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-{"config":{"lang":["en"],"separator":"[\\s\\u200b\\-_,:!=\\[\\]()\"`/]+|\\.(?!\\d)|&[lg]t;|(?!\\b)(?=[A-Z][a-z])","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"awesome-gee-community-catalog","text":"The awesome-gee-community-catalog is an unfunded open source grassroots project with a mission to help collect community sourced and community generated geospatial datasets. Our goal is to make data accessible and tie it to an analysis platform fostering accessibility and reducing digital divide. This catalog lives and serves alongside the Google Earth Engine data catalog. This collaborative effort not only offers openly available, preprocessed research datasets but also caters to frequently requested ones under various open licenses. Stay updated by signing up for email updates, ensuring you receive the latest catalog news and in-depth explorations of valuable data.
You can read about the history and how this project started in the Medium Post article here
Community Datasets added by users and made available for use at large
Like, share and support the Github project. And you can now cite it too
"},{"location":"#citation","title":"Citation","text":"Samapriya Roy, Swetnam, T., & Saah, A. (2024). samapriya/awesome-gee-community-datasets: Community Catalog (3.1.0).\nZenodo. https://doi.org/10.5281/zenodo.14042069\nWelcome to the Awesome GEE Community Catalog, a comprehensive resource for discovering and contributing geospatial datasets designed for use with Google Earth Engine. The awesome-gee-community-catalog is an unfunded open source grassroots project with a mission to help collect community sourced and community generated geospatial datasets. Our goal is to make data accessible and tie it to an analysis platform fostering accessibility and reducing digital divide.
The catalog was created by and maintained by Dr. Samapriya Roy and this is currently a one person team. A Google Developer Expert for Google Earth Engine and Senior Product Manager at MAXAR,an open source developer and a Geospatial Consultant & Speaker. Dr. Roy leads Developer Relations and champions open data access. Leveraging geospatial expertise as an affiliate faculty at the University of Hawai\u02bbi at M\u0101noa and a Designated Campus Colleague at the University of Arizona, Dr. Roy further drives the mission of the catalog.
The catalog is further a result of data requests and tutorial contributions from the #awesome community who use the community catalog and input, advise and feedback from community members. Our mission is to make geospatial data accessible and analysis-ready, fostering collaboration and reducing the digital divide. The Awesome GEE Community Catalog thrives on community participation and open-source principles. We aim to build on creating accessibility to high-quality geospatial data, enabling researchers, developers, and enthusiasts to leverage these resources for their projects. This year the National Science Foundation (NSF) ACCESS program granted us 1.5 million Service Units or CPU Core hours to continue the work on the catalog through Jestream2 a NSF project which allow us to preprocess the datasets as requests are made.
"},{"location":"about_us/#community-contributions","title":"Community Contributions","text":"Our catalog is powered by the contributions of the GEE user base. Community members submit datasets that are then reviewed, usually downloaded and preprocess and made Earth Engine ready and finally added to the catalog for everyone to use. This collaborative approach ensures a diverse and rich collection of data, covering a wide range of topics from waterbodies and population distribution to drought monitoring and more. Each contribution helps expand our repository, making it a go-to resource for geospatial data. \ud83d\udd0d
"},{"location":"about_us/#update-schedule","title":"Update Schedule","text":"We understand the importance of keeping datasets current and reliable. While some datasets are regularly updated on a fixed cadence, others follow a more ad hoc schedule. Updates are made as requests come in or as additional information becomes available about a dataset. This flexible approach allows us to respond to the community's needs and maintain the relevance and accuracy of the data we provide. \ud83d\uddd3\ufe0f
We rely on users to spread the word and share the catalog with other users. Please cite and attribute the catalog using our citation making this project more visible and relevant.
To get involved check out our Get Involved Section
"},{"location":"changelog/","title":"Changelog","text":""},{"location":"changelog/#updated-2024-10-27","title":"Updated 2024-10-27","text":"Creating a code of conduct for any community, including the Awesome GEE community catalog on GitHub, is essential to promote a positive and inclusive environment for all participants. Below is a suggested code of conduct that you can use as a starting point:
"},{"location":"code_of_conduct/#awesome-gee-community-catalog-code-of-conduct","title":"Awesome GEE Community Catalog Code of Conduct","text":"The awesome-gee-community-catalog consists of community sourced geospatial datasets made available for use by the larger Google Earth Engine community and shared publicly as Earth Engine assets. The project was started with the idea that a lot of research datasets are often unavailable for direct use and require preprocessing before use. This catalog lives and serves alongside the Google Earth Engine data catalog and also houses datasets that are often requested by the community and under a variety of open license.
You can read about the history and how this project started in the Medium Post article here
Community Datasets added by users and made available for use at large.
"},{"location":"code_of_conduct/#please-cite-and-acknowledge-use","title":"Please Cite and Acknowledge Use","text":"Users of the Awesome GEE Community catalog must cite the work which allows the community and the project to grow. You can always find the latest citation here
"},{"location":"code_of_conduct/#be-respectful-and-inclusive","title":"Be Respectful and Inclusive","text":"Treat all community members, regardless of their background or experience level, with respect and empathy. Harassment, discrimination, or offensive behavior in any form will not be tolerated. Everyone deserves to feel welcome and valued in our community.
"},{"location":"code_of_conduct/#foster-a-positive-and-constructive-environment","title":"Foster a Positive and Constructive Environment","text":"Engage in discussions and debates in a constructive and positive manner. Disagreements are normal, but we expect community members to address conflicts professionally and respectfully. Focus on the ideas, not on personal attacks.
"},{"location":"code_of_conduct/#encourage-diversity","title":"Encourage Diversity","text":"We welcome contributions and ideas from people of all backgrounds, experiences, and identities. Embrace diversity and encourage the participation of individuals with different perspectives. A diverse community enriches everyone's learning and understanding.
"},{"location":"code_of_conduct/#provide-constructive-feedback","title":"Provide Constructive Feedback","text":"When providing feedback on projects or contributions, do so with the intention of helping others improve. Be constructive and offer suggestions for improvement, but avoid overly negative or unhelpful feedback.
"},{"location":"code_of_conduct/#respect-others-work","title":"Respect Others' Work","text":"Give proper credit to original authors and contributors. If you use or modify someone else's work, make sure to attribute them appropriately. Respect the licenses and terms associated with any resources you use or contribute.
"},{"location":"code_of_conduct/#use-welcoming-language","title":"Use Welcoming Language","text":"Use inclusive language in all interactions. Avoid using offensive or exclusionary language, jokes, or slurs. Be mindful of the impact your words may have on others.
"},{"location":"code_of_conduct/#report-inappropriate-behavior","title":"Report Inappropriate Behavior","text":"If you witness or experience any behavior that violates this code of conduct, please report it to the project maintainers at [email address or contact information]. All reports will be treated confidentially, and appropriate action will be taken as necessary.
"},{"location":"code_of_conduct/#compliance","title":"Compliance","text":"Participants who do not follow this code of conduct may face consequences, including but not limited to warnings, temporary bans, or permanent bans from the community.
"},{"location":"code_of_conduct/#be-responsible","title":"Be Responsible","text":"Community members should be responsible for their actions and their impact on others. If you make a mistake or hurt someone, apologize and try to make amends.
"},{"location":"code_of_conduct/#our-pledge","title":"Our Pledge","text":"In the interest of fostering an open and welcoming environment, we, as contributors and maintainers, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
"},{"location":"code_of_conduct/#acknowledgment","title":"Acknowledgment","text":"This code of conduct is adapted from the Contributor Covenant, version 2.0.
This code of conduct is based on the Contributor Covenant, which is a widely used standard for open-source projects. You can include this code of conduct in the README or CONTRIBUTING.md file of your Awesome GEE Community Catalog repository on GitHub. Remember to adapt the [email address or contact information] section to include the appropriate contact details for the project maintainers. It is essential to communicate the code of conduct to all community members and ensure its enforcement to maintain a healthy and respectful community environment.
"},{"location":"history/","title":"Building Data Commons","text":"I am a firm believer that Communities are what communities build together.The power of Google Earth Engine (GEE) lies not just in its processing capabilities, but also in its vibrant community. This community thrives on constant innovation and collaboration, evident in the ongoing iterations and shared code libraries. Inspired by this collaborative spirit, we embarked on a project to create a community-curated data repository \u2013 a space where users could contribute and access valuable geospatial datasets.
The impetus for this project arose from a specific user query. Someone inquired about Facebook's high-resolution population density maps, a dataset absent from the official GEE catalog. This presented a perfect opportunity to experiment with a community-driven data commons. The dataset, hosted by Columbia University, offered detailed population data at an impressive 30-meter resolution.You can read the foundational blog here
This Facebook dataset became the first and most frequently updated entry in the community catalog, now known as the #Awesome GEE Community Catalog.
"},{"location":"history/#guiding-principle","title":"Guiding Principle","text":"The guiding principle behind this catalog draws inspiration from Elinor Ostrom's groundbreaking work on commons governance, a philosophy that has underpinned successful open-source projects like Linux and collaborative platforms like Wikipedia. Just as shared norms within a physical commons benefit everyone, fostering a similar collaborative environment within the digital realm was our goal.The idea was to use the inspiration from Digital Commons and create a Community Data Commons in the form of the #Awesome GEE Community Catalog.
The #Awesome GEE Community Catalog aims to reduce barriers for users by providing easy access to a growing collection of public datasets. This democratizes access to valuable geospatial data, similar to how GEE itself has democratized access to processing capabilities. However, the challenge lies in effectively applying these principles to both large-scale datasets and smaller, user-contributed ones. The Earth Engine ecosystem itself thrives on a culture of community learning, adaptation, and iteration.This community data commons serves as a bridge, connecting users with the datasets they need and fostering further collaboration within the GEE community. The #Awesome GEE Community Catalog represents a collaborative effort, and its continued success relies on the active participation of its users.
"},{"location":"involved/","title":"Stay updated & contribute","text":"The Awesome GEE Community Catalog is created and maintained by Samapriya Roy with data, examples, tutorial contributions from our community. This is a community common meaning it needs involvement to survive as a grassroots open source project. Here are some ways in which you can get involved with this project and check out examples on how you can bring data, examples, bug reports and pull requests to the catalog here. Open up a Github discussion and create a pull request if you notice any issues so I can fix them. Sign Up for Updates: Never miss the latest catalog additions and in-depth explorations by subscribing to catalog updates through out datacommons blog.
"},{"location":"involved/#choose-your-adventure","title":"Choose your adventure","text":"Browse & Star the Catalog
Visit the website and star the Github Repo so it's easily discovered & you get updates.
Browse and Star
Integrate into Your Projects
Build with the datasets in your GEE projects, use example code and cite the project
Build and Cite
Enrich the Community Catalog
Bring datasets of value to the community catalog. Share it with the community by contributing new datasets
Enrich the Catalog
Submit a tutorial or Example
Create and share examples demonstrating how you've leveraged the catalog's data in your projects.
Submit a tutorial
Support the Project & Donate
We are an unfunded project so community donors and sponors make a world of difference to the project.
Support & Donate
Collaborate with Pull requests
Creating a new pull request means you fixed something that I missed & I and the community apppreciate it.
Create a pull request
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"},{"location":"medium_blogs/","title":"Medium Blog posts","text":"Find a list of associated blog posts related to GEE and community catalog here.
Find a list of associated blog posts related to GEE and community catalog here. To get post subscribe to our
The Awesome GEE Community Catalog is an actively maintained and evolving project that serves a diverse user base with versatile backgrounds and needs. To efficiently address the requirements of all our users, evaluate change requests, and fix bugs, update datasets, I put in a lot of work and your contributions are helpful.The catalog is a collaborative effort, and I welcome your contributions! This catalog aims to provide a comprehensive and up-to-date list of community-driven datasets readily accessible within Google Earth Engine (GEE).
The Awesome GEE Community Catalog thrives on community contributions! Whether you've found a valuable dataset, spotted an error, or have a helpful tip to share, there are many ways to get involved. By contributing, you're not only helping us build a valuable resource for the GEE community, but you're also making it easier for others to find and utilize valuable Earth observation data.
"},{"location":"contributing/#how-you-can-contribute","title":"How you can contribute","text":"I know your time is valuable. That's why I've streamlined contributing to the Awesome GEE Community Catalog!
Ready to get started? Let's dive into the specific ways you can contribute!
"},{"location":"contributing/#creating-an-issue","title":"Creating an issue","text":"Bring or Add data to the Community Catalog
Submit or bring your data request to community catalog
Contribute data
Notice an outdated dataset? Submit an update request
Submit update request for dataset in community catalog
Submit an update
Notice a Bug? Submit a Bug report for review
Bug report for dataset in community catalog
Submit a bug report
Have a Tutorial you want to Contribute? Submit one
Submit tutorials for datasets in community catalog
Submit a tutorial
Have a question or need help?
Ask a question on our discussion board and get in touch with our community
Ask a question
Support the Project & Donate
We are an unfunded project so community donors and sponors make a world of difference to the project.
Support & Donate
Want to create a pull request?
Learn how to create a comprehensive and useful pull request (PR)
Create a pull request
Bug reports are useful information for the catalog. This can range for anything from a spelling mistake that breaks integration to change in asset path that may not have been updated in the documentation for example, incorrect doucentation or citation reference and many more. These are different from dataset updates as they do not pertain to availability of updated data or release information.
To submit a bug report for an existing awesome-gee-catalog dataset use this link
"},{"location":"contributing/example/","title":"Submit example for dataset in community catalog","text":"Examples are helpful in understanding different use cases for datasets as well as enabling rich visualization of an existing dataset from domain experts. The template allows you to point to an existing dataset and submit an example code link via code editor/colab link or otherwise for others to use. All example/code contributors get attribution in the code apart from dataset attributions which are already included.
To submit an example for an existing awesome-gee-catalog dataset use this link
"},{"location":"contributing/submit/","title":"Submit or bring your data request to community catalog","text":"The submit data request templates are further subdivded into two templates one for datasets that you might have created vs any dataset that might be valuable to the community catalog and you would like to submit for consideration. For both templates modify the markdown text as needed and fill in the pieces of information that is available to you as in the example below.
To submit a new dataset for the community catalog use this link
To bring your own dataset for the community catalog use this link
The submit updated data request templates is designed for requesting update to an existing data in the community catalog. This can range from new releases to continuous updates. Modify the markdown text in the template as needed and fill in the pieces of information that is available to you as in the example below.
To submit a suggested update to an existing awesome-gee-catalog dataset use this link
"},{"location":"insiders/","title":"Why donate to the Community Catalog","text":"The GEE Community Catalog is an Open Source and unfunded project that is developed and maintained by a one person team. You can read me and the work further in the about me section. While I started this as a personal side project in 2020, the realization was always present that this project has far reaching implications and applications in the larger geospatial community. I realized that this project could benefit not just the research community who are often producing valuable research products from their research but users who are interested in a share collection of community sources data sources. Behind the scenes most community catalog requests for adding a dataset to the community catalog is triaged by me, evaluated based on multiple factors such as license, data size and preprocessing complexity before I start the work on getting it ready.
The Insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. As such this program is for sponsors and data contributors to the project you can sponsor the project by clicking on the sponsor button above \u261d or submit a new dataset or tutorial request. If you fit under any of those categories fill this form to get insiders access. As an Insider, you'll be added to the \"catalog-contributors\" Google Group, giving you early access to datasets as they are processed, reviewed, and even those not yet released. You'll also receive occasional changelog and update emails, and have the ability to post questions, concerns, and thoughts to the group. And if we meet ask me for stickers to help spread the word \ud83d\ude0a.
Over the last 4 years the project backend now includes over 100,000+ lines of code to often preprocess the dataset or make it ingest ready for Google Earth Engine and making it available for the geospatial community of GEE users. Currently the site serves over 500,000 requests from over 160+ countries. This work is built around creating a Community Data Commons and if you can and wish to support and donate to the project which goes towards simple things like cost of hosting, preprocessing feel free to do so using Github Sponsorship Tier setup for this project.
\u00a0 Choose a sponsoring tier
"},{"location":"insiders/insiders_program/","title":"Insiders program","text":"The awesome GEE community catalog insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. As such this program is for sponsors and data contributors to the project you can sponsor the project by clicking on the sponsor button above or submit a new dataset request for example using this template. If you fit under any of those categories fill this form to get insiders access.
What do you get when you sign up for the Insiders program?
Any and all support is appreciated you can sponsor the project using the sponsorship links as well as contributing and helping data curation for the catalog.You can now find a list of insiders only datasets within the catalog for easily locating these.
"},{"location":"projects/","title":"Data Themes","text":"The Awesome GEE (Google Earth Engine) Community Catalog is a valuable resource for researchers, developers, and environmental scientists. It organizes a diverse range of geospatial datasets into thematic groups, making them more accessible and findable. This structured approach allows users to efficiently locate datasets pertinent to their specific fields of study or interest.
Insiders Program and Insiders only datasets
Some datasets are part of the Insiders only datasets and they can be found here. The insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. The Insiders Program grants access to a few special selection of datasets. You can be part of the program click on the link to find out more.
"},{"location":"projects/#thematic-groups","title":"Thematic Groups","text":"The datasets in the Awesome GEE Community Catalog are categorized into several thematic groups, for example:
Population and Socioeconomic Datasets: These datasets provide crucial information on demographics, economic activities, and social indicators, which are essential for urban planning, public health, and socio-economic research.
Hydrology Datasets: This category includes data on water bodies, hydrological cycles, and water quality, supporting research and decision-making in water resource management, flood risk assessment, and environmental conservation.
Global Land Use and Land Cover Datasets: These datasets offer insights into land use patterns and changes in land cover over time, aiding studies in agriculture, forestry, urbanization, and climate change.
Climate and Weather Datasets: Essential for climate science, these datasets include historical and real-time data on weather patterns, temperature, precipitation, and other climatic factors.
While every effort has been made to place datasets in the most suitable thematic groups, it is acknowledged that some datasets may rightfully belong to more than one category. Users are encouraged to explore multiple themes if their research spans across different areas.
"},{"location":"projects/#accessibility-and-findability","title":"Accessibility and Findability","text":"The thematic grouping of datasets in the Awesome GEE Community Catalog enhances their accessibility and findability. By organizing datasets into clearly defined categories, the catalog simplifies the process of searching and identifying relevant data. This organization not only saves time but also ensures that users can easily locate the most appropriate datasets for their specific needs with the changelog recording periodic updates.
"},{"location":"projects/GPWv4/","title":"Gridded Population of the World Version 4 Administrative Unit Center Points with Population Estimates","text":"The Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates, Revision 11 consists of UN WPP-adjusted population estimates and densities for the years 2000, 2005, 2010, 2015 and 2020, as well as the basic demographic characteristics (age and sex) for the year 2010. The data set also includes administrative name, land and water area, and data context by administrative unit center point (centroid) location. The center points are based on approximately 13.5 million input administrative units used in GPWv4, therefore, these files require hardware and software that can read large amounts of data into memory.
Purpose: To provide a vector (point) version of the input administrative units used in GPWv4 with population estimates, densities, 2010 basic demographic characteristics, and administrative name, area, and data context for use in data integration.
The documentation for this data set is available here
Use the following citation
Doxsey-Whitfield, Erin, Kytt MacManus, Susana B. Adamo, Linda Pistolesi, John Squires, Olena Borkovska, and Sandra R. Baptista. \"Taking advantage of the improved availability of census data: a first look at the gridded population of the world, version 4.\" Papers in Applied Geography 1, no. 3 (2015): 226-234.\nvar gpw = ee.FeatureCollection(\"projects/sat-io/open-datasets/sedac/gpw-v4-admin-unit-center-points-population-estimates-rev11\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GPW-v4
Shared License: This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: census geography, GPWv4, gridded population, uniform distribution
Last updated: 2021-04-07
"},{"location":"projects/S2TSLULC/","title":"ESRI 10m Annual Land Cover (2017-2023)","text":"Time series of annual global maps of land use and land cover (LULC) was updated to v3 with global 10m land cover from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These datasets produced by Impact Observatory and licensed by Esri were fetched from Impact Observatory
This map uses an updated model from the 10-class model and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory and you can find it in GEE here. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.
You can find more information starting with the first release here Kontgis, C. (2021, June 24). Mapping the world in unprecedented detail
"},{"location":"projects/S2TSLULC/#citation","title":"Citation","text":"Karra, Kontgis, et al. \u201cGlobal land use/land cover with Sentinel-2 and deep learning.\u201d\nIGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.\nWater Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.
Trees Any significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).
Flooded vegetation Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.
Crops Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.
Built Area Human made structures; major road and rail networks; large homogeneous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.
Bare ground Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.
Snow/Ice Large homogeneous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.
Clouds No land cover information due to persistent cloud cover.
Rangeland Open areas covered in homogeneous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.
For Accuracy Assessment information visit the ESRI release page
Class Value Remapped Value Land Cover Class Hex Code 1 1 Water #1A5BAB 2 2 Trees #358221 4 3 Flooded Vegetation #87D19E 5 4 Crops #FFDB5C 7 5 Built Area #ED022A 8 6 Bare Ground #EDE9E4 9 7 Snow/Ice #F2FAFF 10 8 Clouds #C8C8C8 11 9 Rangeland #C6AD8D
"},{"location":"projects/S2TSLULC/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var esri_lulc_ts= ee.ImageCollection(\"projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS\");\nThis dataset was produced by Impact Observatory for Esri. \u00a9 2021 Esri. This dataset is available under a Creative Commons BY-4.0 license and any copy of or work based on this dataset requires the following attribution:
"},{"location":"projects/S2TSLULC/#changelog","title":"Changelog","text":"Example path was changed
2023-04-10 Added LULC 2022 to collection
Curated in GEE by: Samapriya Roy
Keywords: : landcover, landuse, lulc, 10m, global, world, sentinel, sentinel 2, impact observatory
Last updated: 2024-06-07
"},{"location":"projects/aces_bhutan/","title":"ACES-Enhanced Rice Crop Maps for Bhutan (2016-2022)","text":"Annual crop type rice maps for 2016-2022 for enabling improved food security decision making has remained a challenge in Bhutan. These maps were developed in collaboration with the Bhutan Department of Agriculture and SERVIR. Through focusing on advancing Science, Technology, Engineering, and Mathematics (STEM) in Bhutan, an effort to co-develop a geospatial application known as the Agricultural Classification and Estimation Service (ACES) was created. This dataset and paper focuses on the co-development of an Earth observation informed climate smart crop type framework which incorporates both modeling and training sample collection. The ACES web application and subsequent ACES modeling software package enables stakeholders to more readily use Earth observation into their decision making process. Additionally, this data set and paper offers a transparent and replicable approach for addressing and combating remote sensing limitations due to topography and cloud cover, a common problem in Bhutan. Lastly, this approach resulted in a Random Forest \"LTE 555\" model, from a set of 3,600 possible models, with an overall test Accuracy of 85% and F-1 Score of .88 for 2020. The model was independently validated resulting in an independent accuracy of 83% and F-1 Score of .45 for 2020.
"},{"location":"projects/aces_bhutan/#citation","title":"Citation","text":"Mayer, Timothy, Biplov Bhandari, Filoteo G\u00f3mez Mart\u00ednez, Kaitlin Walker, Stephanie A. Jim\u00e9nez, Meryl Kruskopf, Micky Maganini et al. \"Employing the\nagricultural classification and estimation service (ACES) for mapping smallholder rice farms in Bhutan.\"\nFrontiers in Environmental Science 11 (2023): 1137835.\nvar Bhutan_ACES_Rice_Maps = ee.ImageCollection(\"projects/servir-sco-assets/assets/Bhutan/Rice_Extent_Mapper/Predicted_Rice_Post_Processed_IC\");\nMap.setCenter(90.37, 27.51,8)\nvar palettes = require('users/gena/packages:palettes');\n\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/132/light-gray\", \"Grayscale\");\n\nMap.addLayer(Bhutan_ACES_Rice_Maps,{min: 0,max: 1, palette: [\"fee6ce\",\"fdae6b\",\"e6550d\"]},\n\"ACES Rice Maps 2016-2022 \")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/ACES-BHUTAN
"},{"location":"projects/aces_bhutan/#license","title":"License","text":"This dataset is available under a Creative Commons BY-4.0 license
Keywords: agriculture, land use, land cover, Bhutan, rice
Created & provided by: Mayer et al 2023, NASA SERVIR
Curated by: Mayer et al 2023, NASA SERVIR
"},{"location":"projects/af_cmask/","title":"Ensemble Source Africa Cropland Mask 2016","text":"In order to produce the most accurate cropland layer at 30 m spatial resolution for Africa, the cropland layers extracted from four remote sensing land cover datasets were integrated. The four datasets covered the period 2015 to 2017. Hence, the constructed cropland layer was produced for the nominal year 2016. To build the final layer, the cropland mapping accuracies of the four cropland layers were firstly investigated at the units of Agro-ecological zones. Then, the best cropland layers for all zones were spatially joined. The resulted cropland layer is a binary mask with higher overall accuracy than individual layers and more consistent with FAO official statistics. You can download the datasets here. You can read additional details from the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/af_cmask/#citation","title":"Citation","text":"Nabil, Mohsen, Miao Zhang, Bingfang Wu, Jose Bofana, and Abdelrazek Elnashar. \"Constructing a 30m African Cropland Layer for 2016 by Integrating\nMultiple Remote sensing, crowdsourced, and Auxiliary Datasets.\" Big Earth Data 6, no. 1 (2022): 54-76.\nNabil, Mohsen; Zhang, Miao; Wu, Bingfang; Bofana, Jose; Elnashar, Abdelrazek (2021): A 30m African Cropland Layer for 2016 by Integrating Multiple\nRemote sensing, Crowdsource, and Auxiliary Datasets.. figshare. Dataset. https://doi.org/10.6084/m9.figshare.13520141.v1\nvar af_cropmask_2016 = ee.Image(\"projects/sat-io/open-datasets/landcover/AF_Cropland_mask_30m_2016_v3\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/AF-CROPLAND-MASK-30M-2016
"},{"location":"projects/af_cmask/#license","title":"License","text":"This dataset is made available under the CC BY Attribution 4.0 International License.
Created by: Nabil, Mohsen; Zhang, Miao; Wu, Bingfang; Bofana, Jose; Elnashar, Abdelrazek
Curated in GEE by: Samapriya Roy
Keywords: Agriculture, Africa, cropland, cropland maps, agriculture land use
"},{"location":"projects/af_trees/","title":"High resolution map of African tree cover","text":"This dataset leverages high-resolution satellite imagery obtained from a nano-satellite constellation, accessible in the tropics through Norway's International Climate and Forest Initiative (NICFI) programme. The primary goal of this dataset is to comprehensively map both forest and non-forest trees on a continental scale across Africa, surpassing the precision of previous endeavors in mapping woody vegetation at large scales.
Utilizing a machine learning approach, we employ 3\u2009m PlanetScope satellite imagery to segment tree canopy cover across Africa, reaching the level of individual scattered trees. The dataset provides a detailed quantification of the contribution of trees located outside traditional forested areas to the overall tree cover within each country. Notably, at the continental scale, our analysis reveals that 29% of the total tree cover exists outside regions classified as forests in a contemporary state-of-the-art map based on Sentinel-2 10\u2009m imagery. You can read the paper here
"},{"location":"projects/af_trees/#citation","title":"Citation","text":"Reiner, F., Brandt, M., Tong, X. et al. More than one quarter of Africa\u2019s tree cover is found outside areas previously\nclassified as forest. Nat Commun 14, 2258 (2023). https://doi.org/10.1038/s41467-023-37880-4\nReiner, F., Brandt, M., Tong, X., Skole, D., Kariryaa, A., Ciais, P., Davies, A., Hiernaux, P., Chave, J., Mugabowindekwe, M.,\nIgel, C., Oehmcke, S., Gieseke, F., Li, S., Liu, S., Saatchi, S., Boucher, P., Singh, J., Taugourdeau, S., \u2026 Fensholt, R.\n(2023). Africa tree cover map [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7764460\nvar tree_cover = ee.Image(\"projects/sat-io/open-datasets/PS_AFRICA_TREECOVER_2019_100m_V10\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/PS-AFRICA-TREECOVER
"},{"location":"projects/af_trees/#license","title":"License","text":"This tree cover map is made freely available for non-commercial purposes. All usage of the data must be attributed and should be cited with the paper citation. Please see the NICFI license for full terms of usage, available here
Provided by: Reiner et al
Curated in GEE by: Samapriya Roy
Keywords: Africa, NICFI, Planet, Tree cover, Land cover
Last updated in GEE: 2024-01-18
"},{"location":"projects/agera5_datasets/","title":"AgERA5 (ECMWF) dataset","text":"Daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modeling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models. Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1\u00b0 spatial resolution. The correction to the 0. 1\u00b0 grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1\u00b0 grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1\u00b0 resolution. This way the data are tuned to the finer topography, finer land use pattern, and finer land-sea delineation of the ECMWF HRES model. You can find additional information here and in the climate engine org dataset page here
"},{"location":"projects/agera5_datasets/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Global Spatial resolution 9600 m (1/10-deg) Temporal resolution Daily Time span 1979-01-01 to present Update frequency Updated daily with 7-day lag timeVariables
Variable Details Wind speed ('Wind_Speed_10m_Mean') - Units: Meters/second - Scale factor: 1.0 Minimum temperature, 2m ('Temperature_Air_2m_Min_24h') - Units: Degrees Kelvin - Scale factor: 1.0 Maximum temperature, 2m ('Temperature_Air_2m_Max_24h') - Units: Degrees Kelvin - Scale factor: 1.0 Mean temperature, 2m ('Temperature_Air_2m_Mean_24h') - Units: Degrees Kelvin - Scale factor: 1.0 Max temperature, 2m, daytime ('Temperature_Air_2m_Max_Day_Time') - Units: Degrees Kelvin - Scale factor: 1.0 Mean temperature, 2m, daytime ('Temperature_Air_2m_Mean_Day_Time') - Units: Degrees Kelvin - Scale factor: 1.0 Min temperature, 2m, nighttime ('Temperature_Air_2m_Min_Night_Time') - Units: Degrees Kelvin - Scale factor: 1.0 Mean temperature, 2m, nighttime ('Temperature_Air_2m_Mean_Night_Time') - Units: Degrees Kelvin - Scale factor: 1.0 Dewpoint temperature, 2m ('Dew_Point_Temperature_2m_Mean') - Units: Degrees Kelvin - Scale factor: 1.0 Precipitation ('Precipitation_Flux') - Units: Millimeters - Scale factor: 1.0 Precipitation rain duration fraction ('Precipitation_Rain_Duration_Fraction') - Units: Count - Scale factor: 1.0 Precipitation solid duration fraction ('Precipitation_Solid_Duration_Fraction') - Units: Count - Scale factor: 1.0 Snow depth ('Snow_Thickness_Mean') - Units: Centimeters - Scale factor: 1.0 Snow water equivalent ('Snow_Thickness_LWE_Mean') - Units: Centimeters - Scale factor: 1.0 Vapour pressure ('Vapour_Pressure_Mean') - Units: hPa - Scale factor: 1.0 Downward solar radiation ('Solar_Radiation_Flux') - Units: J m-2d-1 - Scale factor: 1.0 Cloud cover ('Cloud_Cover_Mean') - Units: Fraction - Scale factor: 1.0 Relative humidity, 2m 06h ('Relative_Humidity_2m_06h') - Units: Percent - Scale factor: 1.0 Relative humidity, 2m 15h ('Relative_Humidity_2m_15h') - Units: Percent - Scale factor: 1.0"},{"location":"projects/agera5_datasets/#citation","title":"Citation","text":"Copernicus Climate Change Service (C3S) (2017): ERA5 Ag: Agrometeorological indicators from 1979 to present derived from reanalysis. Copernicus\nClimate Change Service Climate Data Store (CDS), (date of access),\nhttps://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview\n// Read in Image Collection and get first image\nvar agera5_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-ag-era5/daily')\nvar agera5_i = agera5_ic.first()\n\n// Print first image to see bands\nprint(agera5_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(agera5_i.select('Precipitation_Flux'), {min: 0, max: 1, palette: prec_palette}, 'Precipitation_Flux')\nMap.addLayer(agera5_i.select('Temperature_Air_2m_Max_24h').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Temperature_Air_2m_Max_24h')\nMap.addLayer(agera5_i.select('Temperature_Air_2m_Min_24h').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Temperature_Air_2m_Min_24h')\nMap.addLayer(agera5_i.select('Temperature_Air_2m_Mean_24h').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Temperature_Air_2m_Mean_24h')\nMap.addLayer(agera5_i.select('Dew_Point_Temperature_2m_Mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Dew_Point_Temperature_2m_Mean')\nMap.addLayer(agera5_i.select('Snow_Thickness_Mean'), {min: 0, max: 100, palette: prec_palette}, 'Snow_Thickness_Mean')\nMap.addLayer(agera5_i.select('Snow_Thickness_LWE_Mean'), {min: 0, max: 20, palette: prec_palette}, 'Snow_Thickness_LWE_Mean')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/AGERA5-DATASETS
"},{"location":"projects/agera5_datasets/#license","title":"License","text":"Data are subject to the License to Use Copernicus Products: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf
Keywords: climate, reanalysis, near real-time, ECMWF, precipitation, temperature
Dataset provider: Copernicus
Dataset curated in GEE by: Climate Engine Org
"},{"location":"projects/ai0/","title":"Global Aridity Index","text":"The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 3 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon the implementation of a Penman Monteith Evapotranspiration equation for reference crop. The dataset follows the development and is based upon the WorldClim 2.1 at 30 arc seconds or ~ 1km at the equator. You can read the paper here
Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global Aridity Index_ET0 geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals.
"},{"location":"projects/ai0/#data-citation","title":"Data citation","text":"Zomer, Robert; Trabucco, Antonio (2019): Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.7504448.v6\nZomer, R.J.; Xu, J.; Trabuco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database.\nScientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1\nGlobal-AI grid layers are available as monthly averages (12 data layers, i.e. one layer for each month) or as an annual average (1 data layer) for the 1970-2000 period.
"},{"location":"projects/ai0/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aridity_index_yearly = ee.Image(\"projects/sat-io/open-datasets/global_ai/global_ai_yearly\");\nvar aridity_index_monthly = ee.Image(\"projects/sat-io/open-datasets/global_ai/global_ai_monthly\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-ARIDITY-INDEX
Aridity Index Value Climate Class <0.03 Hyper Arid 0.03-0.2 Arid 0.2-0.5 Semi-Arid 0.5-0.65 Dry sub-humid >0.65 Humid
"},{"location":"projects/ai0/#license","title":"License","text":"The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use under the CC BY 4.0 Attribution 4.0 International license.
Data Website: You can download the data and description here
Curated in GEE by: Samapriya Roy
Keywords: aridity index, evapotranspiration, geospatial modeling
Last updated: 2022-09-02
"},{"location":"projects/airtemp/","title":"Global Daily near-surface air temperature (2003-2020)","text":"Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable and critically needed in the scientific and policy communities, but is still not available. In this paper, we developed a global dataset of daily Tmax and Tmin dataset at 1-km resolution from 2003 to 2020 through the combined use of station-based ground Ta measurements and satellite observations (i.e., digital elevation model, and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP).
This gridded 1 km resolution global (50\u00b0?S ~79\u00b0?N) daily maximum and minimum near-surface air temperature dataset (2003 ?? 2020) was generated using a seamless 1 km resolution land surface temperature dataset (2003-2020), a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology.
You can read the preprint here and download the datasets here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/airtemp/#data-preprocessing","title":"Data preprocessing","text":"The datasets were generated regionally and for tmax and tmin. The tmax and tmin were combined into a single collection for the regions generated. Additional metadata called \"prop_type\" was added to allow for filtering along with other metadata like the day of year and the actual date information for date based filtering. The datasets were projected to EPSG 4326 before being ingested to Google Earth Engine.
"},{"location":"projects/airtemp/#citation","title":"Citation","text":"Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Chen, G., Hu, J., and Wang, L.: A global dataset of daily near-surface air temperature at 1-km resolution\n(2003\u20132020), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-233, in review, 2022.\nZhang, Tao; Zhou, Yuyu (2022): A global 1 km resolution daily near-surface air temperature dataset (2003 ?? 2020).\nIowa State University. Collection. https://doi.org/10.25380/iastate.c.6005185.v1\nvar africa = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/africa\");\nvar australia = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/australia\");\nvar eurasia = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/europe_asia\");\nvar latin_america = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/latin_america\");\nvar north_america = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/north_america\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-DAILY-NEAR-SURFACE-AIR-TEMP
"},{"location":"projects/airtemp/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Chen, G., Hu, J., and Wang, L.
Curated in GEE by : Samapriya Roy
keywords: Air Temperature, land surface temperature (LST), SVCM-SP, MODIS, Global
Last updated on GEE: 2022-08-05
"},{"location":"projects/amazon_peat/","title":"Amazonian Peatland Extent","text":"Tropical peatlands represent some of the most carbon-dense terrestrial ecosystems on the planet, playing a significant role in the global carbon cycle. However, substantial uncertainty exists in estimating their global extent and carbon storage potential. This dataset provides the first field-data-driven model of peatland distribution across the Amazon basin, developed using 2,413 ground reference points and a random forest model applied to a combination of remote sensing products.
The model predicts an Amazonian peatland extent of approximately 251,015 km\u00b2 (95th percentile confidence interval: 128,671\u2013373,359 km\u00b2), which is larger than that of the Congo Basin but 30% smaller than other recent model-based estimates. The dataset addresses key spatial gaps in ground reference data, particularly in regions like Brazil and Bolivia, where uncertainty remains high. The model highlights peatland areas with varying degrees of confidence, such as northern Peru, the Rio Negro basin, and Bolivia, providing a critical resource for future research and field validation efforts. You can read the paper here and download the datasets here
Data Highlights
Hastie, A., Householder, J. E., Coronado, E. N. H., Pizango, C. G. H., Herrera, R., L\u00e4hteenoja, O., de Jong, J., Winton, R. S., Corredor, G. A. A., Reyna, J., Montoya,\nE., Paukku, S., Mitchard, E. T. A., \u00c5kesson, C. M., Baker, T. R., Cole, L. E. S., Oroche, C. J. C., D\u00e1vila, N., \u00c1guila, J. D., \u2026 Lawson, I. T. (2024). A new data-driven map\npredicts substantial undocumented peatland areas in Amazonia. Environmental Research Letters, 19(9), 094019. https://doi.org/10.1088/1748-9326/ad677b\nvar Simple_AOI = ee.FeatureCollection(\"users/adamhastie50/Study_area_simplify\");\nvar Amazon_peat_map = ee.Image(\"projects/sat-io/open-datasets/INT_Amazon_peat_map\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/AMAZONIA-PEATMAP
"},{"location":"projects/amazon_peat/#license","title":"License","text":"The datasets are available under a Creative Commons Attribution 4.0 International license.
Created by: Hastie et al 2024
Curated in GEE by: Hastie et al 2024 and Samapriya Roy
Keywords: Peat, Tropical Peat, Amazon basin
Last updated in GEE: 2024-10-21
"},{"location":"projects/annual_nlcd/","title":"Annual NLCD Land Cover Dataset","text":"The USGS Land Cover program integrates methodologies from the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment, and Projection (LCMAP), along with advanced deep learning, to create Annual NLCD a dataset suite that includes six products, each representing various U.S. land cover and change characteristics. The U.S. Geological Survey\u2019s (USGS) Annual NLCD Collection 1.0 leverages innovations from the National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP) projects, incorporating modern deep learning techniques to deliver accurate, annual land cover and surface change data across the U.S.
Since 1985, Annual NLCD provides six products covering land cover, change, confidence, impervious surfaces, and spectral changes based on Landsat data, facilitating resource management and decision-making.These products leverage Landsat satellite data and are intended for applications in science, resource management, and decision-making, spanning from 1985 to 2023. This dataset supports various environmental analyses, such as urban growth studies, wetland monitoring, agricultural management, and climate impact assessments. Its annual updates and classification confidence features provide essential insights for long-term land use planning and change detection. You can acces User Guide here
"},{"location":"projects/annual_nlcd/#dataset-products-and-descriptions","title":"Dataset Products and Descriptions","text":"Land Cover: A sixteen-class system based on the modified Anderson Level II classification, categorizing dominant surface types like water, forests, and urban areas per pixel. RGB values visually differentiate these categories, ensuring compatibility across federal systems.
Land Cover Change: Tracks annual land cover shifts by comparing consecutive years, using concatenated codes (e.g., 9590 for wetland transitions) to identify changes. Areas without change retain their classification.
Land Cover Confidence: Provides confidence scores based on deep learning probabilities, indicating the model\u2019s certainty in class assignments. Scores are uncalibrated but gauge classification reliability.
Fractional Impervious Surface: Measures the percentage of impermeable surfaces (0-100%) within a 30-meter pixel, informing developed area classifications like urban or suburban based on defined thresholds.
Impervious Descriptor: Differentiates urban, non-urban, and road surfaces within developed areas, offering a clear map of roads distinct from other urban features for detailed analysis.
Spectral Change Day of Year: Identifies the day significant spectral changes occur (values 1-366), pinpointing disturbances (e.g., fires) beyond seasonal variations, enabling temporal change tracking.
Class Value Class Name RGB Color 11 Open Water #466b9f 12 Perennial Ice/Snow #d1def8 21 Developed, Open Space #dec5c5 22 Developed, Low Intensity #d99282 23 Developed, Medium Intensity #eb0000 24 Developed, High Intensity #ab0000 31 Barren Land #b3ac9f 41 Deciduous Forest #68ab5f 42 Evergreen Forest #1c5f2c 43 Mixed Forest #b5c58f 52 Shrub/Scrub #ccb879 71 Grassland/Herbaceous #dfdfc2 81 Pasture/Hay #dcd939 82 Cultivated Crops #ab6c28 90 Woody Wetlands #b8d9eb 95 Emergent Herbaceous Wetlands #6c9fb8
Layer Name Class Values (Range) Min Max NoData Value Land Cover Various land cover types (11, 12, ..., 95) N/A N/A 250 Land Cover Change Change class categories AABB AABB 9999 Land Cover Confidence Confidence levels 1 100 250 Fractional Impervious Surface Imperviousness percentage 0 100 250 Impervious Descriptor Impervious surface types (0: Non-Urban, 1: Roads, 2: Urban) N/A N/A 250 Spectral Change Day of Year Julian days of change 1 366 9999
"},{"location":"projects/annual_nlcd/#citation","title":"Citation","text":"U.S. Geological Survey (USGS), 2024, Annual NLCD Collection 1 Science Products: U.S. Geological Survey data release,\nhttps://doi.org/10.5066/P94UXNTS.\nvar nlcd_landcover = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/NLCD-ANNUAL-LANDCOVER
var nlcd_landcover_confidence = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER_CONFIDENCE\");\nvar nlcd_landcover_change = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER_CHANGE\");\nvar nlcd_fractional_impervious_surface = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/FRACTIONAL_IMPERVIOUS_SURFACE\");\nvar nlcd_impervious_descriptor = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/IMPERVIOUS_DESCRIPTOR\");\nvar nlcd_spectral_change_doy = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/SPECTRAL_CHANGE_DOY\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/NLCD-ANNUAL-LANDCOVER-LAYERS
"},{"location":"projects/annual_nlcd/#license","title":"License","text":"NLCD datasets are provided under a Creative Commons Zero v1.0 Universal license.
Provided by: USGS
Curated in GEE by: Samapriya Roy
Keywords: Land Cover, Land Change, Landsat, Deep Learning, Annual NLCD, USGS, Environmental Monitoring
Last updated in GEE: 2024-10-25
"},{"location":"projects/anusplin/","title":"ANUSPLIN Gridded Climate Dataset","text":"The ANUSPLIN Gridded Climate Dataset for Canada is a station based interpolated dataset produced using the Australian National University Spline (ANUSPLIN) model. It is produced by Agriculture and Agri-Food Canada and covers all of Canada. The dataset is available from 1950-2015 at daily and monthly timesteps for maximum temperature, minimum temperature, and total precipitation at 10km (0.1 degree) resolution. The ANUSPLIN Gridded Climate Dataset for Canada is a comprehensive and station-based interpolated dataset that has been meticulously produced using the Australian National University Spline (ANUSPLIN) model. Created by Agriculture and Agri-Food Canada, this dataset encompasses the entire geographical expanse of Canada and offers a valuable resource for researchers and climate enthusiasts alike.
Researchers and users interested in accessing the dataset can find it through the following external links: - Daily Data: ANUSPLIN Gridded Climate Dataset for Canada (Daily) - Monthly Data: ANUSPLIN Gridded Climate Dataset for Canada (Monthly)
It provides a detailed view of climate conditions with data available from 1950 to 2015, offering insights into daily and monthly variations in maximum temperature, minimum temperature, and total precipitation. This dataset offers a valuable resource for climate research, environmental studies, and various applications that require historical climate data for Canada and parts of the United States. Researchers can explore climate trends, assess climate change impacts, and derive valuable insights into the region's climate patterns using this comprehensive dataset.
"},{"location":"projects/anusplin/#dataset-description","title":"Dataset description","text":"Spatial Information
Parameter Value Spatial extent United States and Canada Spatial resolution 10-km (~0.1-deg) Temporal resolution Daily and monthly Time span 1950-01-01 to 2015-12-31 Update frequency StaticVariables
Variable Details Minimum temperature, 2m (\u2018maxt\u2019) - Units: Degrees Celsius - Scale factor: 1.0 Maximum temperature, 2m (\u2018mint\u2019) - Units: Degrees Celsius - Scale factor: 1.0 Precipitation ('pcp') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/anusplin/#citation","title":"Citation","text":"- Hutchinson, M. F., McKenney, D.W., Lawrence, K., Pedlar, J.H., Hopkinson, R.F., Milewska, E., Papadopol, P. (2009).\n\"Development and testing of Canada-Wide Interpolated Spatial Models of Daily Minimum-Maximum Temperature and Precipitation for 1961-2003.\"\nAmerican Meteorological Society(April): 725-741.\n\n- McKenney, D. W., Hutchinson, M.F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R., Price, D., Owen, T. (2011).\n\"Customized spatial climate models for North America.\" Bulletin of American Meteorological Society-BAMS December: 1612-1622.\n// Read in daily and monthly Image Collections and get single image from each collection\nvar anuspline_m_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-anusplin-monthly')\nvar anuspline_m_i = anuspline_m_ic.first()\nvar anuspline_d_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-anusplin-daily')\nvar anuspline_d_i = anuspline_d_ic.first()\n\n// Print each single image to see bands\nprint(anuspline_m_i)\nprint(anuspline_d_i)\n\n// Visualize each band from each single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(anuspline_m_i.select('pcp'), {min: 0, max: 200, palette: prec_palette}, 'pcp, monthly')\nMap.addLayer(anuspline_m_i.select('mint'), {min: -30, max: 30, palette: temp_palette}, 'mint, monthly')\nMap.addLayer(anuspline_m_i.select('maxt'), {min: -30, max: 30, palette: temp_palette}, 'maxt, monthly')\n\nMap.addLayer(anuspline_d_i.select('pcp'), {min: 0, max: 10, palette: prec_palette}, 'pcp, daily')\nMap.addLayer(anuspline_d_i.select('mint'), {min: -30, max: 30, palette: temp_palette}, 'mint, daily')\nMap.addLayer(anuspline_d_i.select('maxt'), {min: -30, max: 30, palette: temp_palette}, 'maxt, daily')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/ANUSPLIN-GRID
"},{"location":"projects/anusplin/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords : climate, precipitation, temperature, AAFC, daily, monthly, reanalysis
Provider: Agriculture and Agri-Food Canada
Curator: ClimateEngine.org
"},{"location":"projects/aogcm_cmip6/","title":"Current and projected climate data for North America (CMIP6 scenarios)","text":"Atmosphere-Ocean General Circulation Model (AOGCM) were developed to simulate climate variability on a wide range of time scales and are often tested in coupled simulations and data assimilation mode. You can read more about AOGCMs and CMIP6 here. The datasets on this page have been developed by AdaptWest, a project funded by the Wilburforce Foundation to develop information resources for climate adaptation planning. The data were generated using the ClimateNA software. ClimateNA uses data from PRISM and WorldClim for current climate, and downscales data from the Coupled Model Intercomparison Project phase 6 (CMIP6) database corresponding to the 6th IPCC Assessment Report for future projections.
Ensemble projections are average projections from 8 CMIP5 models (table below) that were chosen to represent all major clusters of similar AOGCMs. In addition to the ensemble projections, data are also provided from 9 individual AOGCMs (table below) that are representative of the larger ensemble. Nine individual models were selected to represent all major clusters of similar AOGCMs. A broader set of 8 AOGCMs were used to create the ensemble data. Ensemble projections are also provided here for a greater range of time periods and scenarios than are the projections from individual AOGCMs.
AOGCM Ensemble Models AOGCM Individual Models ACCESS-ESM1-5 ACCESS-ESM1-5 BCC-CSM2-MR CNRM-ESM2-1 CNRM-ESM2-1 CanESM5 EC-Earth3 EC-Earth3 GFDL-ESM4 GFDL-ESM4 GISS-E2-1-G GISS-E2-1-G INM-CM5-0 IPSL-CM6A-LR MIROC6 MIROC6 MPI-ESM1-2-HR MPI-ESM1-2-HR MRI-ESM2-0 MRI-ESM2-0 UKESM1-0-LL UKESM1-0-LL
"},{"location":"projects/aogcm_cmip6/#data-citation","title":"Data citation","text":"AdaptWest Project. 2022. Gridded current and projected climate data for North America at 1km resolution,\ngenerated using the ClimateNA v7.30 software (T. Wang et al., 2022). Available at adaptwest.databasin.org.\nYou can read the paper here and cite as as below
AdaptWest Project. 2022. Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.30 software (T. Wang et al., 2022). Available at adaptwest.databasin.org.\nFor further information and citation refer to:\n\nWang, T., A. Hamann, D. Spittlehouse, C. Carroll. 2016. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS One 11(6): e0156720 https://doi.org/10.1371/journal.pone.0156720\n\nMahony, C.R., T. Wang, A. Hamann, and A.J. Cannon. 2022. A global climate model ensemble for downscaled monthly climate normals over North America. International Journal of Climatology. 1-21. https://doi.org/10.1002/joc.7566\nThe current climatic variables included in the datasets for climate normals, AOGCM models and AOGCM ensemble model are listed below
Monthly Variables Description tmin minimum temperature for a given month (\u00b0C) tmax maximum temperature for a given month (\u00b0C) tave mean temperature for a given month (\u00b0C) ppt total precipitation for a given month (mm)
"},{"location":"projects/aogcm_cmip6/#earth-engine-snippet-climate-variables","title":"Earth Engine Snippet Climate variables","text":"var climate_models_ppt = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_ppt\");\nvar climate_models_tave = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tave\");\nvar climate_models_tmax = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tmax\");\nvar climate_models_tmin = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tmin\");\nvar climate_normals_ppt = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_ppt\");\nvar climate_normals_tave = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tave\");\nvar climate_normals_tmax = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tmax\");\nvar climate_normals_tmin = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tmin\");\nvar aogcm_ensemble_ppt = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_ppt\");\nvar aogcm_ensemble_tave = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tave\");\nvar aogcm_ensemble_tmax = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tmax\");\nvar aogcm_ensemble_tmin = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tmin\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/CMIP6-CURRENT-FUTURE-SCENARIOS
"},{"location":"projects/aogcm_cmip6/#post-processing-for-google-earth-engine-v73","title":"Post processing for Google Earth Engine v7.3","text":"var climate_models_bioclim = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_bioclim\");\nvar aogcm_ensemble_bioclim = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_bioclim\");\nvar climate_normals_bioclim = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_bioclim\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/CMIP6-CURRENT-FUTURE-BIOCLIMATIC
There are a total of 33 bioclimatic variables included for the collections and models , the reference table is included below and you can filter using the metadata property bioclim_variable and the property names from the table.
Bioclimatic Variables Description MAT mean annual temperature (\u00b0C) MWMT mean temperature of the warmest month (\u00b0C) MCMT mean temperature of the coldest month (\u00b0C) TD difference between MCMT and MWMT, as a measure of continentality (\u00b0C) MAP mean annual precipitation (mm) MSP mean summer (May to Sep) precipitation (mm) AHM annual heat moisture index, calculated as (MAT+10)/(MAP/1000) SHM summer heat moisture index, calculated as MWMT/(MSP/1000) DD_0 degree-days below 0\u00b0C (chilling degree days) DD5 degree-days above 5\u00b0C (growing degree days) DD_18 degree-days below 18\u00b0C DD18 degree-days above 18\u00b0C NFFD the number of frost-free days FFP frost-free period bFFP the julian date on which the frost-free period begins eFFP the julian date on which the frost-free period ends PAS precipitation as snow (mm) EMT extreme minimum temperature over 30 years EXT extreme maximum temperature over 30 years Eref Hargreave's reference evaporation CMD Hargreave's climatic moisture index MAR mean annual solar radiation (MJ m-2 d-1) (excludes areas south of US and some high-latitude areas) RH mean annual relative humidity (%) CMI Hogg\u2019s climate moisture index (mm) DD1040 (10<DD<40) degree-days above 10\u00b0C and below 40\u00b0C Tave_wt winter (December to February) mean temperature (\u00b0C) Tave_sp spring (March to May) mean temperature (\u00b0C) Tave_sm summer (June to August) mean temperature (\u00b0C) Tave_at autumn (September to November) mean temperature (\u00b0C) PPT_wt winter (December to February) precipitation (mm) PPT_sp spring (March to May) precipitation (mm) PPT_sm summer (June to August) precipitation (mm) PPT_at autumn (September to November) precipitation (mm) PPT_at autumn (September to November) precipitation (mm)"},{"location":"projects/aogcm_cmip6/#known-issues","title":"Known issues:","text":"Some discontinuity in precipitation values occurs along the US/Canada border due to edge-matching issues between the PRISM data for the two nations.
Mean annual solar radiation (MAR) data are provisional and are slated to be revised in an upcoming release of the ClimateNA software.
These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
"},{"location":"projects/aogcm_cmip6/#changelog","title":"Changelog","text":"Data Website: You can download the data and description here
Explore the data in R-Shiny apps here
Created by: AdaptWest Project, Wang, T., A. Hamann, D. Spittlehouse, C. Carroll
Curated in GEE by: Samapriya Roy
Keywords: climate change, global circulation models, gridded climate data, north america,emission scenarios,climate variables
Last updated: 2023-03-24
"},{"location":"projects/aqualink/","title":"Aqualink ocean surface and subsurface temperature subset","text":"Aqualink is a philanthropically funded system to help people manage their local marine ecosystems in the face of increasing Ocean temperatures. The system consists of satellite-connected underwater temperature sensors and photographic surveys to allow for remote collaboration with scientists across the world. This export was created as a subset of datasets and sites available from aqualink.org as part of making ocean temperature readings in situ truly possible and globally accessible. The aqualink buoy is a collaboration of aqualink with sofarocean to deploy this buoy as sensors that capture ocean temperature both at surface and at varying depths. They are also capable of measuring things like wave height and wind conditions among other things. You can read about aqualink buoys here
The datasets were downloaded and processed using the pyaqua tool I wrote earlier and you can read about aqualink and the pyaqua tool here. These represent sea surface temperature as well as temperature at depth. These were generated only for deployed buoys and are exported CSVs are then imported into Google Earth Engine. The datasets have timestamp and value for said variable which can be used further to assess conditions over time.
This is a one year subset only for 56 deployed sites from 2020-01-04 to 2021-01-04 and is a subset for users to test and the format and duration of data might change in the future as this project evolves
"},{"location":"projects/aqualink/#data-citation","title":"Data Citation","text":"Citation rules will vary by journal or need but a good example would be
aqualink.org (2021). Clerke Reef West side, Australia SST. Retrieved from https://aqualink.org/sites/1218\nvar top_temp = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/aqualink_top_temp_2020\");\nvar bottom_temp = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/aqualink_bottom_temp_2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/AQUALINK-SUBSET-2020
"},{"location":"projects/aqualink/#license","title":"License","text":"All work and data under the aqualink project are under an MIT license and free and open to the community.
Created by: aqualink org
Curated by: Samapriya Roy
Keywords: : aqualink, buoy, temperature, sea surface temperature, sst, wave, oceans, bleaching, coral reefs, extreme-sea-level
Last updated: 2022-01-05
"},{"location":"projects/argo/","title":"Argo Float Data(Subset)","text":"Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level. Each instrument (float) spends almost all its life below the surface. The name Argo was chosen because the array of floats works in partnership with the Jason earth observing satellites that measure the shape of the ocean surface. (In Greek mythology Jason sailed on his ship the Argo in search of the golden fleece). To learn more about Argo, how it works, its data and technology, and its scientific and environmental impact, click here.
"},{"location":"projects/argo/#citation","title":"Citation","text":"These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System.
The general Argo DOI is below.
Argo (2000). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182
If you used data from a particular month, please add the month key to the end of the DOI url to make it reproducible. The key is comprised of the hashtag symbol (#) and then numbers. For example, the key for August 2020 is 76230. The citation would look like:
Argo (2020). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) \u2013 Snapshot of Argo GDAC of August 2020. SEANOE. https://doi.org/10.17882/42182#76230
"},{"location":"projects/argo/#argovis-citation","title":"ArgoVis citation","text":"Argovis API was used to parse through and get to the datasets, you can cite argovis using the following
Tucker, T., D. Giglio, M. Scanderbeg, and S.S. Shen, 2020: Argovis: A Web Application for Fast Delivery,\nVisualization, and Analysis of Argo Data. J. Atmos. Oceanic Technol., 37 (3), 401-416\nhttps://doi.org/10.1175/JTECH-D-19-0041.1\nArgo float dataset has been parsed into a small subset of about 20,000 feature collections flattened into a single collection with over 12.1 million features with total distinct argo float count at 866. The argo float property variables and GEE property names are listed below
Property GEE Property Property Type Platform ID pid integer Instrument Type inst_typ integer date date integer date added date_added integer profile number profile_number string maximum pressure max_pres float pres_max_for_TEMP pmax_temp float pres_min_for_TEMP pmin_temp float pres_max_for_PSAL pmax_psal float pres_min_for_PSAL pmin_psal float Temperature temp float Salinity psal float Pressure pres float
"},{"location":"projects/argo/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var argo = ee.FeatureCollection(\"projects/sat-io/open-datasets/argo-subset\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/ARGOFLOAT-SUBSET
"},{"location":"projects/argo/#license","title":"License","text":"Argo data are freely available without restriction and are released in a model similar to a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.
Created by : International Argo Program, Global Data Assembly Centre
Curated in GEE by: Samapriya Roy
Keywords: float, Argo, global ocean observing system, ocean circulation, in-situ, ocean pressure, sea water salinity, sea water temperature, multi-year, weather climate and seasonal observation, global-ocean
Last updated : 2021-07-30
"},{"location":"projects/aster/","title":"ASTER Global Digital Elevation Model (GDEM) v3","text":"The first version of the ASTER GDEM, released in June 2009, was generated using stereo-pair images collected by the ASTER instrument onboard Terra. ASTER GDEM coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99 percent of Earth's landmass.
The improved GDEM V3 adds additional stereo-pairs, improving coverage and reducing the occurrence of artifacts. The refined production algorithm provides improved spatial resolution, increased horizontal and vertical accuracy. The ASTER GDEM V3 maintains the GeoTIFF format and the same gridding and tile structure as V1 and V2, with 30-meter postings and 1 x 1 degree tiles. You can read more about the product in the user guide here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/aster/#source-data-structure","title":"Source Data structure","text":"The data are in Geotiff format, with each file divided into 1x1 degree tiles. To allow for adding a single image instead of a collection output, the zip files were unzipped and a single composite tif file was generated.
"},{"location":"projects/aster/#citation","title":"Citation","text":"NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team. ASTER Global\nDigital Elevation Model V003. 2018, distributed by NASA EOSDIS Land Processes DAAC,\nhttps://doi.org/10.5067/ASTER/ASTGTM.003\nvar gdem = ee.Image(\"projects/sat-io/open-datasets/ASTER/GDEM\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/ASTER-GDEM
"},{"location":"projects/aster/#license","title":"License","text":"All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. This license can thus be treated similar to a public domain CC0 license. ASTER GDEM Version 3 (ASTGTM V003) was released on August, 5, 2019 and contains no redistribution requirements. The LP DAAC kindly requests that you properly cite the data in your research.
Created by: NASA, METI, AIST, Japan Spacesystems and U.S./Japan ASTER Science Team
Curated in GEE by: Samapriya Roy
Keywords: ASTER, DEM, elevation, remote sensing
"},{"location":"projects/astwbd/","title":"ASTER Global Water Bodies Database (ASTWBD) Version 1","text":"The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Water Bodies Database (ASTWBD) Version 1 data product provides global coverage of water bodies larger than 0.2 square kilometers at a spatial resolution of 1 arc second (approximately 30 meters) at the equator, along with associated elevation information.
The ASTWBD data product was created in conjunction with the ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 data product by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was generated using ASTER Level 1A scenes acquired between March 1, 2000, and November 30, 2013. The ASTWBD data product was then generated to correct elevation values of water body surfaces.
To generate the ASTWBD data product, water bodies were separated from land areas and then classified into three categories: ocean, river, or lake. Oceans and lakes have a flattened, constant elevation value. The effects of sea ice were manually removed from areas classified as oceans to better delineate ocean shorelines in high latitude areas. For lake water bodies, the elevation for each lake was calculated from the perimeter elevation data using the mosaic image that covers the entire area of the lake. Rivers presented a unique challenge given that their elevations gradually step down from upstream to downstream; therefore, visual inspection and other manual detection methods were required. You can find above mentioned detail along with description here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/astwbd/#source-data-structure","title":"Source Data structure","text":"The data are in Geotiff format, with each file divided into 1x1 degree tiles. To allow for adding a single image instead of a collection output, the zip files were unzipped and a single composite tif file was generated.
"},{"location":"projects/astwbd/#citation","title":"Citation","text":"NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team. ASTER Global\nDigital Elevation Model V003. 2018, distributed by NASA EOSDIS Land Processes DAAC,\nhttps://doi.org/10.5067/ASTER/ASTGTM.003\nvar astwbd_att = ee.Image(\"projects/sat-io/open-datasets/ASTER/ASTWBD_ATT\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/ASTWBD
"},{"location":"projects/astwbd/#license","title":"License","text":"All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. This license can thus be treated similar to a public domain CC0 license. ASTER GDEM Version 3 (ASTGTM V003) was released on August, 5, 2019 and contains no redistribution requirements. The LP DAAC kindly requests that you properly cite the data in your research.
Created by: NASA, METI, AIST, Japan Spacesystems and U.S./Japan ASTER Science Team
Curated in GEE by: Samapriya Roy
Keywords: ASTER, DEM, elevation, remote sensing, Water Bodies Database
"},{"location":"projects/avhrr-ltdr/","title":"ESA Fire Disturbance Climate Change Initiative (CCI)","text":"The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Pixel v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.
The dataset provides monthly information on global burned area at 0.05-degree spatial resolution (the resolution of the AVHRR-LTDR input data) from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in monthly GeoTIFF files, packed in annual tar.gz files, and it includes 5 files: date of BA detection (labelled JD), confidence label (CL), burned area in each pixel (BA), number of observations in the month (OB) and a metadata file. For further information on the product and its format see the Product User Guide. You can download the datasets from this link
The Spatial resolution of this BA product is 0.05 degrees, which is the resolution of the AVHRR-LTDR input data.
The Coordinate Reference System (CRS) used is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid and using a Plate Carr\u00c3\u00a9e projection with geographical coordinates of equal pixel size.This product is distributed in global monthly files, grouped by year.
"},{"location":"projects/avhrr-ltdr/#details-of-the-pixel-product","title":"Details of the Pixel product","text":"The pixel product is composed of 5 files:
Chuvieco, E.; Pettinari, M.L.; Ot\u00f3n, G. (2020): ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Pixel product, version 1.1.Centre for Environmental Data Analysis, 21 December 2020. doi:10.5285/b1bd715112ca43ab948226d11d72b85e.\nhttps://dx.doi.org/10.5285/b1bd715112ca43ab948226d11d72b85e\nvar BA = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/AVHRR-LTDR/BA\");\nvar CL = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/AVHRR-LTDR/CL\");\nvar JD = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/AVHRR-LTDR/JD\");\nvar OB = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/AVHRR-LTDR/OB\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/ESA-FIRE-DISTURBANCE-CCI
"},{"location":"projects/avhrr-ltdr/#license","title":"License","text":"You can find license information here
Created by: Chuvieco, E.; Pettinari, M.L.; Ot\u00f3n, G, ESA
Curated in GEE by : Samapriya Roy
keywords: ESA, CCI, Pixel, Burned Area, Fire Disturbance, Climate Change, GCOS Essential Climate Variable
Last modified: 2020-12-21
Last updated on GEE: 2024-04-01
"},{"location":"projects/bii/","title":"Biodiversity Intactness Index (BII)","text":"The Biodiversity Intactness Index (BII) measures biodiversity change using abundance data on plants, fungi and animals worldwide. The Index shows how local terrestrial biodiversity responds to human pressures such as land use change and intensification. Generated by Impact Observatory, in collaboration with Vizzuality, these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020. Biodiversity Intactness data is based on the PREDICTS database of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies
Biodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods white paper entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d authored by Francis Gassert, Joe Mazzarello, Sam Hyde.
"},{"location":"projects/bii/#ancillary-dataset-citation","title":"Ancillary dataset Citation","text":"Hudson, Lawrence N., Tim Newbold, Sara Contu, Samantha LL Hill, Igor Lysenko, Adriana De Palma, Helen RP Phillips et al. \"The database of the\nPREDICTS (projecting responses of ecological diversity in changing terrestrial systems) project.\" Ecology and evolution 7, no. 1 (2017): 145-188.\nImpact Observatory and Vizzuality. Biodiversity Intactness Index (BII) [Data set]. Retrieved from [URL of the dataset]\nvar bdi_ic = ee.ImageCollection(\"projects/ebx-data/assets/earthblox/IO/BIOINTACT\")\nvar bdi_2017_20_comp = bdi_ic.mean()\nvar bdi_2017_comp = bdi_ic.filterDate('2017-01-01', '2017-12-31').mean()\n\n\nvar visualization = {\n bands: ['BioIntactness'],\n min: 0,\n max: 1,\n palette: ['e5f5e0', 'a1d99b', '31a354'], 4: ['edf8e9', 'bae4b3', '74c476', '238b45']\n};\n\nMap.addLayer(bdi_2017_20_comp, visualization, \"composite 2017-20\")\nMap.addLayer(bdi_2017_comp, visualization, \"composite 2017\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/biodiversity-ecosystems-habitat/BIODIVERSITY-INTACTNESS-INDEX
"},{"location":"projects/bii/#license","title":"License","text":"The datasets are made available under the CC BY 4.0 license
Keywords: Biodiversity, Ecology, Human Pressures, Gridded data
Dataset producer/licensor/processor: Impact Observatory and Vizzuality
Data hosted in Earth Engine by: Earth Blox
"},{"location":"projects/br_dwgd/","title":"Brazilian Daily Weather Gridded Data(BR-DWGD) 1961-2020","text":"The Comprehensive Brazilian Meteorological Gridded Dataset represents a significant advancement in meteorological research, addressing the growing demand for precise and extensive meteorological data. This dataset builds upon its predecessor by enhancing spatial resolution to 0.1\u00b0 x 0.1\u00b0 and expanding temporal coverage from January 1961 to July 2020. Incorporating elevation and temperature lapse rates, the dataset improves gridded interpolations for minimum and maximum temperatures, while also encompassing other crucial variables such as precipitation, solar radiation, wind speed, and relative humidity.
This dataset derives from a meticulous fusion of data from 11,473 rain gauges and 1,252 weather stations, enabling accurate interpolations. The selection of optimal interpolation methods, determined via ranked cross-validation statistics, underscores the dataset's commitment to precision. With two categories of gridded controls provided, researchers gain tools to assess interpolation accuracy against station data. As a comprehensive resource, the Comprehensive Brazilian Meteorological Gridded Dataset stands poised to catalyze advancements in climate, meteorology, and agricultural studies, offering nuanced insights for multifaceted scientific investigations.
These dataset presents the daily meteorological gridded data set from Brazil (BR-DWGD). The variables are Precipitation (pr, mm); maximum and minimum temperature (Tmax, tmin, \u00b0C); solar radiation (Rs, MJ/m2); relative humidity (RH, %); wind speed at 2 meters (u2, m/s) and evapotranspiration (ETo, mm). The temporal coverage is 1961/01/01-2020/07/31 (except precipitation: 1961/01/01-2022/12/31) and has the spatial resolution 0.1\u00b0 x 0.1\u00b0, just for Brazil territory. You can find links to the dataset here
"},{"location":"projects/br_dwgd/#dataset-post-processing","title":"Dataset post processing","text":"The datasets were provided as multiband netcdf files with each representing a single day since 1961 and then partitioned across 20 year intervals. There were then converted and split into single geotiff images and merged so they could be continious collections with about 21,762 images per collection except Precipitation which extends till 2022. The rain gauge and weather station location data was further added to the assets. The datasets must be scaled and offset should be applied to represent true values and they are included in the table below as well as the sample script.
Variable Variable name Units Offset Scale pr Precipitation mm 225 0.006866665 Eto evapotranspiration mm 0 0.051181102 Tmax maximum temperature C 15 0.001068148 Tmin minimum temperature C 15 0.001068148 RH Relative humidity Percentage 0 0.393700787 RS Solar radiation MJ/m2 0 0.157086614 U2 Wind speed m/s 0 0.059055118"},{"location":"projects/br_dwgd/#citation","title":"Citation","text":"Xavier, A. C., Scanlon, B. R., King, C. W., & Alves, A. I. (2022). New improved Brazilian daily weather gridded data (1961\u20132020).\nInternational Journal of Climatology, 42( 16), 8390\u2013 8404. https://doi.org/10.1002/joc.7731\nvar ET = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/ET\");\nvar PR = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/PR\");\nvar RH = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/RH\");\nvar RS = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/RS\");\nvar TMAX = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/TMAX\");\nvar TMIN = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/TMIN\");\nvar U2 = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/U2\");\nvar RAIN_GAUGES = ee.FeatureCollection(\"projects/sat-io/open-datasets/BR-DWGD/RAIN_GAUGES\");\nvar WEATHER_STATIONS = ee.FeatureCollection(\"projects/sat-io/open-datasets/BR-DWGD/WEATHER_STATIONS\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/BR-DWDG-EXAMPLE
"},{"location":"projects/br_dwgd/#license","title":"License","text":"The datasets are provided under a Attribution 4.0 International (CC BY 4.0) license.
Provided by: Xavier, A. C. et al
Curated in GEE by : Samapriya Roy
Keywords: Brazil, maximum temperature, minimum temperature, precipitation, solar radiation, wind speed, relative humidity, evapotranspiration
"},{"location":"projects/bss/","title":"Bare Earth\u2019s Surface Spectra 1980-2019","text":"This datasets provides global bare surface area and frequency for a 30 year time range using Landsat Imagery in Google Earth Engine.
From the paper we find
Earth\u2019s surface dynamics provide essential information for guiding environmental and agricultural policies. Uncovered and unprotected surfaces experience several undesirable effects, which can affect soil ecosystem functions. We developed a technique to identify global bare surface areas and their dynamics based on multitemporal remote sensing images to aid the spatiotemporal evaluation of anthropic and natural phenomena. Two additional products were obtained with a similar technique: a) Earth\u2019s bare surface frequency, which represents where and how many times a single pixel was detected as bare surface, based on Landsat series, and b) Earth\u2019s bare soil tendency, which represents the tendency of bare surface to increase or decrease. This technique enabled the retrieval of bare surfaces on 32% of Earth\u2019s total land area and on 95% of land when considering only agricultural areas.
Read the paper here
Use the following credit when these datasets or paper is cited:
Dematt\u00ea, Jos\u00e9 AM, et al. \"Bare earth\u2019s Surface Spectra as a proxy for Soil Resource Monitoring.\"\nScientific reports 10.1 (2020): 1-11.\nvar bare_surface = ee.Image('users/geocis/BareSurfaces/BS_1980_2019');\nvar bare_frequency = ee.Image('users/geocis/BareSurfaces/BF_1980_2019');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/BARE_EARTH_SPECTRA
App Website: App link here
Source Code to App: https://code.earthengine.google.com/6b2935468ce30e08ce693a9cc95f943c
Shared License: This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created & Curated by: Dematt\u00ea, Jos\u00e9 AM, et al
Keywords: Bare Earth Surface, Soil, Geomorphology, Landsat, Bare Surface Frequency
Last updated: 2021-06-12
"},{"location":"projects/ca_canopy_ht/","title":"Canopy height: forested ecosystems of Canada","text":"This dataset contains two canopy height maps from forested ecosystems of Canada at 250m spatial resolution \u2014 one using information from the spaceborne LiDAR GEDI, and the other from ICESat-2. GEDI and ICESat-2 are particular in acquiring canopy height information in Canada \u2014 the former providing more accurate information of vegetation, yet not reaching full coverage in Canada, whilst the latter is not specifically designed to provide vegetation information but has a global coverage. We created wall-to-wall maps using ATL08 LiDAR product from the ICESat-2 satellite, and GEDI L2A from GEDI.
The data were download for the mid growing season (June and August 2020). Points were filtered regarding solar background noise and atmospheric scattering, totaling 208,554 points from ICESat-2, and 1,249,354 points for GEDI after filtering and point thinning. These points were associated with 14 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, seasonal Sentinel-2 red and NIR bands, and annual PALSAR-2 HH and HV polarization. Afterwards, the random forest algorithm was used to extrapolate LiDAR observations and develop regression models with the abovementioned spatially continuous variables. GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m in relation to ALS data used for validation, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI.
You can read the complete paper here and download the dataset at this link
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ca_canopy_ht/#citation","title":"Citation","text":"Sothe, Camile, Alemu Gonsamo, Ricardo B. Louren\u00e7o, Werner A. Kurz, and James Snider. \"Spatially Continuous Mapping of Forest Canopy Height in Canada\nby Combining GEDI and ICESat-2 with PALSAR and Sentinel.\" Remote Sensing 14, no. 20 (2022): 5158.\nSothe, Camile; Gonsamo, Alemu; Snider, James; Louren\u00e7o, Ricardo B.; Kurz, Werner A. (2022): Spatially continuous canopy height maps of forested\necosystems of Canada. 4TU.ResearchData. Dataset. https://doi.org/10.4121/21363081.v1\nvar gedi_fc_ht = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/GEDI_forest_canopy_height_250m_v1\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-TREE-CANOPY-HEIGHT-GEDI
"},{"location":"projects/ca_canopy_ht/#earth-engine-snippet-icesat2","title":"Earth Engine Snippet: ICESat2","text":"var icesat_fc_ht = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/ICESat2_forest_canopy_height_250m_v1\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-TREE-CANOPY-HEIGHT-ICESAT
"},{"location":"projects/ca_canopy_ht/#license","title":"License","text":"This dataset is available under a Creative Commons BY-4.0 license
Created by: Sothe,Camile, et al. 2022
Curated in GEE by : Samapriya Roy
Keywords: LiDAR analysis, ICESat-2, GEDI, canopy height distribution, Carbon storage and distribution
Last updated on GEE: 2022-10-20
"},{"location":"projects/ca_fa/","title":"Landsat-derived forest age for Canada's forested ecosystems (2019)","text":"Landsat-derived forest age for Canada\u2019s forested ecosystems 2019. Satellite-based forest age map for 2019 across Canada\u2019s forested ecozones at a 30-m spatial resolution. Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada\u2019s forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an \"old tree\" category.
Forest area codes:
Map for displaying the approach followed to compute forest age for the treed areas in Canada\u2019s forested ecosystems for a given year, in this case 2019.
Forest area codes: 0: Non treed 1: Disturbance approach 2: Recovery approach 3: Allometric approach
See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada\u2019s National Inventory (NFI). Maltman et al. (2023)
"},{"location":"projects/ca_fa/#citation","title":"Citation","text":"Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada\u2019s forested ecosystems.\nRemote Sensing of Environment 290, 113529.\nvar age = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_forest_age_2019\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FOREST-AGE-2019
"},{"location":"projects/ca_fa/#earth-engine-snippet-approach-used-to-compute-the-landsat-derived-forest-age-for-canada-2019","title":"Earth Engine Snippet: Approach used to compute the Landsat-derived forest age for Canada (2019)","text":"var age_appro = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_forest_age_2019_approach\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FOREST-AGE-2019-APPROACH
Download Tool/Code snippets, if any: Download link https://opendata.nfis.org/downloads/forest_change/CA_forest_age_2019.zip
"},{"location":"projects/ca_fa/#required","title":"Required","text":"License Information: Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada)
Created by: Maltman et al. (2023)
Curated in GEE by: Spencer Bronson and Samapriya Roy
Keywords: Forest age, Forest inventory, Land cover, Landsat, Time since disturbance
Last updated: March 15th 2023
"},{"location":"projects/ca_fao/","title":"Canada Landsat derived FAO forest identification (2019)","text":"Landsat-based forest area consistent with FAO definitions for Canada's forested ecosystems. To conform with international reporting guidelines and programs, using Landsat data we map the forest area for Canada following the Food and Agricultural Organization of the United Nations (FAO) definition. The FAO definition incorporates land use, whereby trees removed by fire and harvesting for instance, remain forest as the trees will return. Annually representative maps were produced using over three decades of annual land cover data generated from Landsat derived time series land cover and change information (to generate a spatially explicit estimate of the forest area of Canada in 2019). We mapped the area of 'forest', as defined by the FAO, for Canada's 650 Mha of forested ecozones. The map includes the current forest cover in a given year (i.e. 2019), plus the satellite-based temporally informed forest area where tree cover had been temporarily lost due to fire or harvest. See Wulder et al. (2020) for an overview of the methods, data, image processing, as well as information on accuracy assessment using Canada\u2019s National Inventory (NFI). You can download the dataset here
Forest area codes for the dataset are
0: Non forest 1: Current forest area 2019 2: Temporally informed forest area 2019
"},{"location":"projects/ca_fao/#citation","title":"Citation","text":"Wulder, M.A., T. Hermosilla, G. Stinson, F.A. Gougeon, J.C. White, D.A. Hill, B.P. Smiley. (2020). Satellite-based time series land cover and change\ninformation to map forest area consistent with national and international reporting requirements. Forestry 93(3), 331-343.\nvar ca_fao = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_FAO_forest_2019\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FAO-FOREST-IDENTIFICATION-2019
"},{"location":"projects/ca_fao/#license","title":"License","text":"Open Government Licence - Canada
Created by: Wulder et al. (2020)
Curated by: Spencer Bronson and Samapriya Roy
Keywords: Forest area, temporally informed forest area, disturbance informed forest area, Forest inventory, Land cover, Landsat
Last updated: 2023-03-29
"},{"location":"projects/ca_fires/","title":"Canada 2023 Wildfires","text":"Canada's 2023 wildfire season represented the largest area burned in a single fire season in Canada\u2019s modern history. Using the Tracking Intra- and Inter-year Change (TIIC) algorithm, wildfires occurring within Canada\u2019s forested ecosystems during the 2023 fire season were detected at a 30-m resolution. Time series data used to identify wildfires originated from Sentinel-2A and -2B, and Landsat-8 and -9. Fires have been grouped into two classes based on detection period: summer fires and fall fires. Summer fires were detected between May 30 and September 17, and fall fires were detected between September 17 and October 25. For summer fires, burned pixels were identified by TIIC as changed and typed as fire.
For the fall period, TIIC only detected changes within a 4-km buffer of NRCan fire perimeters (https://cwfis.cfs.nrcan.gc.ca/datamart). This approach was used to limit commission errors that can occur due to known limitations of mapping with optical data in the fall due to phenology, snow cover, or low sun angles. For the 2023 fire season, the TIIC algorithm detected 12.74 Mha of burned area in Canada\u2019s forested ecozones, representing 1.8% of the total forest-dominated ecozone area. Of the 12.74 Mha, 11.57 Mha (90.9%) was burned by summer fires and 1.16 Mha (9.1%) by fall fires (Pelletier et al., 2024). You can download the dataset here
"},{"location":"projects/ca_fires/#citation","title":"Citation","text":"Pelletier, F., Cardille, J.A., Wulder, M.A., White, J.C., Hermosilla, T., 2024. Revisiting the 2023 wildfire season in Canada. Science of Remote Sensing. 10, 100145. https://doi.org/10.1016/j.srs.2024.100145\nvar image = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Wildfire_2023_Summer_Fall\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-2023
"},{"location":"projects/ca_fires/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada.
Created by: Pelletier et al. 2024
Curated in GEE by : Spencer Bronson and Samapriya Roy
keywords: Wildfire, Tracking Intra- Inter-year Change (TIIC), Landsat, Sentinel, Burned Area, Fire Occurrence, Canada
Last updated on GEE: 2024-08-29
"},{"location":"projects/ca_forest_fire/","title":"Canada Landsat Derived Wildfire disturbance & Magnitude 1985-2020","text":"The annual forest change data included in this product is national in scope (entire forested ecosystem) and represents the wall-to-wall characterization of wildfire in Canada at a 30-m spatial resolution. The information outcomes represent 36 years of wildfire change over Canada\u2019s forests, derived from a single, consistent, spatially explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985\u20132020 for Canada's 650 Mha forested ecosystems.
Landsat data has a 30 m spatial resolution, so the change information is highly detailed and informative regarding both natural and human driven changes. These data represent annual stand replacing forest changes. The stand replacing disturbance types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see ( Hermosilla et al. 2016). The data available is Change year for Wildfire Events. You can download the dataset here
"},{"location":"projects/ca_forest_fire/#canada-landsat-derived-forest-wildfire-change-magnitude-dnbr-1985-2020","title":"Canada Landsat-Derived Forest Wildfire Change Magnitude dNBR (1985-2020)","text":"Wildfire change magnitude dNBR 1985-2020. Spectral change magnitude for wildfires that occurred from 1985 and 2020 expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after a given change event. This layer value has been transformed for data storage efficiency. The actual dNBR value can be calculated as follows dNBR = value / 100. Higher dNBR values are related to higher burn severity. You can download the dataset here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ca_forest_fire/#dataset-citation","title":"Dataset Citation","text":"Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Campbell, L.B., 2016. Mass data processing of time series Landsat imagery:\npixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054.\nvar ca_forest_fire = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Fire_1985-2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-1985-2020
var ca_forest_fire_mag = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Wildfire_dNBR_1985_2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-MAGNITUDE-1985-2020
"},{"location":"projects/ca_forest_fire/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada.
Created by: Hermosilla et al. 2016
Curated in GEE by : Spencer Bronson and Samapriya Roy
keywords: Forest Fire, Forest inventory, Land cover, Landsat, Machine learning
Last updated on GEE: 2023-07-02
"},{"location":"projects/ca_forest_harvest/","title":"Canada Landsat Derived Forest harvest disturbance 1985-2020","text":"The annual forest change data included in this product is national in scope (entire forested ecosystem) and represents the wall-to-wall characterization of harvest in Canada at a 30-m spatial resolution. The information outcomes represent 36 years of harvest change over Canada\u2019s forests, derived from a single, consistent, spatially explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985\u20132020 for Canada's 650 Mha forested ecosystems.
Landsat data has a 30 m spatial resolution, so the change information is highly detailed and informative regarding both natural and human driven changes. These data represent annual stand replacing forest changes. The stand replacing disturbance types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see (Hermosilla et al. 2016). The data available is Change year for Harvest Events and can be downloaded here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ca_forest_harvest/#dataset-citation","title":"Dataset citation","text":"Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Campbell, L.B., 2016. Mass data processing of time series Landsat imagery:\npixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054.\nvar ca_forest_harvest = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Harvest_1985-2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FOREST-HARVEST-1985-2020
"},{"location":"projects/ca_forest_harvest/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada.
Created by: Hermosilla et al. 2016
Curated in GEE by : Samapriya Roy
keywords: Forest Harvest, Forest inventory, Land cover, Landsat, Machine learning
Last updated on GEE: 2023-01-28
"},{"location":"projects/ca_lc/","title":"High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2022)","text":"The annual time series of forest land cover maps are national in scope (entire 650 million hectare forested ecosystem) and represent a wall-to-wall land cover characterization yearly from 1984 to 2022. These time-series land cover maps were produced from annual time-series of Landsat image composites, forest change information, and ancillary topographic and hydrologic data following the framework described in Hermosilla et al. (2022), which builds upon the approach introduced in Hermosilla et al. (2018). The methodological innovations included (i) a refined training pool derived from existing land cover products using airborne and spaceborne measures of forest structure; (ii) selection of training samples proportionally to the land cover distribution using a distance=weighted approach; and (iii) generation of regional classification models using a 150x150 km tiling system. Maps are post-processed using disturbance information to ensure logical class transitions over time using a Hidden Markov Model. Hidden Markov Models assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar). For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) No. 112780. DOI: https://doi.org/10.1016/j.rse.2021.112780 and Hermosilla et al. (2018) https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719
The data represents annual forest land cover of Canada's forested ecosystems for 1984-2022. An image compositing window of August 1 -30 days was used to generate the best-available-pixel (BAP) image composites used as the source data for land cover classification. The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada's forests, were led by Canadian Forest Service of Natural Resources Canada, partnered with the University of British Columbia, with support from the Canadian Space Agency, augmented by processing capacity from WestGrid of Compute Canada.
"},{"location":"projects/ca_lc/#citation","title":"Citation","text":"Paper citation
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., 2022. Land cover classification in an era of big and open data: Optimizing localized\nimplementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. No. 112780.\n[Hermosilla et al. 2022](https://www.sciencedirect.com/science/article/pii/S0034425721005009)\nWhen using this data, please cite as:
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., 2022. Land cover classification in an era of big and open data: Optimizing localized\nimplementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. No. 112780.\nDOI: https://doi.org/10.1016/j.rse.2022.112780 [Open Access]\n#686868 Class Code: 0 Unclassified #3333ff Class Code: 20 Water #ccffff Class Code: 31 Snow/Ice #cccccc Class Code: 32 Rock/Rubble #996633 Class Code: 33 Exposed/Barren Land #ffccff Class Code: 40 Bryoids #ffff00 Class Code: 50 Shrubs #993399 Class Code: 80 Wetland #9933cc Class Code: 81 Wetland Treed #ccff33 Class Code: 100 Herbs #006600 Class Code: 210 Coniferous #00cc00 Class Code: 220 Broad Leaf #cc9900 Class Code: 230 Mixedwood
"},{"location":"projects/ca_lc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var forest_lc = ee.ImageCollection(\"projects/sat-io/open-datasets/CA_FOREST_LC_VLCE2\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FORESTED-ECOSYSTEM-LC
"},{"location":"projects/ca_lc/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada).
Created by: Hermosilla et al. 2022
Curated in GEE by : Samapriya Roy
keywords: Land cover; Classification; Machine learning; Land cover change; Landsat; Lidar; ICESat-2
Last updated on GEE: 2024-08-29
"},{"location":"projects/ca_sbfi/","title":"Canadian Satellite-Based Forest Inventory (SBFI)","text":"The Satellite-Based Forest Inventory (SBFI) provides information on Canada\u2019s forested land cover, disturbance recovery, structure, species, and stand age in 2020, as well as stand-replacing disturbances from 1985-2020. The SBFI polygons represent homogeneous forest conditions similar to those of stands delineated in a strategic forest inventory. More than 25 million SBFI polygons were delineated using a multiresolution segmentation algorithm applied to the 2020 Landsat surface-reflectance BAP image composite (30-m spatial resolution), fire year, and harvest year layers derived from Landsat using the C2C approach. A minimum map unit of 0.45 ha (5 pixels) was used to define polygons. The entirety of Canada\u2019s forest ecosystems were mapped using the same data, attributes, and temporal representation, resulting in a common vegetation inventory system of Canada\u2019s ~650 Mha forested ecosystems. Given the large and diverse forest area of Canada, the strength of an SBFI lies in the use of a consistent data source and methodology across jurisdictional boundaries, and managed and unmanaged forest areas, enabling consistently generated synoptic, spatially explicit information outputs. The data included herein are based upon free and open satellite data and information products following established and communicated approaches.
Full descriptions of feature attributes are found within the attached data dictionary or within the downloadable dataset found here
CA SBFI feature attributesGroup Field Description Units ID ID Unique polygon identifier TILE Tile identifier Geometry AREA_HA Area of the polygon ha PERIMETER_M Length of polygon\u2019s boundary m Stratification JURSDICTION Most represented province/territory ECOZONE Most represented terrestrial ecozone as defined by Ecological Stratification Working Group (1996) ECOPROVINCE Most represented ecoprovince as defined by Ecological Stratification Working Group (1996) ECOREGION Most represented ecoregion as defined by Ecological Stratification Working Group (1996) MANAGEMENT Most represented land status from the forest management classification from Stinson et al_ (2019) Land cover LC_WATER Area covered by water % of polygon area LC_SNOW_ICE Area covered by snow/ice % of polygon area LC_ROCK_RUBBLE Area covered by rock/rubble % of polygon area LC_EXPOSED_BARREN Area covered by exposed/barren land % of polygon area LC_BRYOIDS Area covered by bryoids % of polygon area LC_SHRUBS Area covered by shrubs % of polygon area LC_WETLAND Area covered by wetland % of polygon area LC_WETLAND-TREED Area covered by wetland-treed % of polygon area LC_HERBS Area covered by herbs % of polygon area LC_CONIFEROUS Area covered by coniferous % of polygon area LC_BROADLEAF Area covered by broadleaf % of polygon area LC_MIXEDWOOD Area covered by mixedwood % of polygon area LC_TREED Area covered by treed vegetation derived from combining the land cover classes % of polygon area LC_FAO_FOREST Area covered by forest consistent with FAO definitions (Wulder et al_ 2020) % of polygon area LC_WETLAND_VEGETATION Area covered by wetlands derived from combining the land cover classes % of polygon area Disturbances DISTURB_FIRE_PERC Area impacted by fire disturbances % of polygon area DISTURB_FIRE_YEAR Modal year of fire disturbances years DISTURB_FIRE_MAGNITUDE_MIN Minimum value of fire magnitude dNBR DISTURB_FIRE_MAGNITUDE_MAX Maximum value of fire magnitude dNBR DISTURB_FIRE_MAGNITUDE_AVG Average value of fire magnitude dNBR DISTURB_FIRE_MAGNITUDE_SD Standard deviation of fire magnitude dNBR DISTURB_FIRE_MAGNITUDE_MED Median value of fire magnitude dNBR DISTURB_HARVEST_PERC Area impacted by harvesting disturbances % of polygon area DISTURB_HARVEST_YEAR Modal year of harvesting disturbances years Recovery RECOVERY_FIRE_MIN Minimum value of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_FIRE_MAX Maximum value of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_FIRE_AVG Average value of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_FIRE_SD Standard deviation of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_FIRE_MED Median value of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_HARVEST_MIN Minimum value of spectral recovery for harvesting disturbances % of pre-disturbance RECOVERY_HARVEST_MAX Maximum value of spectral recovery for harvesting disturbances % of pre-disturbance RECOVERY_HARVEST_AVG Average value of spectral recovery for harvesting disturbances % of pre-disturbance RECOVERY_HARVEST_SD Standard deviation of spectral recovery for harvesting disturbances % of pre-disturbance RECOVERY_HARVEST_MED Median value of spectral recovery for harvesting disturbances % of pre-disturbance Age AGE_MIN Minimum forest age years AGE_MAX Maximum forest age years AGE_AVG Average forest age years AGE_SD Standard deviation of forest age years AGE_MED Median forest age years AGE_0_10, AGE_10_20, AGE_20_30, AGE_30_40, AGE_40_50, AGE_50_60, AGE_60_70, AGE_70_80, AGE_80_90, AGE_90_100, AGE_100_110, AGE_110_120, AGE_120_130, AGE_130_140, AGE_140_150, AGE_GT_150 Ten-year age class frequency distribution % of treed area in polygon Forest structure STRUCTURE_CANOPY_HEIGHT_MIN Minimum canopy height m STRUCTURE_CANOPY_HEIGHT_MAX Maximum canopy height m STRUCTURE_CANOPY_HEIGHT_AVG Average canopy height m STRUCTURE_CANOPY_HEIGHT_SD Standard deviation of canopy height m STRUCTURE_CANOPY_HEIGHT_MED Median canopy height m STRUCTURE_CANOPY_COVER_MIN Minimum canopy cover % STRUCTURE_CANOPY_COVER_MAX Maximum canopy cover % STRUCTURE_CANOPY_COVER_AVG Average canopy cover % STRUCTURE_CANOPY_COVER_SD Standard deviation of canopy cover % STRUCTURE_CANOPY_COVER_MED Median canopy cover % STRUCTURE_LOREYS_HEIGHT_MIN Minimum Lorey\u2019s height m STRUCTURE_LOREYS_HEIGHT_MAX Maximum Lorey\u2019s height m STRUCTURE_LOREYS_HEIGHT_AVG Average Lorey\u2019s height m STRUCTURE_LOREYS_HEIGHT_SD Standard deviation of Lorey\u2019s height m STRUCTURE_LOREYS_HEIGHT_MED Median Lorey\u2019s height m STRUCTURE_BASAL_AREA_MIN Minimum basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_MAX Maximum basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_AVG Average basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_SD Standard deviation of basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_MED Median basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_TOTAL Total basal area in polygon m2 STRUCTURE_AGB_MIN Minimum aboveground biomass t ha\u22121 STRUCTURE_AGB_MAX Maximum aboveground biomass t ha\u22121 STRUCTURE_AGB_AVG Average aboveground biomass t ha\u22121 STRUCTURE_AGB_SD Standard deviation of aboveground biomass t ha\u22121 STRUCTURE_AGB_MED Median aboveground biomass t ha\u22121 STRUCTURE_AGB_TOTAL Total aboveground biomass in polygon t STRUCTURE_VOLUME_MIN Minimum gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_MAX Maximum gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_AVG Average gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_SD Standard deviation of gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_MED Median gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_TOTAL Total gross stem volume in polygon m3 Tree species SPECIES_NUMBER SPECIES_1 Name of the 1st most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_2 Name of the 2nd most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_3 Name of the 3rd most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_4 Name of the 4th most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_5 Name of the 5th most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_1_PERC Area covered by the 1st most common leading tree species % of treed area in polygon SPECIES_2_PERC Area covered by the 2nd most common leading tree species % of treed area in polygon SPECIES_3_PERC Area covered by the 3rd most common leading tree species % of treed area in polygon SPECIES_5_PERC Area covered by the 5th most common leading tree species % of treed area in polygon SPECIES_CONIFEROUS_PERC Area covered by coniferous tree species % of treed area in polygon SPECIES_CML1 Name of the 1st most common tree species based on the class membership likelihood values SPECIES_CML2 Name of the 2nd most common tree species based on the class membership likelihood values SPECIES_CML3 Name of the 3rd most common tree species based on the class membership likelihood values SPECIES_CML4 Name of the 4th most common tree species based on the class membership likelihood values SPECIES_CML5 Name of the 5th most common tree species based on the class membership likelihood values SPECIES_CML1_PERC Distribution of the class membership likelihood values of the 1st most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML2_PERC Distribution of the class membership likelihood values of the 2nd most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML3_PERC Distribution of the class membership likelihood values of the 3rd most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML4_PERC Distribution of the class membership likelihood values of the 4th most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML5_PERC Distribution of the class membership likelihood values of the 5th most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML_CONIFEROUS_PERC Proportion of class membership likelihood values of coniferous tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML_ASSEMBLAGES Name of the tree species conforming an assemblage SPECIES_CML_ASSEMBLAGES_PERC Proportion of class membership likelihood values conforming the assemblage % of class membership likelihood from treed pixels in polygon Symbology SYMB_LAND_BASE_LEVEL Land base level classification based on the NFI land cover hierarchy (Wulder et al_ 2008) SYMB_LAND_COVER_LEVEL Land cover level classification based on the NFI land cover hierarchy (Wulder et al_ 2008) SYMB_VEGETATION_LEVEL Vegetation level classification based on the NFI land cover hierarchy (Wulder et al_ 2008) SYMB_DISTURBANCE Simplified coding for disturbance type and year SYMB_RECOVERY Simplified coding for spectral recovery SYMB_AGE Simplified coding for forest age
"},{"location":"projects/ca_sbfi/#dataset-postprocessing","title":"Dataset postprocessing","text":"
The tile datasets are merged into a single feature collection for ease of use. The grid file is kept as is for users to understand how the grids are created.
"},{"location":"projects/ca_sbfi/#citation","title":"Citation","text":"Wulder, Michael A., Txomin Hermosilla, Joanne C. White, Christopher W. Bater, Geordie Hobart, and Spencer C. Bronson. \"Development and\nimplementation of a stand-level satellite-based forest inventory for Canada.\" Forestry: An International Journal of Forest Research (2024): cpad065.\nWulder, M.A., Hermosilla, T., White, J.C., Bater, C.W., Hobart, G., Bronson, S.C., 2024. Development and implementation of a stand-level\nsatellite-based forest inventory for Canada. Forestry: An International Journal of Forest Research. https://doi.org/10.1093/forestry/cpad065\nvar sbfi_merged = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/CA_SBFI_MERGED\");\nvar grid_fe = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/GRID_forested_ecosystems\");\nvar grid_labels = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/Grid_Labels\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SBFI
"},{"location":"projects/ca_sbfi/#license","title":"License","text":"This work is licensed under and freely available to the public under the Open Government Licence - Canada.
Created by: Wulder et al. 2024
Curated in GEE by : Samapriya Roy
Keyworks: Landsat, land cover, change detection, forest structure, biomass; NFI
Last updated in GEE: 2024-08-29
"},{"location":"projects/ca_species/","title":"High Resolution Tree Species Information for Canada","text":""},{"location":"projects/ca_species/#distance-to-second-class-for-the-leading-tree-species-map","title":"Distance-to-second class for the leading tree species map","text":"Distance-to-second class (D2SC) value used as indicator of attribution confidence for the leading tree species map produced from surface reflectance values in a spatially exhaustive, 30-m spatial resolution, Landsat image composite representing year 2019 conditions. Also included in the modeling of species are geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Regional classification models were generated based on Canada??s National Forest Inventory using a 150x150 km tiling system. D2SC is computed using the class membership probabilities derived from the first and second most voted classes from the Random Forests models.
"},{"location":"projects/ca_species/#high-resolution-maps-of-tree-species-membership-likelihood","title":"High Resolution Maps of tree species membership likelihood","text":"Tree species maps indicate the class membership probability of all possible classes on a pixel level. The maps are generated using a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Values represent the class membership probabilities derived from the Random Forests votes. Regional classification models were generated based on Canada??s National Forest Inventory (NFI) using a 150x150 km tiling system. The regional classification models utilize and aim to map only the trees species known to be present in a given tiling unit based on the information provided by the NFI.
"},{"location":"projects/ca_species/#high-resolution-map-of-leading-tree-species-distribution","title":"High Resolution Map of leading tree species distribution","text":"Leading tree species map produced from a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (xxxx). Regional classification models were generated based on Canada??s National Forest Inventory using a 150x150 km tiling system. The leading tree species are defined by representing the most voted tree species from the Random Forests classification models (i.e. the class with the highest class membership probability).
For an overview on the data, image processing, and methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) https://doi.org/10.1016/j.rse.2022.113276
"},{"location":"projects/ca_species/#citation","title":"Citation","text":"Hermosilla, T., Bastyr, A., Coops, N.C., White, J.C., Wulder, M.A., 2022. Mapping the presence and distribution of tree species in Canada's forested ecosystems. Remote Sensing of Environment 282, 113276.\nvar D2SC = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/DISTANCE2SECOND\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-DISTANCE-2-SECOND-CLASS
"},{"location":"projects/ca_species/#earth-engine-snippet-tree-species-membership-likelihood","title":"Earth Engine Snippet: tree species membership likelihood","text":"var membership_likelihood_prob = ee.ImageCollection(\"projects/sat-io/open-datasets/CA_FOREST/SPECIES_CLASS_MEMBERSHIP_PROBABILITIES\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SPECIES-CLASS-MEM-PROBABILITIES
"},{"location":"projects/ca_species/#earth-engine-snippet-leading-tree-species","title":"Earth Engine Snippet: leading tree species","text":"var lead_tree_species = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/LEAD_TREE_SPECIES\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-LEAD-TREE-SPECIES
"},{"location":"projects/ca_species/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada.
Created by: Hermosilla et al. 2022
Curated in GEE by : Samapriya Roy
keywords: Tree species, Forest inventory, Land cover, Landsat, Machine learning, Classification
Last updated on GEE: 2022-10-11
"},{"location":"projects/ca_species_ts/","title":"Canada Long Term Tree Species (1984-2022)","text":"In this dataset, we share maps of annual dominant tree species (also known as leading tree species) from 1984-2022 covering the entirety of Canada\u2019s 650 Mha forested ecosystems using Landsat time-series imagery at a 30-m spatial resolution. Classifications are based on regionally representative Random Forests model using local training samples from Canada\u2019s National Forest Inventory (Hermosilla et al., 2022). Descriptive metrics provide information on spectral, geographic, climatic, and topographic characteristics. Initial annual tree species classifications were subjected to a time series post-classification process using the forward-backward Hidden Markov Model to improve the temporal consistency of tree species transitions within the time series. Assessment of the annual species maps using independent validation data resulted in an overall accuracy of 86.1% \u00b1 0.14% (95%-confidence interval). These data allow consistent comparison of trends and rates of change in tree species composition nationally and across regions using a common time frame, spatial resolution, and analytical approach.
"},{"location":"projects/ca_species_ts/#citation","title":"Citation","text":"Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Bater, C.W., Hobart, G.W., 2024. Characterizing long-term tree species dynamics in Canada's forested ecosystems using annual time series remote sensing data. Forest Ecology and Management 572, 122313. https://doi.org/10.1016/j.foreco.2024.122313 (Hermosilla et al. 2024)\nYou can download the files here, found under the title: Annual Tree Species 1984-2022 and Species_Names here
"},{"location":"projects/ca_species_ts/#dataset-post-processing","title":"Dataset Post processing","text":"The datasets were provided as an earth engine folder with images and have been converted to an imagecollection and start and end date have been added to each image in the image collection for filtering.
"},{"location":"projects/ca_species_ts/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var ca_species_ts = ee.ImageCollection(\"projects/sat-io/open-datasets/CA_FOREST/SPECIES-1984-2022\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SPECIES-TIME-SERIES
"},{"location":"projects/ca_species_ts/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government License - Canada.
Created by: Hermosilla et al. 2024
Curated in GEE by : Spencer Bronson & Samapriya Roy
Keywords: Landsat, Time series analysis, Land cover, Land cover change, Forest succession, Dominant species
Last updated :2024-10-15
"},{"location":"projects/caml/","title":"Cyanobacteria Aggregated Manual Labels (CAML)","text":"Continuous monitoring for cyanobacteria blooms in small, inland water bodies via in-situ sampling and analysis can be challenging not only due to the number and locations of water bodies to cover, but also due to the dynamic nature of algal growth and toxin production. Detection targets vary with cyanobacteria strains as well as physical, chemical, and biological factors. Ground monitoring also lacks consistency as sampling methods, frequency, and analytical techniques vary from region to region. However, remote sensing allows systematic data collection over a large area to identify regions with potential harmful algal growth. We introduce the Cyanobacteria Aggregated Manual Labels (CAML), a large dataset of in-situ cyanobacteria measurements for investigations of cyanobacteria detection and severity classification in inland water bodies across the United States. Relevant satellite imagery from publicly available endpoints are applicable to use when applying the CAML dataset to models.
The dataset labels ground measurements of cyanobacteria cell counts at 23,570 points in U.S. inland water bodies over 2013 - 2021. Algorithms trained on this data could be used to estimate cyanobacteria cell counts in water bodies for timely water quality and public health interventions and to gain an understanding of environmental and anthropogenic factors associated with cyanobacteria incidence and proliferation. Data is provided in a comma-separated values (CSV) format. You can find the dataset here
Severity levels are based on World Health Organization (WHO) cyanobacteria density thresholds.
However users should feel free to to use their own thresholds as makes sense for their needs.
"},{"location":"projects/caml/#dataset-citation","title":"Dataset Citation","text":"S. Gupta, E. Gelbart, R. Gupta, K. Wetstone, and E. Dorne (2024). Cyanobacteria Aggregated Manual Labels Dataset (NASA and DrivenData). SeaBASS. http://dx.doi.org/10.5067/SeaBASS/CAML/DATA001\nvar caml = ee.FeatureCollection(\"projects/sat-io/open-datasets/HAB-DETECTION/CAML_cyanobacteria_abundance_20211229_R1\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/CYANOBACTERIA-AGG-MANUAL-LABELS
"},{"location":"projects/caml/#license","title":"License","text":"Following the NASA Earth Science Data and Information Policy, all SeaBASS data are publicly available.
Provided by: NASA, UC Berkeley,DrivenData, ASRC Federal
Curated in GEE by: Samapriya Roy
Keywords: water quality, HAB, Cyanobacteria, Manual Labels, Ground data
Last updated: 2024-03-20
"},{"location":"projects/can_drought_outlook/","title":"Canadian Drought Outlook","text":"The Canadian Drought Outlook raster dataset is produced by the Agriculture and Agri-Food Canada (AAFC). The Canadian Drought Outlook predicts whether drought across Canada will emerge, stay the same or get better over the target month. In calculating the outlook, consideration is given to Agroclimate indices, such as the Standard Precipitation Index (SPI), the Standard Precipitation Evaporation Index (SPEI), and the Palmer Drought Severity Index (PDSI). The drought outlook is issued on the first Thursday of each calendar month and is valid for 32 days from that date. You can get additional information about this dataset here and on the climate engine org dataset page here.
"},{"location":"projects/can_drought_outlook/#dataset-description","title":"Dataset Description","text":"Categorical Values
Value Interpretation -9999 NoData Value 0 No data 1 Drought removal 2 Drought improves 3 Drought develops 4 Drought persists 5 Drought worsensSpatial Information
Parameter Value Spatial extent Canada Spatial resolution ~0.8-km (1/100-deg) Temporal resolution Monthly Time span 2021-06-01 to present Update frequency Updated first week of each monthVariables
Variable Details Drought category ('drought_outlook_class') - Units: Drought outlook classification - Scale factor: 1.0"},{"location":"projects/can_drought_outlook/#citation","title":"Citation","text":"Agriculture and Agri-Food Canada, 2021, \"Canadian Drought Outlook\", Agroclimate, Geomatics and Earth Observation Division, Science and Technology\nBranch.\n// Read in Image Collection and get single image\nvar cdo_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-aafc-cdo-monthly')\nvar cdo_i = cdo_ic.first()\n\n// Print image to see bands\nprint(cdo_i)\n\n// Visualize a single image\nvar cdo_palette = [\"#ffffff\", \"#4a7733\", \"#dfb73d\", \"#b6a083\", \"#775412\", \"#c24d1b\"]\nMap.addLayer(cdo_i, {min:0, max:4, palette: cdo_palette}, 'cdo_i')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/CANADA-DROUGHT-OUTLOOK
"},{"location":"projects/can_drought_outlook/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: drought, Canada, forecast, AAFC
Provided by: Agriculture and Agri-Food Canada (AAFC)
Curated in GEE by: Climate Engine Org
"},{"location":"projects/canopy/","title":"ETH Global Sentinel-2 10m Canopy Height (2020)","text":"The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change, and prevent biodiversity loss. Here, we present the first global, wall-to-wall canopy height map at 10 m ground sampling distance for the year 2020. No single data source meets these requirements: dedicated space missions like GEDI deliver sparse height data, with unprecedented coverage, whereas optical satellite images like Sentinel-2 offer dense observations globally, but cannot directly measure vertical structures. By fusing GEDI with Sentinel-2, we have developed a probabilistic deep learning model to retrieve canopy height from Sentinel-2 images anywhere on Earth, and to quantify the uncertainty in these estimates.
The presented approach reduces the saturation effect commonly encountered when estimating canopy height from satellite images, allowing to resolve tall canopies with likely high carbon stocks. According to our map, only 5% of the global landmass is covered by trees taller than 30 m. Such data play an important role for conservation, e.g., we find that only 34% of these tall canopies are located within protected areas. Our model enables consistent, uncertainty-informed worldwide mapping and supports an ongoing monitoring to detect change and inform decision making. The approach can serve ongoing efforts in forest conservation, and has the potential to foster advances in climate, carbon, and biodiversity modelling. You can download the cloud optimized geotiffs here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/canopy/#citation","title":"Citation","text":"Lang, Nico, Walter Jetz, Konrad Schindler, and Jan Dirk Wegner. \"A high-resolution canopy height model of the Earth.\" arXiv preprint arXiv:2204.08322 (2022).\nvar canopy_height = ee.Image(\"users/nlang/ETH_GlobalCanopyHeight_2020_10m_v1\");\nvar standard_deviation = ee.Image(\"users/nlang/ETH_GlobalCanopyHeightSD_2020_10m_v1\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-10m-CANOPY-HEIGHT
GEE app link: https://nlang.users.earthengine.app/view/global-canopy-height-2020
GEE app source code link: https://code.earthengine.google.com/fefca6457efb90c0a3f8ae9806bee792
"},{"location":"projects/canopy/#license","title":"License","text":"The ETH Global Canopy Height 2020 product is provided free of charge, without restriction of use. For the full license information see the Creative Commons Attribution 4.0 International License publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.
Created by: Lang, Nico, Walter Jetz, Konrad Schindler, and Jan Dirk Wegner
Curated by: Samapriya Roy
Keywords: Sentinel-2, Forest, Canopy Height, Machine Learning, CNN
Last updated on GEE: 2022-03-29
"},{"location":"projects/carbon_projects/","title":"Carbon Offset Project Boundaries","text":"Nature-based climate solutions (NBS) have become an important component of strategies aiming to reduce atmospheric CO2 and mitigate climate change impacts. Carbon offsets have emerged as one of the most widely implemented NBS strategies, however, these projects have also been criticized for exaggerating offsets. Verifying the efficacy of NBS-derived carbon offset is complicated by a lack of readily available geospatial boundary data. Herein, we detail methods and present a database of nature-based offset project boundaries. This database provides the locations of 575 NBS projects distributed across 55 countries. Geospatial boundaries were aggregated using a combination of scraping data from carbon project registries (n=433, 75.3%) as well as manual georeferencing and digitization (n=127, 22.1%). Database entries include three varieties of carbon projects: avoided deforestation, afforestation, reforestation and re-vegetation, and improved forest management. An accuracy assessment of the georeferencing and digitizing process indicated a high degree of accuracy (intersection over union score of 0.98 \u00b1 0.015).
You can read the preprint here and find the dataset here.
"},{"location":"projects/carbon_projects/#database-notes","title":"Database notes","text":"The project accounting area is defined as the geographical area of the project that was used to calculate carbon credit issuance.
This database does not represent a census of nature-based carbon projects and does not contain all varieties of nature-based carbon projects.
Users should verify that any georeferencing inaccuracies will not significantly impact their analyses.
The boundaries included in the database reflect the data available in the registries at the time of access, with some projects regularly updating their information.
We note that we were unable to assess the accuracy of the boundaries constructed from linear features or from a developer provided protocol.
Karnik, Akshata, John Kilbride, Tristan Goodbody, Rachael Ross, and Elias Ayrey. \"An open-access database of nature-based carbon offset project\nboundarie.\" (2024).\nKarnik, A., Kilbride, J., Goodbody, T., Rachel, R., & Ayrey, E. (2024). A global database of nature-based carbon offset project boundaries [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11459391\nvar carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL');\n\nvar visParams = {\n palette: ['#9ab555'],\n min: 0.0,\n max: 1550000.0,\n opacity: 0.8,\n};\nvar carbonoffsets = ee.Image().float().paint(carbonoffsetscol, 'REP_AREA');\n\nMap.addLayer(carbonoffsets, visParams, 'Existing carbon projects area');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-CARBON-OFFSET-PROJECTS
"},{"location":"projects/carbon_projects/#license","title":"License","text":"This dataset is made available under Creative Commons Attribution 4.0 International license.
Keywords: carbon, carbon offsets, NBS, climate change, Nature-based climate solutions, Carbon offsets, Geospatial boundaries, Georeferencing,Forest carbon
Curated in GEE by: Filipe Silveiran and Samapriya Roy
Last updated : 2024-09-07
"},{"location":"projects/cc/","title":"Chesapeake Bay High Resolution Land Cover Dataset (2013-2014)","text":"This raster dataset was developed as part of the Land Cover Project, a cooperative agreement between the Chesapeake Conservancy and the National Park Service funded through an interagency agreement with the Environmental Protection Agency. Virginia Geographic Information Network (VGIN) coordinated with Worldview Solutions the creation of a separate VA statewide high-resolution land cover dataset, which has unique class names and descriptions. For the purposes of a matching bay-wide dataset, this VA dataset was reclassified and some classes were edited to better match the Chesapeake Bay Dataset class definitions below.
High Resolution mapping was used to develop consistent and extremely accurate land cover dataset for all the counties that comprise the Chesapeake Bay watershed. This land cover was created based on 2014 National Agriculture Imagery Program (NAIP) aerial imagery.
Dataset created and developed by the Conservation Innovation Center at the Chesapeake Conservancy. The creation of this dataset was made possible as a result of a cooperative agreement between the Chesapeake Conservancy and the National Park Service being funded through an interagency agreement with the Environmental Protection Agency. This dataset will enhance the ability to guide the most efficient use of resources in the Bay as well as aid the identification of priorities for conservation and restoration. Created based on 2013/2014 National Agriculture Imagery Program (NAIP) aerial imagery. You can find additional information here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/cc/#citation","title":"Citation","text":"Conservation Innovation Center at the Chesapeake Conservancy. Chesapeake Bay High Resolution Land Cover Dataset (2013-2014). Accessed [Month\nYear] at https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/\nvar chesapeake = ee.Image(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/Baywide_13Class_20132014\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CHESEPEAKE_BAY_2013-2014
"},{"location":"projects/cc/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. The organizations responsible for generating and funding this dataset make no representations of any kind including, but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data. Although every effort has been made to ensure the accuracy of information, errors may be reflected in data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors.
Produced by: Conservation Innovation Center at the Chesapeake Conservancy
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, Chesapeake Bay
Last updated on GEE: 2022-06-12
"},{"location":"projects/ccap_lc/","title":"C-CAP High-Resolution Land Cover","text":"The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats. The timeframe for this metadata is summer 2016. These maps are developed utilizing high resolution National Agriculture Imagery Program (NAIP) imagery, and can be used to track changes in the landscape through time. This trend information gives important feedback to managers on the success or failure of management policies and programs and aid in developing a scientific understanding of the Earth system and its response to natural and human-induced changes. This understanding allows for the prediction of impacts due to these changes and the assessment of their cumulative effects, helping coastal resource managers make more informed regional decisions. NOAA C-CAP is a contributing member to the Multi-Resolution Land Characteristics consortium and C-CAP products are included as the coastal expression of land cover within the National Land Cover Database.
These detailed products bring NOAA\u2019s national land cover mapping framework to the local level and are developed for specific project-based geographies (not the entire coastal land cover mapping boundary). Data are often developed in partnership with state and local groups. Attributes for this product are as follows: 0 Background, 1 Unclassified (Cloud, Shadow, etc), 2 Impervious, 3 4 5 Developed Open Space, 6 Cultivated Land, 7 Pasture/Hay, 8 Grassland, 9 Deciduous Forest, 10 Evergreen Forest, 11 Mixed Forest, 12 Scrub/Shrub, 13 Palustrine Forested Wetland, 14 Palustrine Scrub/Shrub Wetland, 15 Palustrine Emergent Wetland, 16 Estuarine Forested Wetland, 17 Estuarine Scrub/Shrub Wetland, 18 Estuarine Emergent Wetland, 19 Unconsolidated Shore, 20 Bare Land, 21 Open Water, 22 Palustrine Aquatic Bed, 23 Estuarine Aquatic Bed, 24 Tundra, 25 Snow/Ice, Recommended Citation. NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database.
This dataset was created by NOAA's Ocean Service, Office for Coastal Management (OCM). Random Forest Classification: The initial 1m spatial resolution 6 class high resolution land cover product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. You can read a sample metadata file here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ccap_lc/#citation","title":"Citation","text":"National Oceanic and Atmospheric Administration, Office for Coastal Management. \u201cName of Data Set.\u201d Coastal Change Analysis Program (C-\nCAP) High-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed [Month Year] at www.coast.noaa.gov/\nhtdata/raster1/landcover/bulkdownload/hires/.\nThe regional land cover dataset files were downloaded for each region. If the files were img then they were convert to GeoTIFF. Each region was converted into a collection and start and end dates were added based on available information and filenames.
"},{"location":"projects/ccap_lc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var CCAP_AS_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_AS_LC\");\nvar CCAP_CA_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_CA_LC\");\nvar CCAP_CT_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_CT_LC\");\nvar CCAP_GU_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_GU_LC\");\nvar CCAP_HI_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_HI_LC\");\nvar CCAP_LA_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_LA_LC\");\nvar CCAP_MA_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_MA_LC\");\nvar CCAP_ME_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_ME_LC\");\nvar CCAP_MP_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_MP_LC\");\nvar CCAP_OH_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_OH_LC\");\nvar CCAP_OR_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_OR_LC\");\nvar CCAP_PR_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_PR_LC\");\nvar CCAP_RI_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_RI_LC\");\nvar CCAP_VI_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_VI_LC\");\nvar CCAP_WA_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_WA_LC\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-HRLC-HI
"},{"location":"projects/ccap_lc/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.
Produced by: NOAA's Ocean Service, Office for Coastal Management (OCM)
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, OBIA, NOAA
Last updated on GEE: 2022-06-12
"},{"location":"projects/ccap_mlc/","title":"C-CAP Medium-Resolution Land Cover Beta","text":"The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats. These maps are developed utilizing high resolution National Agriculture Imagery Program (NAIP) imagery, and can be used to track changes in the landscape through time. This trend information gives important feedback to managers on the success or failure of management policies and programs and aid in developing a scientific understanding of the Earth system and its response to natural and human-induced changes. This understanding allows for the prediction of impacts due to these changes and the assessment of their cumulative effects, helping coastal resource managers make more informed regional decisions. NOAA C-CAP is a contributing member to the Multi-Resolution Land Characteristics consortium and C-CAP products are included as the coastal expression of land cover within the National Land Cover Database.
These data should be considered to be BETA-level or draft products. They are based on 1-meter land cover mapping that were entirely automated and the relationship of those data to existing wetlands data. As such, there may be issues that result from the different vintages of these products, as well as the errors included in each. While not perfect, the data should provide an example of what level of detail would be possible through such higher-resolution mapping. These data are not jurisdictional or intended for use in litigation. NOAA does not assume liability for any damages or misrepresentations caused by inaccuracies in the data, or as a result of the data used on a particular system. NOAA makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.
These detailed products bring NOAA\u2019s national land cover mapping framework to the local level and are developed for specific project-based geographies (not the entire coastal land cover mapping boundary). Data are often developed in partnership with state and local groups. Attributes for this product are as follows: 0 Background, 1 Unclassified (Cloud, Shadow, etc), 2 Impervious, 3 4 5 Developed Open Space, 6 Cultivated Land, 7 Pasture/Hay, 8 Grassland, 9 Deciduous Forest, 10 Evergreen Forest, 11 Mixed Forest, 12 Scrub/Shrub, 13 Palustrine Forested Wetland, 14 Palustrine Scrub/Shrub Wetland, 15 Palustrine Emergent Wetland, 16 Estuarine Forested Wetland, 17 Estuarine Scrub/Shrub Wetland, 18 Estuarine Emergent Wetland, 19 Unconsolidated Shore, 20 Bare Land, 21 Open Water, 22 Palustrine Aquatic Bed, 23 Estuarine Aquatic Bed, 24 Tundra, 25 Snow/Ice, Recommended Citation. NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database.
This dataset was created by NOAA's Ocean Service, Office for Coastal Management (OCM). Random Forest Classification: The initial 1m spatial resolution 6 class high resolution land cover product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. You can read a sample metadata file here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ccap_mlc/#citation","title":"Citation","text":"National Oceanic and Atmospheric Administration, Office for Coastal Management. \u201cName of Data Set.\u201d Coastal Change Analysis Program (C-\nCAP) 10m-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed [Month Year] at www.coast.noaa.gov/\nhtdata/raster1/landcover/bulkdownload/hires/.\nNational Oceanic and Atmospheric Administration, Office for Coastal Management. \u201cName of Data Set.\u201d Coastal Change Analysis Program (C-\nCAP) 30m-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed [Month Year] at www.coast.noaa.gov/\nhtdata/raster1/landcover/bulkdownload/hires/.\nThe regional land cover dataset files were downloaded for each region. If the files were img then they were convert to GeoTIFF. Each region was converted into a collection and start and end dates were added based on available information and filenames.
"},{"location":"projects/ccap_mlc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var CCAP_LC10 = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/ccap_10m\");\nvar CCAP_LC30 = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/ccap_30m\");\nSample Code LC: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-LC-BETA
var CCAP_IMP30 = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/ccap_30m_impervious\");\nSample Code Impervious: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-IMPERVIOUS
"},{"location":"projects/ccap_mlc/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.
Produced by: NOAA's Ocean Service, Office for Coastal Management (OCM)
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, OBIA, NOAA
Last updated on GEE: 2022-06-12
"},{"location":"projects/ccap_wpotential/","title":"C-CAP Wetland Potential 30m","text":"The probability rating which covers landcover mapping provides a continuum of wetness from dry to water. The layer is not a wetland classification but provides the wetland likelihood at a specific location. The rating was developed through a modelling process combining multiple GIS and remote sensing data sets including soil characteristics, elevation, existing wetland inventories, hydrographical extents and satellite imagery . Data can be downloaded here. This classification is based on GIS and remote sensing data sets with variable ranges from the 1977 to 2010.
This dataset was created by NOAA's Ocean Service, Office for Coastal Management Initial Classification: 1m spatial resolution land cover data developed by the Chesapeake Bay Conservancy, University of Vermont Spatial Analysis Laboratory, and The Virginia Geographic Information Network (VGIN) was the starting point for this dataset. This product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework applied to NAIP imagery and Lidar data. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. The resulting objects are the primary units for analysis. The original dataset can be downloaded here:https://chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/.
The resulting 1-meter land cover was then resampled to a 10-meter raster. This was done using a 10x10 focal pixel window to compute the percent value associated with either the impervious or tree classes, for each land cover type in the 100 pixel neighborhood around each target pixel, and subsequently performing a nearest neighbor resampling of the resulting values. The output values were then coded to the appropriate percentage (between 0 and 100). This 10-meter raster was resampled a second time, using the average of values within a 3x3 focal pixel window in order to obtain the appropriate values over each 30-meter pixel area. Output values between 0 and 100 represent the appropriate percentage mapped within each pixel. Class 127 identifies areas not included in this mapping, or no data areas.
"},{"location":"projects/ccap_wpotential/#class-values","title":"Class values","text":"0: This is the value for nodata. 1: The value indicates there is an extremely low likelihood of wetness. 2-9: The value indicates a likelihood of wetness, where 1 is very unlikely and 9 is highly likely. 10: The value indicates open water.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ccap_wpotential/#citation","title":"Citation","text":"National Oceanic and Atmospheric Administration, Office for Coastal Management. \u201cName of Data Set.\u201d Coastal Change Analysis Program (C-\nCAP) Wetland Potential Layer: NOAA Office for Coastal Management. Accessed [Month Year] at https://coast.noaa.gov/htdata/raster1/landcover/bulkdownload/wetlandpotential/.\nvar ccap_wetland_potential = ee.Image(\"projects/sat-io/open-datasets/NOAA/conus_ccap_wetland_potential\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-WETLAND-POTENTIAL
"},{"location":"projects/ccap_wpotential/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.
Produced by: NOAA's Ocean Service, Office for Coastal Management (OCM)
Curated in GEE by: Samapriya Roy
Keywords: Wetland, Coastal data, NOAA, Remote Sensing
Last updated on GEE: 2022-05-17
"},{"location":"projects/cci_agb/","title":"ESA CCI Global Forest Above Ground Biomass","text":"This dataset provides estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019, and 2020. These estimates are derived from a combination of Earth observation data, depending on the year, obtained from the Copernicus Sentinel-1 mission, Envisat's ASAR instrument, and JAXA's Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from other Earth observation sources. The dataset has been generated as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) program by the Biomass CCI team.
The dataset includes multi-temporal observations at L-band for all biomes and for each year. The above-ground biomass (AGB) maps utilize revised allometries, which are now based on a more extensive collection of spaceborne LiDAR data from the GEDI and ICESat-2 missions. The retrieval algorithm now incorporates temporal information to capture and preserve biomass dynamics as expressed in the remote sensing data.
The data products consist of two (2) global layers that include estimates of:
Above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots
Per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/cci_agb/#dataset-preprocessing-for-gee","title":"Dataset preprocessing for GEE","text":"Santoro, M.; Cartus, O. (2023): ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years\n2010, 2017, 2018, 2019 and 2020, v4. NERC EDS Centre for Environmental Data Analysis, 21 April 2023. doi:10.5285/af60720c1e404a9e9d2c145d2b2ead4e.\nhttps://dx.doi.org/10.5285/af60720c1e404a9e9d2c145d2b2ead4e\nvar agb = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/ESA_CCI_AGB\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/ESA-CCI-ABOVEGROUND-BIOMASS
"},{"location":"projects/cci_agb/#license-and-access","title":"License and Access","text":"Use of these data is covered by the license information found here. The CCI BIOMASS datasets have been processed by the CCI BIOMASS consortium led by the University of Aberystwyth (U.K.). They are made available to the public by ESA and the consortium. When using these data you must cite them correctly using the citation given on the catalogue record. The dataset is under a public access with access to these data available to both registered and non-registered users
Created by: CCI BIOMASS consortium led by the University of Aberystwyth (U.K.)
Curated in GEE by: Samapriya Roy
Keywords: satellite observation, forest, biomass
Created: 2023-02-07
Last updated in GEE: 2023-07-02
"},{"location":"projects/cci_lc/","title":"CCI LAND COVER S2 PROTOTYPE LAND COVER 20M MAP OF AFRICA 2016","text":"The CCI Land Cover (LC) S2 prototype is a high resolution LC map at 20m over Africa based on 1 year of Sentinel-2A observations from December 2015 to December 2016. The main objective of the 'S2 prototype LC map at 20m of Africa 2016' release was to collect users feedback for further improvements. The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.
The legend of the S2 prototype LC 20m map of Africa 2016 was built after reviewing various existing typologies (e.g. LCCS, LCML\u2026), global (e.g. GLC-share, GlobeLand30) and national experiences (Africover, SERVIR-RMCD). The legend includes 10 generic classes that appropriately describe the land surface at 20m: \"trees cover areas\", \"shrubs cover areas\", \"grassland\", \"cropland\", \"vegetation aquatic or regularly flooded\", \"lichen and mosses / sparse vegetation\", \"bare areas\", \"built up areas\", \"snow and/or ice\" and \"open water\".
Among the Land Cover classes, two of them were largely identified thanks to external dataset: the \"open water\" class was based on the Global Surface Water product from JRC/EC and the \"urban areas\" relied both on the Global Human Settlement Layer from JRC/EC and on the Global Urban Footprint from DLR. Two classification algorithms, the Random Forest (RF) and Machine Learning (ML), were chosen to transform the cloud-free reflectance composites generated by the pre-processing module into a land cover map. The two maps resulting from both approaches are then combined either to select the best representation of a land cover class which will be part of the final S2 prototype LC 20m map of Africa 2016 or, in case of unreliable LC class delineation, the reference layer is used to consolidate the land cover classification.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/cci_lc/#earth-engine-snippet","title":"Earth Engine snippet","text":"var esa_cci = ee.Image(\"projects/sat-io/open-datasets/ESA/ESACCI-LC-L4-LC10-Map-20m-P1Y-2016-v10\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCI-LC-20M-AFRICA
"},{"location":"projects/cci_lc/#license-and-terms-of-use","title":"License and Terms of Use","text":"The present product is made available to the public by ESA and the consortium. You may use S2 prototype LC 20m map of Africa 2016 for educational and/or scientific purposes, without any fee on the condition that you credit the ESA Climate Change Initiative and in particular its Land Cover project as the source of the CCI-LC database:
Copyright notice:\n\u00a9 Contains modified Copernicus data (2015/2016)\n\u00a9 ESA Climate Change Initiative - Land Cover project 2017\nBy downloading the prototype product you recognize that this prototype is not a final product and you are aware of the consequences of using a prototype that has not been validated. By downloading the prototype product, you also commit to fill the User Feedback Form (see below). Should you write any scientific publication on the results of research activities that use one or several CCI-LC products as input, you shall acknowledge the ESA CCI Land Cover project in the text of the publication and provide the project with an electronic copy of the publication (due@esa.int).
If you wish to use S2 prototype LC 20m map of Africa 2016 in advertising or in any commercial promotion, you shall acknowledge the ESA CCI Land Cover project and you must submit the layout to the project for approval beforehand (due@esa.int).
Created by: ESA
Curated in GEE by : Samapriya Roy
keywords: Landcover, Sentinel-2 Landcover, ESA, Africa Landcover
Last updated on GEE: 2023-02-27
"},{"location":"projects/cems_fire/","title":"CEMS Fire Danger Indices","text":"Fire danger indices from the ECMWF, calculated using weather forecasts from historical simulations provided by ECMWF ERA5 reanalysis.The CEMS Fire Danger Indices dataset provides a comprehensive set of indices designed to assess and quantify fire danger and wildfire risk at regional and global scales. You can get additional information on the dataset here and on the climateengine.org dataset page here
Build-up Index: The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes.
Burning Index: The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same.
Drought Code: The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800.
Drought Factor: The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum
Duff Moisture Code: The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150.
Energy Release Component: The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential \"heat release\" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or \"build-up\" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative.
Fine Fuel Moisture Code: The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96.
Fire Daily Severity Rating: Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value.
Fire Danger Index: The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation
Fire Weather Index:The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions.
Ignition Component: The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions.
Initial Fire Spread Index: The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1.
Keetch-Byram Drought Index: The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation.
Spread Component: The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit.
Spatial Extent Global Spatial Resolution ~25km (0.25 deg) Temporal Resolution Daily Time Span 1940-01-01 to Present Update Frequency Updated daily with one week lag
Variables Build-up Index ('build_up_index') - Units: Dimensionless - Scale Factor: 1.0 Burning Index ('burning_index') - Units: Dimensionless - Scale Factor: 1.0 Drought Code ('drought_code') - Units: Dimensionless - Scale Factor: 1.0 Drought Factor ('drought_factor') - Units: Dimensionless - Scale Factor: 1.0 Duff Moisture Code ('duff_moisture_code') - Units: Dimensionless - Scale Factor: 1.0 Energy Release Component ('energy_release_component') - Units: J/m2 - Scale Factor: 1.0 Fine Fuel Moisture Code ('fine_fuel_moisture_code') - Units: Dimensionless - Scale Factor: 1.0 Fire Daily Severity Rating ('fire_daily_severity_rating') - Units: Dimensionless - Scale Factor: 1.0 Fire Danger Index ('fire_danger_index') - Units: Dimensionless - Scale Factor: 1.0 Fire Weather Index ('fire_weather_index') - Units: Dimensionless - Scale Factor: 1.0 Ignition Component ('ignition_component') - Units: % - Scale Factor: 1.0 Initial Fire Spread Index ('initial_fire_spread_index') - Units: Dimensionless - Scale Factor: 1.0 Keetch-Byram Drought Index ('keetch_byram_drought_index') - Units: Dimensionless - Scale Factor: 1.0 Spread Component ('spread_component') - Units: Dimensionless - Scale Factor: 1.0
"},{"location":"projects/cems_fire/#citation","title":"Citation","text":"Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libert\u00e1, G., & Krzeminski, B. (2020). ERA5-based global\nmeteorological wildfire danger maps. Scientific data, 7(1), 1-11. 'Contains modified Copernicus Climate Change Service information [Year]'\n\nCopernicus Climate Change Service, Climate Data Store, (2019): Fire danger indices historical data from the Copernicus Emergency Management\nService. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.0e89c522 (Accessed on DD-MMM-YYYY)\n// Read in Image Collection and get most recent image\nvar cems_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-cems-fire-daily-4-1')\nvar cems_i = cems_ic.filterDate('2023-01-01', ee.Date(Date.now())).sort('system:time_start', false).first()\n\n// Print first image to see bands\nprint(cems_i)\n\n// Visualize select bands from first image - additional variables are available in the Image Collection\nvar fire_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(cems_i.select('burning_index'), {min: 0, max: 50, palette: fire_palette}, 'burning_index')\nMap.addLayer(cems_i.select('fire_weather_index'), {min: 0, max: 50, palette: fire_palette}, 'fire_weather_index')\nMap.addLayer(cems_i.select('fire_danger_index'), {min: 0, max: 50, palette: fire_palette}, 'fire_danger_index')\nMap.addLayer(cems_i.select('ignition_component'), {min: 0, max: 50, palette: fire_palette}, 'ignition_component')\nMap.addLayer(cems_i.select('spread_component'), {min: 0, max: 10, palette: fire_palette}, 'spread_component')\nMap.addLayer(cems_i.select('energy_release_component'), {min: 0, max: 50, palette: fire_palette}, 'energy_release_component')\nMap.addLayer(cems_i.select('fire_daily_severity_rating'), {min: 0, max: 50, palette: fire_palette}, 'fire_daily_severity_rating')\nThe license for CEMS Fire Danger Indices data is the Copernicus Licence to Use Copernicus Products (the \"Licence\"). The Licence is a modified Creative Commons Attribution 4.0 International (CC BY 4.0) license, with the following additional terms: * You must acknowledge the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF) as the source of the data. * You must not use the data for commercial purposes without prior permission from the European Commission. * You must not modify the data in a way that could mislead the public about its source or accuracy.
Data are subject to the License to Use Copernicus Products found here
Keywords: ECMWF, Copernicus, wildfire, climate, reanalysis, ERA5, daily, near real-time
Provided by : Copernicus
Curated in GEE by: ClimateEngine Org
"},{"location":"projects/cflux/","title":"Global Forest Carbon Fluxes (2001-2023)","text":"Net forest carbon flux represents the net exchange of carbon between forests and the atmosphere between 2001 and 2023, calculated as the balance between carbon emitted by forests and removed by (or sequestered by) forests during the model period (megagrams CO2 emissions/ha). Net carbon flux is calculated by subtracting average annual gross removals from average annual gross emissions in each modeled pixel; negative values are where forests were net sinks of carbon and positive values are where forests were net sources of carbon between 2001 and 2023. Net fluxes are calculated following IPCC Guidelines for national greenhouse gas inventories in each pixel where forests existed in 2000 or were established between 2000 and 2012 according to the Global Forest Change tree cover change data of Hansen et al. (2013). This layer reflects the cumulative net flux during the model period (2001-2023) and must be divided by 23 to obtain average annual net flux; net flux values cannot be assigned to individual years of the model.
Forest carbon removals from the atmosphere (sequestration) by forest sinks represent the cumulative carbon captured (megagrams CO2/ha) by the growth of established and newly regrowing forests during the model period between 2001-2023. Removals include accumulation of carbon in both aboveground and belowground live tree biomass. Following IPCC Tier 1 assumptions for forests remaining forests, removals by dead wood, litter, and soil carbon pools are assumed to be zero. In each pixel, carbon removals are calculated following IPCC Guidelines for national greenhouse gas inventories where forests existed in 2000 or were established between 2000 and 2012 according to the Global Forest Change tree cover loss data of Hansen et al. (2013). Carbon removed by each pixel is based on maps of forest type (e.g., mangrove, plantation), ecozone (e.g., humid Neotropics), forest age (e.g., primary, old secondary), and number of years of carbon removal. This layer reflects the cumulative removals during the model period (2001-2023) and must be divided by 23 to obtain an annual average during the model duration; removal rates cannot be assigned to individual years of the model.
Forest carbon emissions represent the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modeled year (megagrams CO2 emissions/ha, between 2001 and 2022). Emissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil) and greenhouse gases (CO2, CH4, N2O). Emissions estimates for each pixel are calculated following IPCC Guidelines for national greenhouse gas inventories where stand-replacing disturbance occurred, as mapped in the Global Forest Change annual tree cover loss data of Hansen et al. (2013). The carbon emitted from each pixel is based on carbon densities in 2000, with adjustment for carbon accumulated between 2000 and the year of disturbance. Emissions reflect a gross estimate, i.e., carbon removals from subsequent regrowth are not included. Instead, gross carbon removals resulting from subsequent regrowth after clearing are accounted for in the companion forest carbon removals layer. The fraction of carbon emitted from each pixel upon disturbance (emission factor) is affected by several factors, including the direct driver of disturbance, whether fire was observed in the year of or preceding the observed disturbance event, whether the disturbance occurred on peat, and more. All emissions are assumed to occur in the year of disturbance. Emissions can be assigned to a specific year using the Hansen tree cover loss data.
All three layers are part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist governments and non-government actors with tracking greenhouse gas fluxes from forests and decreasing emissions or increasing removals by forests. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). Please also find the dataset on Global Forest Watch
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/cflux/#dataset-updates","title":"Dataset updates","text":"Each year, the tree cover loss, drivers of tree cover loss, and burned area are updated. In 2023 and 2024, a few model input data sets and constants were changed as well, as described below. Please refer to this blog post for more information.
"},{"location":"projects/cflux/#citation","title":"Citation","text":"Harris, N.L., Gibbs, D.A., Baccini, A. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234\u2013240 (2021).\nhttps://doi.org/10.1038/s41558-020-00976-6\nvar emissions = ee.ImageCollection(\"projects/sat-io/open-datasets/forest_carbon_fluxes/gross_emissions\");\nvar removals = ee.ImageCollection(\"projects/sat-io/open-datasets/forest_carbon_fluxes/gross_removals\");\nvar net_flux = ee.ImageCollection(\"projects/sat-io/open-datasets/forest_carbon_fluxes/net_flux\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FOREST-CARBON-FLUXES
"},{"location":"projects/cflux/#license","title":"License","text":"The Global Forest Carbon Fluxes (2001-2023) products are provided free of charge, without restriction of use. For the full license information see the Creative Commons Attribution 4.0 International License publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.
Created by: Harris, N.L., Gibbs, D.A., Baccini, A. et al
Curated in GEE by: Samapriya Roy
Keywords: Carbon emissions, forest change, climate, carbon
Last updated on GEE: 2024-06-06
"},{"location":"projects/cflux/#changelog","title":"Changelog","text":"The Climate Hazards Center InfraRed Precipitation With Station Data-Prelim (CHIRPS-Prelim) is a blend of CHIRPS data with in situ precipitation data to unbias the data and enhance its accuracy. The process of generating CHIRPS- Prelim is similar to the CHIRPS process, with the main difference being its reliance on Global Telecommunication System (GTS) stations only, which are available in near-real time. Blending of CHIRP with GTS-only stations allows for the latency of CHIRPS- Prelim to be <5 days. Note that, in general, the differences in CHIRPS-Prelim and CHIRPS are within acceptable limits, as both data sets share the same climatological mean. You can find additional information here and on climate org dataset page here.
This dataset is to be used in conjunction with CHIRPS Pentad/Daily collections, which are Earth Engine assets at: - UCSB-CHG/CHIRPS/PENTAD
Spatial Information
Parameter Value Spatial extent Global Spatial resolution 4.8-km grid (1/20 deg) Temporal resolution 5-day (pentad) Time span 2015 to present Update frequency Updated weeklyVariables
Variable Details Precipitation ('precipitation') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/chirps_prelim/#citation","title":"Citation","text":"Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.\nP., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p.,\nhttp://dx.doi.org/10.3133/ds832\n// Read in Image Collections and get single image\nvar chirps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-chirps-prelim-pentad')\nvar chirps_i = chirps_ic.first()\n\n// Print single image to see bands\nprint(chirps_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(chirps_i.select('precipitation'), {min: 0, max: 200, palette: prec_palette}, 'precipitation')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CHIRPS-PRELIM
"},{"location":"projects/chirps_prelim/#license","title":"License","text":"This datasets are in the public domain. To the extent possible under law, Pete Peterson has waived all copyright and related or neighboring rights to Climate Hazards Group Infrared Precipitation with Stations (CHIRPS).
Keywords: precipitation, near real-time, climate, CHIRPS
Provided by: Climate Hazards Group Infrared Precipitation with Stations (CHIRPS)
Curated in GEE by: Climate Engine org
"},{"location":"projects/cisi/","title":"Harmonized Global Critical infrastructure & Index (CISI)","text":"Critical infrastructure (CI) is fundamental for the functioning of a society and forms the backbone for socio-economic development. Natural and human-made threats, however, pose a major risk to CI. Therefore, geospatial data on the location of CI are fundamental for in-depth risk analyses, which are required to inform policy decisions aiming to reduce risk. We present a first-of-its-kind globally harmonized spatial dataset for the representation of CI.
In this study the users generated: (1) a harmonized detailed geospatial data of the world\u2019s main CI systems into a single geospatial database; and (2) a Critical Infrastructure Spatial Index (CISI) to express the global spatial intensity of CI. The datasets are generated from Open Streetmap extract from 8th January 2021 using https://planet.openstreetmap.org/. You can read the full paper here. You can download the spatial extracts for both the feature type and the Critical Infrastructure Spatial Index (CISI) here
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/cisi/#paper-citation","title":"Paper citation","text":"Nirandjan, S., Koks, E.E., Ward, P.J. et al. A spatially-explicit harmonized global dataset of critical infrastructure. Sci Data 9, 150 (2022).\nhttps://doi.org/10.1038/s41597-022-01218-4\nNirandjan, Sadhana, Koks, Elco E., Ward, Philip J., & Aerts, Jeroen C.J.H. (2021). A spatially-explicit harmonized global dataset of critical infrastructure (v1.0.0.)\n[Data set]. Zenodo. https://doi.org/10.5281/zenodo.4957647\nvar global_CISI = ee.Image(\"projects/sat-io/open-datasets/CISI/global_CISI\");\nvar infrastructure = ee.ImageCollection(\"projects/sat-io/open-datasets/CISI/amount_infrastructure\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/CRITICAL-INF-SPATIAL-INDEX(CISI)
"},{"location":"projects/cisi/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International license.
Produced by : Nirandjan, S., Koks, E.E., Ward, P.J. et al
Curated in GEE by : Samapriya Roy
Keywords: : Development indicator, global spatial data, gridded data, critical infrastructure, spatial index
Last updated on GEE: 2022-05-12
"},{"location":"projects/climate_trace/","title":"Climate Trace Global Emissions Data","text":"Climate TRACE is a non-profit coalition that has unveiled an open emissions database containing more than 352 million assets. The database provides a comprehensive accounting of greenhouse gas (GHG) emissions based primarily on direct, independent observation. It includes every country and territory in the world and covers various emitting activities such as energy production, industrial processes, and land use. The data are derived from satellites, remote sensing, and other public and commercial sources, making it the most comprehensive and granular dataset of recent GHG emissions ever created. The inventory allows for transparent assessment of each country's progress toward emission reduction goals.
For more detailed information, you can visit the Climate TRACE website at climatetrace.org.
"},{"location":"projects/climate_trace/#dataset-preprocessing","title":"Dataset preprocessing","text":"The downloaded datasets were processed to get system:time_start and system:time_end in epoch time and these were added to the data column in GEE. Each sector and their associated emissions dataset sources were processed providing 38,731,650 total features. Not all sectors had emissions locations and some were provided only at a country level.
"},{"location":"projects/climate_trace/#citation","title":"Citation","text":"Climate TRACE - Tracking Realtime Atmospheric Carbon Emissions (2022), Climate TRACE Emissions Inventory,\nhttps://climatetrace.org [Date Accessed].\nFor individual sectors refer to citation information from the downloads page.
"},{"location":"projects/climate_trace/#metadata-descriptors","title":"Metadata Descriptors","text":"Expand to show data attributes and definitions for the emissions databaseData-attribute Definition source_id The internal Climate TRACE identifier for each individual source of emissions. source_name Name of the entity or source that produced the emissions. source_type Description of the emission source classification. iso3_country Corresponds to the ISO 3166-1 alpha-3 specification of the country where the entity is physically located. original_inventory_sector Intergovernmental Panel on Climate Change (IPCC) emissions sector to which the emissions source belongs. start_time The time using Coordinated Universal Time (UTC) of emissions, either as an instance of start time of observation. end_time The time using Coordinated Universal Time (UTC) of emissions, either as an instance of end time of observation. lat Approximate latitude location of the source. lon Approximate longitude location of the source. geometry_ref Corresponds to the reference id to the geopackage file present in the downloads. gas Greenhouse gases for which emissions are reported in metric tonnes. emissions_quantity Quantity of gas emitted in metric tonnes. temporal_granularity Resolution of the data available. activity Activity of the entity producing the emissions, not including units. activity_units Units of reported \"activity\". emissions_factor Emissions factor of reported activity. emissions_factor_units Units of reported \"emissions factor\" field. capacity Capacity of the entity producing emissions, not including units. capacity_units Units of reported \"capacity\" field. capacity_factor Corresponds to the ratio of the actual source output (activity) to the source capacity. capacity_factor_units Units of repored \"capacity_factor\" field. other1 Additional data field available for the sub-sector. other1_def Definition of reported data of Other1 field. other2 Additional data field available for the sub-sector. other2_def Definition of reported data of Other2 field. other3 Additional data field available for the sub-sector. other3_def Definition of reported data of Other3 field. other4 Additional data field available for the sub-sector. other4_def Definition of reported data of Other4 field. other5 Additional data field available for the sub-sector. other5_def Definition of reported data of Other5 field. other6 Additional data field available for the sub-sector. other6_def Definition of reported data of Other6 field. other7 Additional data field available for the sub-sector. other7_def Definition of reported data of Other7 field. other8 Additional data field available for the sub-sector. other8_def Definition of reported data of Other8 field. other9 Additional data field available for the sub-sector. other9_def Definition of reported data of Other9 field. other10 Additional data field available for the sub-sector. other10_def Definition of reported data of Other10 field. created_date Date emissions source was added to the Climate TRACE database. modified_date Last date on which any updates were made to the dataset for the specific source.
"},{"location":"projects/climate_trace/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aluminum = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/aluminum_emissions-sources\");\nvar bauxiteMining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/bauxite-mining_emissions-sources\");\nvar cement = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/cement_emissions-sources\");\nvar chemicals = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/chemicals_emissions-sources\");\nvar coalMining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/coal-mining_emissions-sources\");\nvar copperMining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/copper-mining_emissions-sources\");\nvar croplandFires = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/cropland-fires_emissions-sources\");\nvar domesticAviation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/domestic-aviation_emissions-sources\");\nvar domesticShipping = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/domestic-shipping_emissions-sources\");\nvar electricityGeneration = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/electricity-generation_emissions-sources\");\nvar entericFermentationCattleFeedlot = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/enteric-fermentation-cattle-feedlot_emissions-sources\");\nvar entericFermentationCattlePasture = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/enteric-fermentation-cattle-pasture_emissions-sources\");\nvar forestLandClearing = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/forest-land-clearing_emissions-sources\");\nvar forestLandDegradation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/forest-land-degradation_emissions-sources\");\nvar forestLandFires = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/forest-land-fires_emissions-sources\");\nvar internationalAviation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/international-aviation_emissions-sources\");\nvar internationalShipping = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/international-shipping_emissions-sources\");\nvar ironMining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/iron-mining_emissions-sources\");\nvar manureLeftOnPastureCattle = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/manure-left-on-pasture-cattle_emissions-sources\");\nvar manureManagementCattleFeedlot = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/manure-management-cattle-feedlot_emissions-sources\");\nvar netForestLand = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/net-forest-land_emissions-sources\");\nvar netShrubgrass = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/net-shrubgrass_emissions-sources\");\nvar netWetland = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/net-wetland_emissions-sources\");\nvar oilAndGasRefining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/oil-and-gas-refining_emissions-sources\");\nvar otherManufacturing = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/other-manufacturing_emissions-sources\");\nvar petrochemicals = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/petrochemicals_emissions-sources\");\nvar pulpAndPaper = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/pulp-and-paper_emissions-sources\");\nvar removals = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/removals_emissions-sources\");\nvar riceCultivation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/rice-cultivation_emissions-sources\");\nvar roadTransportation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/road-transportation_emissions-sources\");\nvar shrubgrassFires = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/shrubgrass-fires_emissions-sources\");\nvar solidWasteDisposal = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/solid-waste-disposal_emissions-sources\");\nvar steel = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/steel_emissions-sources\");\nvar syntheticFertilizerApplication = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/synthetic-fertilizer-application_emissions-sources\");\nvar wastewaterTreatmentAndDischarge = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/wastewater-treatment-and-discharge_emissions-sources\");\nvar waterReservoirs = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/water-reservoirs_emissions-sources\");\nvar wetlandFires = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/wetland-fires_emissions-sources\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/CLIMATE-TRACE-EMISSIONS
"},{"location":"projects/climate_trace/#license","title":"License","text":"All Climate TRACE data is freely available under the Creative Commons Attribution 4.0 International Public License, unless otherwise noted below
Created by: Climate Trace
Curated in GEE by: Samapriya Roy
Keywords: Climate Trace, Emissions, Sectors, Agriculture, Buildings, Fossil Fuel Operations, Forestry And Land Use, Manufacturing, Mineral Extraction, Power, Transportation, Waste
Last updated: 2024-01-17
"},{"location":"projects/cloudsen12/","title":"CloudSEN12 Global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2","text":"CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs) that consists of 49,400 image patches (IP) that are evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge cloud detection algorithms. Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates that match one of the following cloud cover groups:
clear (0%)
low-cloudy (1% - 25%)
almost clear (25% - 45%)
mid-cloudy (45% - 65%)
cloudy (65% >)
The dataset is available here. For more details check out the website and you can read the preprint of the paper here
"},{"location":"projects/cloudsen12/#data-citation","title":"Data Citation","text":"Aybar, C. et al. CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.\nScience Data Bank https://doi.org/10.57760/sciencedb.06669 (2022).\nAybar, C., Ysuhuaylas, L., Loja, J. et al. CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.\nSci Data 9, 782 (2022). https://doi.org/10.1038/s41597-022-01878-2\nCurrently included layers are:
"},{"location":"projects/cloudsen12/#earth-engine-snippet-hand-crafted-labels-high-quality","title":"Earth Engine Snippet: Hand-crafted labels - high-quality","text":"var cs12_high = ee.ImageCollection(\"projects/sat-io/open-datasets/cloudsen12/high\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-HIGH-QUALITY
"},{"location":"projects/cloudsen12/#earth-engine-snippet-hand-crafted-labels-scribble","title":"Earth Engine Snippet: Hand-crafted labels - scribble","text":"var cs12_scribble = ee.ImageCollection(\"projects/sat-io/open-datasets/cloudsen12/scribble\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-SCRIBBLE-QUALITY
"},{"location":"projects/cloudsen12/#earth-engine-snippet-hand-crafted-labels-nolabel","title":"Earth Engine Snippet: Hand-crafted labels - nolabel","text":"var cs12_nolabel = ee.ImageCollection(\"projects/sat-io/open-datasets/cloudsen12/nolabel\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-NO-LABEL
"},{"location":"projects/cloudsen12/#earth-engine-snippet-ips-footprint","title":"Earth Engine Snippet: IPs footprint","text":"var cs12_geom = ee.ImageCollection(\"projects/sat-io/open-datasets/cloudsen12/footprint\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-FOOTPRINT
"},{"location":"projects/cloudsen12/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated in GEE by: Samapriya Roy
Keywords: cloud, deep learning, Sentinel-2, Sentinel-1, U-Net
Last updated: 2022-09-18
"},{"location":"projects/cmapper/","title":"Carbon Mapper Data Portal Methane Emissions","text":"NoteThis dataset is currently only available to those in the insiders program
The Carbon Mapper data portal focused on collecting individual high emission methane point sources. The Carbon mapper portal provides methane plume imagery with emission rates and uncertainties from strong point sources as observed from NASA\u2019s next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and ASU\u2019s Global Airborne Observatory (GAO) airborne platforms.
As per the portal, these systems have near-identical capabilities and serve as prototypes for future sustained global monitoring. The plume concentration maps are available at spatial resolutions ranging from 3 to 8 meters (depending on aircraft altitude), allowing for precise attribution to individual emission sources.The API end point was used to query the overall area over North America yielding 8327 total sites.
You can use the download button too to download the curated zipped plume and geotiff data if you are inclined to use those. You can read their FAQ here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/cmapper/#citation","title":"Citation","text":"As per the portal the following papers were used to generate the portal and the user should use the appropriate ones as deemed fit
Multi-basin analysis: San Joaquin, Permian, Uinta, Denver-Julesburg, Marcellus (Data from 2020-2021)\n\nCusworth, D. H., Thorpe, A. K., Ayasse, A. K., Stepp, D., Heckler, J., Asner, G. P., Miller, C. E., Chapman, J. W., Eastwood, M. L., Green, R. O.,\nHmiel, B., Lyon, D., & Duren, R. M. (2022). Strong methane point sources contribute a disproportionate fraction of total emissions across multiple\nbasins in the U.S. PNAS. https://www.pnas.org/doi/10.1073/pnas.2202338119\n\nGulf of Mexico offshore platforms (Data from 2021)\nAyasse, A. K., Thorpe, A. K., Cusworth, D. H., Kort, E. A., Negron, A. G., Heckler, J., Asner, G., & Duren, R. M. (2022). Methane remote sensing and\nemission quantification of offshore shallow water oil and gas platforms in the Gulf of Mexico. Environmental Research Letters, 17(8), 084039.\nhttps://doi.org/10.1088/1748-9326/ac8566\n\nPermian point-source data (Data from 2019)\nCusworth, D. H., Duren, R. M., Thorpe, A. K., Olson-Duvall, W., Heckler, J., Chapman, J. W., Eastwood, M. L., Helmlinger, M. C., Green, R. O.,\nAsner, G. P., Dennison, P. E., & Miller, C. E. (2021). Intermittency of large methane emitters in the Permian Basin. Environmental Science &\nTechnology Letters, 8(7), 567\u2013573. https://doi.org/10.1021/acs.estlett.1c00173\n\nCalifornia methane point-source data (Data from 2016-2017)\nDuren, R. M., Thorpe, A. K., Foster, K. T., Rafiq, T., Hopkins, F. M., Yadav, V., Bue, B. D., Thompson, D. R., Conley, S., Colombi, N. K.,\nFrankenberg, C., McCubbin, I. B., Eastwood, M. L., Falk, M., Herner, J. D., Croes, B. E., Green, R. O., & Miller, C. E. (2019). California\u2019s methane\nsuper-emitters. Nature, 575(7781), 180\u2013184. https://doi.org/10.1038/s41586-019-1720-3\n\nDuren, R., Thorpe, A., & McCubbin, I. (2020). The California Methane Survey Final Report, CEC-500-2020-047. https://ww2.energy.ca.gov/\n2020publications/CEC-500-2020-047/CEC-500-2020-047.pdf\nBased on the Plume extracts that were also available from the data portal certain assumptions and metadata fields were renamed. The campaign-id field was used a plume-id field for all of these observation. While there is a separate plume-id field in the original metadata it seems to be missing for some observations and as such the use of campaign id. This was also to keep alignment with the way in which the plume extracts were created by the data portal itself. Flux rate and Flux uncertainity were also renamed to emission and emission uncertainity.
The plume geotiffs were provided as RGB for rendering to a fixed hex code and color palette, so the metadata was attached to each of these raster geotiff in the collection. The portal also provided the RGB underlying imagery and that too was ingested in a separate collection with the same metadata for easy join. Finally the results were exported into a table, all downloaded for S3 URLs were automated using a custom script I wrote and included in the metadata.
"},{"location":"projects/cmapper/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var plume_geotiffs = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon-mapper/plume_geo\");\nvar rgb_geotiffs = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon-mapper/rgb_geo\");\nvar plume_features = ee.FeatureCollection(\"projects/sat-io/open-datasets/carbon-mapper/plume_feature\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/CARBON-MAPPER-METHANE-EMISSIONS
"},{"location":"projects/cmapper/#license","title":"License","text":"Carbon Mapper data is provided for non-commercial purposes subject to the Modified Creative Commons Attribution ShareAlike 4.0 International Public License (\"CC License\"). All third-party use of the data is subject to the CC license at all times. The license details includes terms for Non commercial use and share alike clauses and you can read through the modified terms.
Provided by: Carbon Mapper, Inc.
Curated in GEE by : Samapriya Roy
keywords: Methane Emissions, AVIRIS-NG, Global Airborne Observatory, Plume Emissions , Carbon Mapper Data Portal
Last updated on GEE: 2023-04-16
"},{"location":"projects/cpc_morph/","title":"Climate Prediction Center (CPC) Morphing Technique (MORPH)","text":"The Satellite Precipitation - CMORPH Climate Data Record (CDR) consists of satellite precipitation estimates that have been bias corrected and reprocessed using the Climate Prediction Center (CPC) Morphing Technique (MORPH) to form a global, high resolution precipitation analysis at a 25-km (\u00bd-deg x \u00bd-deg) spatial resolution updated daily from 1980-present. Data is reprocessed on a global grid with daily temporal resolution. You can get additional information here or on climate engine org page here.
"},{"location":"projects/cpc_morph/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Global Spatial resolution 25-km (\u00bd-deg x \u00bd-deg) Temporal resolution Daily Time span 1998-01-01 to present Update frequency Updated daily with 2-day lagVariables
Variable Details Precipitation ('precip') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/cpc_morph/#citation","title":"Citation","text":"Xie, Pingping; Joyce, Robert; Wu, Shaorong; Yoo, S.-H.; Yarosh, Yelena; Sun, Fengying; Lin, Roger, NOAA CDR Program (2019): NOAA Climate Data Record\n(CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1 [indicate subset].\nNOAA National Centers for Environmental Information.\n// Read in Image Collections and get single image\nvar cmorph_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-cpc-cmorph/daily')\nvar cmorph_i = cmorph_ic.first()\n\n// Print single image to see bands\nprint(cmorph_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(cmorph_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CPC-MORPH
"},{"location":"projects/cpc_morph/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: NOAA, global, precipitation, climate, near real-time, NWS, CPC, MORPH, CMORPH
Dataset provided by: NOAA
Dataset curatd in GEE by: Climate Engine Org
"},{"location":"projects/csb/","title":"USDA Crop Sequence Boundaries 2016-2023","text":"The Crop Sequence Boundaries (CSB), developed in collaboration with the USDA's Economic Research Service, provides estimates of field boundaries, crop acreage, and crop rotations across the contiguous United States. This dataset utilizes satellite imagery along with other public data and is open source, enabling users to conduct area and statistical analysis of planted U.S. commodities. It offers valuable insights into farmer cropping decisions and practices.
NASS required a representative field dataset for predicting crop planting based on common rotations like corn-soy, while the Economic Research Service (ERS) employs the CSB to study changes in farm management practices such as tillage and cover cropping over time. The CSB dataset represents non-confidential single crop field boundaries within a specified timeframe. It does not include personal identifying information, ownership boundaries, or tax parcels. The data is sourced from satellite imagery and publicly available information, excluding contributions from producers or agencies like the Farm Service Agency. For access and further information, you can visit the CSB website.Explore the CSB GitHub repository for the codebase, and review the metadata associated with the dataset.
Crop Sequence Boundaries (CSB) represent geospatial algorithm-generated field polygons, originating from the NASS Cropland Data Layer (CDL). These polygonal entities cater to the demands of applications reliant on gridded datasets, necessitating analytical units for streamlined data aggregation. The primary objective of CSBs is to furnish comprehensive coverage spanning the contiguous 48 United States, ensuring precision and replicability across multiple years. These structures are forged by amalgamating historical CDLs within a specified time frame, while also integrating road and rail networks to accurately depict crop sequences within these simulated fields. The dataset is available for 2016 to 2023 growing seasons.
"},{"location":"projects/csb/#citations","title":"Citations","text":"Hunt, Kevin A., Jonathon Abernethy, Peter Beeson, Maria Bowman, Steven Wallander, and Ryan Williams. \"Crop Sequence Boundaries (CSB): Delineated\nFields Using Remotely Sensed Crop Rotations.\"\n\nAbernethy, Jonathon, Peter Beeson, Claire Boryan, Kevin Hunt, and Luca Sartore. \"Preseason crop type prediction using crop sequence boundaries.\" Computers and Electronics in Agriculture 208 (2023): 107768.\nThe datasets are made available as feature collections in Earth Engine for each state the 1623 reprents the year 2016-2023 growing season. The state names are part of the feature collection name. While it may not be necessary it is possible to merge them into a single collection and I created that for those would want to run some analysis on a combined feature collection.
"},{"location":"projects/csb/#earth-engine-snippet-source","title":"Earth Engine Snippet: Source","text":"var csbal23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBAL1623\");\nvar csbar23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBAR1623\");\nvar csbaz23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBAZ1623\");\nvar csbca23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBCA1623\");\nvar csbco23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBCO1623\");\nvar csbct23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBCT1623\");\nvar csbde23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBDE1623\");\nvar csbga23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBGA1623\");\nvar csbfl23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBFL1623\");\nvar csbia23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBIA1623\");\nvar csbid23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBID1623\");\nvar csbil23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBIL1623\");\nvar csbin23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBIN1623\");\nvar csbks23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBKS1623\");\nvar csbky23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBKY1623\");\nvar csbla23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBLA1623\");\nvar csbma23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMA1623\");\nvar csbmd23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMD1623\");\nvar csbme23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBME1623\");\nvar csbmi23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMI1623\");\nvar csbmn23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMN1623\");\nvar csbmo23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMO1623\");\nvar csbms23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMS1623\");\nvar csbmt23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMT1623\");\nvar csbne23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNE1623\");\nvar csbnh23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNH1623\");\nvar csbnj23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNJ1623\");\nvar csbnm23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNM1623\");\nvar csbnv23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNV1623\");\nvar csbny23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNY1623\");\nvar csbnc23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNC1623\");\nvar csbnd23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBND1623\");\nvar csboh23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBOH1623\");\nvar csbok23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBOK1623\");\nvar csbor23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBOR1623\");\nvar csbpa23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBPA1623\");\nvar csbri23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBRI1623\");\nvar csbsc23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBSC1623\");\nvar csbsd23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBSD1623\");\nvar csbtn23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBTN1623\");\nvar csbtx23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBTX1623\");\nvar csbut23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBUT1623\");\nvar csbvt23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBVT1623\");\nvar csbva23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBVA1623\");\nvar csbwa23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBWA1623\");\nvar csbwv23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBWV1623\");\nvar csbwi23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBWI1623\");\nvar csbwy23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBWY1623\");\nvar combined_csb= ee.FeatureCollection('projects/sat-io/open-datasets/USDA/CSB_1623');\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/USDA-CSB-APP
App code: You can find the app here
"},{"location":"projects/csb/#license-and-liability","title":"License and Liability","text":"The USDA NASS Crop Sequence Boundaries and the data offered at https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries are provided to the public as is and are considered public domain and free to redistribute. Users of the Crop Sequence Boundaries (CSB) are solely responsible for interpretations made from these products. The CSB are provided 'as is' and the USDA NASS does not warrant results you may obtain using the data. Contact our staff at (SM.NASS.RDD.GIB@usda.gov) if technical questions arise.
Created by: USDA NASS, USDA ERS
Curated in GEE by : USDA NASS, USDA ERS, Samapriya Roy
keywords: agricultural lands, USDA, crop layer, CDL, crop sequence boundary
Last updated in GEE: 2024-05-25
"},{"location":"projects/csb/#changelog-notes-from-source","title":"Changelog notes from Source","text":"This dataset presents an index developed to assess carbon security across the sagebrush steppe in the Great Basin. The Carbon Security Index (CSI) incorporates data from three key sources: fractional plant cover from the Rangeland Analysis Platform, a fire probability model specific to the Great Basin (Smith et al., 2023), and a resistance and resilience dataset for the sagebrush steppe (Chambers et al., 2014, 2017).
The CSI is calculated as follows:
CSI = Preferred Rangeland Cover Index + Resistance & Resilience \u2013 P(Fire)
The resulting index ranges from -2 to +2 and allows for spatial comparisons of carbon security across the region.
NoteThe associated paper for this dataset has been accepted as part of a special issue but does not yet have a DOI assigned. A preprint version was not made available. Please note that the listed citations do not refer to this paper.
"},{"location":"projects/csi/#supplemental-citation","title":"Supplemental Citation","text":"[Smith, J.T., Allred, B.W., Boyd, C.S., Davies, K.W., Jones, M.O., Kleinhesselink, A.R., Maestas, J.D., Naugle, D.E. (2023). Where There's Smoke, There's Fuel: Dynamic Vegetation Data Improve Predictions of Wildfire Hazard in the Great Basin. Rangeland Ecol. Manage. 89:20-32. https://doi.org/10.1016/j.rama.2022.07.005](https://doi.org/10.1016/j.rama.2022.07.005)\n\n[Chambers, J.C., Miller, R.F., Board, D.I., Pyke, D.A., Roundy, B.A., Grace, J.B., Schupp, E.W., Tausch, R.J. (2014). Resilience and Resistance of Sagebrush Ecosystems: Implications for State and Transition Models and Management Treatments. Rangeland Ecol. Manage. 67:440-454. https://doi.org/10.2111/REM-D-13-00074.1](https://doi.org/10.2111/REM-D-13-00074.1)\n\n[Chambers, J.C., Maestas, J.D., Pyke, D.A., Boyd, C.S., Pellant, M., Wuenschel, A. (2017). Using Resilience and Resistance Concepts to Manage Persistent Threats to Sagebrush Ecosystems and Greater Sage-grouse. Rangeland Ecol. Manage. 70:149-164. https://doi.org/10.1016/j.rama.2016.08.005](https://doi.org/10.1016/j.rama.2016.08.005)\nvar carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL');\n\nvar visParams = {\n palette: ['#9ab555'],\n min: 0.0,\n max: 1550000.0,\n opacity: 0.8,\n};\nvar carbonoffsets = ee.Image().float().paint(carbonoffsetscol, 'REP_AREA');\n\nMap.setCenter(-52.692,-2.628,6)\nMap.addLayer(carbonoffsets, visParams, 'Existing carbon projects area');\n\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/15/subtle-grayscale\", \"Greyscale\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CARBON-SECURITY-INDEX
"},{"location":"projects/csi/#license","title":"License","text":"This dataset is made available under Creative Commons Attribution 4.0 International license.
Keywords: rangelands, sagebrush steppe, Great Basin, ecosystem function, environmental assessment and monitoring, environmental management
Curated in GEE by: Samapriya Roy
Last updated : 2024-07-24
"},{"location":"projects/daily_lst/","title":"MODIS Gap filled Long-term Land Surface Temperature Daily (2003-2020)","text":"High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. In this study the authors developed a novel spatiotemporal gap-filling framework by implementing data preprocessing (filtering pixels with low data quality and gap-filling missing values at one overpass using values at one of the other three overpasses each day) and spatiotemporal fitting (filtering the long-term trend (overall mean) of observations in each pixel, and then spatiotemporally interpolating residuals between observations and overall mean values for each day, and finally adding the overall mean and interpolated residuals), to generate a seamless high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. The paper on the gap-filling method will be published in near future.
The method was implemented to create a global gap filled LST observation.. The cross-validation indicates that the average root mean squared error (RMSE) for mid-daytime (1:30pm) and mid-nighttime (1:30am) LST is 1.88K and 1.33K, respectively. The gap-filled LST in the unit of 0.1 Celsius temperature (0.1 degree C) .You can read the abstract here
The datasets and entire collection is available at Figshare.
"},{"location":"projects/daily_lst/#citation","title":"Citation","text":"Paper Citation
Li, Xiaoma, Yuyu Zhou, Ghassem R. Asrar, and Zhengyuan Zhu. \"Creating a seamless 1 km resolution daily land surface\ntemperature dataset for urban and surrounding areas in the conterminous United States.\" Remote Sensing of\nEnvironment 206 (2018): 84-97.\nAbstract Citation
Zhang, Tao, Yuyu Zhou, and Zhengyuan Zhu. \"A spatiotemporal gap-filling method for building a seamless MODIS land\nsurface temperature dataset.\" In AGU Fall Meeting Abstracts, vol. 2020, pp. GC127-01. 2020.\nCollection Citation
Zhang, Tao; Zhou, Yuyu; Zhu, Zhengyuan; Li, Xiaoma; Asrar, Ghassem (2021): A global seamless 1 km resolution daily\nland surface temperature dataset (2003 \u2013 2020). Iowa State University. Collection. https://doi.org/10.25380/iastate.c.5078492.v1\nvar gf_day_1km = ee.ImageCollection(\"projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km\");\nvar gf_night_1km = ee.ImageCollection(\"projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/MODIS-GAPFILLED-LST-DAILY
"},{"location":"projects/daily_lst/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Zhang, Tao; Zhou, Yuyu; Zhu, Zhengyuan; Li, Xiaoma; Asrar, Ghassem
Curated in GEE by: Samapriya Roy
Keywords: Land Surface Temperature, LST, MODIS, gapfilled
Last updated: 2021-10-03
"},{"location":"projects/daylight_maps/","title":"Daylight Map Distribution map data","text":"Daylight is a complete distribution of global, open map data that\u2019s freely available with support from community and professional mapmakers. We combine the work of global contributors to projects like OpenStreetMap with quality and consistency checks from Daylight mapping partners to create a free, stable, and easy-to-use street-scale global map. Daylight Map Distribution will include a new dataset consisting of vectorized landcover features derived from the European Space Agency\u2019s 2020 World Cover (10m) rasters. This dataset provides global coverage and is suitable for use in maps up to 1:1 million (zoom level 8).
"},{"location":"projects/daylight_maps/#dataset-structure","title":"Dataset structure","text":"Dataset releases are created by the team periodically and will be ingested accordingly into the GEE collection.
"},{"location":"projects/daylight_maps/#attribution","title":"Attribution","text":"* \u00a9 OpenStreetMap contributors available under the Open Database License (www.openstreetmap.org/copyright)\n* Building data \u00a9 OpenStreetMap contributors, Microsoft, Esri Community Maps contributors\n* Australia Building Footprints (github.com/microsoft/AustraliaBuildingFootprints)\n* Canadian Building Footprints (github.com/microsoft/CanadianBuildingFootprints)\n* Uganda/Tanzania Building Footprints (github.com/microsoft/Uganda-Tanzania-Building-Footprints)\n* US Building Footprints (github.com/microsoft/USBuildingFootprints)\nvar water_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/DAYLIGHTMAP/water_polygons\");\nvar land_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/DAYLIGHTMAP/land_polygons\");\nLandcover layer: ESA 2020
var landcover = ee.FeatureCollection(\"projects/sat-io/open-datasets/DAYLIGHTMAP/LANDCOVER_ESA_2020\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/DAYLIGHT-LANDCOVER
"},{"location":"projects/daylight_maps/#license","title":"License","text":"Daylight Map Distribution is open data, licensed under the Open Data Commons Open Database License (ODbL). Daylight is built from upstream sources, primarily from OpenStreetMap contributors with optional additions from Esri Community Maps Contributors and Microsoft Corporation.
Provided by: Daylight Map Distribution
Curated in GEE by: Samapriya Roy
Keywords: Daylight Map Distribution, landcover, land polygons, water polygons, OSM, OpenStreetMap
Last updated in GEE: 2023-10-20
"},{"location":"projects/dea_croplands/","title":"Digital Earth Africa's cropland extent map Africa 2019","text":"These maps shows the estimated location of croplands in the following countries for the period January to December 2019 where cropland is defined as a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date. It was also noted that \"This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation.\" The provisional cropland extent maps have a resolution of 10 metres, and were built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent maps were built seperately using extensive training data from Eastern, Western, Northern, and Sahel Africa, coupled with a Random Forest machine learning model. A detailed exploration of the methods used to produce the cropland extent map can be found in the Jupyter Notebooks in DE Africa\u2019s crop-mask. Easiest place to download the datasets is from AWS Open data registry
The products contain three measurements:
mask: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification.
prob: This band displays the prediction probabilities for the \u2018crop\u2019 class. As this service uses a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted \u2018crop\u2019, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at > 50 % will produce a map identical to mask.
filtered: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has been filtered using an image segmentation algorithm see this paper for details on the algorithm used. During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as a complement to the mask band; small commission errors are removed by object-based filtering, and the \u2018salt and pepper\u2019 effect typical of classifying pixels is diminished.
You can details on the method and more here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/dea_croplands/#preprocessing-for-gee","title":"Preprocessing for GEE","text":"All images were download and merged into single collections. The metadata tags including regions, versions were maintained from the STAC metadata JSON files provided for the regional downloads. Validation datasets for each region were downloaded ingested and merged into a single feature collection.
"},{"location":"projects/dea_croplands/#earth-engine-snippet","title":"Earth Engine snippet","text":"var filtered = ee.ImageCollection(\"projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/filtered\")\nvar mask = ee.ImageCollection(\"projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/mask\");\nvar prob = ee.ImageCollection(\"projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/prob\");\nvar validation = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/validation\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/DEA-CROPLAND-EXTENT
"},{"location":"projects/dea_croplands/#license","title":"License","text":"This dataset is made available under the CC BY Attribution 4.0 International License.
Created by: Digital Earth Africa
Curated by: Samapriya Roy
Keywords: agriculture, cog, deafrica, earth observation, food security, geospatial, satellite imagery, stac,sustainability
Last updated in GEE: 2023-03-13
"},{"location":"projects/dea_lc/","title":"Digital Earth Australia(DEA) Landsat Land Cover 25m v1.0.0","text":"Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures.\u202fA consistent, Australia-wide land cover product helps\u202funderstanding of\u202fhow the different parts of the environment change and\u202finter-relate.\u202fEarth observation data recorded over\u202fa period of time\u202ffirstly allows the observation of\u202fthe state of land cover at a specific time and\u202fsecondly the way that land cover changes by comparison between times.
DEA Land Cover provides annual land cover classifications for Australia using the Food and Agriculture\u202fOrganisation\u202fLand Cover Classification System taxonomy Version 2 (Di Gregorio and Jansen, 1998; 2005). DEA Land Cover\u202fclassifications have been generated by combining quantitative (continuous) or qualitative (thematic) environmental information (referred to as Essential Descriptors; EDs) derived from Landsat\u202fsatellite\u202fsensor data. Several EDs have been generated previously by Geoscience Australia, including annual water summaries (Mueller et al., 2016), vegetation fractional cover (Scarth et al., 2010), mangrove extent (Lymburner et al., 2020) and the Inter Tidal Extent Model (ITEM; Sagar et al., 2017), whilst others have been developed more recently. These EDs have been combined to generate detailed, consistent and expandable annual classifications of Australia\u2019s land cover from 1988 through to 2020.\u202f
Additional information including descriptors above can be found here and you can also explore the map here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/dea_lc/#data-preprocessing","title":"Data preprocessing","text":"Datasets were downloaded from Amazon S3 buckets and each years tiles were composited to create single year derivatives. Start and end date for years were also added to metadata and the collection included the version 1.0.0 as v100. Since this is a classified imagery MODE pyramiding scheme was used and default nodata value from tif at 0 was utilized for no data value during upload.
"},{"location":"projects/dea_lc/#citation","title":"Citation","text":"Lucas R, Mueller N, Siggins A, Owers C, Clewley D, Bunting P, Kooymans C, Tissott B, Lewis B, Lymburner L, Metternicht G. Land Cover Mapping using Digital Earth Australia. Data.\n2019; 4(4):143. https://doi.org/10.3390/data4040143\n\nChristopher J. Owers, Richard M. Lucas, Daniel Clewley, Carole Planque, Suvarna Punalekar, Belle Tissott, Sean M. T. Chua, Pete Bunting, Norman Mueller & Graciela Metternicht\n(2021) Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development, Big Earth Data, 5:3, 368-\n390, DOI: 10.1080/20964471.2021.1948179\nvar dea_lcv100 = ee.ImageCollection(\"projects/sat-io/open-datasets/DEA/landcover_v100\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/DEA-LANDSAT-LC
"},{"location":"projects/dea_lc/#license","title":"License","text":"Creative Commons Attribution-Share Alike 4.0 International License
Created by: Geoscience Australia and Digital Earth Australia
Curated by: Samapriya Roy
Keywords: : Australia, land cover, remote sensing, landsat, satellite
Last updated in GEE: 2022-03-05
"},{"location":"projects/dea_shorlines/","title":"Digital Earth Australia Coastlines","text":"Digital Earth Australia Coastlines is a continental dataset that includes annual shorelines and rates of coastal change along the entire Australian coastline from 1988 to the present. The product combines satellite data from Geoscience Australia's Digital Earth Australia program with tidal modelling to map the most representative location of the shoreline at mean sea level for each year. The product enables trends of coastal retreat and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades.
The ability to map shoreline positions for each year provides valuable insights into whether changes to our coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers and policy makers to assess impacts from the range of drivers impacting our coastlines and potentially assist planning and forecasting for future scenarios. You can find additional details here and you can download the datasets here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
Annual shoreline vectors that represent the median or \u2018most representative\u2019 position of the shoreline at approximately 0 m Above Mean Sea Level for each year since 1988. Dashed shorelines have low certainty and Annual shorelines include the following attribute fields:
Attribute Description year The year of each annual shoreline. certainty A column providing important data quality flags for each annual shoreline. tide_datum The tide datum of each annual shoreline (e.g. \u201c0 m AMSL\u201d). id_primary The name of the annual shoreline\u2019s Primary sediment compartment from the Australian Coastal Sediment Compartments framework."},{"location":"projects/dea_shorlines/#citation","title":"Citation","text":"Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. 2021. Digital Earth Australia\nCoastlines. Geoscience Australia, Canberra. https://doi.org/10.26186/116268\nBishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia's dynamic\ncoastline at mean sea level using three decades of Landsat imagery. Remote Sensing of\nEnvironment, 267, 112734. Available: https://doi.org/10.1016/j.rse.2021.112734\n\nNanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into\nAustralia's coastal change using a national dataset derived from the multi-decadal Landsat\narchive. Estuarine, Coastal and Shelf Science, 265, p.107712. Available: https://doi.org/10.1016/\nj.ecss.2021.107712\n\nBishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., & Sixsmith, J. (2019). Sub-pixel\nwaterline extraction: Characterising accuracy and sensitivity to indices and spectra. Remote\nSensing, 11(24), 2984. Available: https://www.mdpi.com/2072-4292/11/24/2984\nvar shoreline_annual = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_shorelines_annual\");\nvar hotspot_zoom_1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_hotspots_zoom_1\");\nvar hotspot_zoom_2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_hotspots_zoom_2\");\nvar hotspot_zoom_3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_hotspots_zoom_3\");\nvar rate_of_change = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_shorelines_annual\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/DEA-Shorelines
"},{"location":"projects/dea_shorlines/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Digital Earth Australia
Curated in GEE by : Samapriya Roy
Keywords : Sea, ocean and coast, marine and coastal, coast, erosion, waterline extraction, subpixel waterlines, coastal change, DEA CoastLines, coastline data, coastal erosion
"},{"location":"projects/dea_shorlines/#changelog","title":"Changelog","text":"Last updated : 2024-09-13
"},{"location":"projects/deaf_shorlines/","title":"Digital Earth Africa Coastlines","text":"The Digital Earth Africa Coastlines is a continental dataset that includes annual shorelines and rates of coastal change along the entire African coastline. This is a provisional service that has been generated for 2000 to 2021 and we would like to improve and operationalize with users.
The product combines satellite data from the Digital Earth Africa program with tidal modelling to map the typical location of the coastline at mean sea level each year. The product enables trends of coastal erosion and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades.
The ability to map shoreline positions for each year provides valuable insights into whether changes to the coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers, and policy makers to assess impact from the range of drivers impacting the coastlines and potentially assist planning and forecasting for future scenarios. You can find additional details here and you can download the datasets here
"},{"location":"projects/deaf_shorlines/#acknowledgment","title":"Acknowledgment","text":"The Coastlines algorithms incorporated in this product are the work of Robbi-Bishop Taylor, Rachel Nanson, Stephen Sagar, and Leo Lymburner, Geoscience Australia. Digital Earth Africa acknowledges the work done by the Centre de Suivi Ecologique (CSE), Dakar, in assessing the accuracy of the service with the participation of West African WACA stakeholders. Acknowledgements also go to the Regional Center for Mapping Resources for Development (RCMRD) for stakeholder engagement and feedback. Digital Earth Africa thanks the Digital Earth Africa Product Development task team for the co-design, the co-development and early feedback on the Service.
"},{"location":"projects/deaf_shorlines/#citation","title":"Citation","text":"Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. 2021. Digital Earth Australia\nCoastlines. Geoscience Australia, Canberra. https://doi.org/10.26186/116268\nvar shoreline_annual = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_shorelines_annual\");\nvar hotspot_zoom_1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_hotspots_zoom_1\");\nvar hotspot_zoom_2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_hotspots_zoom_2\");\nvar hotspot_zoom_3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_hotspots_zoom_3\");\nvar rate_of_change = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_rates_of_change\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/DEAF-Shorlines-V040
"},{"location":"projects/deaf_shorlines/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Digital Earth Africa
Curated in GEE by : Samapriya Roy
Keywords : Sea, ocean and coast, marine and coastal, coast, erosion, waterline extraction, subpixel waterlines, coastal change, DEAF CoastLines, coastline data, coastal erosion
Last updated : 2023-03-26
"},{"location":"projects/delta_dtm/","title":"DeltaDTM Global coastal digital terrain model","text":"DeltaDTM is a global coastal Digital Terrain Model (DTM) with a horizontal spatial resolution of 1 arcsecond (\u223c30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. It improves upon the accuracy of existing global elevation datasets by correcting Copernicus DEM with spaceborne lidar data from the ICESat-2 and GEDI missions. This correction process involves bias correction, filtering of non-terrain cells (e.g., vegetation and buildings), and gap filling using interpolation. DeltaDTM specifically focuses on low-lying coastal areas (below 10 m above Mean Sea Level) that are particularly vulnerable to sea-level rise, subsidence, and extreme weather events.
DeltaDTM is a valuable resource for a wide range of applications, including coastal management, flood modeling, and adaptation planning. Its improved accuracy enables more precise assessments of coastal flood risks and supports the development of effective mitigation and adaptation strategies. The dataset is freely available in the public domain and can be easily accessed and utilized by researchers, policymakers, and coastal communities. You can read the paper here and download the dataset here.
"},{"location":"projects/delta_dtm/#citation","title":"Citation","text":"Pronk, M., Hooijer, A., Eilander, D. et al. DeltaDTM: A global coastal digital terrain model. Sci Data 11, 273 (2024).\nhttps://doi.org/10.1038/s41597-024-03091-9\nPronk, Maarten (2024): DeltaDTM: A global coastal digital terrain model. Version 2. 4TU.ResearchData. dataset.\nhttps://doi.org/10.4121/21997565.v2\nvar delta_dtm = ee.Image(\"projects/sat-io/open-datasets/DELTARES/deltadtm_v1\");\nvar elevation = delta_dtm.select('b1');\nelevation = elevation.updateMask(elevation.neq(10));\n\n//Setup basemaps\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/132/light-gray\", \"Grayscale\");\n\nvar elevationVis = {\n min: 0,\n max: 10.0,\n // cmocean deep\n palette: [\"281a2c\", \"3f396c\", \"3e6495\", \"488e9e\", \"5dbaa4\", \"a5dfa7\", \"fdfecc\"]\n};\n\nMap.setCenter(103, 0, 7); // South East Asia\nMap.addLayer(elevation, elevationVis, 'DeltaDTM');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/DELTA-DTM
"},{"location":"projects/delta_dtm/#license","title":"License","text":"DeltaDTM is licensed as CC-BY 4.0. DeltaDTM is produced using Copernicus WorldDEM-30 \u00a9 DLR e.V. 2010-2014 and \u00a9 Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.
Created by: Deltares, Pronk, M., Hooijer, A., Eilander, D. et al 2024
Curated in GEE by: Maarten Pronk and Samapriya Roy
Keywords: Altimetry, Digital Elevation Model (DEM), Digital terrain model (DTM), elevation, GEDI, ICESat-2, LiDAR
Last updated in GEE: 2023-10-30
"},{"location":"projects/dynqual/","title":"DynQual Global Surface Water Quality Dataset","text":"Maintaining optimal surface water quality is essential for preserving ecosystems and ensuring safe human water utilization. However, our understanding of surface water quality relies heavily on data from monitoring stations, which are spatially limited and temporally fragmented. Addressing these limitations, we introduce the dynamical surface water quality model (DynQual). This model offers simulations of water temperature (Tw), as well as concentrations of total dissolved solids (TDS), biological oxygen demand (BOD), and fecal coliform (FC). DynQual operates at a daily time step and boasts a spatial resolution of 5\u2009arcmin (\u223c\u200910\u2009km).
This comprehensive global model allows us to evaluate its performance against real-world water quality observations. In addition, we present insights into spatial patterns and temporal trends of TDS, BOD, and FC concentrations spanning the years 1980 to 2019. Our analysis identifies dominant sectors contributing to surface water pollution. Remarkably, DynQual reveals widespread multi-pollutant hotspots, particularly in northern India and eastern China, though water quality issues extend across all regions worldwide. The most significant declines in water quality have occurred in developing regions, especially sub-Saharan Africa and South Asia. Researchers can access the open-source model code (Jones et al., 2023) as well as the global datasets encompassing simulated hydrology, Tw, TDS, BOD, and FC at 5\u2009arcmin resolution on a monthly basis (Jones et al., 2022b). These datasets hold the potential to enhance diverse studies ranging from ecological research to human health and water scarcity assessments. Discover more at DynQual Model Code and Global Surface Water Quality Datasets. You can read the full paper here
The constituents, such as total dissolved solids (TDS), biological oxygen demand (BOD), and fecal coliform (FC), are concentrations. The routed versions of these constituents (routed_TDS, routed_BOD, and routed_FC) are masses of pollutants. These values represent the pollution without considering dilution, and therefore more accurately reflect the transport or \"export\" of pollution through the river network.
Please refer to the original paper on guidance on displaying the concentration maps, the authors recommend these are only plotted above a given discharge/channelStorage threshold
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/dynqual/#datasets-preprocessing","title":"Datasets preprocessing","text":"The datasets were downloaded and converted from NetCDF to Geotiff format for ingestion. Since this was a multi band monthly aggregated image and I wanted to allow the user to slice by time frame, the image bands were seperated as individual images and the overall results are image collections with date range information attached.
"},{"location":"projects/dynqual/#citation","title":"Citation","text":"Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.:\nDynQual v1.0: a high-resolution global surface water quality model, Geosci. Model Dev., 16, 4481\u20134500, https://doi.\norg/10.5194/gmd-16-4481-2023, 2023.\nJones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., & van Vliet, M. T. H.\n(2022). Global monthly hydrology and water quality datasets, derived from the dynamical surface water quality\nmodel (DynQual) at 10 km spatial resolution [Data set]. In Geoscientific Model Development (Vol. 16, pp.\n4481\u20134500). Zenodo. https://doi.org/10.5281/zenodo.7139222\nvar fc = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/fecal-coliform\");\nvar routed_fc = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/routed-fc\");\nvar discharge = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/discharge\");\nvar storage = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/channel-storage\");\nvar avg_annualDischarge = ee.Image(\"projects/sat-io/open-datasets/DYNQUAL/discharge-avg-annual\");\nvar tds = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/total-dissolved-solids\");\nvar routed_tds = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/routed-tds\");\nvar bod = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/biological-oxygen-demand\");\nvar routed_bod = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/routed-bod\");\nvar water_temp = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/water-temperature\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/DYNQUAL-EXAMPLE
"},{"location":"projects/dynqual/#license","title":"License","text":"Creative Commons Attribution 4.0 International Public License
Created by: Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.
Curated in GEE by: Samapriya Roy
Keywords: water quality, discharge, water temperature, total dissolved solids, TDS, salinity, biological oxygen demand, BOD, fecal coliform, FC
"},{"location":"projects/edge_matched/","title":"Edge-matched Global, Subnational and operational Boundaries","text":""},{"location":"projects/edge_matched/#global-edge-matched-subnational-boundaries-humanitarian-edge-matched","title":"Global Edge-matched Subnational Boundaries: Humanitarian Edge Matched","text":"Uses OCHA Common Operational Datasets (COD) when available, falling back to geoBoundaries for regions without coverage. Represents the latest available data for humanitarian operational use. Uses the OpenStreetMap International ADM0 worldview for edge-matching. You can find dataset links in different formats here
"},{"location":"projects/edge_matched/#license","title":"License","text":"The humanitaring edge matched datasets are under a Open Data Commons Open Database License (ODbL) license.
"},{"location":"projects/edge_matched/#attribution","title":"Attribution","text":"FieldMaps, OCHA, geoBoundaries, U.S. Department of State, OpenStreetMap
"},{"location":"projects/edge_matched/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var adm1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm1_polygons\");\nvar adm2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm2_polygons\");\nvar adm3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm3_polygons\");\nvar adm4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm4_polygons\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/HUMANITARIAN-EDGEMATCHED
"},{"location":"projects/edge_matched/#global-edge-matched-subnational-boundaries-open-edge-matched","title":"Global Edge-matched Subnational Boundaries: Open Edge Matched","text":"Uses geoBoundaries exclusively to ensure all data comes from sources with clearly defined licenses. Suitable for academic or commercial use. Uses the U.S. Geological Survey International ADM0 worldview for edge-matching. You can find dataset links in different formats here
"},{"location":"projects/edge_matched/#license_1","title":"License","text":"The open datasets are under a Creative Commons Attribution 4.0 International (CC BY 4.0) and any derived work must include attribution.
"},{"location":"projects/edge_matched/#attribution_1","title":"Attribution","text":"FieldMaps, geoBoundaries, U.S. Department of State, U.S. Geological Survey
"},{"location":"projects/edge_matched/#earth-engine-snippet_1","title":"Earth Engine Snippet","text":"var adm1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-open/adm1_polygons\");\nvar adm2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-open/adm2_polygons\");\nvar adm3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-open/adm3_polygons\");\nvar adm4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-open/adm4_polygons\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/OPEN-EDGEMATCHED
"},{"location":"projects/edge_matched/#international-adm0-boundaries","title":"International ADM0 boundaries","text":"International boundaries are built using either OpenStreetMap or U.S. Geological Survey coastlines. Within each set, ADM0 layers comes in several versions to represent different world views of disputed areas. Starting with the \"All\" version, areas are dissolved together based on varying international recognition. Only international boundaries were ingested, where the default version, uses OpenStreetMap (download) for coastlines so that it aligns with web maps using OSM for basemaps or other data. Specialty version, uses U.S. Geological Survey (download) for coastlines so that intellectual property and related rights in this dataset are absent.
"},{"location":"projects/edge_matched/#license_2","title":"License","text":"The OSM edge matched datasets are under a Open Data Commons Open Database License (ODbL) license while the USGS datasets are under a CC0 or public domain license.
"},{"location":"projects/edge_matched/#attribution_2","title":"Attribution","text":"The OSM datasets have attribution keywords defined such as FieldMaps, U.S. Department of State, OpenStreetMap
"},{"location":"projects/edge_matched/#earth-engine-snippet_2","title":"Earth Engine Snippet","text":"var adm0_osm = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/field-maps/OSM_adm0_polygons\");\nvar adm0_usgs = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/field-maps/USGS_adm0_polygons\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/AMD0-EDGEMATCHED
"},{"location":"projects/edge_matched/#common-operation-dataset-edge-matched","title":"Common Operation dataset Edge Matched","text":"The COD layers are obtained from the Humanitarian Data Exchange at the source URLs below before processed for edge matching. Extended layers can be downloaded and clipped to any ADM0. You can download the dataset in different formats here. Datasets were merged to create singular representation at each hierarchy so all ADM1 for example and ADM2 for example. Each component of the dataset retains the original license provided.
"},{"location":"projects/edge_matched/#earth-engine-snippet_3","title":"Earth Engine Snippet","text":"var adm1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm1_cod\");\nvar adm2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm2_cod\");\nvar adm3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm3_cod\");\nvar adm4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm4_cod\");\nThe Humanitarian Data Exchange at the source URLs below before processed for edge matching. Extended layers can be downloaded and clipped to any ADM0. You can download the dataset and find license information heredifferent formats here. Datasets were merged to create singular representation at each hierarchy so all ADM1 for example and ADM2 for example. Each component of the dataset retains the original license provided.
var adm = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm1_geoboundaries\");\nvar adm = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm2_geoboundaries\");\nvar adm = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm3_geoboundaries\");\nvar adm = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm4_geoboundaries\");\nsample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GEOBOUNDARIES-EDGEMATCHED
Curated in GEE by : Samapriya Roy
Keywords: FieldMaps, U.S. Department of State, OpenStreetMap,U.S. Geological Survey, geoboundaries
Last updated on GEE: 2022-10-30
"},{"location":"projects/elc/","title":"Continental-scale land cover mapping at 10 m resolution over Europe","text":"A land cover classification for Europe at 10 m resolution produced with a machine learning workflow driven by Sentinel optical and radar satellite imagery. The classification model was trained on land cover reference data form the LUCAS (Land Use/Cover Area frame Survey) dataset. The map represents conditions in 2018. You can read the preprint here
The pixel values, their definitions and suggested hex color codes include: 0 (not mapped #000000), 1 (Artificial land, #CC0303), 2 (Cropland, #CDB400), 3 (Woodland, #235123), 4 (Shrubland, #B76124), 5 (Grassland, #92AF1F), 6 (Bare land, #F7E174), 7 (Water/permanent snow/ice, #2019A4), 8 (Wetland, #AEC3D6).
"},{"location":"projects/elc/#citation","title":"Citation","text":"Venter, Zander S., and Markus AK Sydenham. \"Continental-scale land cover mapping at 10 m resolution\nover Europe (ELC10).\" arXiv preprint arXiv:2104.10922 (2021).\nvar elc10= ee.ImageCollection(\"projects/sat-io/open-datasets/NINA/ELC10\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/EUROPE-10m-LULC
Category Land Cover Class Hex Code 1 Artificial land #CC0303 2 Cropland #CDB400 3 Woodland #235123 4 Shrubland #B76124 5 Grassland #92AF1F 6 Bare land #F7E174 7 Water/permanent snow/ice #2019A4 8 Wetland #AEC3D6"},{"location":"projects/elc/#dataset-citation","title":"Dataset Citation","text":"Venter, Zander S., & Sydenham, Markus A.K. (2020). ELC10: European 10 m resolution land cover map 2018\n(Version 01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4407051\nCreative Commons Attribution-Share Alike 4.0 International License
Created by: Venter, Zander S., & Sydenham, Markus A.K.
Curated by: Samapriya Roy
Keywords: : land use, europe, land cover, remote sensing, copernicus, sentinel, satellite
Last updated: 2021-04-29
"},{"location":"projects/elc_gdp/","title":"Global Electric Consumption revised GDP","text":"In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1\u2009km\u2009\u00d7\u20091\u2009km gridded nighttime light data during 1992\u20132019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1\u2009km\u2009\u00d7\u20091\u2009km gridded revised real GDP and electricity consumption during 1992\u20132019 based on our calibrated nighttime light data.
Gridded population and nighttime light data are the most popular proxy tools, and have been adopted extensively because of their strong correlation with economic output and electricity use. Finally the authors note that although nighttime light data as a single indicator may ignore factors such as value added or reduced by forestry or desertification, it is still an effective proxy for calibrating economic growth.You can read the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/elc_gdp/#paper-citation","title":"Paper Citation","text":"Chen, J., Gao, M., Cheng, S., Hou, W., Song, M., Liu, X., & Liu, Y. (2022). Global 1 km\u00d7 1 km gridded revised real gross domestic product and\nelectricity consumption during 1992\u20132019 based on calibrated nighttime light data. Scientific Data, 9(1), 1-14. https://doi.org/10.1038/\ns41597-022-01322-5\nChen, Jiandong; Gao, Ming (2021): Global 1 km \u00d7 1 km gridded revised real gross domestic product and electricity consumption during 1992-2019 based\non calibrated nighttime light data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.17004523.v1\nvar global_ec = ee.ImageCollection(\"projects/sat-io/open-datasets/GRIDDED_EC\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GRIDDED-ELECTRICITY-CONSUMPTION
"},{"location":"projects/elc_gdp/#earth-engine-snippet-gridded-gdp-based-on-electricity-consumption","title":"Earth Engine Snippet: GRIDDED GDP based on Electricity Consumption","text":"var global_elc_gdp = ee.ImageCollection(\"projects/sat-io/open-datasets/GRIDDED_EC-GDP\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GRIDDED-ELECTRICITY-CONSUMPTION-GDP
"},{"location":"projects/elc_gdp/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated in GEE by: Samapriya Roy
Keywords: GDP, Electricity Consumption, Night Lights
Last updated: 2022-09-27
"},{"location":"projects/electric_grid/","title":"Facebook Electrical Distribution Grid Maps","text":"Facebook has produced a model to help map global medium voltage (MV) grid infrastructure, i.e. the distribution lines which connect high-voltage transmission infrastructure to consumer-serving low-voltage distribution. The data found here are model outputs for six select African countries: Malawi, Nigeria, Uganda, DRC, Cote D\u2019Ivoire, and Zambia. The grid maps are produced using a new methodology that employs various publicly-available datasets (night time satellite imagery, roads, political boundaries, etc) to predict the location of existing MV grid infrastructure. The model documentation and code are also available , so data scientists and planners globally can replicate the model to expand model coverage to other countries where this data is not already available.
Building Electrical Grid Maps begins by taking monthly images from the VIIRS satellite, and creating a composite. We then apply a custom image processing filter to remove background and reflected light, and identify locations that consistently demonstrate night-time lighting. These then serve as a proxy for the existence of grid electricity. Using known electrical grids as templates based on data available from energydata.info, we employ a custom algorithm to connect the communities and infer grid paths based on their likelihood to follow roads, avoid water, and follow the shortest paths possible. You can find the model code and documentation here: https://github.com/facebookresearch/many-to-many-dijkstra
Note: current model accuracy is approximately 70% when compared to existing ground-truthed data. Accuracy can be further improved by integrating other locally-relevant information into the model and running it again.
"},{"location":"projects/electric_grid/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var gmv_raster = ee.ImageCollection(\"projects/sat-io/open-datasets/facebook/global_medium_voltage_grid\")\nvar gmv_vector = ee.FeatureCollection(\"projects/sat-io/open-datasets/facebook/gmv_grid\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/FACEBOOK-ELECTRICAL-DIST-GRID-MAPS
"},{"location":"projects/electric_grid/#resolution","title":"Resolution:","text":"geotiff is provided at Bing Tile Level 20
"},{"location":"projects/electric_grid/#location","title":"Location","text":"C\u00f4te d'Ivoire, Democratic Republic of the Congo, Malawi, Nigeria, Uganda, Zambia
"},{"location":"projects/electric_grid/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 License.
Curated by: Samapriya Roy
Keywords: Electrical Distribution Grid, Facebook, Ivory Coast, Democratic Republic of the Congo, Malawi, Nigeria, Uganda, Zambia
Last updated: 2021-04-17
"},{"location":"projects/energy_farms/","title":"Harmonised global datasets of wind and solar farm locations and power","text":"Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet\u2019s limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code to enable users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate these data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.
Data is available for download from figshare here
"},{"location":"projects/energy_farms/#data-citation","title":"Data Citation","text":"Dunnett, S. Harmonised global datasets of wind and solar farm locations and power. figshare. Dataset. https://doi.org/10.6084/m9.figshare.11310269 (2020)\nDunnett, S., Sorichetta, A., Taylor, G. et al. Harmonised global datasets of wind and solar farm locations and power. Sci Data 7, 130 (2020). https://doi.org/10.1038/s41597-020-0469-8\nvar wind_farms = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_wind_farms_2020\");\nvar solar_farms = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_solar_farms_2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/HARMONIZED-WIND-SOLAR-FARMS
"},{"location":"projects/energy_farms/#property-mapping","title":"Property Mapping","text":"Name Detail X X coordinate (.csv only) Y Y coordinate (.csv only) x_id unique identifier for data record GID_0 ISO3 country code panels number of panels turbines number of turbines panel.area total panel area in km2\u00a0(p_area\u00a0for .gdb files) landscape.area landscape area in km2\u00a0(l_area\u00a0for .gdb files) water binary response indicating whether data record is classified as water urban binary response indicating whether data record is classified as urban centre power estimated power capacity in MW"},{"location":"projects/energy_farms/#license","title":"License","text":"Data adapted or built on OpenStreetMap data are required to be distributed under the same licence. These data are therefore made available under the Open Data Commons Open Database License (ODbL). Personal figshare accounts cannot currently present data under this licence so the data are currently (incorrectly) presented under a CC0 licence as a stopgap until this changes.
Created by: Dunnett et al.
Curated by: Samapriya Roy
Keywords: solar, wind, energy, renewable
Last updated: 2021-08-31
"},{"location":"projects/eog_viirs_ntl/","title":"EOG Annual VIIRS Night Time Light","text":"NoteThis dataset is currently only available to those in the insiders program
A new consistently processed time series of annual global VIIRS nighttime lights has been produced from monthly cloud-free average radiance grids spanning 2012 to 2021 is processed by the Earth Observation Group(EOG). The new methodology (version 2.1) is a modification of the original VNL V1 that is available in the Earth Engine catalogue. Compared to VNL V1, this improved VNL V2.1 version removes ephemeral lights and background noise.
In both methods there is an initial filtering to remove sunlit, moonlit and cloudy pixels, leading to rough composites that contains lights, fires, aurora and background. In the original method, the rough annual composites are made from a full year of nightly DNB data. In the new method, the rough composites are made on monthly increments and then combined to form rough annual composites. Both methods employ outlier removal to discard biomass burning pixels and isolate the background.
In the original method the outlier removal is performed on scattergrams generated for each 15 arc second grid cell, with outliers clipped off from both the high and low radiance sides of the scattergram. The discard of outlier pixels proceeds until the scattergram\u2019s standard deviation stabilizes. The new method uses the twelve-month median radiance to discard high and low radiance outliers, filtering out most fires and isolating the background. Background areas are zeroed out in both methods using the data range (DR) calculated from 3x3 grid cells. In both methods, the DR threshold for background is indexed to cloud-cover levels, with higher DR thresholds in areas having low numbers of cloud-free coverages. In the new method, particular attention is given to setting a single DR threshold for distinguishing lit grid cells from background for each 15 arc second grid cell. This is achieved by setting the DR threshold from a multiyear maximum median and a corresponding multiyear percent cloud-cover grids. The multiyear approach makes it possible to detect lighting present in each 15 arc second grid cell with a single DR threshold across all the years in the series. You can get additional information here and download v2.1 here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/eog_viirs_ntl/#data-preprocessing","title":"Data preprocessing","text":"Datasets were preprocessed and metadata was added to each image in collection. The year 2012 had two datasets for a varying period and as such was excluded from the collection for now and maybe added later.
"},{"location":"projects/eog_viirs_ntl/#citation","title":"Citation","text":"Elvidge, C.D, Zhizhin, M., Ghosh T., Hsu FC, Taneja J. Annual time series of global VIIRS nighttime lights derived from monthly averages:2012 to\n2019. Remote Sensing 2021, 13(5), p.922, https://doi.org/10.3390/rs13050922\nvar average = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/average\");\nvar maximum = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/maximum\");\nvar median = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/median\");\nvar minimum = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/minimum\");\nvar average_masked = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/average_masked\");\nvar median_masked = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/median_masked\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/EOG-VNL-V21
"},{"location":"projects/eog_viirs_ntl/#license","title":"License","text":"Public domain license with properitary license language.
Colorado School of Mines data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no\nrestrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and\nis being provided without restriction on use and distribution.\nProvided by: Colorado School of Mines, Elvidge et al. 2019
Curated in GEE by : Samapriya Roy
keywords: Nighttime lights, VIIRS, Annual time series, Earth Observation Group , EOG, Colorado School of Mines
Last updated on GEE: 2023-01-28
"},{"location":"projects/era5_heat/","title":"ERA5-HEAT Dataset","text":"This dataset provides a complete historical reconstruction for a set of indices representing human thermal stress and discomfort in outdoor conditions. This dataset, also known as ERA5-HEAT (Human thErmAl comforT) represents the current state-of-the-art for bioclimatology data record production. The dataset is organized around two main variables: 1) the mean radiant temperature (MRT) and 2) the universal thermal climate index (UTCI) These variables describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, ventilation and radiation.
The dataset is computed using the ERA5 reanalysis from the European Centre for Medium-Range Forecasts (ECMWF). ERA5 combines model data with observations from across the world to provide a globally complete and consistent description of the Earth\u2019s climate and its evolution in recent decades. ERA5 is regarded as a good proxy for observed atmospheric conditions. Additional external information is available on this product here.
"},{"location":"projects/era5_heat/#dataset-description","title":"Dataset Description","text":"Spatial Information
Attribute Details Spatial extent Global Spatial resolution 27.75km (.25 deg) Temporal resolution Daily Time span 1940-01-01 to present Update frequency Updated daily with lag of 2 weeksVariables
Variable Units Scale factor Description Mean Radiant Temperature (\u2018mrt_mean\u2019, \u2018mrt_max\u2019, \u2018mrt_min\u2019, \u2018mrt_median\u2019) Degrees Kelvin 1.0 Provided in four bands for daily mean, maximum, minimum, and median. Universal Thermal Climate Index (\u2018utci_mean\u2019, \u2018utci_max\u2019, \u2018utci_min\u2019, \u2018utci_median\u2019) Degrees Kelvin 1.0 Provided in four bands for daily mean, maximum, minimum, and median."},{"location":"projects/era5_heat/#citation","title":"Citation","text":"Di Napoli C., Barnard C., Prudhomme C., Cloke HL and Pappenberger F. (2020): Thermal comfort indices derived from ERA5 reanalysis. Copernicus\nClimate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.553b7518 (Accessed on DD-MMM-YYYY)\n// Read in Image Collection and get first image\nvar era5_heat_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-era5-heat')\nvar era5_heat_i = era5_heat_ic.first()\n\n// Print first image to see bands\nprint(era5_heat_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(era5_heat_i.select('mrt_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Mean Radiant Temperature, Daily Mean')\nMap.addLayer(era5_heat_i.select('utci_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Universal Thermal Climate Index, Daily Mean')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/ERA5-HEAT
"},{"location":"projects/era5_heat/#license","title":"License","text":"ECMWF is available under an open license with terms of agreement available here
Keywords: heat exposure, climate, reanalysis, global, era5, thermal, public health, climate engine
Provided by: Copernicus
Curated in GEE by: Climate Engine Org
"},{"location":"projects/esa_iq/","title":"ESA WorldCover 10 m 2020 V100 InputQuality","text":"The ESA WorldCover 10 m 2020 V100 product is delivered in 3x3 degree tiles as Cloud Optimized GeoTIFFs (COGs) in EPSG:4326 projection (geographic latitude/longitude CRS). There are 2651 tiles and more information on accessing this dataset can be found here. The current collection focused on the Input Quality layers only, the Map layer is available in Google Earth Engine as an image collection.
The input quality layer is a per pixel quality indicator showing the quality of the Earth Observation (EO) input data. The layer is a 3 band GeoTIFF with
\u2022 Band 1: Number of Sentinel-1 GAMMA0 observations used in the classification workflow
\u2022 Band 2: Number of Sentinel-2 L2A observations used in the classification workflow
\u2022 Band 3 Percentage (0-100) of invalid S2 observations discarded in the classification workflow (after cloud and cloud shadow filtering).
Combining Band 2 and Band 3 yields the total absolute number of valid Sentinel-2 L2A observations used in the classification workflow.
"},{"location":"projects/esa_iq/#license","title":"License","text":"The ESA WorldCover product is provided free of charge, without restriction of use. For the full license information see the Creative Commons Attribution 4.0 International License.
Publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.
"},{"location":"projects/esa_iq/#citation","title":"Citation","text":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A.,\nPaccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li,\nLinlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. ESA WorldCover 10 m 2020 v100.\nhttps://doi.org/10.5281/zenodo.5571936\nvar esa_iq = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA_WorldCover_Input_Quality\");\nData access page: ESA_WorldCover_v100
Provided by: Zanaga et al, ESA WorldCover consortium
Curated in GEE by: Samapriya Roy
Keywords: : land, cover, land use, land cover, lulc, 10m, global, world, sentinel-1, sentinel 2, ESA
Last updated: 2021-11-01
"},{"location":"projects/esrilc2020/","title":"ESRI 2020 Global Land Use Land Cover from Sentinel-2","text":"This layer displays a global map of land use/land cover (LULC). The map is derived from ESA Sentinel-2 imagery at 10m resolution. It is a composite of LULC predictions for 10 classes throughout the year in order to generate a representative snapshot of 2020. This map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.
The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map of 2020.
Processing platform Sentinel-2 L2A/B data was accessed via Microsoft\u2019s Planetary Computer and scaled using Microsoft Azure Batch.
You can find more information here Kontgis, C. (2021, June 24). Mapping the world in unprecedented detail
"},{"location":"projects/esrilc2020/#citation","title":"Citation","text":"Karra, Kontgis, et al. \u201cGlobal land use/land cover with Sentinel-2 and deep learning.\u201d\nIGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.\nWater Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.
Trees Any significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).
Grass Open areas covered in homogeneous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures.
Flooded vegetation Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.
Crops Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.
Scrub/shrub Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants
Built Area Human made structures; major road and rail networks; large homogeneous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.
Bare ground Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.
Snow/Ice Large homogeneous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.
Clouds No land cover information due to persistent cloud cover.
For Accuracy Assessment information visit the ESRI release page
Category Land Cover Class Hex Code 1 No Data #FFFFFF 2 Water #1A5BAB 3 Trees #358221 4 Grass #A7D282 5 Flooded Vegetation #87D19E 6 Crops #FFDB5C 7 Scrub/Shrub #EECFA8 8 Built Area #ED022A 9 Bare Ground #EDE9E4 10 Snow/Ice #F2FAFF 11 Clouds #C8C8C8
"},{"location":"projects/esrilc2020/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var esri_lulc2020= ee.ImageCollection(\"projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m\")\nTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.
"},{"location":"projects/esrilc2020/#credits-attributions-and-license","title":"Credits, Attributions and License","text":"This dataset was produced by Impact Observatory for Esri. \u00a9 2021 Esri. This dataset is available under a Creative Commons BY-4.0 license and any copy of or work based on this dataset requires the following attribution:
This dataset is based on the dataset produced for the Dynamic World Project\nby National Geographic Society in partnership with Google and the World Resources Institute.\nData download page: Esri 2020 Land Cover Downloader
Curated in GEE by: Samapriya Roy
Keywords: : land, cover, land use, land cover, lulc, 10m, global, world, sentinel, sentinel-2, sentinel 2, impact observatory, impact, 2020, deep learning
Last updated: 2021-06-25
"},{"location":"projects/et0/","title":"Global Reference Evapotranspiration Layers","text":"The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 3 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon the implementation of a Penman Monteith Evapotranspiration equation for reference crop. The dataset follows the development and is based upon the WorldClim 2.1 at 30 arc seconds or ~ 1km at the equator. You can read the paper here.
Potential Evapo-Transpiration (PET) is a measure of the ability of the atmosphere to remove water through Evapo-Transpiration (ET) processes. Among several equations to estimate PET, a FAO application of the Penman-Monteith equation (Allen et al. 1998), here referred as FAOPM, is currently widely considered as a standard method (Walter et al. 2000). The FAO introduced the definition of PET as the ET of a reference crop (ET0) under optimal conditions, having the characteristics of well-watered grass with an assumed height of 12 centimeters, a fixed surface resistance of 70 seconds per meter and an albedo of 0.23 (Allen et al. 1998). The FAO-PM is a predominately physically based approach, which can be used globally because it does not require estimations of additional site-specific parameters. However, a major drawback of the FAO-PM method is its relatively high need for specific data for a variety of parameters (i.e. windspeed, relative humidity, solar radiation, etc.).
"},{"location":"projects/et0/#data-citation","title":"Data citation","text":"Zomer, Robert; Trabucco, Antonio (2019): Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.7504448.v6\nZomer, R.J.; Xu, J.; Trabuco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database.\nScientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1\nGlobal-ET0 grid layers are available as monthly averages (12 data layers, i.e. one layer for each month) or as an annual average (1 data layer) as well as standard deviation for annual average for the 1970-2000 period.
"},{"location":"projects/et0/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var et_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/global_et0/global_et0_monthly\");\nvar et_yearly = ee.Image(\"projects/sat-io/open-datasets/global_et0/global_et0_yearly\");\nvar et_yearly_sd = ee.Image(\"projects/sat-io/open-datasets/global_et0/global_et0_yearly_sd\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-ET0
"},{"location":"projects/et0/#license","title":"License","text":"The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use under the CC BY 4.0 Attribution 4.0 International license.
Data Website: You can download the data and description here
Curated in GEE by: Samapriya Roy
Keywords: aridity index, evapotranspiration, geospatial modeling
Last updated: 2022-09-02
"},{"location":"projects/fabdem/","title":"FABDEM (Forest And Buildings removed Copernicus 30m DEM)","text":"FABDEM (Forest And Buildings removed Copernicus DEM) removes building and tree height biases from the Copernicus GLO 30 Digital Elevation Model (DEM) (Airbus, 2020). The data is available at 1 arc second grid spacing (approximately 30m at the equator) for the globe. The authors use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (\u223c30 m) grid spacing. You can read the paper here and the overall dataset can be downloaded here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/fabdem/#source-data-structure","title":"Source Data structure","text":"The data are in Geotiff format, with each file divided into 1x1 degree tiles. Files are divided into 10x10 degree zipped folders (detailed in Data structure section below). Files are labelled using the south-west corner of the tile. For example N51E005_FABDEM_V1-0.tif has an extent from 51-52 degrees N, 5-6 degrees E.
Zipped folders are labeled with the southwest corner to northeast corner. For example For example N10E010-N20E020_FABDEM_V1-0.zip has an extent from 10-20 degrees N, 10-20 degrees E.
"},{"location":"projects/fabdem/#citation","title":"Citation","text":"Hawker, Laurence, Peter Uhe, Luntadila Paulo, Jeison Sosa, James Savage, Christopher Sampson, and Jeffrey Neal. \"A 30m global map of elevation with\nforests and buildings removed.\" Environmental Research Letters (2022).\nvar fabdem = ee.ImageCollection(\"projects/sat-io/open-datasets/FABDEM\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/FABDEM
"},{"location":"projects/fabdem/#license","title":"License","text":"The FABDEM dataset is licensed under a Creative Commons \"CC BY-NC-SA 4.0\" license.
This is a non-commercial and ShareAlike license. In other words, FABDEM may not be used for commercial purposes, and if it is remixed, transformed or built upon you must redistribute your contributions under the same license.
When using the data, users must include the below statements, as per the requirement of the original license.
FABDEM is produced using Copernicus WorldDEM-30 \u00a9 DLR e.V. 2010-2014 and \u00a9 Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the Copernicus program by law or by delegation do not incur any liability for any use of the Copernicus WorldDEM-30
The original license can be found: https://docs.sentinel-hub.com/api/latest/static/files/data/dem/resources/license/License-COPDEM-30.pdf
Created by: Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., & Neal, J
Curated by: Samapriya Roy
Keywords: digital elevation model, bare-earth, terrain, remote sensing, machine learning
"},{"location":"projects/fabdem/#changelog","title":"Changelog","text":"This dataset is part of project but is not part of a peer reviewed publication. This will be updated if and when this is converted into a paper and as it progresses through review and publication cycles.Please keep this into consideration while using this dataset
fiboa is a collaborative initiative aimed at enhancing farm field boundary data interoperability and associated agricultural data. Introduced recently, fiboa is more than just a specification; it's a comprehensive system encompassing data adhering to the specification, ongoing discussions to refine the specs, and a vibrant community actively contributing to its development. This project focuses on fostering the creation of more data and open data concerning field boundaries and agriculture to facilitate informed decision-making processes. The emphasis lies on practical application and iterative refinement, rather than the pursuit of a perfect ontology in isolation. You can read about this project here.
"},{"location":"projects/fiboa_uk/#datasets","title":"Datasets","text":""},{"location":"projects/fiboa_uk/#uk-fields","title":"UK Fields","text":"The ukfields dataset is a publicly accessible Earth Engine asset comprising automatically delineated field boundaries across England, Wales, Scotland, and Northern Ireland. This dataset provides comprehensive field boundary information for the United Kingdom, derived from harmonic composites of Sentinel 2 imagery captured in 2021. The delineation process utilized the Segment Anything Model (SAM) developed by Meta, facilitating efficient field segmentation at scale. Furthermore, the segmented fields have been accurately masked to a 2021 Dynamic World composite of cropland, ensuring precise representation within the dataset.
"},{"location":"projects/fiboa_uk/#dataset-preprocessing","title":"Dataset preprocessing","text":"This dataset was further processed to drop empty geometries from the feature collection
"},{"location":"projects/fiboa_uk/#dataset-citation","title":"Dataset Citation","text":"Bancroft, S., & Wilkins, J. (2024). UKFields (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11110206\nvar uk_fields = ee.FeatureCollection(\"projects/sat-io/open-datasets/UK-FIELDS\");\n\nMap.centerObject(uk_fields.first(),12)\nvar empty = ee.Image().byte();\nvar outline = empty.paint({\n featureCollection: uk_fields,\n color: 'random',\n width: 3\n});\n\nMap.addLayer(outline.randomVisualizer(), {opacity:0.8}, 'UK Fields')\nMap.setOptions(\"SATELLITE\")\nThis product is licensed under a Creative Commons Attribution 4.0 International license.
Curated in GEE by: Samapriya Roy and Samuel Bancroft
Keywords: fields, agriculture, UK, england, scotland, wales, northern-ireland
Last updated: 2024-05-11
"},{"location":"projects/firms_vector/","title":"Archival NRT FIRMS Global VIIRS and MODIS vector data","text":"The Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m thermal anomalies / active fire product provides data from the VIIRS sensor aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 satellites. The 375 m data complements Moderate Resolution Imaging Spectroradiometer (MODIS) fire detection; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375 m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
VIIRS NRT 375 m active fire products are from: Suomi NPP (VNP14IMGTDL_NRT) and NOAA-20, formally known as JPSS-1, (VJ114IMGTDL_NRT).
"},{"location":"projects/firms_vector/#notes","title":"Notes","text":"Archival data was downloaded for each year and ingested as shapefiles. You can download the archives here
You can read more about the MODIS product here and the VIIRS product here
"},{"location":"projects/firms_vector/#citations","title":"Citations","text":"NRT VIIRS 375 m Active Fire product VJ114IMGTDL_NRT distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. doi: 10.5067/FIRMS/VIIRS/VJ114IMGT_NRT.002\nNRT VIIRS 375 m Active Fire product VNP14IMGT distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. doi:10.5067/FIRMS/VIIRS/VNP14IMGT_NRT.002\nMODIS Collection 61 NRT Hotspot / Active Fire Detections MCD14DL distributed from NASA FIRMS.Available on-line [https://earthdata.nasa.gov/firms]. 10.5067/FIRMS/MODIS/MCD14DL.NRT.0061\nMODIS Collection 6 Hotspot / Active Fire Detections MCD14ML distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. doi: 10.5067/FIRMS/MODIS/MCD14ML\nThe MODIS and VIIRS yearly exports were ingested and names based on their years (MODIS 2000-2020) and (VIIRS 2012-2021)
MODIS Path: projects/sat-io/open-datasets/MODIS_MCD14DL/MCD14DL_YYYY Example Path: projects/sat-io/open-datasets/MODIS_MCD14DL/MCD14DL_2000
VIIRS Path: projects/sat-io/open-datasets/VIIRS/VNP14IMGTDL_NRT_YYYY Example Path: projects/sat-io/open-datasets/VIIRS/VNP14IMGTDL_NRT_2012
"},{"location":"projects/firms_vector/#earth-engine-snippet","title":"Earth Engine Snippet","text":"Sample paths are provided for two years only change the year to get different years
var viirs_2012 = ee.FeatureCollection(\"projects/sat-io/open-datasets/VIIRS/VNP14IMGTDL_NRT_2012\");\nvar modis_2012 = ee.FeatureCollection(\"projects/sat-io/open-datasets/MODIS_MCD14DL/MCD14DL_2012\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/ARCHIVAL-NRT-FIRMS-VIIRS-DATA
"},{"location":"projects/firms_vector/#license","title":"License","text":"The FIRMS data is distributed under a license similar to Public domain license and distributed by Land, Atmosphere Near real-time Capability for EOS (LANCE) for Fire Information for Resource Management System (FIRMS)
"},{"location":"projects/firms_vector/#acknowledgements","title":"Acknowledgements","text":"We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) (https://earthdata.nasa.gov/firms), part of NASA's Earth Observing System Data and Information System (EOSDIS).
Created by: Land, Atmosphere Near real-time Capability for EOS (LANCE) for Fire Information for Resource Management System (FIRMS), NASA
Curated in GEE by : Samapriya Roy
Keywords: Archival fire, MODIS, VIIRS, Daytime, Nigh time, Thermal anomalies, FIRMS, LANCE, NASA, vector
Last updated: 2022-04-28
Last updated on GEE: 2022-04-28
"},{"location":"projects/flood/","title":"Global large flood events : Dartmouth Flood Observatory (1985-2016)","text":""},{"location":"projects/flood/#abstract","title":"Abstract","text":"The information presented highlights large flood events from 1985 to 2016 identified by the Dartmouth Flood Observatory. For more information visit . For mapping purposes, some types of flood events have been merged into one, under the \"MAINCAUSEF\" attribute. Please refer to the \"MAINCAUSE\" attribute for original data.
"},{"location":"projects/flood/#edition","title":"Edition","text":"G.R.Brakenridge (2017). Global Active Archive of Large Flood Events.\nDartmouth Flood Observatory, University of Colorado.\nRetrieved from https://floodobservatory.colorado.edu/Archives/index.html
"},{"location":"projects/flood/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var flood_events = ee.FeatureCollection(\"projects/sat-io/open-datasets/events/large_flood_events_1985-2016\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/GLOBAL-LARGE-FLOOD-EVENTS
"},{"location":"projects/flood/#data-quality","title":"Data Quality","text":"Each entry in the table and related \"area affected\" map outline represents a discrete flood event. However, repeated flooding in some regions is a complex phenomenon and may require a compromise between aggregating and dividing such events. The listing is comprehensive and global in scope. Deaths and damage estimates for tropical storms are totals from all causes, but tropical storms without significant river flooding are not included. Supplemental Information
The information presented in the Dartmouth Flood Observatory Archive is derived from news, governmental, instrumental, and remote sensing sources. The archive is \"\"active\"\" because current events are added immediately.
"},{"location":"projects/flood/#license-and-restrictions","title":"License and Restrictions","text":"Unless otherwise specified, no restriction applies.
Source http://ihp-wins.unesco.org/layers/geonode:types_flood_events1
For additional information, visit: floodobservatory.colorado.edu/Archives/index.html
Curated by: Samapriya Roy
Keywords: : flood events, flood type, flood cause, Dartmouth Flood Observatory, Intergovernmental Hydrologic Programme
Last updated: 2021-04-29
"},{"location":"projects/floodplain_lc/","title":"Mississippi River Basin Floodplain Land Use Change (1941-2000)","text":"A comprehensive dataset quantifying floodplain land use change along the 3.3 million km2 Mississippi River Basin (MRB) covering 60 years (1941\u20132000) at 250-m resolution.
"},{"location":"projects/floodplain_lc/#citation","title":"Citation","text":"Rajib, A., Zheng, Q., Golden, H.E, Wu, Q., Lane, C.R., Christensen, J.R., Morrison, R.R., Annis, A., & Nardi, F. (2021). The changing face of\nfloodplains in the Mississippi River Basin detected by a 60-year land use change dataset. _Scientific Data_, 8, 271.\nhttps://doi.org/10.1038/s41597-021-01048-w\nvar MRB_boundary = ee.FeatureCollection('users/giswqs/MRB/MRB_Boundary');\nvar floodplain = ee.Image('users/giswqs/MRB/USGS_Floodplain');\nvar img_1950 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1950');\nvar img_1960 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1960');\nvar img_1970 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1970');\nvar img_1980 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1980');\nvar img_1990 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1990');\nvar img_2000 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_2000');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/MISSISSIPPI-RIVER-BASIN-LUC
Earth Engine App: https://giswqs.users.earthengine.app/view/mrb-floodplain
"},{"location":"projects/floodplain_lc/#license","title":"License","text":"This dataset is shared under a Creative Commons Attribution-Share Alike 4.0 International License
Provided by: Rajib et al 2021
Curated by: Qiusheng Wu
Keywords: land use, floodplain, Mississippi River Basin, hydrology, river basin, ecysostems
Last updated: October 2021
"},{"location":"projects/forest_roads/","title":"Congo Basin Forest Roads","text":"This dataset provides high-detail mapping of road development in the tropical forests of the Congo Basin, created using Sentinel-1 and Sentinel-2 satellite imagery combined with a deep learning model. It delivers up-to-date road maps that are openly available and provide essential insights for forest conservation, sustainable management, and policy decisions.
Road construction in the Congo Basin forests, primarily driven by selective logging, poses significant ecological and climate risks. However, the full extent of these road networks, especially in remote areas, has been poorly understood. This dataset reveals all road networks established since 2019, providing a critical tool for studying the effects of logging, monitoring illegal forest activities, and assessing human impact on tropical forests at a large scale.
The road detection method integrates Sentinel-1 radar and Sentinel-2 optical imagery. Sentinel-2 provides high-resolution optical data in clear weather, while Sentinel-1's radar technology can penetrate clouds, offering consistent observation even during the rainy season. This combination ensures precise monthly updates on narrow and transient road segments. The map covers road development across the six Congo Basin countries: Cameroon, Central African Republic, Democratic Republic of the Congo, Equatorial Guinea, Gabon, and Republic of the Congo. This version presents 5 years of road development (46,311 km) from 2019-2023. You can read more in the paper here.
Attribute Description NetworkID A unique ID for each connected road network. SegLenM The length of the road segment (in meters). NetLenM The length of the connected road network (in meters). Month The road segment opening month. Year The road segment opening year. MonthNum The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations."},{"location":"projects/forest_roads/#citation","title":"Citation","text":"Slagter, Bart, Kurt Fesenmyer, Matthew Hethcoat, Ethan Belair, Peter Ellis, Fritz Kleinschroth, Marielos Pe\u00f1a-Claros, Martin Herold, and Johannes Reiche. \"Monitoring road\ndevelopment in Congo Basin forests with multi-sensor satellite imagery and deep learning.\" Remote Sensing of Environment (2024): 114380.\nSlagter, B., Fesenmyer, K., Hethcoat, M., Belair, E., Ellis, P., Kleinschroth, F., Pe\u00f1a-Claros, M., Herold, M., & Reiche, J. (2024). Forest roads (Congo Basin) [Data set]. In\nRemote Sensing of Environment: Vol. xxx (1.02, Number xxx). Zenodo. https://doi.org/10.5281/zenodo.13739812\nvar forest_roads = ee.FeatureCollection(\"projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/FOREST-ROADS
Earth Engine App: https://nrtwur.users.earthengine.app/view/forest-roads
"},{"location":"projects/forest_roads/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Slagter et al. 2024
Curated in GEE by: Bart Slagter & Samapriya Roy
Keywords : Congo Basin, forest roads, road development, Sentinel-1, Sentinel-2, deep learning, selective logging, deforestation, illegal logging, forest conservation
Last updated in GEE: 2024-09-10
"},{"location":"projects/fpar/","title":"Sensor-Independent MODIS & VIIRS LAI/FPAR CDR 2000 to 2022","text":"This geospatial dataset encompasses crucial biophysical parameters, namely Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR), indispensable for characterizing terrestrial ecosystems. The dataset addresses the limitations observed in existing global LAI/FPAR products, including challenges related to spatial-temporal coherence and accuracy.
Drawing from a range of long-term global LAI/FPAR products, including MODIS&VIIRS, this dataset facilitates a comprehensive analysis of vegetation dynamics and their interplay with climate change. Developed as a Sensor-Independent (SI) LAI/FPAR Climate Data Record (CDR), this dataset is derived from Terra-MODIS, Aqua-MODIS, and VIIRS LAI/FPAR standard products.
Encompassing a substantial temporal scope spanning from 2000 to 2022, the SI LAI/FPAR CDR provides valuable insights at various spatial resolutions: 500 meters, 5 kilometers, and 0.05 degrees. Its temporal granularity includes 8-day intervals and bimonthly frequency. To facilitate diverse analyses and applications, this dataset is accessible in both sinusoidal and WGS1984 projections. It represents a comprehensive and refined resource for studying terrestrial ecosystems and their response to climate dynamics. You can read the paper here
"},{"location":"projects/fpar/#citation","title":"Citation","text":"Pu, J., Yan, K., Roy, S., Zhu, Z., Rautiainen, M., Knyazikhin, Y., and Myneni, R. B.: Sensor-independent LAI/FPAR CDR:\nreconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022, Earth Syst. Sci.\nData, 16, 15\u201334, https://doi.org/10.5194/essd-16-15-2024, 2024\nPu, J., Roy, S., Knyazikhin, Y., & Myneni, R. (2023). Sensor-Independent LAI/FPAR CDR [Data set]. In Sensor-independent LAI/\nFPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022 (Vol.\n16, Number 1, pp. 15\u201334). Zenodo. https://doi.org/10.5281/zenodo.8076540\nvar wgs_500m_8d = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_500m_8d\");\nvar wgs_5km_8d = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_5km_8d\");\nvar wgs_005degree_8d = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_005degree_8d\");\nvar wgs_500m_bimonthly = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_500m_bimonthly\");\nvar wgs_5km_bimonthly = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_5km_bimonthly\");\nvar wgs_005degree_bimonthly = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_005degree_bimonthly\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LAI-FPAR-2000-2022
"},{"location":"projects/fpar/#license","title":"License","text":"The dataset is under a Creative Commons Attribution 4.0 International.
Provided by: Jiabin et al
Curated in GEE by : Samapriya Roy
keywords: Sensor-Independent, Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR), Climate Dataset Record (CDR)
Last updated on GEE: 2023-06-09
"},{"location":"projects/france5m/","title":"DEM France (Continental) 5m IGN RGE Alti","text":"The RGE ALTI\u00ae 5m dataset describes the ground elevation of France with a spatial resolution of 5x5m. It is produced by the National Institute of Geographic and Forest Information (IGN - https://www.ign.fr/).IGN or the National Institute for Geographic and Forest Information, is the State operator for geographic and forest information. The Institute intervenes in support of the evaluation and implementation of public risk prevention and regional planning policies The full dataset description is available here. The RGE ALTI\u00ae is updated from surveys obtained by airborne LIDAR or by correlation of aerial images. You can find the dataset description here. You can also find the Google Translated version of the document in English here.
"},{"location":"projects/france5m/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The dataset was preprocessed by Guillaume Attard from the ASCII files and converted into sub regional datasets. These images were then combined into a single Earth Engine image.
"},{"location":"projects/france5m/#suggested-citation-under-etalab-license","title":"Suggested Citation under ETALAB license","text":"Additional information on license and citation guide can be found here
Ministry of xxx\u2014Original data downloaded from http://www.data.gouv.fr/fr/ datasets/xxx/, updated on 14 February 2017.\nvar rge_alti5 = ee.Image(\"projects/sat-io/open-datasets/IGN_RGE_Alti_5m\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/IGN-RGE-France-DEM-5m
"},{"location":"projects/france5m/#license","title":"License","text":"The dataset is licensed under a Etalab Open License 2.0. The \u201cReuser\u201d is free to reuse the \u201cInformation\u201d - to reproduce it, copy it, - to adapt, modify, extract and transform it, to create \"Derived Information\", products or services, - to communicate, distribute, redistribute, publish and transmit it, - to exploit it for commercial purposes, for example by combining it with other information, or by including it in its own product or application.
Created by: National Institute of Geographic and Forest Information (IGN)
Curated in GEE by: Guillaume Attard and Samapriya Roy
Keywords: digital elevation model, terrain, remote sensing, France
"},{"location":"projects/fret/","title":"Forecast Reference Crop Evapotranspiration (FRET)","text":"The National Weather Service is now producing Forecast Reference Crop Evapotranspiration (FRET), a forecast estimate of the amount of evapotranspiration for a well-watered reference crop (grass or alfalfa) under prescribed conditions for a 24 hour period. Weekly FRET forecast calculations and NLDAS derived reference crop ET Climatology and departure from normal are available as well. The Forecast Reference Evapotranspiration (FRET) are for a short canopy (or 12cm grasses). The short canopy ET values are calculated using the Penman-Monteith Reference Evapotranspiration Equations, adopted by the Environmental Water Resources Institute - American Society of Civil engineers (ASCE-EWRI, 2004), and the National Weather Service forecast of temperatures, relative humidity, wind, and cloud cover. This product will be issued daily by 8 am local time, year round. You can get additional information about the dataset here and here. You can further find information about this on the climate engine org data page.
"},{"location":"projects/fret/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Conterminous United States Spatial resolution 4000 m (1/24-deg) Temporal resolution Daily Time span Next 1-7 days (updated every hour) Update frequency HourlyVariables
Variable Details ASCE Grass Reference Evapotranspiration (ETo) - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/fret/#citation","title":"Citation","text":"Palmer, C., Osborne, H., Krone-Davis, P., Melton, F., & Hobbins, M. National Weather Service\u2013Forecast Reference Evapotranspiration (FRET).\n// Read in Image Collection and get single image\nvar fret_ic = ee.ImageCollection('projects/climate-engine/fret/forecast/eto')\nvar fret_i = fret_ic.first()\n\n// Print image to see bands\nprint(fret_i)\n\n// Visualize a single image\nvar fret_palette = [\"#ffffb2\", \"#fed976\", \"#feb24c\", \"#fd8d3c\", \"#fc4e2a\", \"#e31a1c\", \"#b10026\"]\nMap.addLayer(fret_i, {min:0, max:10, palette: fret_palette}, 'fret_i')\nSample Code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/US-FRET
"},{"location":"projects/fret/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: drought, aridity, evaporative demand, ASCE, evapotranspiration, climate, forecast, CONUS, United States
Provided by: NOAA
Curated in GEE by: Climate Engine Org
"},{"location":"projects/gabam/","title":"30m Global Annual Burned Area Maps (GABAM)","text":"Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released.
30 m resolution global annual burned area maps (GABAM) of 1990-2021 are released for free download. The annual burned area map is defined as spatial extent of fires that occurs within a whole year and not of fires that occurred in previous years. GABAM was generated via an automated pipeline based on Google Earth Engine (GEE), using all the available Landsat images on GEE platform. The product was projected in a Geographic (Lat/Long) projection at 0.00025 degree\u200b\u200b (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10 degree \u00d7 10 degree tiles spanning the range 180W\u2013180E and 80N\u201360S.
You can get links to download the data here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/gabam/#paper-citation","title":"Paper Citation","text":"Long, Tengfei, Zhaoming Zhang, Guojin He, Weili Jiao, Chao Tang, Bingfang Wu, Xiaomei Zhang, Guizhou Wang, and Ranyu Yin. 2019. \"30 m Resolution Global Annual\nBurned Area Mapping Based on Landsat Images and Google Earth Engine\" Remote Sensing 11, no. 5: 489. https://doi.org/10.3390/rs11050489\nLong Tengfei; Zhang Zhaoming; He Guojin, 2021, \"30 m Resolution Global Annual Burned Area Product\", https://doi.org/10.7910/DVN/3CTMKP, Harvard Dataverse, V1\nTile names were modified to attach _year to allow for name based sorting as well as start and end year dates were added to each image in the collection.
Note: The user did notice that some 8 years of datasets were missing or not provided by the author such as 1986,1988,1990,1991,1993,1994,1997,1999
"},{"location":"projects/gabam/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var gabam = ee.ImageCollection(\"projects/sat-io/open-datasets/GABAM\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/GLOBAL-ANNUAL-BURNED-AREA-MAPS
"},{"location":"projects/gabam/#license","title":"License","text":"These datasets are made available under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information here.
"},{"location":"projects/gabam/#changelog","title":"Changelog","text":"Created by: Long Tengfei; Zhang Zhaoming; He Guojin
Curated in GEE by : Samapriya Roy
keywords: Global Fire, burned area, GABAM, remote sensing, Earth Engine
Last modified: 2022-07-14
Last updated on GEE: 2024-02-04
"},{"location":"projects/gcb/","title":"Global Channel Belt (GCB)","text":"The Global Channel Belt (GCB) datasets describes the global extent of river channel belts. A two-tier single-threaded (e.g., meandering) versus multi-threaded (e.g., braided) classification is provided showing the likely morphology of the associated active river channel. In addition, the GCB model provides a new global classification of riverine and lacustrine environments based on the mapped extent of the river channel belts.
To read more about the dataset check out the Nature Communications article here. The datasets are also publically available on the Zenodo data repository here.
"},{"location":"projects/gcb/#citation","title":"Citation","text":"Nyberg, B., Henstra, G., Gawthorpe, R.L. et al. Global scale analysis on the extent of river channel belts. Nat Commun 14, 2163 (2023).\nThe combined value of the 'Meandering' and 'Braided' bands yield the confidence of the channel belt extent. The remaining percentage out of a total 100, is the confidence that a pixel is classified as background (or non riverine).
"},{"location":"projects/gcb/#environments-dataset","title":"Environments Dataset","text":"var gcb = ee.Image('projects/sat-io/open-datasets/GCB/GRMM'); // Global Channel Belt Prediction 0 to 100% confidence\nvar env = ee.Image('projects/sat-io/open-datasets/GCB/Env'); // Global Depositional Environment Classifications\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-CHANNEL-BELT
For use as a basemap in QGIS or another GIS software use the following XYZ layer links.
https://storage.googleapis.com/ge_rivermaps/GRMM/tiles/{z}/{x}/{y} #GCB Map\nhttps://storage.googleapis.com/ge_rivermaps/riverClasses/tile/{z}/{x}/{y} #Environments Map\nThis dataset is available under the Creative Commons Attribution 4.0 International
Curated by: Bj\u00f6rn Nyberg
Curated in GEE by: Bj\u00f6rn Nyberg and Samapriya Roy
Keywords: Rivers, Hydrology, Morphology, Landforms, Ecosystems
Last updated: June 8, 2022
"},{"location":"projects/gcc/","title":"Global 1-km Cloud Cover","text":"The Cloud Cover Frequency dataset v1.0 measures over 15 years of twice daily MODIS images to analyze and quantify cloud dynamics and cloud predictions over areas. This allows us to understand global cloud heterogeneity over a spatial and temporal scale. The study establises a baseline for temporal variability of cloud forest, dynamics and allows for users to determine temporal windows of imaging and cloud free snapshots. The complete description of the project can be found here.
Please use Citation:
Wilson AM, Jetz W (2016) Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions. PLoS Biol 14(3):\ne1002415. doi:10.1371/journal. pbio.1002415\nShared Under: Creative Commons Attribution-Non Commercial 4.0 International License.
"},{"location":"projects/gcc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"//EarthEnv Cloud Frequency v1.0\nvar cloud_forest_prediction = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_CloudForestPrediction\");\nvar interannual_sd = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_interannualSD\");\nvar intrannual_sd = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_intraannualSD\");\nvar mean_annual = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_meanannual\");\nvar monthly_mean = ee.ImageCollection(\"projects/sat-io/open-datasets/gcc/MODCF_monthlymean\");\nvar seasonality_concentration = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_seasonality_concentration\");\nvar seasonality_rgb = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_seasonality_rgb\");\nvar seasonality_theta = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_seasonality_theta\");\nvar seasonality_visct = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_seasonality_visct\");\nvar spatial_sd_1deg = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_spatialSD_1deg\");\nProject Website: http://www.earthenv.org/cloud
App Website: App link here
Metadata link: http://www.earthenv.org/metadata/Cloud_DataDescription.pdf
Curated by: Samapriya Roy
Created by: Wilson AM, Jetz W 2016
Keywords: Earthenv, cloud concentration, seasonality, MODIS, Global Cloud
Last updated: Refer to webpage
"},{"location":"projects/gcd/","title":"Global Database of Cement Production Assets","text":"The Global Database of Cement Production Assets provides information on global cement production plants that are operational today. The database contains 3,117 cement plants with exact geolocation and provides information about ownership, production type, plant type, capacity and production start year where available.
The process consists of three steps: the mixing of limestone with other materials; the heating of the limestone mixture to produce clinker and the grinding of clinker with different ingredients to produce cement. The grinding process can happen in integrated facilities where the clinker is also produced or in independent grinding facilities closer to its end market. While the bulk of greenhouse gas emissions associated with cement production stem from clinker production and integrated facilities, the database covers both integrated as well as independent grinding facilities.
"},{"location":"projects/gcd/#citation","title":"Citation","text":"McCarten, M., Bayaraa, M., Caldecott, B., Christiaen, C., Foster, P., Hickey, C., Kampmann, D.,\nLayman, C., Rossi, C., Scott, K., Tang, K., Tkachenko, N., and Yoken, D. 2021.\nGlobal Database of Cement Production Assets. Spatial Finance Initiative\nAdditional Information about the Spatial Finance Initiative can be found here
SNo Field Field_Description GEE_Field 1 accuracy The accuracy of the latitude and longitude accuracy 2 capacity Total cement production capacity (millions of tons) cap 3 capacity_source Source used to obtain or estimate the capacity, either a link to reported capacity information or \"Estimated.\" If \"Estimated\" then the capacity has been modelled based on annotated kiln and plant dimensions. cap_sr 4 city City in which the plant is located city 5 country Country in which the plant is located country 6 country_code Three-digit country code defined in ISO 3166-1 numeric country_code 7 iso3 Three-letter country code defined in ISO 3166-1 alpha 3 iso3 8 owner_name Name of the primary owner of the plant ow_name 9 owner_permid PermID of the primary owner of the plant* ow_pid 10 owner_source Source reporting the ownership link between the plant and owner ow_source 11 ownership_stake The percentage ownership attributed to the parent company if the plant is a joint venture. If the plant is majority owned by a single parent company then this column will be blank ow_stake 12 ownership_stake_2 The percentage ownership attributed to the 2nd parent company if the plant is a joint venture ow_stake2 13 parent_exchange The primary exchange for the ultimate parent, if the company is publicly traded pr_exc 14 parent_exchange_2 The primary exchange for the 2nd ultimate parent, if the company is publicly traded pr_exc2 15 parent_holding_status The holding status of the ultimate parent (Private or Public) pr_hstat 16 parent_holding_status_2 The holding status of the 2nd ultimate parent (Private or Public) pr_hstat_2 17 parent_lei Legal Entity Identifier (LEI) of the ultimate parent of the owner of the plant pr_lei 18 parent_lei_2 Legal Entity Identifier (LEI) of the 2nd ultimate parent pr_lei2 19 parent_name Name of the ultimate parent of the owner of the plant pr_name 20 parent_name_2 Name of the 2nd ultimate parent of the owner of the plant pr_name2 21 parent_permid PermID of the ultimate parent of the owner of the plant* pr_pid 22 parent_permid_2 PermID of the 2nd ultimate parent of the owner of the plant* pr_pid2 23 parent_ticker The primary ticker for the ultimate parent, if the company is publicly traded pr_tkr 24 parent_ticker_2 The primary ticker for the 2nd ultimate parent, if the company is publicly traded pr_tkr2 25 plant_type The type of cement plant (Integrated or Grinding) plant_type 26 production_type The production process used to produce the clinker at Integrated plants (Wet or Dry) prod_type 27 region Region in which the plant is located region 28 state State or province in which the plant is located state 29 status Current plant operating status status 30 sub_region Subregion in which the plant is located sub_region 31 uid Unique identifier for the cement plant uid 32 year Year the plant started production year"},{"location":"projects/gcd/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var global_cement = ee.FeatureCollection(\"projects/sat-io/open-datasets/SFI/global_cement_database_20210701\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-CEMENT-PRODUCTION-ASSETS
"},{"location":"projects/gcd/#acknowledgements","title":"Acknowledgements","text":"Both databases have been developed by the Oxford Sustainable Finance Programme, Satellite Applications Catapult, and The Alan Turing Institute as part of the Spatial Finance Initiative \u2018s GeoAsset Project. Project FAQ's can be found here
"},{"location":"projects/gcd/#license","title":"License","text":"The Global Database of Cement Production Assets can be used by others and is available under a CC BY 4.0 license
Data download page: Download Request Form
Curated in GEE by: Samapriya Roy
Keywords: : GeoAsset Project, Oxford Sustainable Finance Programme, Satellite Applications Catapult, Alan Turing Institute, McCarten et al , cement , Global database
Last updated: 2021-07-16
"},{"location":"projects/gcd_assets/","title":"Global database of cement production assets and upstream suppliers","text":"The Global Cement Production Dynamics dataset provides a comprehensive asset-level view of the cement industry, addressing the growing climate and sustainability concerns faced by cement producers and investors. It integrates greenhouse gas emissions disclosure, sourcing patterns of raw materials, and the age of production plants as key variables. Leveraging innovative techniques, including geospatial computer vision and Large Language Modelling, the dataset offers a holistic understanding of global cement production dynamics. It serves various applications, such as environmental impact assessment, investment decision-making, industry research, and policy development, contributing to more informed and responsible decision-making within the sector.
The dataset contains spatial information for 3,117 cement production assets, offering near-approximate location data within a 10-kilometer radius. This data, available for download in Excel format from the Dryad Repository, includes precise coordinates (WGS84), city, state, country, ISO codes, sub-region, and region, obtained through reverse geocoding. Plant-specific details encompass plant type (integrated or grinding), production process (wet or dry), capacity, and the year of production commencement, with corresponding sources for capacity data. Ownership information is also comprehensive, encompassing direct or subsidiary owner names, ultimate parent details, PermID where available, Legal Entity Identifier (LEI), holding status (public or private), ticker, and exchange for ultimate parents. In cases of joint ventures, information for both ultimate parents is provided, alongside source links for ownership data. You can read the paper here.
"},{"location":"projects/gcd_assets/#dataset-structure","title":"Dataset structure","text":"The datasets were renamed to end with the date of dataset upload to Dryad Repository and the primary layer consisting of the assets database includes the following fields. You can expand this section to get all field names or use the example code
Expand to show field names and description for primary asset databaseField Description uid Unique identifier for the cement plant city City in which the plant is located state State or province in which the plant is located country Country in which the plant is located iso3 Three-letter country code defined in ISO 3166-1 alpha 3 country_code Three-digit country code defined in ISO 3166-1 numeric region Region in which the plant is located sub_region Subregion in which the plant is located latitude Latitude for the geolocation of the plant (based on WGS84) longitude Longitude for the geolocation of the plant (based on WGS84) accuracy The accuracy of the latitude and longitude status Current plant operating status plant_type The type of cement plant (Integrated or Grinding) production_type The production process used to produce the clinker at Integrated plants (Wet or Dry) confdnc Accuracy of production capacity (in cases where numerous values are reported) capacity Total cement production capacity (millions of tons) capacity_source Source used to obtain the capacity estimate (news media, company website, or company disclosure reports) year Year the plant started production owner_permid PermID of the primary owner of the plant* owner_name Name of the primary owner of the plant owner_source Source reporting the ownership link between the plant and owner parent_permid PermID of the ultimate parent of the owner of the plant* parent_name Name of the ultimate parent of the owner of the plant ownership_stake The percentage ownership attributed to the parent company if the plant is a joint venture. If the plant is majority owned by a single parent company, then this column will be blank ('n/a') parent_lei Legal Entity Identifier (LEI) of the ultimate parent of the owner of the plant parent_holding_status The holding status of the ultimate parent (Private or Public) parent_ticker The primary ticker for the ultimate parent, if the company is publicly traded parent_exchange The primary exchange for the ultimate parent, if the company is publicly traded parent_permid_2 PermID of the 2nd ultimate parent of the owner of the plant* parent_name_2 Name of the 2nd ultimate parent of the owner of the plant ownership_stake_2 The percentage ownership attributed to the 2nd parent company if the plant is a joint venture parent_lei_2 Legal Entity Identifier (LEI) of the 2nd ultimate parent parent_holding_status_2 The holding status of the 2nd ultimate parent (Private or Public) parent_ticker_2 The primary ticker for the 2nd ultimate parent, if the company is publicly traded parent_exchange_2 The primary exchange for the 2nd ultimate parent, if the company is publicly traded sourcing Locally sourced, imported, or hybrid supply of input production materials raw_mtrl Typology of raw input materials (limestone, clay, gypsum, sand, coal) clinker Whether clinker was used as an input material
The second dataset provides facility and supplier information
Expand to show field names and description for facility and supplier informationField Description uid Unique identifier for the cement plant facility.country Country in which the plant is located supplier.country Country in which facility-supplier (mine) is located supplier.latitude Latitude for the geolocation of the facility-supplier (based on WGS84 (EPSG:4326)) supplier.longitude Longitude for the geolocation of the facility-supplier (based on WGS84 (EPSG:4326))
"},{"location":"projects/gcd_assets/#citation","title":"Citation","text":"Tkachenko, N., Tang, K., McCarten, M. et al. Global database of cement production assets and upstream suppliers. Sci Data 10, 696 (2023).\nhttps://doi.org/10.1038/s41597-023-02599-w\nTkachenko, Nataliya et al. (2023). Global database of cement production assets and upstream suppliers [Dataset]. Dryad.\nhttps://doi.org/10.5061/dryad.6t1g1jx4f\nvar assets_db = ee.FeatureCollection(\"projects/sat-io/open-datasets/SFI/global_cement_db_assets_20231004\");\nvar suppliers_producers_db = ee.FeatureCollection(\"projects/sat-io/open-datasets/SFI/global_cement_db_suppliers_20231004\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-CEMENT-SUPP-PROD-DB
"},{"location":"projects/gcd_assets/#license","title":"License","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license
Data download page: Dryad data download page
Provided by: Tkachenko, N., Tang, K., McCarten, M. et al
Curated in GEE by: Samapriya Roy
Keywords: : Computer vision, Remote sensing, Computer and information sciences, asset-level data, Decarbonisation, LLMs, spatial finance, supply chains, sustainable finance, cement
Last updated: 2023-10-18
"},{"location":"projects/gcep30/","title":"GFSAD Global Cropland Extent Product (GCEP)","text":"The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data of the globe for nominal year 2015 at 30 meter resolution. The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GCEP data product uses the pixel-based supervised classifiers, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products.
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
The datasets are coded as follows and you can find individual links to datasets here
Class Label Name Description 0 Ocean Ocean and Water bodies 1 Non-croplands Non-croplands 2 Croplands Croplands"},{"location":"projects/gcep30/#citation","title":"Citation","text":"Thenkabail, P.S., Teluguntla, P.G., Xiong, J., Oliphant, A., Congalton, R.G., Ozdogan, M., Gumma, M.K., Tilton, J.C., Giri, C., Milesi, C., Phalke,\nA., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., and Foley, D., 2021, Global Cropland-Extent Product at 30-m Resolution (GCEP30)\nDerived from Landsat Satellite Time-Series Data for the Year 2015 Using Multiple Machine-Learning Algorithms on Google Earth Engine Cloud: U.S.\nGeological Survey Professional Paper 1868, 63 p., https://doi.org/10.3133/pp1868.\nGFSAD30 Cropland Extent 2015 Africa 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001\n\nGFSAD30 Cropland Extent 2015 30 m Australia, New Zealand, China, Mongolia 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AUNZCNMOCE.001\n\nGFSAD30 Cropland Extent 2015 Europe, Central Asia, Russia, Middle East 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001\n\nGFSAD30 Cropland Extent 2015 North America 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30NACE.001\n\nGFSAD30 Cropland Extent 2015 South Asia, Afghanistan, Iran 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SAAFGIRCE.001\n\nGFSAD30 Cropland Extent 2015 South America 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SACE.001\n\nGFSAD30 Cropland Extent 2015 Southeast and Northeast Asia 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SEACE.001\n\nGFSAD30 Cropland Extent 2015 Global Validation\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30VAL.001\nvar gcep30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GFSAD/GCEP30\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GCEP-30-CROPLAND-EXTENT
"},{"location":"projects/gcep30/#license","title":"License","text":"GFSAD GCEP 30 data are freely available to the public (similar to a CC0 license) and are generated by leveraging other national programs including the Landsat satellite program.
Created by: U.S. Geological Survey Center for Earth Resources Observation and Science (EROS)
Curated by: Samapriya Roy
Keywords: Landsat, Global Food, Cropland Extent, GEE, USGS, EROS
Last updated in GEE: 2023-03-01
"},{"location":"projects/gci/","title":"Areas of global conservation value","text":"A series of global priority layers are provided from the NatureMap project. These maps were created by jointly optimizing biodiversity and NCPs such as carbon and/or water. They describe on a continuous scale the amount of land area with the greatest upper potential value for expanding conservation efforts.
NoteConservation in this context should be interpreted as not prescriptive (e.g. specifically the establishment of protected areas), but rather means that a certain area harbour great biophysical potential for contributing to the conservation of biodiversity, carbon and water assets.
"},{"location":"projects/gci/#usage-notes","title":"Usage notes","text":"The datasets cover roughly the period of 2015 to 2019 and with a spatial resolution of 10 km (50 km versions are available as well on the data repository). The datasets were copied over from the source paths to harmonize path and naming conventions within the community catalog and all dunder characters (double underscores __) were removed with a single underscore. Folder names were also split with a hyphen to separate the words like biodiversity-carbon rather than biodiversitycarbon.
The layers can be navigated openly through a dedicated Earth engine app (conservation importance).Coarser grained versions at 50km are also available see Zenodo data repository but not uploaded to Google Earth Engine.
"},{"location":"projects/gci/#citation","title":"Citation","text":"- Jung, M., Arnell, A., de Lamo, X. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat Ecol Evol 5, 1499\u20131509 (2021). https://doi.org/10.1038/s41559-021-01528-7\n\n- Jung, M., Arnell, A., De Lamo, X., Garc\u00eda-Rangelm, S., Lewis, M., Mark, J., Merow, C., Miles, L., Ondo, I., Pironon, S., Ravilious, C., Rivers, M., Schepashenko, D., Tallowin, O., van Soesbergen, A., Govaerts, R., Boyle, B. L., Enquist, B. J., Feng, X., \u2026 Visconti, P. (2021). Areas of global importance for conserving terrestrial biodiversity, carbon, and water (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5006332\n// ------------------------ //\n// Import the layers\n// -- Biodiv --\nvar biodiv_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity/minshort_speciestargets_biome_esh10km_repruns10_ranked\");\nvar biodiv_pa_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity/minshort_speciestargets_biome_withPA_esh10km_repruns10_ranked\");\nvar biodiv = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity/minshort_speciestargets_esh10km_repruns10_ranked\");\nvar biodiv_pa = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity/minshort_speciestargetswithPA_esh10km_repruns10_ranked\");\n// -- Biodiv Carbon--\nvar biodivcarbon_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon/minshort_speciestargets_biome_carbon_esh10km_repruns10_ranked\");\nvar biodivcarbon_pa_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon/minshort_speciestargets_biome_withPA_carbon_esh10km_repruns10_ranked\");\nvar biodivcarbon = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon/minshort_speciestargets_carbon_esh10km_repruns10_ranked\");\nvar biodivcarbon_pa = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon/minshort_speciestargetswithPA_carbon_esh10km_repruns10_ranked\");\n// -- Biodiv water--\nvar biodivwater_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-water/minshort_speciestargets_biome_water_esh10km_repruns10_ranked\");\nvar biodivwater_pa_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-water/minshort_speciestargets_biome_withPA_water_esh10km_repruns10_ranked\");\nvar biodivwater = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-water/minshort_speciestargets_water_esh10km_repruns10_ranked\");\nvar biodivwater_pa = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-water/minshort_speciestargetswithPA_water_esh10km_repruns10_ranked\");\n// -- Biodiv carbonwater--\nvar biodivcarbonwater_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon-water/minshort_speciestargets_biome_carbon_water_esh10km_repruns10_ranked\");\nvar biodivcarbonwater_pa_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon-water/minshort_speciestargets_biome_withPA_carbon_water_esh10km_repruns10_ranked\");\nvar biodivcarbonwater = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon-water/minshort_speciestargets_carbon_water_esh10km_repruns10_ranked\");\nvar biodivcarbonwater_pa = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon-water/minshort_speciestargetswithPA_carbon_water_esh10km_repruns10_ranked\");\n// ------------------------ //\n\n// Define SLD style of discrete intervals to apply to the image.\nvar default_colours = {min: 1, max: 100, palette: ['7a0403','c92903','f56918','fbb938','c9ef34','74fe5d','1be5b5','35abf8','4662d8','30123b']};\n\n// Default entries\nvar what = \"Biodiversity, carbon and water\";\n\n// Visualize\nMap.addLayer(biodivcarbon, default_colours, \"Biodiversity and Carbon\", true);\n\n// The layers are area-consistent, thus through subsetting it becomes possible to identify for example\n// the 10% of land-areas with the greatest conservation value for biodiversity\n\nvar bio30x30 = biodiv.expression(\"b(0) <= 10\");\nMap.addLayer(bio30x30.mask(bio30x30.eq(1)), {'palette':['red']}, \"Top 10% value for biodiversity only\", false);\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/biodiversity-ecosystems-habitat/GLOBAL-CONSERVATION-IMP-BIODIV-CARBON-WATER
"},{"location":"projects/gci/#enter-license-information","title":"Enter license information","text":"The datasets are provided under a CC-BY-SA 4.0
"},{"location":"projects/gci/#additional-resources","title":"Additional resources","text":"You can explore the dataset layers using this app
Keywords: biodiversity, conservation importance, priorities, carbon, water
Provided by: IIASA
Curated in GEE by: IISA, Samapriya Roy
Last updated in GEE: 2023-10-31
"},{"location":"projects/gci30/","title":"Global Cropping Intensity Dataset 30m","text":"The Global Cropping Intensity Dataset is the first high-resolution (30m) cropping intensity product covering the entire globe. This dataset is constructed using reconstructed time series data of the Normalized Difference Vegetation Index (NDVI) from multiple satellite sources, including Landsat-8, Sentinel-2, and MODIS. The dataset quantifies cropping intensity by enumerating the total number of valid cropping cycles, determined through a binary crop phenophase profile that distinguishes between growing and non-growing periods. To calculate average cropping intensity, the total number of valid cropping cycles is divided by three (representing three years from 2016 to 2018). The implementation of this algorithm leverages the capabilities of the Google Earth Engine (GEE) cloud computing platform to produce global cropping intensity products. You can find additional information in the paper here
"},{"location":"projects/gci30/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The dataset comprises 504 separate GeoTIFF files, each projected in GCS_WGS_1984. The spatial resolution is 0.00026949459 degrees, with each file covering an area of 10\u00b0 \u00d7 10\u00b0. Files are named according to the format: Cropping_Intensity_30m_2016_2018_$regions$.tif, where \"regions\" designates the hemispherical and latitudinal/longitudinal coordinates of the top-left corner. Each file contains two layers:
The dataset includes two key layers for the average cropping intensity and the total number of crop cycles between 2016 and 2018. The Average Cropping Intensity layer classifies cropping patterns into three distinct values: '1' for single cropping, '2' for double cropping, and '3' for triple cropping. Areas with no data or masked regions are assigned a value of '-1'. The Total Number of Crop Cycles layer provides the original counts of crop cycles within the same period. Continuous cropping or instances of more than three crop cycles per year are indicated with a value of '127', while areas with no data or masked regions are also assigned '-1'. The images were ingested as a single image collection.
"},{"location":"projects/gci30/#citation","title":"Citation","text":"Zhang, Miao, Bingfang Wu, Hongwei Zeng, Guojin He, Chong Liu, Shiqi Tao, Qi Zhang et al. \"GCI30: A global dataset of 30-m cropping intensity using\nmultisource remote sensing imagery.\" Earth System Science Data Discussions 2021 (2021): 1-22.\nZhang, Miao; Wu, Bingfang; Zeng, Hongwei; He, Guojin; Liu, Chong; Nabil, Mohsen; Tian, Fuyou; Bofana, Jos\u00e9; Wang, Zhengdong; Yan, Nana, 2020,\n\"GCI30: Global Cropping Intensity at 30m resolution\", https://doi.org/10.7910/DVN/86M4PO, Harvard Dataverse, V2\nvar GCI30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GCI30\");\n\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/72543/assassins-creed-iv\", \"Greyscale\");\n\n// Average Cropping Intensity (Single/Double/Triple Cropping)\nvar average_cropping_intensity = GCI30.median().mask(GCI30.median().neq(-1));\nvar cropping_intensity_palette = ['#ffeda0', '#feb24c', '#f03b20', '#bd0026'];\n\nMap.addLayer(average_cropping_intensity.select('b1'), {\n min: 1,\n max: 3,\n palette: cropping_intensity_palette\n}, 'Average Crop Intensity Single/Double/Triple Cropping');\n\n// Total Number of Crop Cycles\nvar total_crop_cycles = GCI30.median().mask(GCI30.median().neq(-1));\n\n// Recode value 127 to 4 to make the palette continuous\nvar recoded_crop_cycles = total_crop_cycles.select('b2').remap([127], [4]);\n\nvar crop_cycles_palette = ['#762a83', '#af8dc3', '#e7d4e8', '#d9f0d3', '#7fbf7b', '#1b7837'];\n\nMap.addLayer(total_crop_cycles.select('b2'), {\n min: 1,\n max: 4,\n palette: crop_cycles_palette\n}, 'Total Number of Crop Cycles (Recode)');\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GCI30
"},{"location":"projects/gci30/#license","title":"License","text":"The Global Cropping Intensity Dataset is licensed under a CC0 1.0 Universal license.
Created by: Zhang et al 2021
Curated in GEE by: Samapriya Roy
Keywords : cropping intensity,crop cycle,average crop intensity
Last updated in GEE: 2024-10-02
"},{"location":"projects/gcl/","title":"Global Consensus Landcover","text":"The datasets integrate multiple global remote sensing-derived land-cover products and provide consensus information on the prevalence of 12 land-cover classes at 1-km resolution. For additional information about the integration approach and the evaluations of the datasets.
"},{"location":"projects/gcl/#paper-citation","title":"Paper citation","text":"Tuanmu, M.-N. and W. Jetz. 2014. A global 1-km consensus land-cover product for biodiversity and ecosystem modeling.\nGlobal Ecology and Biogeography 23(9): 1031-1045.\nvar barren = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/barren\");\nvar cultivated_and_managed_vegetation = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/cultivated_and_managed_vegetation\");\nvar deciduous_broadleaf_trees = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/deciduous_broadleaf_trees\");\nvar evergreen_deciduous_needleleaf_trees = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/evergreen-deciduous_needleleaf_trees\");\nvar evergreen_broadleaf_trees = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/evergreen_broadleaf_trees\");\nvar herbaceous_vegetation = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/herbaceous_vegetation\");\nvar mixed_other_trees = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/mixed-other_trees\");\nvar open_water = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/open_water\");\nvar regularly_flooded_vegetation = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/regularly_flooded_vegetation\");\nvar shrubs = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/shrubs\");\nvar snow_ice = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/snow-ice\");\nvar urban_built_up = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/urban-built-up\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:earthenv-bd-ecosystems-clim-layers/GLOBAL-CONSENSUS-LANDCOVER
"},{"location":"projects/gcl/#license","title":"License","text":"EarthEnv Global 1-km Consensus Land Cover Version 1 by Tuanmu & Jetz is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Permissions beyond the scope of this license may be available at http://www.earthenv.org/landcover.html.
"},{"location":"projects/gcl/#dataset-citation","title":"Dataset citation","text":"Tuanmu, M.-N. and W. Jetz. 2014. A global 1-km consensus land-cover product for biodiversity and ecosystem modeling.\nGlobal Ecology and Biogeography 23(9): 1031-1045. Data available on-line at http://www.earthenv.org/.\nProject Website: http://www.earthenv.org/landcover
App Website: App link here
Curated by: Samapriya Roy
Keywords: Earthenv, barren, cultivated and managed vegetation, deciduous broadleaf trees, evergreen broadleaf trees, mixed other trees, shrubs, urban built up, evergreen deciduous needleleaf trees, mixed other trees
Last updated: 2021-05-09
"},{"location":"projects/gcn250/","title":"Global Hydrologic Curve Number(GCN250)","text":"The GCN250 is a globally consistent, gridded dataset defining CNs at the 250\u2009m spatial resolution from new global land cover (300\u2009m) and soils data (250\u2009m). GCN250 represents runoff for a combination of the European space agency global land cover dataset for 2015 (ESA CCI-LC) resampled to 250\u2009m and geo-registered with the hydrologic soil group global data product (HYSOGs250m) released in 2018. The potential application of this data includes hydrologic design, land management applications, flood risk assessment, and groundwater recharge modeling. The CN values vary depending on antecedent runoff conditions (ARC), which is affected by the rainfall intensity and duration, total rainfall, soil moisture conditions, cover density, stage of growth, and temperature
"},{"location":"projects/gcn250/#paper-citation","title":"Paper Citation","text":"Jaafar, H.H., Ahmad, F.A. & El Beyrouthy, N. GCN250, new global gridded curve numbers for hydrologic modeling and design.\nSci Data 6, 145 (2019). https://doi.org/10.1038/s41597-019-0155-x\nvar GCN250_Average = ee.Image(\"users/jaafarhadi/GCN250/GCN250Average\")\nvar GCN250_Dry = ee.Image(\"users/jaafarhadi/GCN250/GCN250Dry\")\nvar GCN250_Wet = ee.Image(\"users/jaafarhadi/GCN250/GCN250Wet\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-HYDROLOGIC-CURVE-NUMBER
Extra Info: Dataset is also available as an Earth Engine App (Global Hydrologic Curve Number Explorer) It allows users to visualize the gridded hydrologic curve number dataset at ~250m resolution globally.
Link to the app: https://jaafarhadi.users.earthengine.app/view/hydrologic-curve-number#GEE
Link to app code: https://code.earthengine.google.com/086bf71b76fcaae637df3df2148fbce4
"},{"location":"projects/gcn250/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created and Curated by: Hadi H. Jaafar, Farah A. Ahmad, Naji El Beyrouthy
Keywords: Curve Number, Runoff, Hydrology
Last updated: 2019-06-20
"},{"location":"projects/gdat/","title":"Global Dam Tracker (GDAT) Database","text":"The Global Dam Tracker (GDAT) is one of the most comprehensive geo-referenced global dam databases, encompassing over 35,000 dams worldwide. It includes accurate geo-coordinates, satellite-derived catchment areas, and detailed attribute information such as completion year, dam height, length, purpose, and installed capacity. GDAT is built upon existing global datasets and enriched with regional data from governments, non-profits, and academic sources, especially in the Global South, which often lacks detailed coverage. The dataset is designed for inter-temporal analysis, allowing users to assess the environmental and socioeconomic impacts of dam construction over the past three decades, particularly focusing on changes in global surface water coverage. You can find the paper here and the dataset repository on Zenodo.
"},{"location":"projects/gdat/#citation","title":"Citation","text":"Zhang, Alice Tianbo, and Vincent Xinyi Gu. \"Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information.\"\nScientific data 10, no. 1 (2023): 111.\nZhang, A. T., & Gu, V. X. (2023). Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information [Data set].\nIn Scientific Data (Version v1, Vol. 10, Number 1, p. 111). Zenodo. https://doi.org/10.5281/zenodo.7616852\nvar gdat_catchments = ee.FeatureCollection(\"projects/sat-io/open-datasets/GDAT/GDAT_V1_CATCHMENTS\");\nvar gdat_dams = ee.FeatureCollection(\"projects/sat-io/open-datasets/GDAT/GDAT_V1_DAMS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/hydrology/GLOBAL-DAM-TRACKER
"},{"location":"projects/gdat/#license-information","title":"License Information","text":"The dataset is available under a Creative Commons 4.0 International License.
Provided by: Zhang et al. 2023
Curated in GEE by: Samapriya Roy
Keywords: River Barriers, Reservoirs, Hydropower Dams, Water Storage, Flood Control, Aquatic Ecosystems
Last updated: 2024-10-27
"},{"location":"projects/gde/","title":"Global Groundwater-Dependent Ecosystems (GDEs)","text":"Groundwater is the most widespread source of liquid freshwater, yet its critical role in supporting diverse ecosystems often goes unrecognized. The location and extent of groundwater-dependent ecosystems (GDEs) remain largely unknown in many regions, leading to inadequate protection measures. This dataset offers a high-resolution (approximately 30m) map of GDEs, revealing their presence on over one-third of the global drylands analyzed, including key biodiversity hotspots. GDEs are found to be more extensive and contiguous in areas dominated by pastoralism with lower groundwater depletion rates, indicating that many GDEs have likely already been lost due to unsustainable water and land use practices.
About 53% of the mapped GDEs are located in regions experiencing declining groundwater trends, underscoring the urgent need for protective measures. Despite their importance, only 21% of GDEs are within protected areas or regions with sustainable groundwater management policies, highlighting a significant gap in conservation efforts. Additionally, this dataset explores the connection between GDEs and cultural, socio-economic factors in the Greater Sahel region, emphasizing their role in supporting biodiversity and rural livelihoods. The GDE map is a crucial tool for policymakers, conservationists, and stakeholders to prioritize and develop strategies for safeguarding these vital ecosystems at local, regional, and international levels. You can read the paper here and individual tiles can be downloaded here.
"},{"location":"projects/gde/#citation","title":"Citation","text":"Rohde, M.M., Albano, C.M., Huggins, X. et al. Groundwater-dependent ecosystem map exposes global dryland protection needs.\nNature 632, 101\u2013107 (2024). https://doi.org/10.1038/s41586-024-07702-8\nRohde, M. M., Albano, C., Huggins, X., Klausmeyer, K., & Sharman, A. (2024). Data, code, and outputs for: groundwater-dependent ecosystem map\nexposes global dryland protection needs [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11062894\nvar imageCollection = ee.ImageCollection(\"projects/sat-io/open-datasets/GlobalGDEMap_v6_TNC\");\nprint(imageCollection)\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:biodiversity-ecosystems-habitat/GROUNDWATER-DEP-ECOSYSTEMS
Earth Engine App: https://codefornature.projects.earthengine.app/view/global-gde
"},{"location":"projects/gde/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: TNC, Rohde, M.M., Albano, C.M., Huggins, X. et al
Curated by: TNC & Samapriya Roy
Keywords: : Global Groundwater-Dependent Ecosystems, GDE Mapping, Conflict Hotspots, Climate Change, Food Security
Last updated: 2024-08-28
"},{"location":"projects/gdis/","title":"Geocoded Disasters (GDIS) Dataset (1960\u200a\u2013\u200a2018)","text":"The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region). You can access the dataset from NASA SEDAC and read the full paper here
"},{"location":"projects/gdis/#data-citation","title":"Data Citation","text":"Rosvold, E., and H. Buhaug. 2021. Geocoded Disasters (GDIS) Dataset. Palisades, NY: NASA Socioeconomic Data and\nApplications Center (SEDAC). https://doi.org/10.7927/zz3b-8y61. Accessed DAY MONTH YEAR.\nRosvold, E.L., Buhaug, H. GDIS, a global dataset of geocoded disaster locations. Sci Data 8, 61 (2021).\nhttps://doi.org/10.1038/s41597-021-00846-6\nvar gdis= ee.FeatureCollection(\"projects/sat-io/open-datasets/gdis_1960-2018\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/GEOCODED-DISASTERS-DATASET
"},{"location":"projects/gdis/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: NASA Socioeconomic Data and Applications Center (SEDAC)
Curated by: Samapriya Roy
Keywords: : droughts,earthquakes,floods,landslides,cyclones,volcanic eruptions
Last updated: 2021-08-16
"},{"location":"projects/gdw/","title":"Global Dam Watch (GDW) Database","text":"The Global Dam Watch (GDW) database provides a comprehensive, geo-referenced global repository of river barriers and reservoirs for large-scale analysis. Version 1.0 includes 41,145 river barriers and 35,295 associated reservoir polygons, detailing barrier attributes like height, purpose, year, volume, and discharge. The database is harmonized with global river networks (HydroSHEDS and RiverATLAS) to facilitate hydrological analysis and evaluate upstream/downstream effects. It integrates multiple sources, including satellite-derived data and machine learning techniques, to achieve consistent global coverage and supports various applications such as environmental impact assessments and freshwater system management. You can read more about the database in their paper
The GDW v1.0 database consists of two GIS layers: a point layer containing representative barrier locations with attributes, and a polygon layer of corresponding reservoir outlines with attributes. Each barrier point lies within its reservoir polygon, allowing spatial joining based on location. Both attribute tables share the same unique identification number for each barrier-reservoir pair. Version 1.0 includes 41,145 barrier points and 35,295 reservoir polygons, meaning 5,850 barrier locations have no polygon. These include navigation locks, diversion barrages, flood-event storage check dams, weirs, other instream control barriers, or dams under construction without filled reservoirs. The dataset and its accompanying resources are accessible through the Global Dam Watch platform https://www.globaldamwatch.org and the Figshare repository https://doi.org/10.6084/m9.figshare.25988293.
"},{"location":"projects/gdw/#citation","title":"Citation","text":"Lehner, Bernhard, Penny Beames, Mark Mulligan, Christiane Zarfl, Luca De Felice, Arnout van Soesbergen, Michele Thieme et al. \"The Global Dam Watch database of river barrier and reservoir information for large-scale applications.\" Scientific Data 11, no. 1 (2024): 1069.\nLehner, Bernhard; Beames, Penny; Mulligan, Mark; Zarfl, Christiane; De Felice, Luca; van Soesbergen, Arnout; et al. (2024). Global Dam Watch database version 1.0.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.25988293.v1\nvar gdw_barriers = ee.FeatureCollection(\"projects/sat-io/open-datasets/GDW/GDW_BARRIERS_V1_0\");\nvar gdw_reservoirs = ee.FeatureCollection(\"projects/sat-io/open-datasets/GDW/GDW_RESERVOIRS_V1_0\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/hydrology/GLOBAL-DAM-WATCH-DATABASE
"},{"location":"projects/gdw/#license-information","title":"License Information","text":"The GDW database is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Provided by: Lehner et al 2024, Global Dam Watch
Curated in GEE by: Samapriya Roy
Keywords: River Barriers, Reservoirs, Hydropower Dams, Water Storage, Flood Control, Aquatic Ecosystems
Last updated: 2024-10-27
"},{"location":"projects/gebco/","title":"General Bathymetric Chart of the Oceans (GEBCO)","text":"The GEBCO_2023 Grid is a global terrain model for ocean and land, providing elevation data in meters on a 15 arc-second interval grid. This means that the grid has a spatial resolution of about 1 kilometer at the equator. The data values are pixel-center registered, meaning that they refer to the elevation at the center of each grid cell.
The grid is accompanied by a Type Identifier (TID) Grid, which provides information on the types of source data that the GEBCO_2023 Grid is based on. The primary GEBCO_2023 grid contains land and ice surface elevation information. A version is also made available with under-ice topography/bathymetry information for Greenland and Antarctica.
The GEBCO_2023 Grid was published in April 2023 and is the fifth GEBCO grid developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and GEBCO, which aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor and make it available to all. The GEBCO_2023 Grid is a valuable resource for a variety of applications, including oceanography, geology, marine biology, climate change research, and disaster management.For information on the data sets included in the GEBCO_2021 Grid, please see the list of contributions included in this release of the grid.
"},{"location":"projects/gebco/#data-citation-attribution","title":"Data Citation & Attribution","text":"GEBCO Compilation Group (2023) GEBCO 2023 Grid (doi:10.5285/f98b053b-0cbc-6c23-e053-6c86abc0af7b)\nvar gebco_grid = ee.ImageCollection(\"projects/sat-io/open-datasets/gebco/gebco_grid\");\nvar gebco_sub_ice_topo = ee.ImageCollection(\"projects/sat-io/open-datasets/gebco/gebco_sub-ice-topo\");\nvar gebco_tid_grid = ee.ImageCollection(\"projects/sat-io/open-datasets/gebco/gebco_tid_grid\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/GEBCO
"},{"location":"projects/gebco/#gebco-type-identifier-tid-grid-coding","title":"GEBCO Type Identifier (TID) grid coding","text":"TID Definition 0 Land Direct measurements 10 Singlebeam - depth value collected by a single beam echo-sounder 11 Multibeam - depth value collected by a multibeam echo-sounder 12 Seismic - depth value collected by seismic methods 13 Isolated sounding - depth value that is not part of a regular survey or trackline 14 ENC sounding - depth value extracted from an Electronic Navigation Chart (ENC) 15 Lidar - depth derived from a bathymetric lidar sensor 16 Depth measured by optical light sensor 17 Combination of direct measurement methods Indirect measurements 40 Predicted based on satellite-derived gravity data - depth value is an interpolated value guided by satellite-derived gravity data 41 Interpolated based on a computer algorithm - depth value is an interpolated value based on a computer algorithm (e.g. Generic Mapping Tools) 42 Digital bathymetric contours from charts - depth value taken from a bathymetric contour data set 43 Digital bathymetric contours from ENCs - depth value taken from bathymetric contours from an Electronic Navigation Chart (ENC) 44 Bathymetric sounding - depth value at this location is constrained by bathymetric sounding(s) within a gridded data set where interpolation between sounding points is guided by satellite-derived gravity data 45 Predicted based on helicopter/flight-derived gravity data Unknown 70 Pre-generated grid - depth value is taken from a pre-generated grid that is based on mixed source data types, e.g. single beam, multibeam, interpolation etc. 71 Unknown source - depth value from an unknown source 72 Steering points - depth value used to constrain the grid in areas of poor data coverage"},{"location":"projects/gebco/#license","title":"License","text":"The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid indicates that the user accepts the conditions of use and disclaimer information given below. Users are free to: Copy, publish, distribute and transmit The GEBCO Grid. Adapt The GEBCO Grid. Commercially exploit The GEBCO Grid, by, for example, combining it with other information, or by including it in their own product or application.
Produced by : General Bathymetric Chart of the Oceans (GEBCO), Nippon Foundation-GEBCO Seabed 2030 Project
Curated by: Samapriya Roy
Keywords: :\"Nippon Foundation-GEBCO Seabed 2030 Project, GEBCO, General Bathymetric Chart of the Oceans, Bathymetry , Elevation\"
Last updated: 2023-08-28
"},{"location":"projects/gee_sebal/","title":"geeSEBAL-MODIS Continental scale ET for South America","text":"The geeSEBAL-MODIS Version 0-02 Evapotranspiration (ET) product is an 8-day product produced at 500 meter pixel resolution. The algorithm for ET calculation is based on the SEBAL model and the FAO Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, land cover and land surface temperature (LST).
The pixel values for the layers are the average of all eight days within the period, multiplied by 1000. Images must be multiplied by 0.001 for the actual values. Note that the last 8-day period of each year is a 5 or 6-day composite period, depending on the year. The dataset is available from 2002-07-01 to 2022-12-31. Band information is the following
Name Description Min Max Units Scale Offset ET_24h Daily actual evapotranspiration 0 6601 mm day-1 0.001 0 FE Evaporative fraction 0 1017 [-] 0.001 0 ETr Reference evapotranspiration -220 7543 mm day-1 0.001 0 Rn24h_G Daily average net radiation -84478 198081 w m-2 0.001 0 LE Instantaneous latent heat flux 0 2478572 w m-2 0.001 0 H Instantaneous sensible heat flux -829456 964618 w m-2 0.001 0 Rn Instantaneous net radiation -5435 753836 w m-2 0.001 0 G Instantaneous soil heat flux -2039206 102284 w m-2 0.001 0 End_Members Cold and hot endmember candidates. Pixels with -1.0 values are cold candidates and pixels equal to 1.0 are hot endmember candidates. -121 172 [-] 0.001 0 LST_lat Land surface temperature, corrected by the adiabatic lapse rate and normalized by the solar zenith angle. 272879 330413 K 0.001 0 LST_dem Land surface temperature, corrected by the adiabatic lapse rate 254456 331505 K 0.001 0"},{"location":"projects/gee_sebal/#citation-preprint","title":"Citation Preprint","text":"Comini, Bruno & Ruhoff, Anderson & Laipelt, Leonardo & Fleischmann, Ayan & Huntington, Justin & Morton, Charles & Melton, Forrest & Erickson,\nTyler & Roberti, D\u00e9bora & Souza, Vanessa & Biudes, Marcelo & Machado, Nadja & Santos, Carlos & Cosio, Eric. (2023). geeSEBAL-MODIS: Continental\nscale evapotranspiration based on the surface energy balance for South America. 10.13140/RG.2.2.17579.11041.\nvar geesebal = ee.ImageCollection(\"projects/et-brasil/assets/geesebal/myd11a2/sa/v0-02\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GEESEBAL-ET-SOUTH-AMERICA
"},{"location":"projects/gee_sebal/#license","title":"License","text":"The datset is made available under a CC-BY-4.0 license.
Curated by: Bruno Comini de Andrade, Anderson Ruhoff, Leonardo Laipelt
Keywords: evapotranspiration, South America, water resources
Last updated: 2023-03-26 https://code.earthengine.google.com/ba815cfffab1b2f60ef92446693b9170
"},{"location":"projects/geoboundary/","title":"geoBoundaries Global Database of Political Administrative Boundaries","text":"Produced and maintained by the William & Mary geoLab since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database is an online, open license resource of boundaries (i.e., state, county) for every country in the world. We currently track 199 total entities, including all 195 UN member states, Greenland, Taiwan, Niue, and Kosovo. All boundaries are available to view or download in common file formats, including shapefiles; the only requirement for use is acknowledgement. The most up-to-date information about geoBoundaries can be found at www.geoboundaries.org.
All boundary types have been ingested and are include the following with Admin level varying from 0-4 which have been ingested.
HPSCU - High Precision Single Country Unstandardized. The premier geoBoundaries release, representing the highest precision files available for every country in the world. No standardization is performed on these files, so (for example) two countries may overlap in the case of contested boundaries.
HPSCGS - High Precision Single Country Globally Standardized. A version of geoBoundaries high precision data that has been clipped to the U.S. Department of State boundary file, ensuring no contested boundaries or overlap in the dataset. This globally standardized product may have gaps between countries. If you need a product with no gaps, we recommend our simplified global product.
SSCU - Simplified Single Country Unstandardized. A simplified version of every file available for every country in the world. No standardization is performed on these files, so (for example) two countries may overlap in the case of contested boundaries.
SSCGS - Simplified Single Country Globally Standardized. A version of geoBoundaries simplified data that has been clipped to the U.S. Department of State boundary file, ensuring no contested boundaries or overlap in the dataset. This globally standardized product may have gaps between countries.
CGAZ - Comprehensive Global Administrative Zones. A global composite of the SSCGS ADM0, ADM1 and ADM2, with gaps filled between borders. Also available at higher levels of simplification.
Feature Collection Admin Levels HPSCU ADM0,ADM1,ADM2,ADM3,ADM4 HPSCGS ADM0,ADM1,ADM2,ADM3,ADM4 SSCU ADM0,ADM1,ADM2,ADM3,ADM4 SSCGS ADM0,ADM1,ADM2,ADM3,ADM4 CGAZ ADM0,ADM1,ADM2
"},{"location":"projects/geoboundary/#citation","title":"Citation","text":"You can read the paper here and cite using citation below
Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020)\ngeoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4):\ne0231866. https://doi.org/10.1371/journal.pone.0231866\nYou can also find more information on the webpage along with the github project page
"},{"location":"projects/geoboundary/#data-preprocessing-for-gee","title":"Data Preprocessing for GEE","text":"To make the datasets more amenable they were downloaded using the API and all features in a folder were then merged into single collections pertaining to varying boundary type and admin levels. There might be some missing pieces owing to issues with downloads and or upload to GEE but care has been taken to minimize those efforts.
"},{"location":"projects/geoboundary/#earth-engine-datasets","title":"Earth Engine Datasets","text":"var CGAZ_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/CGAZ_ADM0');\nvar CGAZ_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/CGAZ_ADM1');\nvar CGAZ_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/CGAZ_ADM2');\nvar HPSCGS_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM0');\nvar HPSCGS_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM1');\nvar HPSCGS_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM2');\nvar HPSCGS_ADM3 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM3');\nvar HPSCGS_ADM4 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM4');\nvar HPSCU_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM0');\nvar HPSCU_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM1');\nvar HPSCU_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM2');\nvar HPSCU_ADM3 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM3');\nvar HPSCU_ADM4 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM4');\nvar SSCGS_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM0');\nvar SSCGS_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM1');\nvar SSCGS_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM2');\nvar SSCGS_ADM3 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM3');\nvar SSCGS_ADM4 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM4');\nvar SSCU_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM0');\nvar SSCU_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM1');\nvar SSCU_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM2');\nvar SSCU_ADM3 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM3');\nvar SSCU_ADM4 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM4');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GEOBOUNDARIES
"},{"location":"projects/geoboundary/#code-attribution","title":"Code Attribution","text":"Code Provided by Ujaval Gandhi
"},{"location":"projects/geoboundary/#license","title":"License","text":"Individual data files in the geoBoundaries database are governed by the license or licenses identified within the metadata for each respective boundary and are all variants of partially or completely open licenses. All referenced licenses can be read in their entirety here. Computer code and derivative works generated by the geoBoundaries project are released under the Attribution 4.0 International (CC BY 4.0) license.
Produced and maintained by the William & Mary geoLab since 2017
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: : Metadata, Political Geography, Open Data, Built Structures, Physical Mapping
Last updated: 2021-07-10
"},{"location":"projects/geomorpho90/","title":"Geomorpho90m Geomorphometric Layers","text":"Topographical relief comprises the vertical and horizontal variations of the Earth\u2019s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90\u2009m) and 7.5 arc-second (~250\u2009m) resolutions under the WGS84 geodetic datum, and 100\u2009m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography.
Geomorpho90m is a set of geomorphometric variables derived from MERIT-DEM. The are available at 3 resolutions the ingested ones are the 3 arc-second (~90m) resolution.The layers can be downloaded from OpenTopography or from Google Drive.
Read about the methodology here
Use the following credit when these datasets are cited:
Amatulli, Giuseppe, Daniel McInerney, Tushar Sethi, Peter Strobl, and Sami Domisch. \"Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers.\" Scientific Data 7, no. 1 (2020): 1-18.\nvar geom = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/geom\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GEOMORPHO90-GEOMORPHOLOGICAL-FORMS
"},{"location":"projects/geomorpho90/#first-order-derivatives","title":"First order derivatives","text":"var slope = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/slope\");\nvar aspect = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/aspect\");\nvar aspect_cosine = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/aspect-cosine\");\nvar aspect_sine = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/aspect-sine\");\nvar eastness = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/eastness\");\nvar northness = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/northness\");\nvar convergence = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/convergence\");\nvar spi = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/spi\");\nvar cti = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/cti\");\nvar dx = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dx\");\nvar dy = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dy\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GEOMORPHO90-FIRST-ORDER-DERIVATIVE
"},{"location":"projects/geomorpho90/#second-order-derivatives","title":"Second order derivatives","text":"var dxx = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dxx\");\nvar dxy = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dxy\");\nvar dyy = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dyy\");\nvar pcurv = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/pcurv\");\nvar tcurv = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/tcurv\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GEOMORPHO90-SECOND-ORDER-DERIVATIVE
"},{"location":"projects/geomorpho90/#ruggedeness","title":"Ruggedeness","text":"var elev_stdev = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/elev-stdev\")\nvar vrm = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/vrm\");\nvar roughness = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/roughness\");\nvar tri = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/tri\");\nvar tpi = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/tpi\");\nvar dev_magnitude = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dev-magnitude\");\nvar dev_scale = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dev-scale\");\nvar rough_magnitude = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/rough-magnitude\");\nvar rough_scale = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/rough-scale\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GEOMORPHO90-RUGGEDENESS
"},{"location":"projects/geomorpho90/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: Geomorpho90m, geomorphometric layers, MERIT DEM, topographic index, terrain ruggedness index, slope
Last updated: 2023-04-04
"},{"location":"projects/gfa/","title":"Global Fire Atlas (2003-2016)","text":"The Global Fire Atlas is a new freely available global dataset that tracks the daily dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Data are available in easily accessible GIS-layers and can also be explored online here and a detailed description of the underlying methodology is provided by Andela et al. (2019).
The data provide unique insights in the environmental conditions that give rise to the world's most extreme wildfires. The world's largest wildfires were found in sparsely populated arid and semiarid grasslands and shrublands of interior Australia, Africa, and Central Asia. Strikingly, fires of these proportions were nearly absent in similar ecosystems of North and South America, possibly due to higher landscape fragmentation and different management practices, including active fire suppression.
While the world's largest fires occurred in more arid ecosystems, the longest fires burned for over 2 months in seasonal regions of the humid tropics and high-latitude forests. In these sparsely populated high fuel-load systems fires can continuously burn as long as weather conditions are favorable. Abnormal weather conditions often synchronized the occurrence of multiple extreme wildfires across larger regions. Global patterns of fire velocity were reversely related to fuel loads, and the highest fire velocities typically occurred in areas of low fuel loads.
"},{"location":"projects/gfa/#citation","title":"Citation","text":"Andela, Niels, Douglas C. Morton, Louis Giglio, Ronan Paugam, Yang Chen, Stijn Hantson, Guido R. Werf, and James T. Randerson.\n\"The Global Fire Atlas of individual fire size, duration, speed and direction.\" Earth System Science Data 11, no. 2 (2019): 529-552.\nvar day_of_burn = ee.ImageCollection(\"projects/sat-io/open-datasets/global-fire-atlas/day_of_burn\");\nvar fire_direction = ee.ImageCollection(\"projects/sat-io/open-datasets/global-fire-atlas/fire_direction\");\nvar fire_line = ee.ImageCollection(\"projects/sat-io/open-datasets/global-fire-atlas/fire_line\");\nvar fire_speed = ee.ImageCollection(\"projects/sat-io/open-datasets/global-fire-atlas/fire_speed\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/GLOBAL-FIRE-ATLAS
Data Location Online Webpage https://www.globalfiredata.org/fireatlas.html User Guide https://glihtdata.gsfc.nasa.gov/files/fire_atlas/Fire_Atlas_user_guide.pdf Overall data hosted by NASA https://glihtdata.gsfc.nasa.gov/files/fire_atlas/The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire
"},{"location":"projects/gfa/#global-fire-atlas-earth-engine-snippet","title":"Global Fire Atlas: Earth Engine Snippet","text":"var ignitions_2003 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2003\");\nvar ignitions_2004 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2004\");\nvar ignitions_2005 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2005\");\nvar ignitions_2006 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2006\");\nvar ignitions_2007 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2007\");\nvar ignitions_2008 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2008\");\nvar ignitions_2009 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2009\");\nvar ignitions_2010 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2010\");\nvar ignitions_2011 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2011\");\nvar ignitions_2012 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2012\");\nvar ignitions_2013 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2013\");\nvar ignitions_2014 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2014\");\nvar ignitions_2015 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2015\");\nvar ignitions_2016 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2016\");\nvar perimeter_2003 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2003\");\nvar perimeter_2004 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2004\");\nvar perimeter_2005 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2005\");\nvar perimeter_2006 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2006\");\nvar perimeter_2007 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2007\");\nvar perimeter_2008 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2008\");\nvar perimeter_2009 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2009\");\nvar perimeter_2010 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2010\");\nvar perimeter_2011 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2011\");\nvar perimeter_2012 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2012\");\nvar perimeter_2013 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2013\");\nvar perimeter_2014 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2014\");\nvar perimeter_2015 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2015\");\nvar perimeter_2016 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2016\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/GLOBAL-FIRE-ATLAS
GEE_Property_Name Property_Name Property_Example end_DOY: End Day of Year 33 end_date: End Date 2/2/2003 expansion: Daily fire expansion (km2\u00a0day-1) 0.21 fire_ID: Fire_ID 226089 fire_line: Daily Fire Line 0.46 lat: Latitude 39.8896 lon: Longitude -0.3178 perimeter: Perimeter (km) 1.85 size: Size(km2) 0.21 speed: Speed (km day-1) 0.46 start_DOY: Start Day of Year 33 start_date: Start Date 2/2/2003 tile_ID: Tile_ID h17v05"},{"location":"projects/gfa/#license-usage","title":"License & Usage","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: Human Dimensions, Natural Hazards, Wild Fires, Burned Area, Biosphere, Ecological Dynamics, Fire Ecology, Fire Dynamics, Fire Occurrence
Contact details & Created by: Niels Andela (niels.andela@nasa.gov)
Last updated: 2019-04-24
"},{"location":"projects/gfch/","title":"Global Forest Canopy Height from GEDI & Landsat","text":"A new, 30-m spatial resolution global forest canopy height map was developed through the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides footprint-based measurements of vegetation structure, including forest canopy height between 52\u00b0N and 52\u00b0S globally. The Global Land Analysis and Discover team at the University of Maryland (UMD GLAD) integrated the GEDI data available to date (April-October 2019) with the year 2019 Landsat analysis-ready time-series data (Landsat ARD). The GEDI RH95 (relative height at 95%) metric was used to calibrate the model. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling. The \u201cmoving window\u201d locally calibrated and applied bagged regression tree ensemble model was implemented to ensure high quality of forest height prediction and global map consistency. The model was extrapolated in the boreal regions (beyond the GLAD data range) to create the global forest height prototype map.
The global forest height map is a prototype product that has known issues related to GEDI data quality and Landsat optical time-series data availability and feasibility of characterizing forest structure. GEDI data overestimate forest height on slopes within temperate and subtropical mountain grasslands, e.g. in New Zealand and Lesotho. Tree height over cities and suburbs may be confounded with building height, as GEDI data do not discriminate between the height of vegetation and man-made objects. The GEDI calibration uncertainties (specifically, geolocation precision and land surface height estimation) may be responsible for some of the map errors. The forest height model saturated above 30m and does not adequately represent the height of the tallest trees. The global product will be updated in the future to address most of the issues. The newly processed GEDI data will include refinements to land surface detection algorithms, an urban mask, and improved geolocation. Planned integration of higher spatial resolution Sentinel-2 data will allow the implementation of texture metrics. Application of advanced machine learning tools (namely, convolution neural networks) will be tested to improve forest height modeling accuracy.
Map data within the GEDI data range provided in the geographic coordinates using the WGS84 reference system. 8-bit unsigned LZW-compressed GeoTiff. Pixel size is 0.00025 x 0.00025 degree. Data aggregated into continental mosaics which can be downloaded here. Pixel values: 0-60 Forest canopy height, meters, 101 Water, 102 Snow/ice ,103 No data
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/gfch/#citation","title":"Citation","text":"P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C. E. Silva, J. Armston, R.\nDubayah, J. B. Blair, M. Hofton (2020). https://doi.org/10.1016/j.rse.2020.112165\nvar gf = ee.ImageCollection(\"projects/sat-io/open-datasets/GLAD/GEDI_V27\");\nvar gbf = ee.ImageCollection(\"projects/sat-io/open-datasets/GLAD/GEDI_V25_Boreal\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FOREST-CANOPY-HT-GEDI-LANDSAT
Earth Engine app: http://glad.earthengine.app/view/global-forest-canopy-height-2019
"},{"location":"projects/gfch/#license","title":"License","text":"The global dataset is available online, with no charges for access and no restrictions on subsequent redistribution or use, as long as the proper citation is provided as specified by the Creative Commons Attribution License (CC BY)
Provided by : Potapov et al. 2020
Curated in GEE by : Potapov et al and Samapriya Roy
Keywords: GEDI, Canopy Height, Landsat, Tree
Last updated: 2020-07-25
"},{"location":"projects/gfm_100/","title":"Global Forest Management dataset 2015","text":"The global forest management dataset was published by Lesiv et al. (2022) in Nature Scientific Data. The resultant map at 100m resolution detials six global forest management categories, including 1) naturally regenerating forest without any signs of human activities, 2) naturally regenerating forest with signs of human activities, 3) planted forest, 4) short rotation plantations for timber, 5) oil palm plantations, and 6) agroforestry. The dataset included reference dataset of 226\u2009K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki.
Some of the published dataset included here are
Some of the fields were encoded to remove special characters and renamed for clarity.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/gfm_100/#citation","title":"Citation","text":"Lesiv, Myroslava, Dmitry Schepaschenko, Marcel Buchhorn, Linda See, Martina D\u00fcrauer, Ivelina Georgieva, Martin Jung et al. \"Global forest management\ndata for 2015 at a 100 m resolution.\" Scientific data 9, no. 1 (2022): 199.\nvar class_prob = ee.Image(\"projects/sat-io/open-datasets/GFM/ProbaV_LC100_epoch2015_global_v203\");\nvar crowdsourced_data = ee.FeatureCollection(\"projects/sat-io/open-datasets/GFM/original_crowdsourced_data\");\nvar validation_data = ee.FeatureCollection(\"projects/sat-io/open-datasets/GFM/validation_data_set\");\nvar fml = ee.Image(\"projects/sat-io/open-datasets/GFM/FML_v3-2\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FOREST-MANAGEMENT-DATASET-2015
"},{"location":"projects/gfm_100/#license","title":"License","text":"This dataset is available under a Creative Commons BY-4.0 license with attribution.
Provided by : Myroslava et al. 2022
Curated in GEE by : Samapriya Roy
Keywords: forest management, plantations, land use, map, remote sensing
Last updated in GEE: 2023-05-11
"},{"location":"projects/gfplain250/","title":"Global high-resolution floodplains (GFPLAIN250m)","text":"The GFPLAIN250m includes raster data of Earth's floodplains identified using a geomorphic approach presented in Nardi et al. (2006, 2018). The 250m floodplain dataset is derived by processing the NASA SRTM Digital Elevation model gathered from http://srtm.csi.cgiar.org/, and in particular the 250-m SRTM version 4.1 DTM. The coding used for each continent and additional information are detailed in the metadata included in the GFPLAIN250m data repository. You can find the dataset here
You can read the full paper here. The elevation data are processed by a fast geospatial tool for floodplain mapping available for download at https://github.com/fnardi/GFPLAIN. As per the paper, the GFPLAIN250m dataset can support many applications, including flood hazard mapping, habitat restoration, development studies, and the analysis of human-flood interactions.
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/gfplain250/#paper-citation","title":"Paper Citation","text":"Nardi, F. et al. GFPLAIN250m, a global high-resolution dataset of Earth\u2019s floodplains.\nSci. Data. 6:180309 doi: 10.1038/sdata.2018.309 (2019).\nNardi, Fernando; Annis, Antonio (2018): GFPLAIN250m. figshare. Dataset.\nhttps://doi.org/10.6084/m9.figshare.6665165.v1\nvar gfplain250 = ee.ImageCollection(\"projects/sat-io/open-datasets/GFPLAIN250\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-HIGHRES-FLOODPLAINS
"},{"location":"projects/gfplain250/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created by: Nardi, F. et al
Curated by: Samapriya Roy
Keywords: Floodplain, Digital Elevation Model (DEM), Terrain analysis, river networks, landscape features
Last updated: 2018-11-11
"},{"location":"projects/gfv/","title":"Global Freshwater Variables","text":"The dataset consists of near-global, spatially continuous, and freshwater-specific environmental variables in a standardized 1km grid. We delineated the sub-catchment for each grid cell along the HydroSHEDS river network and summarized the upstream environment (climate, topography, land cover, surface geology and soil) to each grid cell using various metrics (average, minimum, maximum, range, sum, inverse distance-weighted average and sum). All variables were subsequently averaged across single lakes and reservoirs of the Global lakes and Wetlands Database that are connected to the river network. Monthly climate variables were summarized into 19 long-term climatic variables following the \u201cbioclim\u201d framework.
"},{"location":"projects/gfv/#paper-citation","title":"Paper citation","text":"Domisch, S., Amatulli, G., and Jetz, W. (2015) Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution.\nScientific Data 2:150073 doi:10.1038/sdata.2015.73\nvar annual_air_temperature_range_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/annual_air_temperature_range_avg\");\nvar annual_sum_of_precipitation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/annual_sum_of_precipitation_avg\");\nvar barren_lands_sparse_vegetation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/barren_lands_sparse_vegetation_avg\");\nvar catchment_size_sum = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/catchment_size_sum\");\nvar cultivated_and_managed_vegetation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/cultivated_and_managed_vegetation_avg\");\nvar deciduous_broadleaf_trees_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/deciduous_broadleaf_trees_avg\");\nvar elevation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/elevation_avg\");\nvar elevation_range = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/elevation_range\");\nvar evergreen_broadleaf_trees_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/evergreen_broadleaf_trees_avg\");\nvar evergreen_deciduous_needleleaf_trees_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/evergreen_deciduous_needleleaf_trees_avg\");\nvar herbaceous_vegetation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/herbaceous_vegetation_avg\");\nvar mean_annual_air_temperature_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/mean_annual_air_temperature_avg\");\nvar mixed_other_trees_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/mixed_other_trees_avg\");\nvar open_water_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/open_water_avg\");\nvar precambrian_surface_lithology_wsum = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/precambrian_surface_lithology_wsum\");\nvar precipitation_seasonality_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/precipitation_seasonality_avg\");\nvar quaternary_surface_lithology_wsum = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/quaternary_surface_lithology_wsum\");\nvar regularly_flooded_shrub_herbaceous_vegetation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/regularly_flooded_shrub_herbaceous_vegetation_avg\");\nvar shrubs_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/shrubs_avg\");\nvar slope_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/slope_avg\");\nvar slope_range = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/slope_range\");\nvar snow_ice_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/snow-ice_avg\");\nvar stream_length_sum = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/stream_length_sum\");\nvar urban_builtup_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/urban_builtup_avg\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:earthenv-bd-ecosystems-clim-layers/GLOBAL-FRESHWATER-VARIABLES
"},{"location":"projects/gfv/#license","title":"License","text":"EarthEnv Near-global environmental information for freshwater ecosystems in 1km resolution Version 1 by Domisch et al. is licensed under a \u201cCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\u201d. Permissions beyond the scope of this license may be available at http://www.earthenv.org/.
"},{"location":"projects/gfv/#dataset-citation","title":"Dataset citation","text":"Domisch, S., Amatulli, G., and Jetz, W. (2015) Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution.\nScientific Data 2:150073 doi: 10.1038/sdata.2015.73. Data available online at http://www.earthenv.org/.\nProject Website: http://www.earthenv.org/streams
App Website: App link here
Curated by: Samapriya Roy
Keywords: Earthenv, stream length, urban builtup, slope, shrubs, precambrian surface lithology, barren_lands, precipitation seasonality, herbaceous vegetation
Last updated: 2021-05-09
"},{"location":"projects/gfwed/","title":"Global Fire WEather Database (GFWED)","text":"The Global Fire WEather Database (GFWED) integrates different weather factors influencing the likelihood of a vegetation fire starting and spreading at daily temporal resolution and a ~50-km (0.5-deg x 0.625-deg) spatial resolution from 1980-present. It is based on the Fire Weather Index (FWI) System, the most widely used fire weather system in the world. The FWI System was developed in Canada, and is composed of three moisture codes and three fire behavior indices. The moisture codes capture the moisture content of three generalized fuel classes and the behavior indices reflect the spread rate, fuel consumption and intensity of a fire if it were to start. Details on the development and testing of GFWED can be found in Field et al. (2015) and the evaluation of GFWED products in Field (2020a). Applications of the FWI System can be found in Taylor and Alexander (2006) and technical descriptions are provided by van Wagner (1987) and Dowdy et al. (2009). Additional information about this dataset can be found here and here. You can also find this dataset at the climate engine org page here.
Spatial Extent Global Spatial Resolution ~50-km (0.5-deg x 0.625-deg) Temporal Resolution Daily Time Span 1980-04-02 to present Update Frequency Updated daily with 5-month lag time Variables Fire Weather Index ('FWI') - Units: Unitless - Scale Factor: 1.0
"},{"location":"projects/gfwed/#citation","title":"Citation","text":"Field, R.D., A.C. Spessa, N.A. Aziz, A. Camia, A. Cantin, R. Carr, W.J. de Groot, A.J. Dowdy, M.D. Flannigan, K. Manomaiphiboon, F. Pappenberger, V.\nTanpipat, and X. Wang, 2015: Development of a global fire weather database. Nat. Hazards Earth Syst. Sci., 15, 1407-1423, doi:10.5194/\nnhess-15-1407-2015.\n\nMERRA-2 Overview: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Ronald Gelaro, et al., 2017, J. Clim.,\ndoi: 10.1175/JCLI-D-16-0758.1\n// Read in Image Collection and get first image\nvar merra2_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-merra2_fwi-daily')\nvar merra2_i = merra2_ic.filterDate('2020-08-01', '2020-08-05').first()\n\n// Print first image to see bands\nprint(merra2_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar fwi_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(merra2_i.select('FWI'), {min: 0, max: 100, palette: fwi_palette}, 'FWI')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/GLOBAL-FIRE-WEATHER-DB
Sample Script:
"},{"location":"projects/gfwed/#license","title":"License","text":"NASA Earth science data are made freely available to the public to the fullest extent possible, consistent with applicable laws and regulations. NASA Earth science data are not subject to copyright.
Keywords: climate, fire, wildfire, NASA, MERRA2, daily, global, GFWED
Provided by : GFWED development is supported by the NASA Precipitation Measurement Missions Science Team and the NASA Group on Earth Observations Work Program
Curated in GEE by: Climate Engine Org
"},{"location":"projects/ghap/","title":"Global High Air Pollutants(GHAP) PM2.5 Concentrations (2017-2022)","text":"This dataset is the first big data-derived gapless (100% spatial coverage) daily, monthly, and annual PM2.5 concentration product at 1km resolution (D1K, M1K, and Y1K) for global land areas from 2017 to 2022 and part of Global High Air Pollutants Dataset (GHAP). Leveraging machine learning and big data techniques, the dataset provides unprecedented insights into the spatiotemporal variability of PM2.5 pollution. In particular, it reveals extensive exposure to unhealthy air globally, identifies disparities in exposure between developed/developing countries, urban/rural areas, and within cities, captures the impact of events like COVID-19 lockdowns on air quality, and provides insights into nature-induced pollution episodes (e.g., biomass burning). The dataset boasts high quality, with a cross-validation coefficient of determination (CV-R2) of 0.91 and a root-mean-square error (RMSE) of 9.20 \u00b5g m-3 on the daily basis.
Data Coverage: Variables: Scaling factor Temporal: Daily data from 2017 to 2022 PM2.5 concentration (\u03bcg/m\u00b3) 0.1 Spatial: Global coverage (land areas)"},{"location":"projects/ghap/#citation","title":"Citation","text":"Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3\nDataset citations are available based on the year and how the releases were packaged. You can find them all here and find an example below
Wei, J., & Li, Z. (2024). GlobalHighPM2.5 (2022) (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10795662\nvar GHAP_DAILY = ee.ImageCollection(\"projects/sat-io/open-datasets/GHAP/GHAP_D1K_PM25\");\nvar GHAP_MONTHLY = ee.ImageCollection(\"projects/sat-io/open-datasets/GHAP/GHAP_M1K_PM25\");\nvar GHAP_YEARLY = ee.ImageCollection(\"projects/sat-io/open-datasets/GHAP/GHAP_Y1K_PM25\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GHAP-DATASETS
"},{"location":"projects/ghap/#license","title":"License","text":"These datasets are licensed under a Creative Commons Attribution 4.0 International license.
Provided by: Wei et al
Curated in GEE by: Samapriya Roy
Keywords: PM2.5, Pollutants, Air Quality, Air Pollutants, GHAP
Last updated in GEE: 2024-06-09
"},{"location":"projects/ghh/","title":"Global Habitat Heterogeneity","text":"The datasets contain 14 metrics quantifying spatial heterogeneity of global habitat at multiple resolutions based on the textural features of Enhanced Vegetation Index (EVI) imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). For additional information about the metrics and the evaluations of their utility for biodiversity modeling. The dataset is generated at 1km, 5km and 25km resolution and only the 1km assets are listed here, simply replace _1km by _5km and _25km as needed.
"},{"location":"projects/ghh/#paper-citation","title":"Paper Citation","text":"Tuanmu, M.-N. and W. Jetz. (2015) A global, remote sensing-based characterization of terrestrial habitat heterogeneity\nfor biodiversity and ecosystem modeling. Global Ecology and Biogeography. DOI: 10.1111/geb.12365.\nvar cov = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/coefficient_of_variation_1km\");\nvar contrast = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/contrast_1km\");\nvar corr = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/correlation_1km\");\nvar dissimilarity = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/dissimilarity_1km\");\nvar entropy = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/entropy_1km\");\nvar homogeneity = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/homogeneity_1km\");\nvar maximum = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/maximum_1km\");\nvar mean = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/mean_1km\");\nvar pielou = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/pielou_1km\");\nvar range = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/range_1km\");\nvar shannon = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/shannon_1km\");\nvar simpson = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/simpson_1km\");\nvar sd = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/standard_deviation_1km\");\nvar uniformity = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/uniformity_1km\");\nvar variance = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/variance_1km\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:earthenv-bd-ecosystems-clim-layers/GLOBAL-HABITAT-HETEROGENEITY
"},{"location":"projects/ghh/#license","title":"License","text":"Global Habitat Heterogeneity Metrics Version 1 by Tuanmu & Jetz is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Permissions beyond the scope of this license may be available at http://www.earthenv.org/texture.html.
"},{"location":"projects/ghh/#dataset-citation","title":"Dataset Citation","text":"Tuanmu, M.-N. and W. Jetz. (2015) A global, remote sensing-based characterization of terrestrial habitat heterogeneity\nfor biodiversity and ecosystem modeling. Global Ecology and Biogeography. DOI: 10.1111/geb.12365.\nProject Website: http://www.earthenv.org/texture
App Website: App link here
Curated by: Samapriya Roy
Keywords: Earthenv, habitat heterogeneity, shannon, simpson, pielou, dissimilarity, homogeneity, variance, contrast
Last updated: 2021-05-09
"},{"location":"projects/ghm/","title":"Global human modification v1.5","text":"This updates v1 to v1.5, which provides additional datasets on 6 major stressors at 300 m resolution and two additional time steps (1995 and 2005), as well as reflecting minor data update and processing refinements. Users are advised to use these data instead of \"CSP gHM: Global Human Modification\" (https://developers.google.com/earth-engine/datasets/catalog/CSP_HM_GlobalHumanModification). These data were updated June 17, 2023.
Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 1995, 2000, 2005, 2010, 2015, and 2017 to evaluate temporal changes and spatial patterns of land use modification of terrestrial lands (excluding Antarctica). These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (0.09 km^2 resolution), temporally consistent (for 1990-2015, every 5 years), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset that represents ~2017 conditions and has 14 stressors for an even more comprehensive dataset, but the 2017 results should not be used to calculate change with the other datasets (1990-2015).You can read the paper here. The v1.5 datasets can also be accessed at.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ghm/#updates-changelog-reasoning","title":"Updates Changelog & reasoning","text":"This updates v1 to v1.5, which provides additional datasets on 6 major stressors at 300 m resolution and two additional time steps (1995 and 2005), as well as reflecting minor data updates and processing refinements. Specifically:
Datasets are provided for each of the 6 stressor groups: built-up areas (BU), agricultural/timber harvest (AG), extractive energy and mining (EX), human intrusions (HI), natural system modifications (NS), and transportation & infrastructure (TI), available now at 300 m resolution for each of the time steps in the 1990-2015 time series.
It provides the addition\u00a0datasets for the years 1995 and 2005, calculated using linear interpolation when stressor data do not provide data at the specific year.
The ESA 150 m water-mask dataset (Lamarche et al. 2017) was used to provide better and more consistent alignment of datasets at the ocean-land-inland water interfaces.
The built-up stressor uses an updated version of the Global Human Settlement Layer (v2022A).
Values provided are 32-bit floating point values, with human modification values ranging from 0.0 to 1.0.
Theobald, David M., Christina Kennedy, Bin Chen, James Oakleaf, Sharon Baruch-Mordo, and Joe Kiesecker. \"Earth transformed: detailed mapping of\nglobal human modification from 1990 to 2017.\" Earth System Science Data 12, no. 3 (2020): 1953-1972.\nTheobald, David M., Kennedy, Christina, Chen, Bin, Oakleaf, James, Baruch-Mordo, Sharon, & Kiesecker, Joe. (2023). Data for detailed temporal\nmapping of global human modification from 1990 to 2017 (v1.5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7534895\nvar waterMask = ee.Image(\"projects/sat-io/open-datasets/GHM/ESACCI-LC-L4-WB-Ocean-Land-Map-150m-P13Y-2000-v40\");\nvar H2017static = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2017_300_60land\")\nvar H2015change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2015c_300_60land\");\nvar H2010change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2010c_300_60land\");\nvar H2005change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2005c_300_60land\");\nvar H2000change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2000c_300_60land\");\nvar H1995change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_1995c_300_60land\");\nvar H1990change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_1990c_300_60land\");\nvar H2017_AG = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-AG\");\nvar H2017_BU = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-BU\");\nvar H2017_EX = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-EX\");\nvar H2017_HI = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-HI\");\nvar H2017_NS = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-NS\");\nvar H2017_TI = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-TI\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GLOBAL-HUMAN-MODIFICATION
Earth Engine App: https://davidtheobald8.users.earthengine.app/view/global-human-modification-change
"},{"location":"projects/ghm/#license","title":"License","text":"This dataset is available under a CC-BY-SA-4.0.
Curated by: David M. Theobald & Samapriya Roy
Keywords: Global human modification, land use, human pressures, biodiversity
Last updated: June 17, 2023
"},{"location":"projects/ghsl/","title":"Global Human Settlement Layer 2023","text":"The Global Human Settlement Layer (GHSL) project is a comprehensive initiative that generates global spatial data and evidence-based analytics, offering insights into the distribution and characteristics of human presence on Earth. The project follows an open and unrestricted data and methods access policy. The knowledge derived from GHSL plays a crucial role in shaping European policies, fostering public discussions, and facilitating the implementation of international frameworks like the 2030 Development Agenda. This release offers enhanced built-up area information, including surface, volume, and height measurements, along with population data. Moreover, it introduces a new settlement model and a classification system for administrative and territorial units based on the \"Degree of Urbanisation\" framework. The GHSL Data Package 2023 consists of multitemporal products, that offers an insight into the human presence in the past (epochs from 1975 through 2020, 5 years interval) and the future (2025 and 2030). The datasets included can be found below along with descriptors and dataset citations. Methodological citations and additional details on the products themselves can be found here.
"},{"location":"projects/ghsl/#dataset-details","title":"Dataset details","text":"Pesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from\nSentinel2 composite and Landsat, multitemporal (1975-2030). European Commission, Joint Research Centre\n(JRC) [Dataset] doi: 10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA PID:\nhttp://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea\n\nPesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-H R2023A - GHS building height, derived from AW3D30,\nSRTM30, and Sentinel2 composite (2018). European Commission, Joint Research Centre (JRC) [Dataset] doi:\n10.2905/85005901-3A49-48DD-9D19-6261354F56FE PID: http://data.europa.eu/89h/85005901-3a49-\n48dd-9d19-6261354f56fe\n\nPesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-V R2023A - GHS built-up volume grids derived from\njoint assessment of Sentinel2, Landsat, and global DEM data, multitemporal (1975-2030). European\nCommission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/AB2F107A-03CD-47A3-85E5-139D8EC63283\nPID: http://data.europa.eu/89h/ab2f107a-03cd-47a3-85e5-139d8ec63283\n\nPesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-C R2023A - GHS Settlement Characteristics, derived\nfrom Sentinel2 composite (2018) and other GHS R2023A data. European Commission, Joint Research Centre\n(JRC) [Dataset] doi: 10.2905/3C60DDF6-0586-4190-854B-F6AA0EDC2A30 PID:\nhttp://data.europa.eu/89h/3c60ddf6-0586-4190-854b-f6aa0edc2a30\n\nSchiavina, Marcello; Freire, Sergio; Alessandra Carioli; MacManus, Kytt (2023): GHS-POP R2023A - GHS\npopulation grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:\n10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE PID: http://data.europa.eu/89h/2ff68a52-5b5b-4a22-\n8f40-c41da8332cfe\n\nSchiavina, Marcello; Melchiorri, Michele; Pesaresi, Martino (2023): GHS-SMOD R2023A - GHS settlement layers,\napplication of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A,\nmultitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:\n10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA PID: http://data.europa.eu/89h/a0df7a6f-49de-46ea9bde-563437a6e2ba\nvar GHS_BUILT_S_2018 = ee.ImageCollection(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_S_E2018_GLOBE_R2023A_54009_10_V1_0\");\nvar GHS_BUILT_S_2030 = ee.Image(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_S_E2030_GLOBE_R2023A_54009_100_V1_0\");\nvar GHS_BUILT_H = ee.Image(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_H_AGBH_E2018_GLOBE_R2023A_54009_100_V1_0\");\nvar GHS_BUILT_V = ee.Image(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_V_E2030_GLOBE_R2023A_54009_100_V1_0\");\nvar GHS_BUILT_C = ee.ImageCollection(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_C_MSZ_E2018_GLOBE_R2023A_54009_10_V1_0\");\nvar GHS_POP_1975_2030 = ee.ImageCollection(\"projects/sat-io/open-datasets/GHS/GHS_POP\");\nvar GHS_SMOD_1975_2030 = ee.ImageCollection(\"projects/sat-io/open-datasets/GHS/GHS_SMOD\")\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/JRC-GHSL-2023
"},{"location":"projects/ghsl/#license","title":"License","text":"The GHSL has been produced by the EC JRC as open and free data. Reuse is authorised, provided the source is acknowledged. For more information, please read the use conditions European Commission Reuse and Copyright Notice.
Created by: ESA & JRC
Curated in GEE by : Samapriya Roy
keywords: Global Population, Population count, Urban structure, Built up area, Built up volume, Building height
Last modified: 2022-01-20
Last updated on GEE: 2023-09-06
"},{"location":"projects/gid/","title":"Global Database of Iron and Steel Production Assets","text":"The Global Database of Iron and Steel Production Assets provides information on global iron and steel production plants that are operational today. The database contains 1,598 production plants with exact geolocation and provides information about ownership, production type, plant type, capacity and production start year where available.
Primary steel production processes (blast furnace, basic oxygen furnace or open-hearth furnaces), typically use coal as an energy source and take place in large integrated facilities. Whereas secondary steel production processes (electric arc furnaces) typically use electricity as an energy source and take place in so called \u2018mini-mills\u2019. The database captures a wide range of assets across the steel production process, including the procurement and processing of raw materials (in particular coking and pelletisation plants), the production of crude steel (integrated plants and mini-mills) and the production of finished steel products (downstream plants).
"},{"location":"projects/gid/#citation","title":"Citation","text":"McCarten, M., Bayaraa, M., Caldecott, B., Christiaen, C., Foster, P., Hickey, C., Kampmann, D.,\nLayman, C., Rossi, C., Scott, K., Tang, K., Tkachenko, N., and Yoken, D., 2021.\nGlobal Database of Iron and Steel Production Assets. Spatial Finance Initiative\nAdditional Information about the Spatial Finance Initiative can be found here
SNo Field Field_Description GEE_Field 1 uid Unique identifier for the plant uid 2 city City in which the plant is located city 3 state State or province in which the plant is located state 4 country Country in which the plant is located country 5 iso3 Three-letter country code defined in ISO 3166-1 alpha 3 iso3 6 country_code Three-digit country code defined in ISO 3166-1 numeric country_code 7 region Region in which the plant is located region 8 sub_region Subregion in which the plant is located sub_region 9 latitude Latitude for the geolocation of the plant (based on WGS84 (EPSG:4326)) latitude 10 longitude Longitude for the geolocation of the plant (based on WGS84 (EPSG:4326)) longitude 11 accuracy The accuracy of the latitude and longitude accuracy 12 status Current plant operating status status 13 plant_type The type of iron and steel production facility. Plant types include: Integrated, Mini-Mill, DRI, Downstream, Coke, and Pelletisation plants. plant_type 14 primary_production_type The primary production type used at the plant prprod_typ 15 primary_product The primary product that is produced at the plant. Product types include: Crude Steel, Finished Steel, Iron, Coke and Pellets. primary_product 16 capacity Total steel production capacity (millions of tons) of the primary product cap 17 capacity_source Source used to obtain capacity information cap_sr 18 year Year the plant started production year 19 owner_permid PermID of the primary owner of the plant* ow_pid 20 owner_name Name of the primary owner of the plant ow_name 21 owner_source Source reporting the ownership link between the plant and owner ow_sr 22 parent_permid PermID of the ultimate parent of the owner of the plant* pr_pid 23 parent_name Name of the ultimate parent of the owner of the plant pr_name 24 ownership_stake The percentage ownership attributed to the parent company if the plant is a joint venture. If the plant is majority owned by a single parent company then this column will be blank ow_stake 25 parent_lei Legal Entity Identifier (LEI) of the ultimate parent of the owner of the plant pr_lei 26 parent_holding_status The holding status of the ultimate parent (Private or Public) pr_hstat 27 parent_ticker The primary ticker for the ultimate parent, if the company is publicly traded pr_tkr 28 parent_exchange The primary exchange for the ultimate parent, if the company is publicly traded pr_exc 29 parent_permid_2 PermID of the 2nd ultimate parent of the owner of the plant* pr_pid2 30 parent_name_2 Name of the 2nd ultimate parent of the owner of the plant pr_name2 31 ownership_stake_2 The percentage ownership attributed to the 2nd parent company if the plant is a joint venture ow_stake2 32 parent_lei_2 Legal Entity Identifier (LEI) of the 2nd ultimate parent pr_lei2 33 parent_holding_status_2 The holding status of the 2nd ultimate parent (Private or Public) pr_hstat2 34 parent_ticker_2 The primary ticker for the 2nd ultimate parent, if the company is publicly traded pr_tkr2 35 parent_exchange_2 The primary exchange for the 2nd ultimate parent, if the company is publicly traded pr_exc2"},{"location":"projects/gid/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var global_steel = ee.FeatureCollection(\"projects/sat-io/open-datasets/SFI/global_steel_database_20210701\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-IRON-STEEL-PRODUCTION-ASSETS
"},{"location":"projects/gid/#acknowledgements","title":"Acknowledgements","text":"Both databases have been developed by the Oxford Sustainable Finance Programme, Satellite Applications Catapult, and The Alan Turing Institute as part of the Spatial Finance Initiative \u2018s GeoAsset Project. Project FAQ's can be found here
"},{"location":"projects/gid/#license","title":"License","text":"The Global Database of Iron and Steel Production Assets can be used by others and is available under a CC BY 4.0 license
Data download page: Download Request Form
Curated in GEE by: Samapriya Roy
Keywords: : GeoAsset Project, Oxford Sustainable Finance Programme, Satellite Applications Catapult, Alan Turing Institute, McCarten et al , Iron, Steel , Global database
Last updated: 2021-07-16
"},{"location":"projects/gimms_ndvi/","title":"GIMMS Normalized Difference Vegetation Index 1982-2022","text":"The PKU GIMMS Normalized Difference Vegetation Index dataset (PKU GIMMS NDVI, version 1.2) offers consistent global NDVI data at half-month intervals and 1/12\u00b0 resolution, spanning from 1982 to 2022. Its primary objective is to address key uncertainties prevalent in existing global long-term NDVI datasets, specifically mitigating the impact of NOAA satellite orbital drift and AVHRR sensor degradation.
This dataset was generated through the utilization of biome-specific Back-Propagation Neural Network (BPNN) models, leveraging the GIMMS NDVI3g product, and drawing from a pool of 3.6 million high-quality global NDVI samples. To extend its temporal coverage up to 2022, a pixel-wise Random Forests fusion method was employed, integrating data from the MODIS NDVI (MOD13C1).Notably, the PKU GIMMS NDVI dataset demonstrates impressive accuracy when assessed against Landsat NDVI samples. It effectively eliminates the adverse effects of satellite orbital drift and sensor degradation, showcasing robust temporal consistency with MODIS NDVI data concerning pixel values and global vegetation trends. Consequently, this dataset holds significant potential as a foundational resource for research in the realm of global change studies.
The dataset is available in two versions for download: one exclusively reliant on AVHRR data covering the period from 1982 to 2015, and the other consolidated with MODIS NDVI, encompassing data from 1982 to 2022. Users are strongly encouraged to utilize the quality control (QC) layer provided within the dataset to enhance data reliability. Additionally, it is recommended to apply a threshold (e.g., 0.1) for the removal of sparse vegetation during trend analysis, following established methodologies (Zhou et al., 2001; Liu et al., 2016).
"},{"location":"projects/gimms_ndvi/#post-processing","title":"Post Processing","text":"The datasets were renames since periods are not allowed in earth engine filenames so v1.2 was renamed to v12 and dates were added as start dates to each image in the collection.
"},{"location":"projects/gimms_ndvi/#dataset-citation","title":"Dataset citation","text":"Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, & Shilong Piao. (2023). Spatiotemporally consistent global dataset of the GIMMS Normalized\nDifference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2) (V1.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8253971\nLi, Muyi, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao. \"Spatiotemporally consistent global dataset of the GIMMS Normalized\nDifference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022.\" Earth System Science Data 15, no. 9 (2023): 4181-4203.\nvar avhrr_modis_consolidated = ee.ImageCollection(\"projects/sat-io/open-datasets/PKU-GIMMS-NDVI/AVHRR_MODIS_CONSOLIDATED\");\nvar avhrr_solely = ee.ImageCollection(\"projects/sat-io/open-datasets/PKU-GIMMS-NDVI/AVHRR_SOLELY\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GIMMS-NDVI-1982-2022
"},{"location":"projects/gimms_ndvi/#license","title":"License","text":"This work is licensed under Creative Commons Attribution 4.0 International license.
Created by: Li, Muyi, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Curated in GEE by : Samapriya Roy
keywords: PKU GIMMS NDVI, Landsat, MODIS, Back Propagation Neural Network
Last updated on GEE: 2023-10-10
"},{"location":"projects/gisa/","title":"Global Impervious Surface Area (1972-2019)","text":"Using more than three million Landsat satellite images, this research developed the first global impervious surface area (GISA) dataset from 1972 to 2019. Based on 120,777 independent and random reference sites from 270 cities all over the world, the omission error, commission error, and F-score of GISA are 5.16%, 0.82%, and 0.954, respectively. Compared to the existing global datasets, the merits of GISA include: (1) it provided the global ISA maps before the year of1985, and showed the longest time span (1972\u20132019), and the highest accuracy (in terms of a large number of randomly selected and third-party validation sample sets); (2) it presented a new global ISA mapping method, including a semi-automatic global sample collection, a locally adaptive classification strategy, and a spatio-temporal post-processing procedure; and (3) it extracted ISA from the whole global land area (not from an urban mask) and hence reduced the underestimation. The GISA can contribute to further understanding on the human's utilization and reformation to nature during the past half century.
Pixel values in each map indicate the first year when ISA was detected. No-data was labeled as 0. A look-up table for the detected year and pixel value is provided as follow:
year of first ISA:[1972, 1978, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]
pixel value[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]
You can download the dataset here
You can read about the methodology in the paper here
"},{"location":"projects/gisa/#citation","title":"Citation","text":"Huang, X., Li, J., Yang, J. et al. 30 m global impervious surface area dynamics\nand urban expansion pattern observed by Landsat satellites: From 1972 to 2019.\nSci. China Earth Sci. (2021). https://doi.org/10.1007/s11430-020-9797-9\nXin Huang, Jiayi Li, Jie Yang, Zhen Zhang, Dongrui Li, & Xiaoping Liu. (2021).\n30 m global impervious surface area dynamics and urban expansion pattern observed\nby Landsat satellites: from 1972 to 2019 (Version 1.0.0)\n[Data set]. http://doi.org/10.1007/s11430-020-9797-9\nvar gisa = ee.ImageCollection(\"projects/sat-io/open-datasets/GISA_1972_2019\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-IMPERVIOUS-SURFACE-AREA
"},{"location":"projects/gisa/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Xin Huang, Jiayi Li, Jie Yang, Zhen Zhang, Dongrui Li, & Xiaoping Liu
Curated in GEE by: Samapriya Roy
Keywords: Landsat, Urban, Google Earth Engine, Impervious area, Urban expansion
Last updated : 2021-08-01
"},{"location":"projects/gisd30/","title":"Global 30m Impervious-Surface Dynamic Dataset (GISD30)","text":"The Global 30\u2009m Impervious-Surface Dynamic Dataset (GISD30) offers an invaluable resource for understanding the ever-changing landscape of impervious surfaces across the globe from 1985 to 2020. This dataset holds profound scientific significance and practical applications in the realms of urban sustainable development, anthropogenic carbon emissions assessment, and global ecological-environment modeling. The GISD30 was meticulously created through an innovative and automated methodology that capitalizes on the strengths of spectral-generalization and automatic-sample-extraction strategies. Leveraging time-series Landsat imagery on the Google Earth Engine cloud computing platform, the dataset provides comprehensive insights into impervious-surface dynamics.
In the dataset creation process, global training samples and corresponding reflectance spectra were automatically derived, enhancing accuracy and reliability. Spatiotemporal adaptive classification models were employed, taking into account the dynamic nature of impervious surfaces across different epochs and geographical tiles. Furthermore, a spatiotemporal-consistency correction method was introduced to enhance the reliability of impervious-surface dynamics. The GISD30 dynamic model exhibits remarkable accuracy, with an overall accuracy of 90.1% and a kappa coefficient of 0.865, validated using a substantial dataset of 23,322 global time-series samples. This dataset provides vital insights into the doubling of global impervious surface area over the past 35 years, from 1985 to 2020, with Asia experiencing the most substantial increase. The GISD30 dataset is freely accessible and serves as a crucial tool for monitoring urbanization at regional and global scales, offering invaluable support for diverse applications. Access the dataset here (Liu et al., 2021b).
The global dynamic dataset was used to label the expansion information in a single band; specifically, the pervious surface and the impervious surface before 1985 were, respectively, labeled 0 and 1, and the expanded impervious surfaces in the periods 1985\u20131990, 1990\u20131995, 1995\u20132000, 2000\u20132005, 2005\u20132010, 2010\u20132015 and 2015\u20132020 were labeled 2, 3, 4, 5, 6, 7 and 8.
Years Impervious Surface Labels Before 1985 1 1985\u20131990 2 1990\u20131995 3 1995\u20132000 4 2000\u20132005 5 2005\u20132010 6 2010\u20132015 7 2015\u20132020 8
"},{"location":"projects/gisd30/#citation","title":"Citation","text":"Zhang, X., Liu, L., Zhao, T., Gao, Y., Chen, X., and Mi, J.: GISD30: global 30\u2009m impervious-surface dynamic dataset from 1985 to 2020 using\ntime-series Landsat imagery on the Google Earth Engine platform, Earth Syst. Sci. Data, 14, 1831\u20131856,\nhttps://doi.org/10.5194/essd-14-1831-2022, 2022\nLiangyun,Liu; Xiao,Zhang; Tingting,Zhao; Yuan,Gao; Xidong,Chen; Jun,Mi. (2021). GISD30: global 30-m impervious surface dynamic dataset from 1985 to\n2020 using time-series Landsat imagery on the Google Earth Engine platform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5220816\nvar gisd30 = ee.Image(\"projects/sat-io/open-datasets/GISD30_1985_2020\");\n\n//zoom to an urban center\nMap.setCenter(31.16387, 30.97292,8)\n\nvar palette = [\"#808080\", \"#006400\", \"#228B22\", \"#32CD32\", \"#ADFF2F\", \"#FFFF00\", \"#FFA500\", \"#FF0000\"];\n\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/132/light-gray\", \"Grayscale\");\n\nMap.addLayer(gisd30,{min:1,max:8,palette:palette},'GISD 30')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/global-landuse-landcover/GLOBAL-IMPERVIOUS-30-GISD
"},{"location":"projects/gisd30/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Zhang, X., Liu, L., Zhao, T., Gao, Y., Chen, X., and Mi, J.
Curated in GEE by: Samapriya Roy
Keywords: Landsat, Urban, Google Earth Engine, Impervious area, Urban expansion, global dataset
Last updated in GEE: 2023-09-12
"},{"location":"projects/giulu/","title":"Global Intra-Urban Land Use","text":"This dataset provides land use maps for the year 2020 for all 4,000+ cities and metropolitan areas in the world with populations exceeding 100,000. The resulting product is the first freely available, global intra-urban land use maps at 5-meter resolution. The data includes a 4-tier land use taxonomy which at its root distinguishes open-space from built-up area. At the second tier, it subdivides the built-up category into nonresidential and residential areas. The third tier distinguishes formal from informal residential land use, and the fourth tier further subdivides formal and informal residential land uses into more detailed categories. Results of a separate road/street classification model based on the same methods are also provided. You can read more in the paper here
"},{"location":"projects/giulu/#citation","title":"Citation","text":"Guzder-Williams, Brookie, Eric Mackres, Shlomo Angel, Alejandro M. Blei, and Patrick Lamson-Hall. \"Intra-urban land use maps for a global sample of\ncities from Sentinel-2 satellite imagery and computer vision.\" Computers, Environment and Urban Systems 100 (2023): 101917.\nvar ULU = ee.ImageCollection('projects/wri-datalab/cities/urban_land_use/V1')\n\n// Define classes and set color parameters.\nvar CLASSES_7=[\n \"open_space\",\n \"nonresidential\",\n \"atomistic\",\n \"informal_subdivision\",\n \"formal_subdivision\",\n \"housing_project\",\n \"road\"]\nvar COLORS_7=[\n '33A02C',\n 'E31A1C',\n 'FB9A99',\n 'FFFF99',\n '1F78B4',\n 'A6CEE3',\n 'bdbdbd']\nvar CLASSES=CLASSES_7\nvar colors=COLORS_7\nvar ULU7Params = {bands: ['lulc'], min: 0, max: 6, opacity: 1, palette: colors};\n\n// Generate image of 6-class land use from the highest probability class at each pixel.\nvar ULUimage = ULU.select('lulc').reduce(ee.Reducer.firstNonNull()).rename('lulc')\nULUimage=ULUimage.mask(ULUimage.mask().gt(0))\n\n// Generate image of road areas based on a pixels with greater than 50% probability of being road.\nvar roadsImage = ULU.select('road').reduce(ee.Reducer.firstNonNull()).rename('lulc')\nvar roadProb = 50\nvar roadsMask = roadsImage.updateMask(roadsImage.gt(roadProb)).where(roadsImage, 1)\n\n// Composite 6-class land use and roads into as single image.\nvar ULUandRoads = ULUimage.where(roadsMask,6).select('lulc')\n\n// Map both the 6-class land use and composite images.\nMap.addLayer(ULUimage, ULU7Params, 'Intra-urban land use, 6-class (2020)');\nMap.addLayer(ULUandRoads, ULU7Params, 'Intra-urban land use, 7-class (2020)');\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-INTRA-URBAN-LANDUSE
"},{"location":"projects/giulu/#license","title":"License","text":"The dataset is provided under a Creative Commons BY-4.0 license
Keywords: Urban land use maps; Land use land cover; Sentinel-2; Neural networks; Computer vision; Supervised classification; Google Earth Engine; Informal settlements
Provided by: WRI
Curated in GEE by: WRI
Last updated in GEE: 2023-05-29
"},{"location":"projects/glacier/","title":"Global Glacier Elevation change products (2000-2019)","text":"This dataset provides a comprehensive and globally consistent record of glacier elevation and mass changes between 2000 and 2019. It utilizes extensive satellite imagery, primarily from NASA's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and advanced processing techniques to offer a high-resolution view of glacier fluctuations worldwide. The full dataset, including global, regional, tile, and per-glacier data, is publicly available here and you can read the full paper here.
While there are additional datasets provided by the publication only the elevation products are currently ingested.
"},{"location":"projects/glacier/#dataset-features","title":"Dataset Features:","text":"The dataset includes elevation change maps at a 100 m resolution, covering glaciers and their surrounding areas, for various time periods: 5-year intervals from 2000 to 2019, 10-year intervals (2000-2009 and 2010-2019), and the entire 20-year period. These maps, provided as GeoTIFF files, are organized by RGI region and split into 1\u00b0 x 1\u00b0 tiles for easier handling. Both the elevation change rates (meters per year) and their associated 1-sigma uncertainties are included, allowing for a comprehensive understanding of glacier elevation changes and their associated confidence levels. The file naming convention clearly identifies the location of each tile using its southwest corner coordinates.
To facilitate regional analysis, the dataset aggregates glacier change data for 19 major glacier regions around the world as defined by the Randolph Glacier Inventory 6.0. Recognizing the inherent uncertainties in such measurements, the dataset provides thorough uncertainty estimates for both elevation and mass changes. These estimates consider factors like observational coverage, spatial correlations due to instrument resolution and uncorrected noise, and the interpolation of the elevation time series. This ensures the reliability and transparency of the data. Further bolstering confidence in the data, the dataset has undergone extensive validation against independent, high-precision measurements from ICESat and Operation IceBridge campaigns, confirming its accuracy and suitability for a wide range of applications.
"},{"location":"projects/glacier/#citation","title":"Citation:","text":"Hugonnet, R., McNabb, R., Berthier, E. et al. Accelerated global glacier mass loss in the early twenty-first century. Nature 592, 726\u2013731 (2021). https://doi.org/10.1038/s41586-021-03436-z\nvar elevation_change = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-GLACIER-MASS-LOSS/elevation-change\");\nvar error = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-GLACIER-MASS-LOSS/elevation-change-error\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/GLACIER-ELEVATION
"},{"location":"projects/glacier/#license","title":"License","text":"This dataset is made available under the Creative Commons Attribution 4.0 International License.
Created by: Hugonnet, R., McNabb, R., Berthier, E. et al. 2021
Curated in GEE by: Samapriya Roy
Keywords: Altimetry, Digital Elevation Model (DEM), ICESat-2, Glaciers, Elevation change, ASTER, ICESat, Operation IceBridge, Randolph Glacier Inventory
Last updated in GEE: 2024-03-04
"},{"location":"projects/glance/","title":"Global Land Cover Estimation (GLanCE)","text":"The Global Land Cover Estimation (GLanCE) dataset provides high-quality, long-term records of annual land cover and land cover change from 2001 to 2019, using Landsat imagery at 30-meter spatial resolution. The dataset covers all global land areas except Antarctica, and includes 10 Science Data Sets (SDSs) that track land cover, land cover changes and greenness dynamics. The Continuous Change Detection and Classification (CCDC) algorithm is used to identify land cover and changes based on all available, clear Landsat observations.
The GLanCE SDSs are organized into three categories:
Version 1 data includes seven layers and their corresponding band names. Note that these band names differ slightly from those listed in the user guide on the LP DAAC website.
This table shows the layer names and their corresponding band names from Version 1 data.
Layer Name Band Name Land Cover Class LC Previous Class prevClass Change Date changeDate EVI2 Median EVI2median EVI2 Amplitude EVI2amplitude EVI2 Rate EVI2rate Change EVI2 Median changeMag
The user manual with more detailed information about each data layer can be found here.
"},{"location":"projects/glance/#notes","title":"Notes","text":"The citation paper will be updated once we finalize the release of V1 data.
"},{"location":"projects/glance/#citation","title":"Citation","text":"Friedl, M.A., Woodcock, C.E., Olofsson, P., Zhu, Z., Loveland, T., Stanimirova, R., Ar\u00e9valo, P., Bullock, E., Hu, K.-T., Zhang, Y., Turlej, K.,\nTarrio, K., McAvoy, K., Gorelick, N., Wang, J.A., Barber, C.P., Souza, C., 2022. Medium Spatial Resolution Mapping of Global Land Cover and Land\nCover Change Across Multiple Decades From Landsat. Frontiers in Remote Sensing 3. https://doi.org/10.3389/frsen.2022.894571\nAr\u00e9valo, P., R. Stanimirova, E. Bullock, Y. Zhang, K. Tarrio, K. Turlej, K. Hu, K. McAvoy, V. Pasquarella, C. Woodcock, P. Olofsson, Z. Zhu, N.\nGorelick, T. Loveland, C. Barber, M. Friedl. Global Land Cover Mapping and Estimation Yearly 30 m V001. 2022, distributed by NASA EOSDIS Land\nProcesses Distributed Active Archive Center, https://doi.org/10.5067/MEaSUREs/GLanCE/GLanCE30.001. Accessed YYYY-MM-DD.\nvar GLANCE = ee.ImageCollection(\"projects/GLANCE/DATASETS/V001\")\nSample Code 1: Load and visualize datasets
Sample Code 2: Load year of change and EVI2 change data
App: https://glance.earthengine.app/view/datasetviewer
"},{"location":"projects/glance/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created and Curated by: Friedl et al 2022
Keywords: land cover, land cover change, greenness, EVI2, CCDC, global, Landsat, NASA MEaSUREs
Last updated in GEE: 2024-09-25
"},{"location":"projects/glance_training/","title":"GLANCE Global Landcover Training dataset","text":"The GLanCE training dataset, available to the public, is designed for regional-to-global land cover and land cover change analyses. With a medium spatial resolution of 30 meters, it spans the years 1984 to 2020 and is geographically and spectrally representative of all global ecoregions. Offering up to 23 land cover characteristics per training unit, it provides a harmonized, standardized, and comprehensive database that includes information on abrupt and gradual land cover change processes, particularly spanning up to 36 years in select regions. The dataset's adaptability allows users to sub-sample and customize it based on their study region, classification algorithm, and desired classification legend, making it a versatile resource for in-depth land cover investigations. You can read about the dataset in the paper here
Column Name Description Lat Latitude Lon Longitude Start_Year Start year of segment, ranging from 1984 to 2020 (integer) End_Year End year of segment, ranging from 1984 to 2020 (integer) Glance_Class_ID_level1 Level 1 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Barren/sparsely vegetated), 5 (Trees), 6 (Shrub), and 7 (Herbaceous) Glance_Class_ID_level2 Level 2 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Soil), 5 (Rock), 6 (Beach/sand), 7 (Deciduous), 8 (Evergreen), 9 (Mixed), 10 (Shrub), 11 (Grassland), 12 (Agriculture), and 13 (Moss/lichen). NaN values present. Leaf_Type Tree leaf type: broadleaf (1), needleleaf (2), and mixed (3). NaN values present. Impervious_Percent Impervious percent for developed samples: low 0%-30% (1), medium 30%-60% (2), and high 60%-100% (3). NaN values present. Tree_Location Binary integer indicating whether trees are on the interior (0) or edge (1) of a forest. NaN values present. Veg_Density Vegetation density for trees and shrubs: sparse 0%-30% (1), open 30%-60% (2), and closed 60%-100% (3). NaN values present. Veg_Modifier Vegetation modifiers, which can include one or more of the following: Cropland, Plantation, Wetland, Riparian/Flood, Mangrove, Greenhouse, and Trees/Shrub Present. NaN values present. Segment_Type Indicates whether a segment is stable (0) or transitional (1). See Section 1 for a detailed description. Land cover for transitional segments is recorded at both the beginning and end of the time segment - typically the first and last three years. NaN values present. Change Indicates presence (1) or absence (0) of land cover change for Level 1 land cover labels. Includes both abrupt change and gradual change (transitional segments (1) from the Segment_Type attribute) if it happened at any time for that training unit. LC_Confidence Interpreter confidence in the Level 1 land cover label from 1 (lowest) to 3 (highest). NaN values present. Level1_Ecoregion Ecoregion Level 1 number based on World Wildlife Fund definitions. For North America we used ecoregions based on the Environmental Protection Agency\u2019s Ecoregions of North America product. Level2_Ecoregion Ecoregion Level 2 number based on the Environmental Protection Agency\u2019s Ecoregions of North America product. This field is available only for North America and is assigned a value of 0 for all other continents. Continent_Code Assigned continent number: North America (1), South America (2), Africa (3), Europe (4), Asia (5), and Oceania (6). Dataset_Code Assigned dataset number: 1, 2, 3, 4, 5, 902, 999, 700, 701, 702, 703, 704, 705, 706, and 707. Numbers correspond to each Dataset as follows: STEP, CLUSTERING, LCMAP, ABoVE, MapBiomas, Feedback, Training_augment, MODIS_algo, GeoWiki, RadEarth, Collaborator_data, BU_team_collected, GLC30, LUCAS, ASB_crop. For details see Scientific Data publication. Glance_ID Unique ID for each sample. ID ID for each unique combination of latitude and longitude. Change units have the same ID but different Glance_ID. Expand to show Glance Training Level descriptorsLevel 1 Level 2 Description Water (1) Water (1) Areas covered with water throughout the year: streams, canals, lakes, reservoirs, oceans. Ice/snow (2) Ice/snow (2) Land areas with snow and ice cover greater than 50% throughout the year. Developed (3) Developed (3) Areas of intensive use; land covered with structures, including any land functionally related to developed/built-up activity. Barren/sparsely vegetated (4) Land comprised of natural occurrences of soils, sand, or rocks where less than 10% of the area is vegetated. Soil (4) Land covered with less than 10% vegetation and dominated by soil. Rock (5) Land covered with less than 10% vegetation and dominated by rocks. Beach/sand (6) Land covered with less than 10% vegetation and dominated by beach/sand. Trees (5) Land where tree cover is greater than 30%. Note that cleared trees (i.e., clear-cuts) are mapped according to\u00a0current\u00a0cover (e.g., barren/sparsely vegetated, shrubs, or herbaceous). Deciduous (7) Land with tree cover greater than 30% and all trees present are deciduous. Evergreen (8) Land with tree cover greater than 30% and all trees present are evergreen. Mixed (9) Land with tree cover greater than 30% and neither deciduous nor evergreen trees dominate. Shrub (6) Shrub (10) Land with less than 30% tree cover, where total vegetation cover exceeds 10% and shrub cover is greater than 10%. Herbaceous (7) Land covered by herbaceous plants. Total vegetation cover exceeds 10%, tree cover is less than 30%, and shrubs comprise less than 10% of the area. Grassland (11) Herbaceous land covered with grass. Agriculture (12) Herbaceous land covered with cultivated cropland. Moss/lichen (13) Herbaceous land covered with lichen and/or moss.
"},{"location":"projects/glance_training/#citation","title":"Citation","text":"
Stanimirova, R., Tarrio, K., Turlej, K., McAvoy K., Stonebrook S., Hu K-T., Ar\u00e9valo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z.,\nBarber C.P., Souza C., Chen S., Wang J.A., Mensah F., Caldero\u0301n-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S.,\nSiampale A., and M.A. Friedl (2023) A global land cover training dataset from 1984 to 2020. Sci Data 10, 879\nhttps://doi.org/10.1038/s41597-023-02798-5\nStanimirova R., Tarrio K., Turlej K., McAvoy K., Stonebrook S., Hu K-T., Ar\u00e9valo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z.,\nBarber C.P., Souza C., Chen S., Wang J.A., Mensah F., Caldero\u0301n-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S.,\nSiampale A., and M.A. Friedl (2023) \"A Global Land Cover Training Dataset from 1984 to 2020\", Version 1.0, Radiant MLHub. [Date Accessed]\nhttps://doi.org/10.34911/rdnt.x4xfh3\nvar glance_training = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLANCE/GLANCE_TRAINING_DATA_V1\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLANCE-TRAINING
"},{"location":"projects/glance_training/#license","title":"License","text":"The dataset is provided under a Creative Commons Attribution 4.0 International Public License, unless otherwise noted.
Created by: Stanimirova et al, Boston University
Curated in GEE by: Samapriya Roy
Keywords: Glance, LULC, training dataset, Medium resolution, global dataset, land use, land cover
Last updated in GEE: 2024-01-02
"},{"location":"projects/glc10/","title":"Finer Resolution Observation and Monitoring of Global Land Cover 10m (FROM-GLC10)","text":"This work and the paper was designed with an aim to classify 10-m resolution images acquired in 2017 with a sensor on board a different satellite. We examined through the 10-m resolution map, FROM-GLC10, and compared it with our 2017 30-m global land cover map, FROM-GLC30. We found while the results are comparable the 10-m map did provide more spatial details. Although an overall accuracy comparable to the 30-m resolution data was achieved, the actual accuracy of the 10-m resolution map can only be properly assessed with test samples collected from the 10-m resolution data. You can read the paper here
"},{"location":"projects/glc10/#about-from-glc","title":"About FROM-GLC","text":"Global land cover data are key sources of information for understanding the complex interactions between human activities and global change. FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover) is the first 30 m resolution global land cover maps produced using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data.
You can download the dataset here the links are directly to a geotiff file and you can use a downloader like Uget to get to the files.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/glc10/#data-preprocessing","title":"Data preprocessing","text":"The datasets were downloaded and a MODE pyramiding scheme was applied owing to the fact that these are classified datasets. The RGB values were provided by the authors and these were converted to hex code for creating a palette. The sample script also takes into consideration remapping the values to provide a more continuous mix max distribution.
"},{"location":"projects/glc10/#data-citation","title":"Data Citation","text":"Chen, B., B. Xu, Z. Zhu, C. Yuan, H. Ping Suen, J. Guo, N. Xu, W. Li, Y. Zhao, and J. J. S. B. Yang. \"Stable classification with limited sample: Transferring a\n30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017.\" Sci. Bull 64 (2019): 370-373.\nClass Value Remapped Hex code Background 0 0 #000000 Cropland 10 1 #a3ff73 Forest 20 2 #267300 Grass 30 3 #4ce600 Shrub 40 4 #70a800 Water 60 5 #005cff Impervious 80 6 #c500ff Bareland 90 7 #ffaa00 Snow/Ice 100 8 #00ffc5 Cloud 120 9 #ffffff
"},{"location":"projects/glc10/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var GLC10 = ee.ImageCollection(\"projects/sat-io/open-datasets/FROM-GLC10\");\nThis dataset is available under a Creative Commons BY-4.0 license.
Curated in GEE by: Samapriya Roy
Keywords: : landcover, landuse, lulc, 10m, global, world, sentinel 2, FROM-GLC
Last updated on GEE: 2022-09-10
"},{"location":"projects/glc_fcs/","title":"GLC_FCS30D Global 30-meter Land Cover Change Dataset (1985-2022)","text":"The GLC_FCS30D dataset represents a pioneering advancement in global land-cover monitoring, offering comprehensive insights into land cover dynamics at a 30-meter resolution spanning the period from 1985 to 2022. Developed using continuous change detection methods and leveraging the extensive Landsat imagery archives within the Google Earth Engine platform, GLC_FCS30D comprises 35 land-cover subcategories with 26 time steps, updated every five years prior to 2000 and annually thereafter. Through a rigorous refinement process, including spatiotemporal classification and temporal-consistency optimization, the dataset achieves high-confidence accuracy, validated with over 84,000 global samples and achieving an overall accuracy of 80.88%. Notably, GLC_FCS30D elucidates significant trends, revealing forest and cropland variations as dominant drivers of global land cover change over the past 37 years, with a net loss of approximately 2.5 million km\u00b2 of forests and a net gain of around 1.3 million km\u00b2 in cropland area. With its diverse classification system, high spatial resolution, and extensive temporal coverage, GLC_FCS30D serves as a valuable resource for climate change research and sustainable development analysis. Access the dataset here.
Expand to show Land Cover classes, RGB values and hex codes
LC Id Classification System RGB value Color 10 Rainfed cropland (255,255,100) 11 Herbaceous cover cropland (255,255,100) 12 Tree or shrub cover (Orchard) cropland (255,255,0) 20 Irrigated cropland (170,240,240) 51 Open evergreen broadleaved forest (76,115,0) 52 Closed evergreen broadleaved forest (0,100,0) 61 Open deciduous broadleaved forest (0.15<fc<0.4) (170,200,0) 62 Closed deciduous broadleaved forest (fc>0.4) (0,160,0) 71 Open evergreen needle-leaved forest (0.15< fc <0.4) (0,80,0) 72 Closed evergreen needle-leaved forest (fc >0.4) (0,60,0) 81 Open deciduous needle-leaved forest (0.15< fc <0.4) (40,100,0) 82 Closed deciduous needle-leaved forest (fc >0.4) (40,80,0) 91 Open mixed leaf forest (broadleaved and needle-leaved) (160,180,50) 92 Closed mixed leaf forest (broadleaved and needle-leaved) (120,130,0) 120 Shrubland (150,100,0) 121 Evergreen shrubland (150,75,0) 122 Deciduous shrubland (150,100,0) 130 Grassland (255,180,50) 140 Lichens and mosses (255,220,210) 150 Sparse vegetation (fc<0.15) (255,235,175) 152 Sparse shrubland (fc<0.15) (255,210,120) 153 Sparse herbaceous (fc<0.15) (255,235,175) 181 Swamp (0,168,132) 182 Marsh (115,255,223) 183 Flooded flat (158,187,215) 184 Saline (130,130,130) 185 Mangrove (245,122,182) 186 Salt marsh (102,205,171) 187 Tidal flat (68,79,137) 190 Impervious surfaces (195,20,0) 200 Bare areas (255,245,215) 201 Consolidated bare areas (220,220,220) 202 Unconsolidated bare areas (255,245,215) 210 Water body (0,70,200) 220 Permanent ice and snow (255,255,255) 0, 250 Filled value (255,255,255)
"},{"location":"projects/glc_fcs/#dataset-postprocessing","title":"Dataset postprocessing","text":"
The datasets consist of about 961 tiles with the annual layers consisting of about 23 years worth of imagery with each band representing a year from 2000 and the 5 year ones start from 1985 with 3 band representing a gap of 5 year so 1985-1990 is b1, 1990-1995 is b2 and 1990-2000 is b3.
"},{"location":"projects/glc_fcs/#citation","title":"Citation","text":"Zhang, X., Zhao, T., Xu, H., Liu, W., Wang, J., Chen, X., and Liu, L.: GLC_FCS30D: the first global 30\u2009m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method, Earth Syst. Sci. Data, 16, 1353\u20131381, https://doi.org/10.5194/essd-16-1353-2024, 2024.\nLiangyun Liu, Xiao Zhang, & Tingting Zhao. (2023). GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification\nsystem from 1985 to 2022 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8239305\nvar annual = ee.ImageCollection(\"projects/sat-io/open-datasets/GLC-FCS30D/annual\");\nvar five_year = ee.ImageCollection(\"projects/sat-io/open-datasets/GLC-FCS30D/five-years-map\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLC-FCS30D
"},{"location":"projects/glc_fcs/#license","title":"License","text":"This work is licensed under and freely available to the public under Creative Commons Attribution 4.0 International license.
Created by: Zhang et al. 2023
Curated in GEE by : Samapriya Roy
Keywords: GLC_FCS30D, 1985-2022, Land-cover change, Landsat, change detection, Google Earth Engine
Last updated in GEE: 2024-02-20
"},{"location":"projects/glo30/","title":"Copernicus Digital Elevation Model (GLO-30 DEM)","text":"The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. We provide two instances of Copernicus DEM named GLO-30 Public and GLO-90. GLO-90 provides worldwide coverage at 90 meters. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Program. Note that in both cases ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs and was downloaded from the Amazon Open Registry. You can read the documentation here
"},{"location":"projects/glo30/#citation","title":"Citation","text":"Copernicus Digital Elevation Model (DEM) was accessed on DATE from\nvar glo30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GLO-30\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/COPERNICUS_GLO30
Earth Engine comparison app: https://samapriya.users.earthengine.app/view/glob-elevation
"},{"location":"projects/glo30/#license","title":"License","text":"GLO-30 Public is available on a free basis for the general public under the terms and conditions of the license found here.
\u00a9 DLR e.V. 2010-2014 and \u00a9 Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.
"},{"location":"projects/glo30/#disclaimer","title":"Disclaimer","text":"The organisations in charge of the Copernicus programme by law or by delegation do not incur any liability for any use of the Copernicus WorldDEM-30.See Article 6\u00a9 in https://docs.sentinel-hub.com/api/latest/static/files/data/dem/resources/license/License-COPDEM-30.pdf
Created by: European Space Agency, COPERNICUS
Curated in GEE by: Samapriya Roy
Keywords: digital elevation model, terrain, remote sensing, esa, copernicus
"},{"location":"projects/global-mining/","title":"Global Highres Mining Footprints","text":"Mining is of major economic, environmental and societal consequence, yet knowledge and understanding of its global footprint is still limited. Here, we produce a global mining land use dataset via remote sensing analysis of high-resolution, publicly available satellite imagery. The dataset comprises 74,548 polygons, covering ~66,000 km2 of features like waste rock dumps, pits, water ponds, tailings dams, heap leach pads and processing/milling infrastructure. Our polygons finely contour the edges of mine features and do not include the space between them. This distinguishes our dataset from others that employ broader definitions of mining lands. Hence, despite our database being the largest to date by number of polygons, comparisons show relatively lower global land use. Our database is made freely available to support future studies of global mining impacts. A series of spatial analyses are also presented that highlight global mine distribution patterns and broader environmental risks.
"},{"location":"projects/global-mining/#citation","title":"Citation","text":"Tang, Liang, and Tim T. Werner. \"Global mining footprint mapped from high-resolution satellite imagery.\"\nCommunications Earth & Environment 4, no. 1 (2023): 134.\nTang, Liang, & Werner, Tim T. (2023). Global mining footprint mapped from high-resolution satellite imagery.\nCommunications earth & environment, 4(134). https://doi.org/10.5281/zenodo.7894216\nvar mining_footprints = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-mining/global_mining_footprints\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-MINING-FOOTPRINTS
"},{"location":"projects/global-mining/#license","title":"License","text":"This dataset is available under a Creative Commons BY-4.0 license
Curated by: Tang, Liang, & Werner, Tim T.
Keywords: Mining, High Resolution, Global, coal, land-use, metal ores, minerals, raw material extraction
Last updated: April 26, 2023
"},{"location":"projects/global_buildings/","title":"Global Google-Microsoft Open Buildings Dataset","text":"This dataset consolidates Google's V3 Open Buildings and Microsoft's most recent Building Footprints, comprising a staggering 2,534,595,270 footprints. As of September 2023, it stands out as the most comprehensive openly accessible dataset. Encompassing 92% of Level 0 administrative boundaries, the dataset is organized into 182 partitions. Each footprint is explicitly labeled with its origin, denoting whether it is from Google or Microsoft. Accessible in cloud-native geospatial formats such as GeoParquet, FlatGeobuf, and PMTiles, this dataset provides a robust resource for various applications. Further details, including the dataset's comprehensive information and methodology, can be explored here and here, respectively.
"},{"location":"projects/global_buildings/#dataset-schema","title":"Dataset Schema","text":"Country level datasets were inegsted while each row in the dataset provides information on a specific building footprint with associated information on individual columns
Please cite the original citations from source dataset including date of access of the combined dataset for citation here is a sample citation
Google-Microsoft Open Buildings - combined by VIDA, https://beta.source.coop/repositories/vida/google-microsoft-open-buildings. Date Accessed: [Insert the date you accessed the webpage in the format YYYY-MM-DD]\nThe datasets were collected from the country level geoparquet files and only a subset of these are mentioned below while an earthengine ls should provide more information on all countries ingested. All feature collections are in the format
projects/sat-io/open-datasets/VIDA_COMBINED/\"3 letter country ISO code\"
for example India would be
var ind = ee.FeatureCollection(\"projects/sat-io/open-datasets/VIDA_COMBINED/IND\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-COMBINED-BUILDING-FOOTPRINTS-VIDA
Earth Engine App: https://sat-io.earthengine.app/view/global-buildings
"},{"location":"projects/global_buildings/#license","title":"License","text":"The data is shared under the Creative Commons Attribution (CC BY-4.0) license and the Open Data Commons Open Database License (ODbL) v1.0 license. As the user, you can pick which of the two licenses you prefer and use the data under the terms of that license.
Contact information: VIDA has provided contact information and if you'd like more information about the dataset or the processing steps, feel free to write an email to darell@vida.place.
Provided by: VIDA, Google, Microsoft
Curated in GEE by: Samapriya Roy
Last updated in GEE: 2023-11-28
"},{"location":"projects/global_earthquakes/","title":"USGS Global Earthquake dataset","text":"The USGS Earthquake Hazards Program (EHP) offers a comprehensive earthquake dataset, serving as a valuable resource for monitoring, research, and earthquake preparedness worldwide. This dataset encompasses information about earthquakes from various sources, including seismic stations, satellite imagery, and ground-based observations. Continuously updated, it contains a staggering collection of millions of records of earthquake daily.
The USGS earthquake dataset serves a multitude of purposes, including earthquake hazard assessment, which aids in identifying earthquake-prone regions and evaluating potential impacts on communities. Additionally, it supports the development of earthquake early warning systems, enabling timely alerts to mitigate disaster. Furthermore, the dataset is instrumental in the creation of earthquake preparedness and response plans, enhancing community resilience. Lastly, it fuels earthquake research endeavors, facilitating investigations into earthquake hazards and mitigation strategies.
"},{"location":"projects/global_earthquakes/#dataset-processing","title":"Dataset processing","text":"Since exports were allowed in specific chunks I wrote a program to fetch these over periods starting from 1923-2024. This can be updated since the records extend all the way to 1900 and only earthquakes greater than 2.5 and those reviewed were selected.
"},{"location":"projects/global_earthquakes/#dataset-citation","title":"Dataset Citation","text":"U.S. Geological Survey (USGS). (YEAR). Earthquake Hazards Program (EHP). Retrieved from https://earthquake.usgs.gov/earthquakes\nYear Value 1923 129 1924 132 1925 162 1926 276 1927 310 1928 310 1929 314 1930 296 1931 304 1932 513 1933 877 1934 609 1935 691 1936 580 1937 510 1938 565 1939 485 1940 523 1941 453 1942 455 1943 394 1944 348 1945 285 1946 526 1947 672 1948 580 1949 594 1950 604 1951 506 1952 861 1953 797 1954 842 1955 575 1956 688 1957 637 1958 595 1959 734 1960 1147 1961 884 1962 1028 1963 1308 1964 1076 1965 1225 1966 1070 1967 1202 1968 1543 1969 1685 1970 1492 1971 2129 1972 1596 1973 5386 1974 6721 1975 8823 1976 7621 1977 6808 1978 6929 1979 8207 1980 9663 1981 7831 1982 8514 1983 10402 1984 9374 1985 10312 1986 12341 1987 10896 1988 11111 1989 12307 1990 12213 1991 12713 1992 19893 1993 16333 1994 17041 1995 18667 1996 18669 1997 17459 1998 19307 1999 19594 2000 18373 2001 20627 2002 23647 2003 24515 2004 27466 2005 31323 2006 32478 2007 30997 2008 33039 2009 15618 2010 25037 2011 23285 2012 19939 2013 20559 2014 28577 2015 27015 2016 25233 2017 22860 2018 37558 2019 26600 2020 32212 2021 28650 2022 26916 2023 27291 2024 13528
"},{"location":"projects/global_earthquakes/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var usgs_earthquakes = ee.FeatureCollection(\"projects/sat-io/open-datasets/USGS/usgs_earthquakes\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/global-events-layers/USGS-EARTHQUAKES
"},{"location":"projects/global_earthquakes/#license","title":"License","text":"These datasets are public domain data with no use restrictions, though if modifications or derivatives of the product(s) are created, then please add some descriptive modifier to the data set to avoid confusion
"},{"location":"projects/global_earthquakes/#changelog","title":"Changelog","text":"Provided by: USGS
Curated in GEE by: Samapriya Roy
Last updated: 2024-07-28
"},{"location":"projects/global_esi/","title":"NOAA Evaporative Stress Index (ESI)","text":"The Evaporative Stress Index (ESI) is produced by the NOAA Center for Satellite Applications and Research (STAR) and USDA-ARS Hydrology and Remote Sensing Laboratory. The Evaporative Stress Index (ESI) is a thermal indicator of anomalous ET conditions that can be used for drought monitoring. The Evaporative Stress Index (ESI) describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. Here, ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST is a fast- response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. The ESI also demonstrates capability for capturing early signals of \u201cflash drought\u201d, brought on by extended periods of hot, dry and windy conditions leading to rapid soil moisture depletion. You can get additional information on this dataset here and climate engine org data page here.
Spatial Information
Parameter Value Spatial extent Global Spatial resolution 4-km (1/24-deg) Temporal resolution Weekly Time span 2001-01-01 to present Update frequency Updated weekly with 1 week lagVariables
Variable Details 4-week Evaporative Stress Index (\u2018ESI_4wk\u2019) - Units: Unitless - Scale factor: 1.0 12-week Evaporative Stress Index (\u2018ESI_12wk\u2019) - Units: Unitless - Scale factor: 1.0"},{"location":"projects/global_esi/#citation","title":"Citation","text":"- Anderson, M. C., J. M. Norman, G. R. Diak, W. P. Kustas, and J. R. Mecikalski, 1997: A two-source time-integrated model for estimating surface\nfluxes using thermal infrared remote sensing. Remote Sens. Environ., 60, 195-216.\n\n- Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas, 2007a: A climatological study of evapotranspiration and moisture\nstress across the continental U.S. based on thermal remote sensing: I. Model formulation. J. Geophys. Res., 112, D10117, doi:10110.11029/\n12006JD007506.\n\n- Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas, 2007b: A climatological study of evapotranspiration and moisture\nstress across the continental U.S. based on thermal remote sensing: II. Surface moisture climatology. J. Geophys. Res., 112, D11112, doi:11110.11029/\n12006JD007507.\n\n- Anderson, M. C., C. R. Hain, B. Wardlow, J. R. Mecikalski, and W. P. Kustas (2011), Evaluation of a drought index based on thermal remote sensing\nof evapotranspiration over the continental U.S., J. Climate, 24, 2025-2044.\n\n- McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. AMS Eighth conf. on Applied\nClimatology, Anaheim, CA, 179-184.\n\n- McKee, T. B., N. J. Doesken, and J. Kleist, 1995: Drought monitoring with multiple time scales. AMS Ninth conf. on Applied Climatology, Dallas,\nTX, 233-236.\n\n- Norman, J. M., W. P. Kustas, and K. S. Humes, 1995: A two-source approach for estimating soil and vegetation energy fluxes from observations of\ndirectional radiometric surface temperature. Agric. For. Met., 77, 263-293.\n\n- Svoboda, M., and Coauthors, 2002: The Drought Monitor. Bull. Amer. Meteorol. Soc., 83, 1181-1190.\n// Read in Image Collection and get first image\nvar esi_4wk_ic = ee.ImageCollection('projects/climate-engine/esi/4wk')\nvar esi_4wk_i = esi_4wk_ic.filterDate('2020-08-01', '2020-08-10').first()\nvar esi_12wk_ic = ee.ImageCollection('projects/climate-engine/esi/12wk')\nvar esi_12wk_i = esi_12wk_ic.filterDate('2020-08-01', '2020-08-10').first()\n\n// Print first image to see bands\nprint(esi_4wk_i)\nprint(esi_12wk_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar esi_palette = [\"#0000aa\", \"#0000ff\", \"#00aaff\", \"#00ffff\", \"#aaff55\", \"#ffffff\", \"#ffff00\", \"#fcd37f\", \"#ffaa00\", \"#e60000\", \"#730000\"]\nMap.addLayer(esi_4wk_i.select('ESI'), {min: -2.5, max: 2.5, palette: esi_palette}, 'ESI_4wk')\nMap.addLayer(esi_12wk_i.select('ESI'), {min: -2.5, max: 2.5, palette: esi_palette}, 'ESI_12wk')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-ESI-10KM
"},{"location":"projects/global_esi/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: Drought, vegetation, remote sensing, climate, USDA-ARS, NOAA, MODIS, LST, global, near real-time
Provided by NOAA,USDA-ARS
Curated in GEE by: Climate Engine Org
"},{"location":"projects/global_fertilizer/","title":"Global Fertilizer Use by crop & country","text":"Understanding how much inorganic fertilizer (referred to as fertilizer) is applied to different crops at national, regional and global levels is an essential component of fertilizer consumption analysis and demand projection. Good information on fertilizer use by crop (FUBC) is rarely available because it is difficult to collect and time-consuming to process and validate. To fill this gap, a first global FUBC report was published in 1992 for the 1990/1991 period, based on an expert survey conducted jointly by the Food and Agriculture Organization (FAO) of the UN, the International Fertilizer Development Center (IFDC) and the International Fertilizer Association (IFA). Since then, similar expert surveys have been carried out and published every two to four years in the main fertilizer-consuming countries. Since 2008 IFA has led these efforts and, to our knowledge, remains the only globally available data set on FUBC. This dataset includes data (in CSV format) from a survey carried out by IFA to represent the 2017\u201318 period as well as a collation of all historic FUBC data.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/global_fertilizer/#dataset-preprocessing","title":"Dataset Preprocessing","text":"LSIB country boundaries were used to join tow table, since the primary table is not geospatial the country names were first refactored to match those of LSIB before creating an internal join. Since these are large geometries the join was converted to centroids for each feature and exported as a feature collection.
"},{"location":"projects/global_fertilizer/#paper-citation","title":"Paper Citation","text":"Ludemann, C.I., Gruere, A., Heffer, P. et al. Global data on fertilizer use by crop and by country. Sci Data 9, 501 (2022).\nhttps://doi.org/10.1038/s41597-022-01592-z\nLudemann, Cameron; Gruere, Armelle; Heffer, Patrick; Dobermann, Achim (2022), Global data on fertilizer use by crop and by country, Dryad,\nDataset, https://doi.org/10.5061/dryad.2rbnzs7qh\nvar global_fertilizer_use = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_fertilizer_use_centroid\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FERTILIZER-USE-CROP-COUNTRY
"},{"location":"projects/global_fertilizer/#license","title":"License","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
Created by: Ludemann, Cameron; Gruere, Armelle; Heffer, Patrick; Dobermann, Achim
Curated in GEE by : Samapriya Roy
keywords: Global fertilizer use, agriculture, FAO, crop
Last modified: 2022-08-22
Last updated on GEE: 2022-09-05
"},{"location":"projects/global_ftype/","title":"Global Natural and Planted Forests","text":"The Global Natural and Planted Forests dataset offers a high-resolution (30-meter) map distinguishing natural from planted forests worldwide as of 2021. Created using over 70 million training samples generated from 30-meter Landsat images (1985\u20132021), this dataset supports improved environmental monitoring and conservation efforts, carbon sequestration assessment, and biodiversity management. The data includes rich spectral, structural, textural, and topographic attributes, enabling users to identify forest types and quantify forest coverage across various spatial scales.
"},{"location":"projects/global_ftype/#data-generation-and-classification","title":"Data Generation and Classification","text":"The dataset leverages a time-series change detection method applied to Landsat imagery, distinguishing planted forests from natural forests based on disturbance frequency. Using a locally adaptive random forest classifier (RF), this method achieved an overall accuracy of 85% when validated against independently interpreted reference data. This high precision makes the dataset an effective tool for global forest resource assessment.
"},{"location":"projects/global_ftype/#key-features","title":"Key Features","text":"The dataset is publicly available and can be accessed via: - Primary Data Source - Supplemented Tiles 300\u2013400
"},{"location":"projects/global_ftype/#citation","title":"Citation","text":"Xiao, Yuelong, Qunming Wang, and Hankui K. Zhang. \"Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m.\"\nJournal of Remote Sensing 4 (2024): 0204.\nXiao, Y. (2024). Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m [Data set].\nZenodo. https://doi.org/10.5281/zenodo.10701417\nvar global_forest_types = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-NATURAL-PLANTED-FORESTS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-NATURAL-PLANTED-FORESTS
"},{"location":"projects/global_ftype/#license","title":"License","text":"This dataset is licensed under a Creative Commons Attribution 4.0 International license.
Provided by: Xiao et al 2024
Curated in GEE by: Samapriya Roy
Keywords: Global Forest Mapping, Natural and Planted Forests, Carbon Sequestration, Forest Cover Classification, Biodiversity Monitoring, Forest Disturbance, Random Forest Classifier
Last updated in GEE: 2024-10-27
"},{"location":"projects/global_fungi/","title":"Global Fungi Database","text":"Fungi are essential components of ecosystems, contributing to vital functions such as carbon cycling, decomposition, plant associations, and pathogenicity. However, our knowledge of fungal biogeography and the factors driving these patterns is limited. To address this gap, we compiled and validated data on soil fungal communities from terrestrial environments, including soil and plant-associated habitats. This valuable dataset, available through the user interface at https://globalfungi.com, encompasses over 600 million observations of fungal sequences derived from more than 17,000 samples, with precise geographic information and metadata from 178 original studies. The dataset, which includes millions of unique nucleotide sequences of the fungal internal transcribed spacers (ITS) 1 and 2, represents an extensive atlas of global fungal distribution. It is designed to facilitate the integration of third-party data, promoting further exploration and enhancing our understanding of fungal biogeography and its environmental drivers. You can read the details in the paper here and you can get to the database here.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/global_fungi/#dataset-preprocessing","title":"Dataset preprocessing","text":"Feature field were transformed to make sure it meets the limits for characters. The overall global extract was created from globalfungi.com and converted to a shapefile. Changes are
'year_of_sampling': 'sample_yr'\n'ITS1_extracted': 'ITS1_extr',\n'ITS3_extracted': 'ITS2_extr'\nV\u011btrovsk\u00fd, Tom\u00e1\u0161, Daniel Morais, Petr Kohout, Cl\u00e9mentine Lepinay, Camelia Algora, Sandra Awokunle Holl\u00e1, Barbara Doreen Bahnmann et al.\n\"GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies.\" Scientific Data 7, no. 1 (2020): 228.\nGlobalFungi dataset release 4 (20.7.2021). Taxonomy based on UNITE version 8.2 (4.2.2020).\nActual number of samples in the database: 57184; actual number of studies included: 515.\nNumber of ITS sequence variants: 481 799 996; number of ITS1 sequences 791 513 743; number of ITS2 sequences 2 892 377 338.\nvar table = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLOBAL-FUNGI-DB/global-fungi-db-20230627\");\nSample code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FUNGI-DATABASE
"},{"location":"projects/global_fungi/#license","title":"License","text":"This database is available under a Creative Commons Attribution 4.0 International License
Curated by: V\u011btrovsk\u00fd, Tom\u00e1\u0161, et al. 2020
Keywords: Global Fungi database, Global dataset, carbon cycling
Last updated: June 27, 2023
"},{"location":"projects/global_irrigation/","title":"Global irrigation areas (2001 to 2015)","text":"About 40% of global crop production takes place on irrigated land, which accounts for approximately 20% of the global farmland. The great majority of freshwater consumption by human societies is associated with irrigation, which contributes to a major modification of the global water cycle by enhancing evapotranspiration and reducing surface and groundwater runoff. In many regions of the world irrigation contributes to streamflow and groundwater depletion, soil salinization, cooler microclimate conditions, and altered land-atmosphere interactions. Despite the important role played by irrigation in food security, water cycle, soil productivity, and near-surface atmospheric conditions, its global extent remains poorly quantified. To date global maps of irrigated land are often based on estimates from circa year 2000. Here we apply artificial intelligence methods based on machine learning algorithms to satellite remote sensing and monthly climate data to map the spatial extent of irrigated areas between 2001 and 2015. We provide global annual maps of irrigated land at \u22489km resolution for the 2001-2015 and we make this dataset available online.
"},{"location":"projects/global_irrigation/#citation","title":"Citation:","text":"Deepak Nagaraj, Eleanor Proust, Alberto Todeschini, Maria Cristina Rulli, Paolo D'Odorico,\nA new dataset of global irrigation areas from 2001 to 2015, Advances in Water Resources,\nVolume 152,2021,103910,ISSN 0309-1708,https://doi.org/10.1016/j.advwatres.2021.103910.\nYou can read the paper here : https://www.sciencedirect.com/science/article/pii/S0309170821000658
"},{"location":"projects/global_irrigation/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var irrigation_maps = ee.ImageCollection(\"users/deepakna/global_irrigation_maps\");\nYou can also get maps for individual years as TIF images:
var highly_irrigated_areas_2001 = ee.Image(\"users/deepakna/global_irrigation_maps/2001\")\n .expression(\"b(0) == 2 ? 1 : 0\");\nMap.addLayer(highly_irrigated_areas_2001.updateMask(highly_irrigated_areas_2001.neq(0))\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-IRRIGATION-AREAS
"},{"location":"projects/global_irrigation/#band-info-irrigation-classes-are-present-in-the-band-classification","title":"Band Info: Irrigation classes are present in the band \"classification\"","text":"Band Value Irrigation Class 0 no or very little irrigation 1 low-to-medium irrigation (<= 2000 hectares in 86 sq km square of land) 2 high irrigation (>2000 hectares in 86 sq km square of land)"},{"location":"projects/global_irrigation/#license","title":"License","text":"Creative Commons Attribution 4.0 International License
Curated by: Deepak Nagaraj
Keywords: Global irrigation, agriculture, water sustainability, machine learning
Last updated: 2020-06-10
"},{"location":"projects/global_mining/","title":"Global Mining Areas and Validation Datasets","text":"This data set provides spatially explicit estimates of the area directly used for surface mining on a global scale. It contains more than 21,000 polygons of activities related to mining, mainly of coal and metal ores. Several data sources were compiled to identify the approximate location of mines active at any time between the years 2000 to 2017. This data set does not cover all existing mining locations across the globe. The polygons were delineated by experts using Sentinel-2 cloudless (https://s2maps.eu by EOX IT Services GmbH (contains modified Copernicus Sentinel data 2017 & 2018)) and very high-resolution satellite images available from Google Satellite and Bing Imagery. The derived polygons cover the direct land used by mining activities, including open cuts, tailing dams, waste rock dumps, water ponds, and processing infrastructure.
The overall accuracy calculated from the control points was 88.4%
Read about the methodology here
Use the following credit when these data are cited:
Maus, Victor; Giljum, Stefan; Gutschlhofer, Jakob; da Silva, Dieison M; Probst, Michael; Gass, Sidnei L B; Luckeneder, Sebastian; Lieber, Mirko; McCallum, Ian (2020): Global-scale mining polygons (Version 1). PANGAEA https://doi.org/10.1594/PANGAEA.910894\nYou can cite the original paper using:
Maus, Victor, Stefan Giljum, Jakob Gutschlhofer, Dieison M. da Silva, Michael Probst, Sidnei LB Gass, Sebastian Luckeneder, Mirko Lieber, and Ian McCallum. \"A global-scale data set of mining areas.\" Scientific Data 7, no. 1 (2020): 1-13.\nvar mining = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-mining/global_mining_polygons\");\nvar validation = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-mining/global_mining_validation\");\n21,000 main polygons and 1000 validation polygons
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-MINING-AND-VALIDATION
Shared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: Mining, High Resolution, Global, coal, land-use, metal ores, minerals, raw material extraction
Last updated: 2021
"},{"location":"projects/global_palm_oil/","title":"Global Oil Palm Dataset 1990-2021","text":"NoteThis dataset is part of a paper in submission and citation and DOI information will be updated accordingly. This will be updated as the paper progresses through review and publication cycles.Please keep this into consideration while using this dataset
The dataset provides a comprehensive global map of oil palm plantations, including both industrial and smallholder plots, at a 10-meter resolution using Sentinel-1 data from 2016 to 2021. Additionally, it includes planting year estimates from 1990 to 2021 at a 30-meter spatial resolution derived from Landsat-5, -7, and -8 imagery. This dataset aims to support environmental monitoring and policy discussions by offering detailed and up-to-date information on the extent and age of oil palm plantations worldwide. You can read the preprint here.
This data repository can be found here offers comprehensive data on global oil palm plantations, including a 10-meter resolution global oil palm extent layer for the year 2021 and a 30-meter resolution oil palm planting year layer spanning from 1990 to 2021. The extent layer was generated using a convolutional neural network applied to Sentinel-1 data, identifying both industrial and smallholder plantations. The planting year layer was developed to detect early oil palm growth stages using the Landsat time series.
The key findings of the dataset reveal a total mapped area of 23.98 million hectares (Mha) of oil palm plantations, comprising 16.66 \u00b1 0.25 Mha of industrial and 7.59 \u00b1 0.29 Mha of smallholder oil palm. The accuracy of the data is high, with producers' and users' accuracy for industrial plantations at 91.9 \u00b1 3.4% and 91.8 \u00b1 1.0%, respectively, and for smallholders at 72.7 \u00b1 1.3% and 75.7 \u00b1 2.5%, respectively. The average age of the plantations is 14.1 years, and 6.28 Mha are over 20 years old, indicating a significant need for replanting within the coming decade.
"},{"location":"projects/global_palm_oil/#data-layers","title":"Data Layers","text":""},{"location":"projects/global_palm_oil/#1-grid_oilpalm2016-2021","title":"1. Grid_OilPalm2016-2021","text":"The oil palm extent and planting year data can be explored through a web map available at: Global Oil Palm Planting Year 1990-2021. This tool allows users to inspect the Landsat time series and view historical satellite images of oil palm plantations.
"},{"location":"projects/global_palm_oil/#citation","title":"Citation","text":"Descals, A., Gaveau, D. L. A., Wich, S., Szantoi, Z., and Meijaard, E.: Global mapping of oil palm planting year from 1990 to 2021\nEarth Syst. Sci Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-157, in review, 2024.\nDescals, A. (2024). Global oil palm extent and planting year from 1990 to 2021 (v1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11034131\n/*\nGlobalOilPalm_YoP_2021: year of oil palm plantation establishment\ngrid_oilpalm: Delineates the 609 grid cells of 100 x 100 km where oil palm was detected\nglobaloilpalm_extent2021: Deep learning classification at a 10-meter spatial resolution\n- **Classes:**\n - [0] Other land covers that are not oil palm.\n - [1] Industrial oil palm plantations.\n - [2] Smallholder oil palm plantations.\nvalidation: Contains 17,812 points used to validate the global oil palm extent and age layers. Each point includes:\n - \u2018Class\u2019: Assigned by visual interpretation (class values same as extent layer).\n - \u2018OP2016-2021\u2019 and \u2018OP2019\u2019: Mapped classes in this dataset and the 2019 global oil palm layer (Descals et al., 2021), respectively.\n\n - **Classes:**\n - [0] Other land covers that are not oil palm.\n - [1] Industrial oil palm plantations.\n - [2] Smallholder oil palm plantations.\n\n*/\nvar grid_oilpalm = ee.FeatureCollection('projects/sat-io/open-datasets/global-oil-palm/Grid_OilPalm_2021_v1-1');\nvar globaloilpalm_extent = ee.ImageCollection('projects/sat-io/open-datasets/global-oil-palm/GlobalOilPalm_extent_2021');\nvar globaloilpalm_yop_2021 = ee.ImageCollection(\"projects/sat-io/open-datasets/global-oil-palm/GlobalOilPalm_YoP_2021\");\nvar validation = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-oil-palm/Validation_points_GlobalOP2016-2021_v1-1\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/global-landuse-landcover/GLOBAL-OIL-PALM-1990-2021-APP
Earth Engine App: https://ee-globaloilpalm.projects.earthengine.app/view/global-oil-palm-planting-year-1990-2021
"},{"location":"projects/global_palm_oil/#license","title":"License","text":"This product is licensed under a Creative Commons Attribution 4.0 International license.
Curated in GEE by: Descals et al 2024 and Samapriya Roy
Keywords: oil palm, planting year, global crop mapping, remote sensing, deep learning, Sentinel-1
Last updated in GEE: 2024-04-28
"},{"location":"projects/global_pm25/","title":"Global Monthly Satellite-derived PM2.5","text":"This dataset provides annual and monthly estimates of ground-level fine particulate matter (PM2.5) from 2000 to 2019. The data are derived by integrating Aerosol Optical Depth (AOD) retrievals from multiple NASA instruments\u2014MODIS, MISR, SeaWIFS, and VIIRS\u2014with the GEOS-Chem chemical transport model. The initial PM2.5 estimates are then calibrated using a residual Convolutional Neural Network (CNN) against global ground-based observations.
"},{"location":"projects/global_pm25/#key-features","title":"Key Features","text":"Updates in V6.GL.02:
Annual and monthly datasets are provided in NetCDF (.nc) format, with gridded files using the WGS84 projection. These estimates are primarily intended to aid in large-scale studies. Annual and coarse-resolution averages correspond to a simple mean of within-grid values. Gridded datasets are provided to allow users to agglomerate data as best meets their particular needs. High-resolution (0.01\u00b0 \u00d7 0.01\u00b0) datasets are gridded at the finest resolution of the information sources that were incorporated but are unlikely to fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. You can read the paper here and download the dataset here
"},{"location":"projects/global_pm25/#citation","title":"Citation","text":"Shen, S. Li, C. van Donkelaar, A. Jacobs, N. Wang, C. Martin, R. V.: Enhancing Global Estimation of Fine Particulate Matter Concentrations by\nIncluding Geophysical a Priori Information in Deep Learning. (2024) ACS ES&T Air. DOI: 10.1021/acsestair.3c00054\nvar pm25_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-SATELLITE-PM25/MONTHLY\")\nvar pm25_yearly = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-SATELLITE-PM25/ANNUAL\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-SATELLITE-PM25
"},{"location":"projects/global_pm25/#license","title":"License","text":"The datasets are made available under the Creative Commons Attribution 4.0 International license.
Keywords: PM2.5, Remote Sensing, MODIS, SeaWIFS, VIIRS, MISR, AOD
Provided by: Atmospheric Composition Analysis Group at Washington University in St Louis
Curated in GEE by: Samapriya Roy
Last updated : 2024-06-19
"},{"location":"projects/global_power/","title":"Predictive mapping of the global power system using open data","text":"Read about the methodology here
Download the dataset here
Use the following credit when these datasets are cited:
Arderne, Christopher, NIcolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data (Version 1.1.1) [Data set]. Nature Scientific Data. Zenodo. http://doi.org/10.5281/zenodo.3628142\nCite the paper using
Arderne, Christopher, Conrad Zorn, Claire Nicolas, and E. E. Koks. \"Predictive mapping of the global power system using open data.\" Scientific data 7, no. 1 (2020): 1-12.\nCurrent version: v1.1.1 released 2020-01-16 You can access the app here: https://gridfinder.org/
"},{"location":"projects/global_power/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var lv = ee.Image(\"projects/sat-io/open-datasets/predictive-global-power-system/lv\");\nvar targets = ee.Image(\"projects/sat-io/open-datasets/predictive-global-power-system/targets\");\nvar transmission = ee.FeatureCollection(\"projects/sat-io/open-datasets/predictive-global-power-system/distribution-transmission-lines\");\nlv is at 250m, targets at 463.83 m
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/PREDICTED-GLOBAL-POWER-SYSTEMS
Shared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: Global transmission lines, electricity, infrastructure, power
Last updated: 2021-04-03
"},{"location":"projects/global_pv/","title":"Global Photovoltaics Inventory (2016-2018)","text":"Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. The authors point out that energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. The authors further locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423\u2009gigawatts (\u221275/+77\u2009gigawatts) at the end of 2018.
For installations over 10,000 m2 (approximately 600 kW), achieved precision was 98.6% relative to our test set, with a modest trade-off in recall which drops to 90% (Supplementary Fig. 6). The IoU of the final dataset for installations over 10,000 m2 is 90%\u2014sufficient for the wide range of uses based on the user report. You can read the paper here
"},{"location":"projects/global_pv/#citation","title":"Citation:","text":"Kruitwagen, L., Story, K.T., Friedrich, J. et al. A global inventory of photovoltaic solar energy generating units.\nNature 598, 604\u2013610 (2021). https://doi.org/10.1038/s41586-021-03957-7\nKruitwagen, Lucas, Story, Kyle, Friedrich, Johannes, Byers, Logan, Skillman, Sam, & Hepburn, Cameron. (2021). A global\ninventory of solar photovoltaic generating units - dataset (1.0.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5005868\nvar predicted_set = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/predicted_set\");\nvar cv_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/cv_polygons\");\nvar cv_tiles = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/cv_tiles\");\nvar test_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/test_polygons\");\nvar test_tiles = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/test_tiles\");\nvar trn_tiles = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/trn_tiles\");\nvar trn_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/trn_polygons\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-PHOTOVOLTAICS-INVENTORY
"},{"location":"projects/global_pv/#layer-name-and-description-table","title":"Layer name and description table","text":"File Name Description trn_tiles 18,570\u00a0rectangular areas-of-interest used for sampling training patch data. trn_polygons 36,882\u00a0polygons obtained from OSM in 2017\u00a0used to label training patches cv_tiles 560\u00a0rectangular areas-of-interest used for sampling cross-validation data seeded from\u00a0WRI GPPDB cv_polygons 6,281 polygons corresponding to all PV solar generating units present in cv_tiles at the end of 2018. test_tiles 122 rectangular regions-of-interest used for building the test set. test_polygons 7,263 polygons corresponding to all utility-scale (>10kW) solar generating units present in test_tiles at the end of 2018. predicted_set 68,661 polygons corresponding to predicted polygons in global deployment, capturing the status of deployed photovoltaic solar energy generating capacity at the end of 2018."},{"location":"projects/global_pv/#license","title":"License","text":"Creative Commons Attribution 4.0 International License
Created by: Kruitwagen et al
Curated by: Samapriya Roy
Keywords: photovoltaic solar remote sensing geospatial data computer vision
Last updated: 2021-10-28
"},{"location":"projects/global_salinity/","title":"Global Soil Salinity Maps (1986-2016)","text":"This dataset includes global soil salinity layers for the years 1986, 1992, 2000, 2002, 2005, 2009 and 2016. The maps were generated with a random forest classifier that was trained using seven soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. The validation accuracy of the resulting maps was in the range of 67\u201370%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Further details are provided in a peer-reviewed journal article (https://doi.org/10.1016/j.rse.2019.111260). The main data page for this dataset can be found here along with links to the VRT and tiff files.
"},{"location":"projects/global_salinity/#paper-citation","title":"Paper Citation","text":"Ivushkin, Konstantin, Harm Bartholomeus, Arnold K. Bregt, Alim Pulatov, Bas Kempen, and Luis De Sousa. \"Global mapping of soil salinity change.\"\nRemote sensing of environment 231 (2019): 111260.\nvar soil_salinity = ee.ImageCollection(\"projects/sat-io/open-datasets/global_soil_salinity\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/GLOBAL-SOIL-SALINITY
"},{"location":"projects/global_salinity/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Ivushkin et al
Curated by: Samapriya Roy
Keywords: : salinity, digital soil mapping, electrical conductivity, global map, soilgrids, landsat, thermal, salinisation
Last updated: 2021-11-25
"},{"location":"projects/global_tcc/","title":"Global 30m Landsat Tree Canopy Cover v4","text":"The Global 30m Landsat Tree Canopy Version 4 (TCC) product is a 30-meter resolution dataset that shows tree canopy coverage per pixel between 0% and 100%. The TCC product was announced in May 2019 and was processed from the Landsat image archive. It replaces the previous version of global tree canopy cover estimates for 2000, 2005, 2010, and 2015. It also includes annual tree cover estimates from 2010 to 2015 for North and South America. The TCC datasets are based on Landsat and Sentinel-2 imagery. The most recent TCC version 2021.4 product suite released in 2023 includes several components, including an annual Science product with maps and data for years 2008-2021. You can find additional information here and download the datasets here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/global_tcc/#citation","title":"Citation","text":"Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K.M., Channan, S., DiMiceli, C., Townshend, J.R.G. (2013). Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS Vegetation Continuous Fields with lidar-based estimates of error.\nInternational Journal of Digital Earth, 130321031236007. doi:10.1080/17538947.2013.786146.\nvar GFCC30TC= ee.ImageCollection(\"projects/sat-io/open-datasets/GFCC30TC\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GFCC30TC-TREE-CANOPY-COVER
"},{"location":"projects/global_tcc/#license","title":"License","text":"This data is open to the public and browse images are freely available without restriction.
Created by: Sexton, J. O et al
Curated in GEE by : Samapriya Roy
keywords: Global Tree Canopy, Forestry, Time series, Landsat
Last updated on GEE: 2023-07-05
"},{"location":"projects/globathy/","title":"GLOBathy (Global lakes bathymetry dataset)","text":"We developed a novel GLObal Bathymetric (GLOBathy) dataset of 1.4+\u2009million waterbodies to align with the well-established global dataset, HydroLAKES. GLOBathy uses a GIS-based framework to generate bathymetric maps based on the waterbody maximum depth estimates and HydroLAKES geometric/geophysical attributes of the waterbodies. The maximum depth estimates are validated at 1,503 waterbodies, making use of several observed data sources.
We also provide estimations for head-Area-Volume (h-A-V) relationships of the HydroLAKES waterbodies, driven from the bathymetric maps of the GLOBathy dataset. The h-A-V relationships provide essential information for water balance and hydrological studies of global waterbody systems. You can read the full paper here
You can find the datasets here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/globathy/#citation","title":"Citation","text":"Khazaei, B., Read, L.K., Casali, M. et al. GLOBathy, the global lakes bathymetry dataset. Sci Data 9, 36 (2022).\nhttps://doi.org/10.1038/s41597-022-01132-9\nKhazaei, Bahram; Read, Laura K; Casali, Matthew; Sampson, Kevin M; Yates, David N (2022): GLOBathy Bathymetry Rasters. figshare.\nDataset. https://doi.org/10.6084/m9.figshare.13404635.v1\nvar globathy = ee.Image(\"projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry\");\nvar globathy_param = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLOBathy/GLOBathy_basic_parameters\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBathy
"},{"location":"projects/globathy/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. The organizations responsible for generating and funding this dataset make no representations of any kind including, but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data. Although every effort has been made to ensure the accuracy of information, errors may be reflected in data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors.
Produced by: Khazaei, B., Read, L.K., Casali, M. et al.
Curated in GEE by: Samapriya Roy
Keywords: bathymetry and depth, lake systems, reservoir management, Hydrological Modelling, Limnology, Geographic information systems (GIS), Geomorphology, topographic analysis
Last updated on GEE: 2022-01-26
"},{"location":"projects/globcover_esa/","title":"GlobCover Global Land Cover","text":"GlobCover is an ESA initiative which began in 2005 in partnership with JRC, EEA, FAO, UNEP, GOFC-GOLD and IGBP. The aim of the project was to develop a service capable of delivering global composites and land cover maps using as input observations from the 300m MERIS sensor on board the ENVISAT satellite mission. ESA makes available the land cover maps, which cover 2 periods: December 2004 - June 2006 and January - December 2009. You can download the datasets here.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/globcover_esa/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var globcoverv23 = ee.Image(\"projects/sat-io/open-datasets/ESA/GLOBCOVER_L4_200901_200912_V23\");\nvar globcoverv22 = ee.Image(\"projects/sat-io/open-datasets/ESA/GLOBCOVER_200412_200606_V22_Global_CLA\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/ESA-GLOBCOVER
"},{"location":"projects/globcover_esa/#license","title":"License","text":"The GlobCover products have been processed by ESA and by the Universit\u00e9 Catholique de Louvain. They are made available to the public by ESA. You may use the GlobCover land cover map for educational and/or scientific purposes, without any fee on the condition that you credit ESA and the Universit\u00e9 Catholique de Louvain as the source of the GlobCover products:
\u00a9 ESA 2010 and UCLouvain\nAccompanied by a link to our ESA DUE GlobCover website: http://due.esrin.esa.int/page_globcover.php\nShould you write any scientific publication on the results of research activities that use GlobCover products as input, you shall acknowledge the ESA GlobCover 2009 Project in the text of the publication and provide ESA with an electronic copy of the publication (due@esa.int). If you wish to use the GlobCover 2009 products in advertising or in any commercial promotion, you shall acknowledge the ESA GlobCover 2009 Project and you must submit the layout to ESA for approval beforehand (due@esa.int).
Created by: ESA and by the Universit\u00e9 Catholique de Louvain
Curated in GEE by : Samapriya Roy
keywords: Landcover, Global Landcover, ESA, JRC, EEA, FAO, UNEP, GOFC-GOLD and IGBP, ENVISAT product
Last updated on GEE: 2023-02-28
"},{"location":"projects/globgm/","title":"GLOBGM v1.0 global-scale groundwater model","text":"The GLOBGM v1.0 dataset marks a significant milestone in global groundwater modeling, offering a parallel implementation of a 30\u2009arcsec PCR-GLOBWB-MODFLOW model. Developed by Jarno Verkaik et al., this dataset presents a comprehensive understanding of global groundwater dynamics at a spatial resolution of approximately 1\u2009km at the Equator. Leveraging two model layers and the MODFLOW 6 framework, the dataset utilizes available 30\u2032\u2032 PCR-GLOBWB data to drive simulations, enabling researchers to explore groundwater flow dynamics on a global scale. The computational implementation is parallelized using the message-passing interface, facilitating efficient processing on distributed memory parallel clusters.
Covering the globe, excluding Greenland and Antarctica, the GLOBGM v1.0 dataset offers insights into various aspects of groundwater behavior. Despite its uncalibrated nature, the dataset undergoes limited evaluation using USGS National Water Information System (NWIS) head observations for the contiguous United States (CONUS). You can read the paper here to understand the methodology better.
"},{"location":"projects/globgm/#data-strucutre","title":"Data strucutre","text":"This table provides a structured overview of the model raster outputs for the GLOBGM dataset, including file paths and descriptions of each file.
File Path Description /steady-state/globgm-heads-lower-layer-ss.tif Computed steady-state groundwater head [m] for the lower model layer /steady-state/globgm-heads-lower-layer-ss.tif Computed steady-state groundwater head [m] for the upper model layer /steady-state/globgm-wtd-ss.tif Computed water table depth [m] (sampled from upper to lower layer) /transient_1958-2015/globgm-wtd-.tif Computed water table depth [m] (sampled from upper to lower layer) /transient_1958-2015/globgm-wtd-bot-*.tif Computed water table depth [m] (lower layer only)"},{"location":"projects/globgm/#citation","title":"Citation","text":"Verkaik, Jarno, Edwin H. Sutanudjaja, Gualbert HP Oude Essink, Hai Xiang Lin, and Marc FP Bierkens. \"GLOBGM v1. 0: a parallel implementation of a 30\narcsec PCR-GLOBWB-MODFLOW global-scale groundwater model.\" Geoscientific Model Development 17, no. 1 (2024): 275-300.\nVerkaik, J., Hughes J.D., Langevin, C.D., (2021). Parallel MODFLOW 6.2.1 prototype release 0.1 (6.2.1_0.1). Zenodo.\nvar wtd = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBGM/TRANSIENT/WTD\");\nvar wtd_bt = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBGM/TRANSIENT/WTD-BOTTOM\");\nvar globgm_wtd_ss = ee.Image(\"projects/sat-io/open-datasets/GLOBGM/STEADY-STATE/globgm-wtd-ss\");\nvar globgm_heads_lower_layer_ss = ee.Image(\"projects/sat-io/open-datasets/GLOBGM/STEADY-STATE/globgm-heads-lower-layer-ss\");\nvar globgm_heads_upper_layer_ss = ee.Image(\"projects/sat-io/open-datasets/GLOBGM/STEADY-STATE/globgm-heads-upper-layer-ss\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBGM-GROUNDWATER-MODEL
"},{"location":"projects/globgm/#license","title":"License","text":"GLOBGM v1.0 is open source and distributed under the terms of GNU General Public License v3.0, or any later version, as published by the Free Software Foundation.
Created by: Verkaik et al. 2024
Curated in GEE by : Samapriya Roy
Keywords: GLOBGM,groundwater,global-scale modeling,PCR-GLOBWB,MODFLOW,high performance computing
Last updated in GEE: 2024-02-04
"},{"location":"projects/glodap/","title":"Global Ocean Data Analysis Project (GLODAP) v2.2023","text":"The Global Ocean Data Analysis Project (GLODAP) v2.2023 represents a significant advancement in the synthesis of ocean biogeochemical bottle data. With a primary focus on seawater inorganic carbon chemistry, this update builds upon GLODAPv2.2022, incorporating several key enhancements. Notably, 43 new cruises have been added to expand the dataset's coverage until 2020. The data quality control process involved the removal of entries with missing temperatures. Moreover, the inclusion of digital object identifiers (DOIs) for each cruise enhances data traceability. GLODAPv2.2022 also includes minor corrections for improved data accuracy.
This dataset encompasses more than 1.4 million water samples from 1108 cruises across the global oceans, covering 12 essential variables such as salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4. The data are available in two formats: the raw data format, updated to WOCE exchange format, and a merged data product with bias-minimizing adjustments. Rigorous quality control procedures were applied, and adjustments were made by comparing new cruise data with the quality-controlled data from GLODAPv2.2020. The dataset is believed to provide accurate measurements within specific limits for each variable.
To access this valuable resource and its documentation, including DOIs, visit the Ocean Carbon Data System of NOAA NCEI at this link. Additionally, the merged data product is available, offering a single global file and regional files for the Arctic, Atlantic, Indian, and Pacific oceans. These files contain ancillary and approximated data, derived from interpolation or calculation. For comprehensive information and data access, please visit GLODAP's official website. Researchers can benefit from this living dataset by following the provided resources and documentation.
"},{"location":"projects/glodap/#data-quality-and-accuracy","title":"Data Quality and Accuracy","text":"The dataset has undergone extensive quality control with a focus on systematic evaluation of bias. The adjustments made aim to remove potential biases stemming from errors related to measurement, calibration, and data handling practices, while preserving known or likely time trends or variations in the evaluated variables.
The compiled and adjusted data product is believed to have a high level of accuracy, with consistent measurements:
Salinity: Better than 0.005 Oxygen: 1% Nitrate: 2% Silicate: 2% Phosphate: 2% Dissolved Inorganic Carbon: 4 \u03bcmolkg\u207b\u00b9 Total Alkalinity: 4 \u03bcmolkg\u207b\u00b9 pH: 0.01\u20130.02 (varies by region) Halogenated Transient Tracers: 5% Other variables in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), have not undergone bias comparison or adjustments.
"},{"location":"projects/glodap/#variable-key","title":"Variable key","text":"Variable / Parameter Abbreviation Unit Observation Type Sampling Instrument Quality Flag Convention Researcher Name Temperature temperature \u00b0C measured, data synthesis CDT, Niskin Bottles WOCE quality control flags are used GLODAP Group Potential temperature theta \u00b0C calculated GLODAP Group Salinity salinity measured, data synthesis CDT, Niskin Bottles WOCE quality control flags are used GLODAP Group Potential density sigma0 kg m\u22123 calculated GLODAP Group Potential density, ref 1000 dbar sigma1 kg m\u22123 calculated GLODAP Group Potential density, ref 2000 dbar sigma2 kg m\u22123 calculated GLODAP Group Potential density, ref 3000 dbar sigma3 kg m\u22123 calculated GLODAP Group Potential density, ref 4000 dbar sigma4 kg m\u22123 calculated GLODAP Group Neutral density gamma kg m\u22123 calculated GLODAP Group Oxygen oxygen \u03bcmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Apparent oxygen utilization aou \u03bcmol kg\u22121 calculated GLODAP Group Nitrate nitrate \u03bcmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Nitrite nitrite \u03bcmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Silicate silicate \u03bcmol kg\u22121 measured, data synthesis CDT, Niskin Bottles WOCE quality control flags are used GLODAP Group Phosphate phosphate \u03bcmol kg\u22121 measured, data synthesis CDT, Niskin Bottles WOCE quality control flags are used GLODAP Group TCO2 tco2 \u03bcmol kg\u22121 measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group TAlk talk \u03bcmol kg\u22121 measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group fCO2 fco2 microatmospheres measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group pH at total scale, 25 \u00b0C and zero dbar of pressure phts25p0 measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group pH at total scale, in situ temperature and pressure phtsinsitutp measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group CFC-11 cfc11 pmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group pCFC-11 pcfc11 ppt measured, data synthesis CTD, Niskin Bottles GLODAP Group CFC-12 cfc12 pmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group pCFC-12 pcfc12 ppt measured, data synthesis CTD, Niskin Bottles GLODAP Group CFC-113 cfc113 pmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group CCl4 ccl4 pmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group pCCl4 pccl4 ppt calculated GLODAP Group SF6 sf6 fmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group pSF6 psf6 ppt calculated GLODAP Group \u03b413C c13 % measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group \u220614C c14 % measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group \u220614C counting error c14err % calculated GLODAP Group 3H h3 TU measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group 3H counting error h3err TU calculated GLODAP Group \u03b43He \u03b43He % measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group 3He counting error he3err % calculated GLODAP Group He He nmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group He counting error heerr % calculated GLODAP Group Neon neon nmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Neon counting error neonerr nmol kg\u22121 calculated GLODAP Group \u03b418O o18 % measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Total organic carbon toc \u03bcmol L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Dissolved organic carbon doc \u03bcmol L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Dissolved organic nitrogen don \u03bcmol L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Total dissolved nitrogen tdn \u03bcmol L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Chlorophyll a chla ug L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group"},{"location":"projects/glodap/#citation","title":"Citation","text":"Whenever GLODAPv2 is used, the following citations must be included:
Olsen, A., R. M. Key, S. van Heuven, S. K. Lauvset, A. Velo, X. Lin, C. Schirnick, A. Kozyr, T. Tanhua, M. Hoppema, S. Jutterstr\u00f6m, R. Steinfeldt,\nE. Jeansson, M. Ishii, F. F. P\u00e9rez and T. Suzuki. The Global Ocean Data Analysis Project version 2 (GLODAPv2) \u2013 an internally consistent data\nproduct for the world ocean, Earth Syst. Sci. Data, 8, 297\u2013323, 2016, doi:10.5194/essd-8-297-2016\n\nLauvset, S. K, R. M. Key, A. Olsen, S. van Heuven, A. Velo, X. Lin, C. Schirnick, A. Kozyr, T. Tanhua, M. Hoppema, S. Jutterstr\u00f6m, R. Steinfeldt, E.\nJeansson, M. Ishii, F. F. P\u00e9rez, T. Suzuki and S. Watelet. A new global interior ocean mapped climatology: the 1\u00b0x1\u00b0 GLODAP version 2, Earth Syst.\nSci. Data, 8, 325\u2013340, 2016, doi:10.5194/essd-8-325-2016\n\nKey, R.M., A. Olsen, S. van Heuven, S. K. Lauvset, A. Velo, X. Lin, C. Schirnick, A. Kozyr, T. Tanhua, M. Hoppema, S. Jutterstr\u00f6m, R. Steinfeldt, E.\nJeansson, M. Ishii, F. F. Perez, and T. Suzuki. 2015. Global Ocean Data Analysis Project, Version 2 (GLODAPv2), ORNL/CDIAC-162, NDP-093. Carbon\nDioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee. doi:10.3334/CDIAC/OTG.\nNDP093_GLODAPv2\nThe matlab derived products add a G2 infront of all column names and they are left in place as is,with an additional column called system:time_start and datetime added to reflect epoch time and UTC datetime derived from the existing columns. Adding the system:time_start and datetime allow for easily filtering across the earth engine collection. While the merged collection is provided individual feature collections are still maintained to provide the user with the flexibility of loading a smaller subset of features for operations.
"},{"location":"projects/glodap/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var merged = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Merged_Master_File_formatted\");\nvar arctic_ocean = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Arctic_Ocean_formatted\");\nvar atlantic_ocean = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Atlantic_Ocean_formatted\");\nvar indian_ocean = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Indian_Ocean_formatted\");\nvar pacific_ocean = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Pacific_Ocean_formatted\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLODAP-V2_2023_MERGED
"},{"location":"projects/glodap/#license","title":"License","text":"The dataset is distributed under a public licese. Distribution liability: NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data. If appropriate, NCEI can only certify that the data it distributes are an authentic copy of the records that were accepted for inclusion in the NCEI archives.
Provided by: NCEI, NOAA, Olsen et al
Curated in GEE by: Samapriya Roy
Last updated in GEE: 2023-10-25
"},{"location":"projects/gloric/","title":"Global River Classification (GloRiC)","text":"The Global River Classification (GloRiC) provides river types and sub-classifications for all river reaches contained in the HydroRIVERS database. GloRiC has been developed by utilizing the river network delineation of HydroRIVERS combined with the hydro-enviromental characteristics from the HydroATLAS database and auxiliary information.
Version 1.0 of GloRiC provides a hydrologic, physio-climatic, and geomorphic sub-classification, as well as a combined river type for every river reach, resulting in a total of 127 river reach types. It also offers a k-means statistical clustering of the reaches into 30 groups. The dataset comprises 8.5 million river reaches with a total length of 35.9 million km.
You can find overall technical documentation here and technical information for GloRiC Canada here
"},{"location":"projects/gloric/#preprocessing","title":"Preprocessing","text":"Besides the global version of GloRiC, there is also a regional version for Canada available, GloRiC-Canada, which follows the same classification principles but with Canada-specific adaptations. GloRiC-Canada categorizes all river reaches of Canada into 23 types.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/gloric/#citation","title":"Citation","text":"Ouellet Dallaire, C., Lehner, B., Sayre, R., Thieme, M. (2019). A multidisciplinary framework to derive global river reach classifications at high\nspatial resolution. Environmental Research Letters, 14(2): 024003. https://doi.org/10.1088/1748-9326/aad8e9\n\nOuellet Dallaire, C., Lehner, B., Creed, I. (2020): Multidisciplinary classification of Canadian river reaches to support the sustainable management\nof freshwater systems. Canadian Journal of Fisheries and Aquatic Sciences, 77(2): 326\u2013341. https://doi.org/10.1139/cjfas-2018-0284\nvar gloric = ee.FeatureCollection(\"projects/sat-io/open-datasets/GloRiC/GloRiC_v10\");\nvar gloric_canada = ee.FeatureCollection(\"projects/sat-io/open-datasets/GloRiC/GloRiC_Canada_v10\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-CLASSIFICATION(GLORIC)
"},{"location":"projects/gloric/#license","title":"License","text":"The data is licensed under a Creative Commons Attribution 4.0 International License (see section 4). By downloading and using the data the user agrees to the terms and conditions of this license.
Created by: Ouellet Dallaire, C., Lehner, B., Sayre, R., Thieme, M. & Schmitt, O
Curated by: Samapriya Roy
Keywords: water,hydrology, rivers, discharge, depth, volume, area, gloric
Last updated: 2022-07-09
"},{"location":"projects/gmd/","title":"Global Mangrove Distribution, Aboveground Biomass, and Canopy Height","text":"This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models. Also provided are (4) in situ field measurement data for selected sites across a wide variety of forest structures (e.g., scrub, fringe, riverine and basin) in mangrove ecotypes of the global equatorial region. Within designated plots, selected trees were identified to species and diameter at breast height (DBH) and tree height was measured using a laser rangefinder or clinometer. Tree density (the number of stems) can be estimated for each plot and expressed per unit area. These data were used to derive plot-level allometry among AGB, basal area weighted height (Hba), and maximum canopy height (Hmax) and to validate the remotely sensed estimates.
Spatially explicit maps of mangrove canopy height and AGB derived from space-borne remote sensing data and in situ measurements can be used to assess local-scale geophysical and environmental conditions that may regulate forest structure and carbon cycle dynamics. Maps revealed a wide range of canopy heights, including maximum values (> 62 m) that surpass maximum heights of other forest types.
There are 348 data files in GeoTIFF format (.tif) with this dataset representing three data products for each of 116 countries. The in situ tree measurements are provided in a single .csv file. You can grab the dataset here
"},{"location":"projects/gmd/#preprocessing","title":"Preprocessing","text":"The tree measurements CSV has lat lon 2,3,4 removed and lat and lon1 were renamed to lat lon. The dataset table is as below. This and additional metadata can be found here. The datasets divided into subsets of 116 datasets each were ingested into Google Earth Engine collections.
Column name Units/format Type Description ID Character A unique identifier for locating a specific observation. A combination of plot_name and tree number fields.\u00a0 region Character Continent or subcontinent of observation subregion Character Country or state of observation biome Character Biome type of observation - all mangrove date yyyy-mm-dd Date Date of observation plot_name Character Plot name tree_number Character Unique identifier for a tree in a specific plot. Trees characterized as \u201cSevere inclined\u201d were sometimes measured without assigning a number. These trees have been assigned M1, M2, M3, etc. within a plot. genus Character Genus species Character Species dbh cm Numeric Diameter at breast height (1.3 m), check tree_notes as some were estimated dbh height meters Numeric height of tree, check tree_notes as some were modeled height live Numeric 1 indicates tree is alive. 0 indicates tree is dead tree_notes Character specific notes about a tree use_for_allometry Numeric 1 indicates tree was used for allometry, 0 indicates it was not.\u00a0 plot_type Character f = fixed plot size, v = variable plot size plot_shape Character shape of plot: s and square; r, circle, and circular. Missing when plot_type = v. baf Numeric basal area factor: with a value of 5 for plot_type = v, otherwise missing plot_area m^2 Numeric plot area lat Numeric latitude of plot location (center of circular and variable shape plots or a plot corner for square plots) lon Numeric longitude of plot location (center of circular and variable shape plots or a plot corner for square plots) collected_by Character Collector of field observations digitized_by Character Performer of GIS activities"},{"location":"projects/gmd/#paper-citation","title":"Paper Citation","text":"Simard, M., L. Fatpyinbo, C. Smetanka, V.H. Rivera-Monroy, E. Castaneda-Moya, N. Thomas, and T. Van der Stocken. 2019. Mangrove canopy height\nglobally related to precipitation, temperature and cyclone frequency. Nature Geoscience, 12: 40\u201345. https://doi.org/10.1038/s41561-018-0279-1\nSimard, M., T. Fatoyinbo, C. Smetanka, V.H. Rivera-monroy, E. Castaneda, N. Thomas, and T. Van der stocken. 2019. Global Mangrove Distribution,\nAboveground Biomass, and Canopy Height. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1665\nVersion 1.3: The in situ tree measurement data file and documentation were added in April 2021. No changes to previously archived data.
Version 1.2: Data files were updated in May 2019 because the height to biomass (AGB) conversion equations in the associated Nature Geoscience publication were correct but were implemented incorrectly when generating the publicly available data files. These have now been corrected. The Hba and Hmax data were updated so that they are now capped at the 95th percentile of the maximum value (55 m), as outlined in the publication. Countries without Hba and Hmax data have been omitted.
Version 1.1: Science-quality data were released in March 2019. All preliminary data files were replaced with new files that incorporated some changes to the aboveground biomass estimation algorithm. In addition, several files with missing data were replaced.
Version 1.0: Preliminary data were released in November 2018 to accompany the publication of the Simard et al, 2019 paper in Nature Geosciences.
var agb = ee.ImageCollection(\"projects/sat-io/open-datasets/global_mangrove_distribution/agb\");\nvar hba95 = ee.ImageCollection(\"projects/sat-io/open-datasets/global_mangrove_distribution/hba95\");\nvar hmax95 = ee.ImageCollection(\"projects/sat-io/open-datasets/global_mangrove_distribution/hmax95\");\nvar americas_tree = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_mangrove_distribution/americas_tree_measurements\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-MANGROVE-BIOMASS-HEIGHT
"},{"location":"projects/gmd/#license","title":"License","text":"Public Domain/No restrictions (CC0): Under the terms of this license you are free to use the material for any purpose without any restrictions.
Created by: Simard et al
Curated by: Samapriya Roy
Keywords: : global mangrove, above ground biomass, canopy height, basal-area weighted height, ecosystem, mangroves
Last updated: 2021-12-15
"},{"location":"projects/gnatsgo/","title":"gNATSGO (gridded National Soil Survey Geographic Database)","text":"The gNATSGO (gridded National Soil Survey Geographic Database) database is a composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. This collection is sourced for the raster data only. Since the original format of the data is proprietary the source COGs are sourced from Planetary Computer STAC catalog.
The gNATSGO database was created by combining data from three sources: the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS). SSURGO is a USDA-NRCS Soil & Plant Science Division (SPSD) flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains.
STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are next-generation soil survey databases developed using advanced digital soil mapping methods. The gNATSGO database is composed primarily of SSURGO data, with STATSGO2 data used to fill in the gaps. The RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is relatively limited at this time but is expected to increase in the coming years. To use the map unit values contained in the mukey raster asset, you will need to join to tables represented as Items in the gNATSGO Tables Collection. Many items have commonly used values encoded in additional raster assets.
Expand to show current asset collectionsTitles STAC Key Roles Description Aws0_5 aws0_5 Data Available water storage estimate (AWS) in a standard zone 1 (0-5 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_20 aws0_20 Data Available water storage estimate (AWS) in standard zone 2 (0-20 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_30 aws0_30 Data Available water storage estimate (AWS) in standard zone 3 (0-30 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_100 aws0_100 Data Available water storage estimate (AWS) in standard zone 4 (0-100 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_150 aws0_150 Data Available water storage estimate (AWS) in standard zone 5 (0-150 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_999 aws0_999 Data Available water storage estimate (AWS) in total soil profile (0 cm to the reported depth of the soil profile), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws5_20 aws5_20 Data Available water storage estimate (AWS) in standard layer 2 (5-20 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws20_50 aws20_50 Data Available water storage estimate (AWS) in standard layer 3 (20-50 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws50_100 aws50_100 Data Available water storage estimate (AWS) in standard layer 4 (50-100 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws100_150 aws100_150 Data Available water storage estimate (AWS) in standard layer 5 (100-150 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws150_999 aws150_999 Data Available water storage estimate (AWS) in standard layer 6 (150 cm to the reported depth of the soil profile), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Soc0_5 soc0_5 Data Soil organic carbon stock estimate (SOC) in standard zone 1 (0-5 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 5 cm. NULL values are presented where data are incomplete or not available. Soc0_20 soc0_20 Data Soil organic carbon stock estimate (SOC) in standard zone 2 (0-20 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 20 cm. NULL values are presented where data are incomplete or not available. Soc0_30 soc0_30 Data Soil organic carbon stock estimate (SOC) in standard zone 3 (0-30 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 30 cm. NULL values are presented where data are incomplete or not available. Soc0_100 soc0_100 Data Soil organic carbon stock estimate (SOC) in a standard zone (0-100 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 100 cm. NULL values are presented where data are incomplete or not available. Soc0_150 soc0_150 Data Soil organic carbon stock estimate (SOC) in a standard zone (0-150 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 150 cm. NULL values are presented where data are incomplete or not available. Soc0_999 soc0_999 Data Soil organic carbon stock estimate (SOC) in a standard zone (0-999 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 999 cm. NULL values are presented where data are incomplete or not available. Soc100_150 soc100_150 Data Soil organic carbon stock estimate (SOC) in a standard zone (100-150 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 100 and 150 cm depth. NULL values are presented where data are incomplete or not available. Soc150_999 soc150_999 Data Soil organic carbon stock estimate (SOC) in a standard zone (150-999 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 150 and 999 cm depth. NULL values are presented where data are incomplete or not available. Soc20_50 soc20_50 Data Soil organic carbon stock estimate (SOC) in a standard zone (20-50 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 20 and 50 cm depth. NULL values are presented where data are incomplete or not available. Soc50_100 soc50_100 Data Soil organic carbon stock estimate (SOC) in a standard zone (50-100 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 50 and 100 cm depth. NULL values are presented where data are incomplete or not available. Soc5_20 soc5_20 Data Soil organic carbon stock estimate (SOC) in a standard zone (5-20 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 5 and 20 cm depth. NULL values are presented where data are incomplete or not available. Mukey mukey Data Map unit key is the unique identifier of a record in the Mapunit table. Use this column to join the Component table to the Map Unit table and the Valu1 table to the MapUnitRaster_10m raster map layer to map valu1 themes. Droughty droughty Data Zone for commodity crops that is less than or equal to 6 inches (152 mm) expressed as \u201c1\u201d for a drought vulnerable soil landscape map unit or \u201c0\u201d for a non-droughty soil landscape map unit or NULL for miscellaneous areas (includes water bodies) or where data were not available. Nccpi3sg nccpi3sg Data National Commodity Crop Productivity Index for Small Grains (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). NULL values are presented where data are incomplete or not available. Tk0_100a tk0_100a Data Thickness of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk0_100s tk0_100s Data Thickness of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Tk0_150a tk0_150a Data Thickness of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk0_150s tk0_150s Data Thickness of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Tk0_999a tk0_999a Data Thickness of soil components used in total soil profile (0 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk0_999s tk0_999s Data Thickness of soil components used in total soil profile (0 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Tk20_50a tk20_50a Data Thickness of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk20_50s tk20_50s Data Thickness of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Musumcpct musumcpct Data The sum of the comppct_r (SSURGO component table) values for all listed components in the map unit. NULL values are presented where data are incomplete or not available. Nccpi3all nccpi3all Data National Commodity Crop Productivity Index that has the highest value among Corn and Soybeans, Small Grains, or Cotton (weighted average) for major earthy components. NULL values are presented where data are incomplete or not available. Nccpi3cot nccpi3cot Data National Commodity Crop Productivity Index for Cotton (weighted average) for major earthy components. NULL values are presented where data are incomplete or not available. Nccpi3soy nccpi3soy Data National Commodity Crop Productivity Index for Soybeans (weighted average) for major earthy components. NULL values are presented where data are incomplete or not available. Pwsl1pomu pwsl1pomu Data Potential Wetland Soil Landscapes (PWSL) is expressed as the percentage of the map unit that meets the PWSL criteria. NULL values are presented where data are incomplete or not available. Rootznaws rootznaws Data Root zone available water storage estimate (RZAWS), expressed in mm, is the volume of plant available water that the soil can store within the root zone. NULL values are presented where data are incomplete or not available. Rootznemc rootznemc Data Root zone depth is the depth within the soil profile that commodity crop roots can effectively extract water and nutrients for growth. NULL values are presented where data are incomplete or not available. Tk50_100a tk50_100a Data Thickness of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk50_100s tk50_100s Data Thickness of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Musumcpcta musumcpcta Data The sum of the comppct_r (SSURGO component table) values used in the available water storage calculation for the map unit. NULL values are presented where data are incomplete or not available. Musumcpcts musumcpcts Data The sum of the comppct_r (SSURGO component table) values used in the soil organic carbon calculation for the map unit. NULL values are presented where data are incomplete or not available. Nccpi3corn nccpi3corn Data National Commodity Crop Productivity Index for Corn (weighted average) for major earthy components. NULL values are presented where data are incomplete or not available. Pctearthmc pctearthmc Data The National Commodity Crop Productivity Index map unit percent earthy is the map unit summed comppct_r for major earthy components. NULL values are presented where data are incomplete or not available. Tk100_150a tk100_150a Data Thickness of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk100_150s tk100_150s Data Thickness of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Tk150_999a tk150_999a Data Thickness of soil components used in standard layer 6 (150 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk150_999s tk150_999s Data Thickness of soil components used in standard layer 6 (150 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.
"},{"location":"projects/gnatsgo/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aws0_100 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_100');\nvar aws0_150 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_150');\nvar aws0_20 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_20');\nvar aws0_30 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_30');\nvar aws0_5 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_5');\nvar aws0_999 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_999');\nvar aws100_150 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws100_150');\nvar aws150_999 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws150_999');\nvar aws20_50 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws20_50');\nvar aws50_100 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws50_100');\nvar aws5_20 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws5_20');\nvar mukey = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/mukey');\nvar soc0_100 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_100');\nvar soc0_150 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_150');\nvar soc0_20 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_20');\nvar soc0_30 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_30');\nvar soc0_5 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_5');\nvar soc0_999 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_999');\nvar soc100_150 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc100_150');\nvar soc150_999 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc150_999');\nvar soc20_50 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc20_50');\nvar soc50_100 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc50_100');\nvar soc5_20 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc5_20');\nvar droughty = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/droughty');\nvar musumcpct = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/musumcpct');\nvar musumcpcta = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/musumcpcta');\nvar musumcpcts = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/musumcpcts');\nvar nccpi3all = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3all');\nvar nccpi3corn = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3corn');\nvar nccpi3cot = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3cot');\nvar nccpi3sg = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3sg');\nvar nccpi3soy = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3soy');\nvar pctearthmc = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/pctearthmc');\nvar pwsl1pomu = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/pwsl1pomu');\nvar rootznaws = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/rootznaws');\nvar rootznemc = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/rootznemc');\nvar tk0_100a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_100a');\nvar tk0_100s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_100s');\nvar tk0_150a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_150a');\nvar tk0_150s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_150s');\nvar tk0_20a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_20a');\nvar tk0_20s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_20s');\nvar tk0_30a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_30a');\nvar tk0_30s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_30s');\nvar tk0_5a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_5a');\nvar tk0_5s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_5s');\nvar tk0_999a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_999a');\nvar tk0_999s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_999s');\nvar tk100_150a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk100_150a');\nvar tk100_150s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk100_150s');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/gNATSGO-DATABASE
"},{"location":"projects/gnatsgo/#license","title":"License","text":"The data from the Gridded National Soil Survey Geographic Database (gNATSGO) provided by the USDA Natural Resources Conservation Service (NRCS) is available under the Public Domain license (CC0 1.0 Universal Public Domain Dedication).
Provided by: United States Department of Agriculture, Natural Resources Conservation Service
Hosted by: Microsoft
Curated in GEE by: Samapriya Roy
Keywords: Soil Survey, USDA, NRCS, Raster Data, Gridded Data
Last updated: 2024-10-22
"},{"location":"projects/gnatsgo/#changelog","title":"Changelog","text":"GOODD, a global dataset of more than 38,000 georeferenced dams as well as their associated catchments. The source paper presents the development of the global database through systematic digitisation of satellite imagery globally by a small team and highlights the various approaches to bias estimation and to validation of the data. The following datasets are provided (a) raw digitised coordinates for the location of dam walls (that may be useful for example in machine learning approaches to dam identification from imagery), (b) a global vector file of the watershed for each dam.
Read the paper for methodology and further details here
You can download the dataset here
"},{"location":"projects/goodd/#data-citation","title":"Data Citation","text":"van Soesbergen, Arnout; Mulligan, Mark; S\u00e1enz, Leonardo (2020): GOODD global dam dataset\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.9747686.v1\nMulligan, Mark, Arnout van Soesbergen, and Leonardo S\u00e1enz.\n\"GOODD, a global dataset of more than 38,000 georeferenced dams.\"\nScientific Data 7, no. 1 (2020): 1-8.\nvar catchments = ee.FeatureCollection(\"projects/sat-io/open-datasets/GOODD/GOOD2_catchments\");\nvar dams = ee.FeatureCollection(\"projects/sat-io/open-datasets/GOODD/GOOD2_dams\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-GEOREF-DATABASE-DAMS
"},{"location":"projects/goodd/#license","title":"License","text":"The dataset is released under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
Created by : Mulligan, Mark, Arnout van Soesbergen, and Leonardo S\u00e1enz
Curated in GEE by: Samapriya Roy
Keywords: Global dams, global catchments, vector, Hydrology
Last updated : 2021-07-24
"},{"location":"projects/gowt/","title":"Global offshore wind turbine dataset","text":"This dataset including two contents including the validation datasets, only the location dataset has been ingested and the validation dataset can be downloaded. The location dataset consist of geocoded information on global offshore wind turbines (OWTs) derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. It identified 6,924 wind turbines comprising of more than 10 nations. Data is available at 10 m spatial resolution, providing an explicit dataset for planning, monitoring, and managing marine space. The global OWTs are stored in Shapefile (.shp) format. The attributes and metadata are organized with referenced to the WGS84 datum, and each record consists of seven attributes: centroid latitude (centr_lat), centroid longitude (centr_lon), continent, country, sea area (sea_area), appearance year (occ_year) and month (occ_month).
You can download both location and validation datasets here
You can read about the methodology in the paper here
"},{"location":"projects/gowt/#citation","title":"Citation","text":"Zhang, T., Tian, B., Sengupta, D. et al. Global offshore wind turbine dataset.\nSci Data 8, 191 (2021). https://doi.org/10.1038/s41597-021-00982-z\nZhang, Ting; Tian, Bo; Sengupta, Dhritiraj; Zhang, Lei; Si, Yali (2020):\nGlobal offshore wind turbine dataset. figshare. Dataset.\nhttps://doi.org/10.6084/m9.figshare.13280252.v5\nvar gowt = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_offshore_wind_turbine_v1-3\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-OFFSHORE-WIND-TURBINES
"},{"location":"projects/gowt/#license","title":"License","text":"The dataset is released under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
Created by : Zhang, T., Tian, B., Sengupta, D. et al
Curated in GEE by: Samapriya Roy
Keywords: Offshore energy systems, Coastal engineering, Google Earth Engine Platform, Ocean engineering, Marine planning, Coastal management, Sentinel-1
Last updated : 2021-07-27
"},{"location":"projects/gpm/","title":"Global Precipitation Measurement (GPM)","text":"Global Precipitation Measurement (GPM) is an international satellite mission to provide next-generation observations of rain and snow worldwide every three hours. NASA and the Japanese Aerospace Exploration Agency (JAXA) launched the GPM Core Observatory satellite on February 27th, 2014, carrying advanced instruments that set a new standard for precipitation measurements from space. The data they provide is used to unify precipitation measurements made by an international network of partner satellites to quantify when, where, and how much it rains or snows around the world. Note: Additional bands (\u2018HQprecipitation\u2019, \u2018IRprecipitation\u2019, \u2018precipitationUncal\u2019, \u2018randomError\u2019) are provided with documentation provided here. You can find additional information here and climate engine org dataset page here.
This dataset is derived from the Earth Engine asset, NASA/GPM_L3/IMERG_V06, using the processing steps: 1. The GPM Daily dataset hosted on Climate Engine is sourced from the 30-min data. These data are summed to daily based on 0 UTC time zone. 2. This collection contains provisional products that are regularly replaced with updated versions when the data become available. This transition typically occurs about 1-2 years out.
Spatial Information
Parameter Value Spatial extent Global Spatial resolution 11-km (1/10-deg) Temporal resolution Daily Time span 2000-06-01 to present Update frequency Updated daily with 2 day lag timeVariables
Variable Details Precipitation (Calibrated) - daily data is derived from 30-minute data ('precipitationCal') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/gpm/#citation","title":"Citation","text":"Jackson, Gail & Berg, Wesley & Kidd, Chris & Kirschbaum, Dalia & Petersen, Walter & Huffman, George & Takayabu, Yukari. (2018). Global Precipitation Measurement (GPM): Unified Precipitation Estimation from Space. 10.1007/978-3-319-72583-3_7.\n// Read in Image Collections and get single image\nvar gpm_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-gpm-imerg-daily')\nvar gpm_i = gpm_ic.first()\n\n// Print single image to see bands\nprint(gpm_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(gpm_i.select('precipitationCal'), {min: 0, max: 200, palette: prec_palette}, 'precipitationCal')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-PRECIP-MEASUREMENT
"},{"location":"projects/gpm/#license","title":"License","text":"NASA promotes the full and open sharing of all data with research and applications communities, private industry, academia, and the general public.
Keywords: precipitation, climate, NASA, JAXA, satellite, near real-time
Provided by: Climate Engine Org, NASA
Curated in GEE by: Climate Engine Org
"},{"location":"projects/gridded_gdp_hdi/","title":"Gridded Global GDP and HDI (1990-2015)","text":"Two global key indicators of development are Gross Domestic Product (GDP) and Human Development Index (HDI). While \u2018GDP measures the monetary value of final goods and services\u2014that is, those that are bought by the final user\u2014produced in a [given area] in a given period of time, HDI is a composite index of \u2018average achievement in key dimensions of human development:
Gap-filled multiannual datasets in gridded form for Gross Domestic Product (GDP) and Human Development Index (HDI). To provide a consistent product over time and space, the sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics. This resulted in annual gridded datasets for GDP per capita (PPP), total GDP (PPP), and HDI, for the whole world at 5\u2009arc-min resolution for the 25-year period of 1990\u20132015. Additionally, total GDP (PPP) is provided with 30\u2009arc-sec resolution for three time steps (1990, 2000, 2015).
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
Dataset Dimensions Note GDP per capita (PPP) Timesteps: 26 Gridded GDP per capita, derived from a combination of sub-national and national datasets GDP (PPP)-5\u2009arc-min Timesteps: 26 Total GDP (PPP) of each grid cell, derived from GDP per capita (PPP) which is multiplied by gridded population data HYDE 3.2 GDP (PPP)-30\u2009arc-sec Timesteps: 3 Total GDP (PPP) of each grid cell, derived from GDP per capita (PPP) which is multiplied by gridded population data GHS Pedigree of GDP data Timesteps: 26 Reports the scale (national, sub-national) and type (reported, interpolated, extrapolated) of each year of data HDI Timesteps: 26 Gridded HDI, derived from a combination of sub-national and national datasets Pedigree of HDI data Timesteps: 26 Reports the level (national, sub-national) and type (reported, interpolated, extrapolated) of each year of data Administrative units Products: 2 Represents the administrative units used for GDP per capita (PPP) and HDI. National admin units have id 1\u2013999, sub-national ones 1001-"},{"location":"projects/gridded_gdp_hdi/#dataset-notes","title":"Dataset notes","text":"Units for GDP is US dollar
Pedigree GDP: Pedigree index numbers, coded as follows: 1-regional reported; 2-regional interpolated; 3-regional extrapolated; 5-national reported; 6-national interpolated; 7-national extrapolated
Pedigree HDI: Pedigree index numbers, coded as follows: 1-regional reported; 2-regional scaled; 4-national reported; 5-national interpolated; 6-national extrapolated; 7-no data, regional average used
Kummu, M., Taka, M. & Guillaume, J. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990\u20132015. Sci Data 5, 180004 (2018).\nhttps://doi.org/10.1038/sdata.2018.4\nKummu, Matti; Taka, Maija; Guillaume, Joseph H. A. (2020), Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015,\nDryad, Dataset, https://doi.org/10.5061/dryad.dk1j0\nvar gdp_ppp = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/GDP_PPP_1990_2015_5arcmin_v2\");\nvar gdp_ppp_30arc = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/GDP_PPP_30arcsec_v3\");\nvar gdp_per_capita = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/GDP_per_capita_PPP_1990_2015_v2\");\nvar hdi = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/HDI_1990_2015_v2\");\nvar admin_areas = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/admin_areas_GDP_HDI\");\nvar pedigree_gdp = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/pedigree_GDP_per_capita_PPP_1990_2015_v2\");\nvar pedigree_hdi = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/pedigree_HDI_1990_2015_v2\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GLOBAL-GDP-HDI
"},{"location":"projects/gridded_gdp_hdi/#license","title":"License","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
Produced by : Kummu, Matti; Taka, Maija; Guillaume, Joseph H. A.
Curated in GEE by : Samapriya Roy
Keywords: : Development indicator, global spatial data, gridded data, Gross Domestic Product (GDP), Human Development Index (HDI), Purchasing Power Parity (PPP)
Last updated on GEE: 2022-04-30
"},{"location":"projects/gridded_livestock/","title":"Gridded Livestock Density Kazakhstan (2000-2019)","text":"This dataset provides medium-resolution (1 km) gridded livestock density estimates for horses and small ruminants (sheep and goats) in Kazakhstan from 2000 to 2019. The database was developed using random forest regression modeling, incorporating vegetation proxies, climatic factors, socioeconomic variables, topographic data, and proximity forcing variables. Each file is saved with an acronym of 'sheep_goat' for small ruminants (Sheep & goat combined) and 'horse' for horses, followed by an underscore and a year. Missing data are represented by \"No data.\"
For detailed methodology, validation results, and further insights, please refer to the associated publication: \"Gridded livestock density database and spatial trends for Kazakhstan\" you can read the paper here and the dataset is available here.
"},{"location":"projects/gridded_livestock/#citation","title":"Citation","text":"Kolluru, V., John, R., Saraf, S. et al. Gridded livestock density database and spatial trends for Kazakhstan. Sci Data 10, 839 (2023).\nhttps://doi.org/10.1038/s41597-023-02736-5\nKOLLURU, VENKATESH; John, Ranjeet; Saraf, Sakshi; Chen, Jiquan; Hankerson, Brett; Robinson, Sarah; et al. (2023). Gridded livestock density database and spatial trends for\nKazakhstan. figshare. Dataset. https://doi.org/10.6084/m9.figshare.23528232.v3\nvar sheep_goat_collection = ee.Image(\"projects/sat-io/open-datasets/GRIDDED-LIVESTOCK/KZ_SHEEP_GOAT_DENSITY_DB\");\nvar horse_collection = ee.Image(\"projects/sat-io/open-datasets/GRIDDED-LIVESTOCK/KZ_HORSE_DENSITY_DB\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/KZ-GRIDDED-LIVESTOCK
"},{"location":"projects/gridded_livestock/#license","title":"License","text":"The datasets are available under a Creative Commons Attribution 4.0 International license.
Created by: Kolluru et al 2023
Curated in GEE by: Kolluru et al 2023 and Samapriya Roy
Keywords: livestock, machine learning, random forest, population, small ruminant, grazing, vegetation, grasslands, kazakhstan
Last updated in GEE: 2024-10-21
"},{"location":"projects/gridded_ppt/","title":"High-resolution gridded precipitation dataset for Peruvian and Ecuadorian watersheds (1981-2015)","text":"RAIN4PE is a novel daily gridded precipitation dataset obtained by merging multi-source precipitation data (satellite-based Climate Hazards Group InfraRed Precipitation, CHIRP (Funk et al. 2015), reanalysis ERA5 (Hersbach et al. 2020), and ground-based precipitation) with terrain elevation using the random forest regression method. Furthermore, RAIN4PE is hydrologically corrected using streamflow data in catchments with precipitation underestimation through reverse hydrology. Hence, RAIN4PE is the only gridded precipitation product for Peru and Ecuador, which benefits from maximum available in-situ observations, multiple precipitation sources, elevation data, and is supplemented by streamflow data to correct the precipitation underestimation over p\u00e1ramos and montane catchments.
Currently included layers are:
"},{"location":"projects/gridded_ppt/#earth-engine-snippet-annual-mean","title":"Earth Engine Snippet: Annual mean","text":"var rain4pe_clim = ee.ImageCollection('users/csaybar/rainpe/annual_mean')\nvar rain4pe_clim = ee.ImageCollection('users/csaybar/rainpe/monthly_clim')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-MONTHLY-CLIM
"},{"location":"projects/gridded_ppt/#earth-engine-snippet-monthly-data","title":"Earth Engine Snippet: Monthly data","text":"var rain4pe_clim = ee.ImageCollection('users/csaybar/rainpe/monthly')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-MONTHLY
"},{"location":"projects/gridded_ppt/#earth-engine-snippet-daily-data","title":"Earth Engine Snippet: Daily data","text":"var rain4pe_daily = ee.ImageCollection('projects/sat-io/open-datasets/rainpe/daily')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-DAILY
"},{"location":"projects/gridded_ppt/#resolution-01-or-roughly-10km-x-10km","title":"Resolution: 0.1\u00b0 (or roughly 10km x 10km)","text":""},{"location":"projects/gridded_ppt/#citation","title":"citation","text":"When using the data please cite:
Fernandez-Palomino, C. A.; Hattermann, F. F.; Krysanova, V.; Lobanova, A.; Vega-J\u00e1come, F.; Lavado, W.;\nSantini, W.; Aybar, C.; Bronstert, A. (2021). Rain for Peru and Ecuador (RAIN4PE). V. 1.0. GFZ Data\nServices. https://doi.org/10.5880/pik.2020.010\nThe data are supplementary material to:
Fernandez-Palomino, C. A.; Hattermann, F. F.; Krysanova, V.; Lobanova, A.; Vega-J\u00e1come, F.; Lavado, W.;\nSantini, W.; Aybar, C.; Bronstert, A. (2021). A novel high-resolution gridded precipitation dataset for\nPeruvian and Ecuadorian watersheds \u2013 development and hydrological evaluation. Journal of\nHydrometeorology. https://doi.org/10.1175/jhm-d-20-0285.1\nThis work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Cesar Aybar & Samapriya Roy
Keywords: precipitation, streamflow, Peru, Ecuador, random forest, SWAT, reverse hydrology, satellite data, Earth observation, GIS.
"},{"location":"projects/grip/","title":"Global Roads Inventory Project global roads database","text":"The Global Roads Inventory Project (GRIP) dataset was developed to provide a more recent and consistent global roads dataset for use in global environmental and biodiversity assessment models like GLOBIO.
The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km).
The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a Creative Commons License (CC-BY 4.0) and is free to use. Read about the methodology here
Download the dataset here
Use the following credit when these datasets are cited:
Meijer, Johan R., Mark AJ Huijbregts, Kees CGJ Schotten, and Aafke M. Schipper. \"Global patterns of current and future road infrastructure.\" Environmental Research Letters 13, no. 6 (2018): 064006.\nvar grip4_africa = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Africa\");\nvar grip4_central_south_america = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Central-South-America\");\nvar grip4_europe = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Europe\");\nvar grip4_north_america = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/North-America\");\nvar grip4_oceania = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Oceania\");\nvar grip4_south_east_asia = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/South-East-Asia\");\nvar grip4_middle_east_central_asia = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Middle-East-Central-Asia\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-ROADS-INVENTORY-PROJECT
Total features: 25,758,453
Shared License: This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: global, road map, infrastructure, global roads inventory project (GRIP), SSP scenarios
Last updated: 2021-04-03
"},{"location":"projects/grn_wrz/","title":"Global river networks & Corresponding Water resources zones","text":"River networks and water resources zones (WRZ) are critical for planning, utilization, development, conservation and management of water resources. Currently, the river network and WRZ of world are most obtained based on digital elevation model data automatically, which are not accurate enough, especially in plains. In addition, the WRZ code is inconsistent with the river network. The authors proposed a series of methods and generated a higher resolution and consistent high-precision global river network and corresponding WRZs at level 1 to 4. This dataset provides an important basis and support for reasonable use of water resources and sustainable social development in the world. You can read the full paper here
Level Categorization for Global River Networks(GRN) and Water Resources Zone(WRZ)
The river at level 1 (L1 river) refers to the river that flows into the sea or lake.
The river at level 2 (L2 river) refers to the river that flows into the L1 river, and its confluence area is larger than one hundredth of the L1 river or 10,000 km2.
The river at level 3 (L3 river) refers to the river that flows into the L2 river, and its confluence area is larger than one hundredth of the L2 river or 1000 km2.
The river at level 4 (L4 river) refers to the river that flows into the L3 river, and its confluence area is large than one hundredth of the L3 river or 100 km2.
The tributaries that do not satisfy the above conditions were neglected.
The WRZ correspond to River Levels
"},{"location":"projects/grn_wrz/#paper-citation","title":"Paper Citation","text":"Yan, D., Wang, K., Qin, T. et al. A data set of global river networks and corresponding water resources zones divisions.\nSci Data 6, 219 (2019). https://doi.org/10.1038/s41597-019-0243-y\nYan, Denghua; Wang, Kun; Qin, Tianling; Weng, Baisha; wang, Hao; Bi, Wuxia; et al. (2019): A data set of global river networks and corresponding\nwater resources zones divisions. figshare. Dataset. https://doi.org/10.6084/m9.figshare.8044184.v6\nThe river networks are ingested for each continent and as provided by the author. The water resources zone on the other hands were available as level based subsets for each continent so a total of 24 files. To make this accessible as large feature collections Levels across multiple continents were merged into single feature collections.
Global River Network Levels Asia Level 1,2,3,4 Africa Level 1,2,3,4 Australia Level 1,2,3,4 Europe Level 1,2,3,4 North America Level 1,2,3,4 South America Level 1,2,3,4 Water Resources Zone Levels Asia Level 1,2,3,4 Africa Level 1,2,3,4 Australia Level 1,2,3,4 Europe Level 1,2,3,4 North America Level 1,2,3,4 South America Level 1,2,3,4 Combined Water Resources Zones Locations Level 1 All Continents Level 2 All Continents Level 3 All Continents Level 4 All Continents
"},{"location":"projects/grn_wrz/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var af_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/af_river\");\nvar as_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/as_river\");\nvar au_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/au_river\");\nvar eu_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/eu_river\");\nvar na_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/na_river\");\nvar sa_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/sa_river\");\nvar WRZ_L1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/WRZ/WRZ_L1\");\nvar WRZ_L2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/WRZ/WRZ_L2\");\nvar WRZ_L3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/WRZ/WRZ_L3\");\nvar WRZ_L4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/WRZ/WRZ_L4\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-NETWORKS-WATER-RESOURCE-ZONES
"},{"location":"projects/grn_wrz/#data-subsets","title":"Data subsets","text":"The Water Resources Zones are also available as level based extracts for each countinent. Use the prefix and the level to get to each feature collection. The format is
projects/sat-io/open-datasets/WRZ/(Level)/(Prefix)_(Level)
Here are the prefix list and some examples
Country Prefix Path Asia as projects/sat-io/open-datasets/WRZ/L1/as_wrz1 Africa af projects/sat-io/open-datasets/WRZ/L2/af_wrz2 Australia au projects/sat-io/open-datasets/WRZ/L3/au_wrz3 Europe eu projects/sat-io/open-datasets/WRZ/L4/eu_wrz4 North America na projects/sat-io/open-datasets/WRZ/L1/na_wrz1 South America sa projects/sat-io/open-datasets/WRZ/L3/sa_wrz3
"},{"location":"projects/grn_wrz/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created by: Yan, D., Wang, K., Qin, T. et al.
Curated by: Samapriya Roy
Keywords: River networks, Water Resources, Hydrology
Last updated: 2019-09-28
"},{"location":"projects/grod/","title":"Global River Obstruction Database (GROD)","text":"GROD v1.1 (filename: GROD_v1.1.csv), or Global River Obstruction Database version 1.1, contains 30549 manually identified human-made structures that obstructing river longitudinal flow. Obstructions have been identified on Google Earth Engine satellite map for all rivers mapped in the Global River Widths from Landsat (GRWL) database. Each obstruction has assigned one of the six types\u2014Dam, Lock, Low head dam, Channel dam, Partial dam 1, Partial dam 2. Details of the mapping process and data quality can be found in the publication and the dataset can be accessed here.
"},{"location":"projects/grod/#citation","title":"Citation","text":"Yang, X., Pavelsky, T.M., Ross, M.R., Januchowski\u2010Hartley, S.R., Dolan, W., Altenau, E.H., Belanger, M., Byron, D., Durand, M., Van Dusen, I. and Galit, H., 2022. Mapping flow\u2010obstructing structures on global rivers. Water Resources Research, 58(1), p.e2021WR030386.\nYang, X., Pavelsky, T. M., Ross, M. R. V., Januchowski-Hartley, S. R., Dolan, W., Altenau, E. H., Belanger, M., Byron, D., Durand, M., Van Dusen,\nI., Galit, H., Jorissen, M., Langhorst, T., Lawton, E., Lynch, R., Mcquillan, K. A., Pawar, S., & Whittemore, A. (2021). Global River Obstruction\nDatabase v1.1 (v1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5793918\nvar grod = ee.FeatureCollection(\"projects/sat-io/open-datasets/GROD/GROD_V11\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-OBSTRUCTION-DATABASE
"},{"location":"projects/grod/#license","title":"License","text":"The datasets are provided under a Creative Commons 4.0 International License.
Provided by: Yang et al 2021
Curated in GEE by: Samapriya Roy
Keywords: river obstruction, dam, lock, low head dam, weir, partial dam, wing dam, dataset, fragmentation, SWOT
Last updated: 2024-04-15
"},{"location":"projects/grwl/","title":"Global River Width from Landsat (GRWL)","text":"The Global River Width from Landsat (GRWL) layers are the major output from the GRWL paper and is extremely large with over 64 million features after joining all the subparts and this is a combination from the subpart files provided by the author. You can read the paper here
The repository consists of 5 total files with each files having subparts
1) Simplified GRWL Vector Product: grwl_SummaryStats_v01_01
The shapefile contains the following attributes:
Index Attribute Description 1 width_min the minimum of river width measurements along the segment at mean discharge (meters) 2 width_med the median of river width measurements along the segment at mean discharge (meters) 3 width_mean the mean of river width measurements along the segment at mean discharge (meters) 4 width_max the maximum of river width measurements along the segment at mean discharge (meters) 5 width_sd the standard deviation of river width measurements along the segment at mean discharge (meters) 6 lakeflag integer specifying if segment is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3) 8 nSegPx number of pixels within the segment (N pixels) 9 Shape_Leng length of the segment (kilometers)2) GRWL Mask (raster): water_mask_v01_01
The file contains the following values:
DN Value Classification DN = 256 No Data DN = 255 River DN = 180 Lake/reservoir DN = 126 Tidal rivers/delta DN = 86 Canal3) GRWL Vector Product: water_vector_v01_01
The shapefile contains the following attributes:
Index Attribute Description 1 utm_east UTM Easting (UTM Zone is given in tile file name; meters) 2 utm_north UTM Northing (UTM Zone is given in tile file name; meters) 3 width_m wetted width of river (meters) [note: width_m == 1 indicates NA (no width data along the centerline) ] 4 nchannels braiding index (-) 5 segmentID unique ID of river segment in each tile 6 segmentInd Index of each observation in each segment. Not sorted by upstream or downstream 7 lakeflag integer specifying if observation is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3). 8 lon Longitude (decimal degrees) 9 lat Latitude (decimal degrees) 10 elev Elevation (meters) \u2013 sampled from the Hydro1k DEMand
4) Location map of the individual GRWL tiles: grwl_tiles
5) River and stream surface area totals by drainage basin (Fig. 4 in Allen & Pavelsky, 2018): rssa_basins
GRWL vector product has a feature Count: 64,572,998 features.
Currently included layers are
"},{"location":"projects/grwl/#earth-engine-snippet","title":"Earth Engine Snippet:","text":"var grwl_summary = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRWL/grwl_SummaryStats_v01_01\");\nvar water_mask = ee.ImageCollection(\"projects/sat-io/open-datasets/GRWL/water_mask_v01_01\");\nvar grwl_water_vector = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRWL/water_vector_v01_01\");\nvar grwl_tiles = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRWL/grwl_tiles\");\nvar grwl_rssa_basins = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRWL/rssa_basins\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-WIDTH-LANDSAT
Resolution: approx 30m
"},{"location":"projects/grwl/#cite-the-dataset-using","title":"Cite the dataset using","text":"Allen, George H., & Pavelsky, Tamlin M. (2018). Global River Widths from Landsat (GRWL) Database (Version V01.01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1297434\nAllen, George H., and Tamlin M. Pavelsky. \"Global extent of rivers and streams.\"\nScience 361, no. 6402 (2018): 585-588.\nShared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: :\"GRWL, Fluvial Geomorphology, Hydrology, Rivers, River Width, Landsat, MNDWI\"
Last updated: 2021-04-17
"},{"location":"projects/gsa/","title":"Global Solar Atlas Datasets","text":"The current version of Global Solar Atlas is v 2.6 released in July 2021. The Global Solar Atlas version 2.0 is an enhancement of the online platform, originally published in 2016 in version 1.0, that offers access to data needed for preliminary assessment of solar energy projects and sites through use of GIS data layers and maps in Download section. This Technical report summarizes delivery of the GSA 2.0 version and compares version 2.0 with previous version 1.0 in terms of enhancement in methodology, data layers and Solargis approach to PV electricity simulation. GSA 2.0 provides an access to long-term averaged yearly (for selected parameters monthly) solar, air temperature, PV power potential data and map products for almost any site on Earth.
The atlas provides an access to long-term averaged yearly (for selected parameters monthly) solar, air temperature, PV power potential data and map products for almost any site on Earth.
"},{"location":"projects/gsa/#attribution-and-license","title":"Attribution and License","text":"If you get the data or use the dataset within the GSA app attribution below, the Works (datasets) themselves are under are licensed under the Creative Commons Attribution 4.0 International license, CC BY 4.0, except where expressly stated that another license applies.
[Data/information/map] obtained from the \u201cGlobal Solar Atlas 2.0, a free, web-based application is developed and\noperated by the company Solargis s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding\nprovided by the Energy Sector Management Assistance Program (ESMAP).\nFor additional information: https://globalsolaratlas.info\nYou can find the Global Solar Atlas here and you can interact and download the datasets here
"},{"location":"projects/gsa/#data-structure","title":"Data Structure","text":"Delivered GIS data include eight parameters in the form of a raster data layers, providing the information on solar resource, photovoltaic power potential, air temperature and terrain elevation on global scale
Data layers are provided in Geographical coordinate system (EPSG:4326) and calculated in 30 arc-sec (nominally 1 km) resolution. On top of this, for more detailed analysis solar resource data (GHI, DIF, GTI and DNI) is also provided in 9 arc-sec (nominally 250 m) resolution. Finally, auxiliary data layer of Optimum angle features with 2 arcmin (nominally 4 km)resolution.
Atlas Variable Atlas GEE Variable Short Name Description DIF dif_LTAy_AvgDailyTotals Diffuse horizontal irradiation Longterm average of diffuse horizontal irradiation DNI dni_LTAy_AvgDailyTotals Direct normal irradiation Longterm average of direct normal irradiation ELE ele_asl Terrain elevation above sea level Terrain elevation GHI ghi_LTAy_AvgDailyTotals Global horizontal irradiation Longterm average of global horizontal irradiation GTI gti_LTAy_AvgDailyTotals Global irradiation for optimally tilted surface Longterm average of global irradiation at optimum tilt OPTA opta_LTAy_AvgDailyTotals Optimum tilt to maximize yearly yield Optimum tilt of photovoltaic modules PVOUT_LTAm pvout_LTAm_AvgDailyTotals Photovoltaic power potential Average Monthly Longterm monthly average of daily totals of potential photovoltaic electricity production PVOUT_LTAy pvout_LTAy_AvgDailyTotals Photovoltaic power potential Average daily Longterm yearly average of daily totals of potential photovoltaic electricity production TEMP temp_2m_agl Temperature at 2m above ground Longterm yearly average of air temperature (1994-2018) "},{"location":"projects/gsa/#earth-engine-datasets","title":"Earth Engine Datasets","text":"var dif = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/dif_LTAy_AvgDailyTotals');\nvar dni = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/dni_LTAy_AvgDailyTotals');\nvar elevation_asl = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/ele_asl');\nvar ghi = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/ghi_LTAy_AvgDailyTotals');\nvar gti = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/gti_LTAy_AvgDailyTotals');\nvar opta = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/opta_LTAy_AvgDailyTotals');\nvar pvout_ltam = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/pvout_LTAm_AvgDailyTotals');\nvar pvout_ltay = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/pvout_LTAy_AvgDailyTotals');\nvar temp_agl = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/temp_2m_agl');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-SOLAR-ATLAS
Produced and maintained by the Global Solar Atlas, ESMAP, Solargis and the World Bank Group (consisting of The World Bank and the International Finance Corporation, or IFC)
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: : Solar, energy, photovoltaic capacity, irradiation, optimally tilted surface, Photovoltaic power potential
Last updated: 2021-10-30
"},{"location":"projects/gshtd/","title":"Global Seamless High-resolution Temperature Dataset (GSHTD)","text":"The Global Seamless High-resolution Temperature Dataset (GSHTD) presented in this study offers a comprehensive and valuable resource for researchers across various fields. Covering the period from 2001 to 2020, this dataset focuses on land surface temperature (Ts) and near-surface air temperature (Ta). A unique feature of GSHTD is its incorporation of seven types of temperature data, including clear-sky daytime and nighttime Ts, all-sky daytime and nighttime Ts, and mean, maximum, and minimum Ta. Notably, the dataset achieves global coverage with an impressive 30 arcsecond or 1km spatial resolution.
The development of GSHTD involves the innovative Estimation of Temperature Difference (ETD) method, enabling the reconstruction of both clear- and cloudy-sky Ts. The dataset is seamless, eliminating missing values, and employs a Cubist machine learning algorithm to enhance accuracy in creating monthly averages of mean, maximum, and minimum Ta data. GSHTD exhibits high accuracy, outperforming existing methods with average mean absolute errors (MAEs) that are significantly lower. This dataset's accessibility at the Middle Yangtze River Geoscience Data Center provides a valuable tool for studies related to climate change, environmental science, ecology, epidemiology, and human health. You can find additional information in the paper here including links to the dataset.
These temperature datasets are not valid over open oceans.
"},{"location":"projects/gshtd/#citation","title":"Citation","text":"Yao, Rui, Lunche Wang, Xin Huang, Qian Cao, Jing Wei, Panxing He, Shaoqiang Wang, and Lizhe Wang. \"Global seamless and high-resolution temperature\ndataset (GSHTD), 2001\u20132020.\" Remote Sensing of Environment 286 (2023): 113422.\nvar all_sky_day = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/ALL_SKY_DAY\");\nvar all_sky_night = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/ALL_SKY_NIGHT\");\nvar clear_sky_day = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/CLEAR_SKY_DAY\");\nvar clear_sky_night = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/CLEAR_SKY_NIGHT\");\nvar tmax = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/TMAX\");\nvar tmean = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/TMEAN\");\nvar tmin = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/TMIN\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GSHTD
"},{"location":"projects/gshtd/#license","title":"License","text":"The dataset is distributed under the Creative Commons Attribution 4.0 International as requested by the authors.
Provided by: Yao et al 2023
Curated in GEE by: Samapriya Roy
Keywords: MODIS, High Resolution Temperature, Seamless, Gap filled, Global dataset
Last updated in GEE: 2024-02-04
"},{"location":"projects/gssr/","title":"Global Storm Surge Reconstruction (GSSR) database","text":"The Global Storm Surge Reconstruction (GSSR) database includes daily maximum surge values for the past at 882 tide gauges distributed along the global coastline. The data-driven models employed for the surge reconstruction were developed by Tadesse et al. (2020). The authors use five different atmospheric reanalysis products with different spatial and temporal resolution to produce surge information for the periods covered by the different reanalyses. The reanalysis that leads to the best validation results is marked with \"best reconstruction\" (note that in some locations data is not available for all reanalyses as there is no overlap in the periods covered by the tide gauges and the reanalysis). You can read the full paper here The full surge reconstruction for each reanalysis (comprised of 882 compressed individual .csv files for the different tide gauges) can be downloaded from the following links:
Tadesse, M.G., Wahl, T. A database of global storm surge reconstructions. Sci Data 8, 125 (2021).\nhttps://doi.org/10.1038/s41597-021-00906-x\nThe combined merged download daily maximum surge values for individual tide gauges and reanalysis products for different sites were merged into master feature collections while still maintaining different reanalyses products. Since site names included special characters like + or # or spaces which are not allowed in GEE naming convention we applied a consistent find and replace strategy to provide some level of consistency between locations. However in any case we hope the lat long provides a more accurate representation of a site. Another thing to note is that the CSVs seemed to have been exported with the index column which is not very useful and especially since it was missing a header, so the index column was removed from all CSVs before being renamed and ingested.
Reanalysis Type GEE Feature Collection name 20-CR Surge Reconstruction [1836 - 2015] 20-CR_surge_reconstruction ERA-20C Surge Reconstruction [1900 - 2010] era-20C_surge_reconstruction ERA-Interim Surge Reconstruction [1979 - 2019] era-Interim_surge_reconstruction MERAA-2 Reconstruction [1980 - 2019] merra-2_surge_reconstruction ERA-Five Reconstruction [1979 - 2019] era-5_surge_reconstruction "},{"location":"projects/gssr/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var surge_20_cr = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/20-CR_surge_reconstruction\");\nvar surge_era_20c = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/era-20C_surge_reconstruction\");\nvar surge_era_interim = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/era-Interim_surge_reconstruction\");\nvar surge_merra_2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/merra-2_surge_reconstruction\");\nvar surge_era_5 = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/era-5_surge_reconstruction\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL-STORM-SURGE-RC
"},{"location":"projects/gssr/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Tadesse, M.G., Wahl, T.
Curated by: Samapriya Roy
Keywords: : 20-CR, ERA-20C, ERA-Interim, MERAA-2, ERA-Five, reanalysis, storm-surge, surge-reconstruction, NCEP, extreme-sea-level
Last updated: 2022-01-04
"},{"location":"projects/gue/","title":"Global urban extents from 1870 to 2100","text":"Long term, global records of urban extent can help evaluate environmental impacts of anthropogenic activities. Remotely sensed observations can provide insights into historical urban dynamics, but only during the satellite era. Here, we develop a 1\u2009km resolution global dataset of annual urban dynamics between 1870 and 2100 using an urban cellular automata model trained on satellite observations of urban extent between 1992 and 2013. Hindcast (1870\u20131990) and projected (2020\u20132100) urban dynamics under the five Shared Socioeconomic Pathways (SSPs) were modeled. We find that global urban growth under SSP5, the fossil-fuelled development scenario, was largest with a greater than 40-fold increase in urban extent since 1870. The high resolution dataset captures grid level urban sprawl over 200 years, which can provide insights into the urbanization life cycle of cities and help assess long-term environmental impacts of urbanization and human\u2013environment interactions at a global scale. You can read the paper here
The dataset includes hindcast urban extent from 1870 to 1990 with a 10-year interval, satellite observed urban extent from 1992 to 2013 at an annual interval, and projected urban extent from 2020 to 2100 under five SSP scenarios with a 10-year interval. The datasets and entire collection are available at Figshare
"},{"location":"projects/gue/#citation","title":"Citation","text":"Li, Xuecao, Yuyu Zhou, Mohamad Hejazi, Marshall Wise, Chris Vernon, Gokul Iyer, and Wei Chen. \"Global urban growth between 1870 and 2100 from\nintegrated high resolution mapped data and urban dynamic modeling.\" Communications Earth & Environment 2, no. 1 (2021): 1-10.\nLi, Xuecao; Zhou, Yuyu (2020): High resolution mapping of global urban extents from 1870 to 2100 by integrating data and model driven approaches.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.9696218\nDates are added to the images and the start and end date are given one year periods to be consistent with a snapshot approach rather than a continuity approach. For the projected extent scenario is added as a metadata to the images for easy sort and use as needed.
"},{"location":"projects/gue/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var hindcast_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/global-urban-extents/hindcast_urban_extent\");\nvar observed_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/global-urban-extents/observed_urban_extent\");\nvar projected_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/global-urban-extents/project_urban_scenarios\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-URBAN-EXTENTS
"},{"location":"projects/gue/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by: Li, Xuecao, Yuyu Zhou, Mohamad Hejazi, Marshall Wise, Chris Vernon, Gokul Iyer, and Wei Chen
Curated in GEE by: Samapriya Roy
Keywords: Cellular Automata, Urban sprawl, temporal trend, Nighttime lights, hindcasts, Forecast
Last updated: 2021-11-09
"},{"location":"projects/gwa/","title":"Global Wind Atlas Datasets","text":"The Global Wind Atlas is a free, web-based application developed to help policymakers, planners, and investors identify high-wind areas for wind power generation virtually anywhere in the world, and then perform preliminary calculations. The Global Wind Atlas facilitates online queries and provides freely downloadable datasets based on the latest input data and modeling methodologies. Users can additionally download high-resolution maps of the wind resource potential, for use in GIS tools, at the global, country, and first-administrative unit (State/Province/Etc.) levels. You can read more about the methods used here
The modeling process is made up of a WAsP calculation of local wind climates for every 250 m at five heights: 10 m; 50 m; 100 m; 150 m and; 200 m. On a 250 m grid, there is a local wind climate estimate for every node. Power density data takes into account geographical variations of air density. Includes variables in the Google Earth Engine collection include, wind speed, air density and power density. Surface roughness length is a property of the surface which can be used to determine the way the horizontal wind speed varies with height. The wind speed at a given height decreases with increasing surface roughness.
Most of the data are named as follows: gwa_{variable}_{height}.tif with GEE collections, where variable is one of and this description below is obtained here
wind-speed - The mean wind speed at the location for the 10 year period
power-density - The mean power density of the wind, which is related to the cube of the wind speed, and can provide additional information about the strength of the wind not found in the mean wind speed alone.
air-density - The air density is found by interpolating the air density from the CFSR reanalysis to the elevation used in the global wind atlas following the approach described in WAsP 12.
RIX - The RIX (Ruggedness IndeX) is a measure of how complex the terrain is. It provides the percent of the area within 10 km of the position that have slopes over 30-degrees. A RIX value greater than 5 suggests that you should use caution when interpreting the results.
The files which do not follow the naming convention above are the capacity-factor layers. The capacity factor layers were calculated for 3 distinct wind turbines, with 100m hub height and rotor diameters of 112, 126, and 136m, which fall into three IEC Classes (IEC1, IEC2, and IEC3). Capacity factors can be used to calculate a preliminary estimate of the energy yield of a wind turbine (in the MW range), when placed at a location. This can be done by multiplying the rated power of the wind turbine by the capacity factor for the location (and the number of hours in a year):
AEP = Prated * CF * 8760 hr/year,
where AEP is annual energy production, Prated is rated power, and CF is capacity factor.
Variable Name Version Heights(in m) Wind Speed 3 10,50,100,150,200 Power Density 3 10,50,100,150,200 Air Density 3 10,50,100,150,200 Capacity Factor IEC1 3 NA Capacity Factor IEC2 3 NA Capacity Factor IEC3 3 NA Ruggedness Index 3 NA
"},{"location":"projects/gwa/#attribution-and-license","title":"Attribution and License","text":"If you get the data or use the dataset within the GWA app attribution below, the Works (datasets) themselves are under are licensed under the Creative Commons Attribution 4.0 International license, CC BY 4.0, except where expressly stated that another license applies.
[Data/information/map obtained from the] \u201cGlobal Wind Atlas 3.0, a free, web-based application developed,\nowned and operated by the Technical University of Denmark (DTU). The Global Wind Atlas 3.0 is released in partnership\nwith the World Bank Group, utilizing data provided by Vortex, using funding provided by\nthe Energy Sector Management Assistance Program (ESMAP). For additional information: https://globalwindatlas.info\u201d\nYou can also find the Global Wind Atlas here and you can interact and download the datasets here
"},{"location":"projects/gwa/#data-preprocessing-for-gee","title":"Data Preprocessing for GEE","text":"Capacity Factors were added onto a single collection and rotor diameter and hub height was added as metadata property for filtering. For variables that have height gradients, height was added as a metadata for filtering.
"},{"location":"projects/gwa/#earth-engine-datasets","title":"Earth Engine Datasets","text":"var air_density = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/air-density');\nvar capacity_factor = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/capacity-factor');\nvar power_density = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/power-density');\nvar rix= ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/ruggedness-index');\nvar wind_speed= ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/wind-speed');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-WIND-ATLAS
Produced and maintained by the Global Wind Atlas, Department of Wind Energy at the Technical University of Denmark (DTU Wind Energy) and the World Bank Group (consisting of The World Bank and the International Finance Corporation, or IFC)
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: : Wind, energy, ruggedness index, capacity factor, wind speed, power density
Last updated: 2021-07-11
"},{"location":"projects/gwl_fcs/","title":"Global 30 m Wetland Map with a Fine Classification System","text":"GWL_FCS30 is a global wetland map with a resolution of 30 meters, designed to provide detailed information on wetland ecosystems worldwide. This dataset spans from 2000 to 2022 and includes a fine classification system that distinguishes between eight specific wetland subcategories. These subcategories encompass both coastal tidal wetlands and inland wetlands, offering a comprehensive view of wetland types across the globe.
The coastal tidal wetlands in this dataset are categorized into mangroves, salt marshes, and tidal flats. For inland wetlands, the classification includes permanent water, swamps, marshes, flooded flats, and saline wetlands. This level of detail is crucial for understanding and managing different wetland types and their ecological functions.
The dataset was created using a novel approach that combines automatic sample extraction from various existing global wetland products with multi-temporal satellite imagery, including Landsat reflectance data and Sentinel-1 SAR imagery. This method allows for capturing the complex temporal dynamics and spectral variations of wetlands. Additionally, a stratified classification strategy was employed, utilizing local adaptive random forest models to ensure precise classification at a high spatial resolution.
The GWL_FCS30 dataset offers a spatial resolution of 30 meters and covers the entire globe. It provides valuable insights into wetland areas and their distribution over the past two decades, with the data presented in square kilometers. This dataset is an important resource for ecological studies, wetland management, and conservation efforts, providing essential information for understanding and preserving wetland ecosystems.
You can read the paper here, and find the complete dataset here
"},{"location":"projects/gwl_fcs/#dataset-preprocessing","title":"Dataset Preprocessing","text":"Yearly images are distributed as zipped files consisting of tiles for global regions which are merged into a single image per year. These are then ingested into a single image collection in GEE.
"},{"location":"projects/gwl_fcs/#citation","title":"Citation","text":"Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., & Liu, W. (2023). GWL_FCS30: a global 30\u2009m wetland map with a fine classification\nsystem using multi-sourced and time-series remote sensing imagery in 2020. *Earth Syst. Sci. Data*, 15, 265\u2013293.\nhttps://doi.org/10.5194/essd-15-265-2023\nLiangyun, L., & Xiao, Z. (2023). Time-series global 30 m wetland maps from 2000 to 2022 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10068479\nClass Code Wetland Subcategory 0 Non-wetland 180 Permanent Water 181 Swamp 182 Marsh 183 Flooded Flat 184 Saline 185 Mangrove Forest 186 Salt Marsh 187 Tidal Flat
"},{"location":"projects/gwl_fcs/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var gwl_fcs30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GWL_FCS30\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GWL-FCS30-WETLANDS
"},{"location":"projects/gwl_fcs/#license","title":"License","text":"These datasets are made available under the Creative Commons Attribution 4.0 International.
Provided by: Zhang et al 2023
Curated in GEE by: Samapriya Roy
Keywords: fine classification system, land cover, wetland, wetland ecosystem
Last updated: 2024-07-28
"},{"location":"projects/habitat/","title":"Global maps of habitat types","text":"We provide a global, spatially explicit characterization of terrestrial and marine habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme, which is widely used in ecological analyses, including for quantifying species\u2019 Area of Habitat. We produced this novel habitat map for the years 2015-2019 by creating a global decision tree that intersects the best currently available global data on elevation and bathymetry, land and ocean cover, climate and land use.
"},{"location":"projects/habitat/#citation","title":"Citation","text":"Jung, M., Dahal, P.R., Butchart, S.H.M. et al. A global map of terrestrial habitat types.\nSci Data 7, 256 (2020). https://doi.org/10.1038/s41597-020-00599-8\n// Level 1 and level 2 for the year 2015\nvar lvl1 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/iucn_habitatclassification_composite_lvl1_ver004\")\nvar lvl2 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/iucn_habitatclassification_composite_lvl2_ver004\")\n\n// Note: Colour code is random\nMap.addLayer(lvl2.randomVisualizer(),{},'Habitat Classification Level 1')\n\n// Changemask for later.\n// Replace year in folder and mask to get a different year (for years 2016-2019)\n//for example 2017 would be var change2017_lvl1 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/changemasks/iucn_habitatclassification_2017changemask_lvl1_ver004\")\nvar change2019_lvl1 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/changemasks/iucn_habitatclassification_2019changemask_lvl1_ver004\").select('comp2019').unmask(0)\nvar change2019_lvl2 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/changemasks/iucn_habitatclassification_2019changemask_lvl2_ver004\").select('comp2019').unmask(0)\nprint(change2019_lvl1)\n\n//Level 1 and level 2 for the year 2019\nvar lvl12019 = change2019_lvl1.firstNonZero(lvl1)\nvar lvl22019 = change2019_lvl2.firstNonZero(lvl2)\n\nMap.addLayer(lvl12019.randomVisualizer(),{},'Level 1 2019')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GLOBAL-HABITAT-TYPES
Extra Info: Code to reproduce the maps can be found here and be visualized here. Default Maps are for the year 2015. Change maps are also available for later years (2016-2019) based on Copernicus only. Note that provided changemasks are cumulative (e.g. the year 2019 includes changes up to 2019). They can be used to updateMask the 2015 image.
https://zenodo.org/record/4058819
"},{"location":"projects/habitat/#source-code-for-dataset","title":"Source Code for dataset","text":"https://github.com/Martin-Jung/Habitatmapping
Created by : Jung, M., Dahal, P.R., Butchart, S.H.M. et al
Curated by: Martin Jung
Keywords: Global habitats, Ecosystems, Integrated map, IUCN, Biodiversity, Species
Last updated: 2020-09-01
"},{"location":"projects/hand/","title":"Global 30m Height Above the Nearest Drainage","text":"Read about the methodology here
Or get it from https://gena.users.earthengine.app/view/global-hand
Use the following credit when these data are cited:
Donchyts, Gennadii, Hessel Winsemius, Jaap Schellekens, Tyler Erickson, Hongkai Gao, Hubert Savenije, and Nick van de Giesen. \"Global 30m Height Above the Nearest Drainage (HAND)\",\nGeophysical Research Abstracts, Vol. 18, EGU2016-17445-3, 2016, EGU General Assembly (2016).\nvar hand30_100 = ee.ImageCollection(\"users/gena/global-hand/hand-100\").mosaic()\nvar hand30_1000 = ee.Image(\"users/gena/GlobalHAND/30m/hand-1000\")\nvar hand90_1000 = ee.Image(\"users/gena/GlobalHAND/90m-global/hand-1000\")\n30 and 90 - cell resolution, 100 and 1000 - number of river head threshold cells
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-HEIGHT-ABV-NEAREST-DRAINAGE
Shared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Source and Curated by: Donchyts/Deltares
Keywords: Global Hand, Hydrology, drainage
Last updated: ~2017
"},{"location":"projects/harvest/","title":"NASA Harvest Layers","text":"This page includes studies and geospatial layers made available as a result of publications from the NASA Harvest group members and made available in Google Earth Engine. This will be updated as newer and updated studies get published.
"},{"location":"projects/harvest/#rapid-response-crop-maps-in-data-sparse-regions","title":"Rapid Response Crop Maps in Data Sparse Regions","text":"We present a method for rapid mapping of croplands in regions where little to no ground data is available. We present results for this method in Togo, where we delivered a high-resolution (10 m) cropland map in under 10 days to facilitate rapid response to the COVID-19 pandemic by the Togolese government. This demonstrated a successful transition of machine learning applications research to operational rapid response in a real humanitarian crisis. All maps, data, and code are publicly available to enable future research and operational systems in data-sparse regions. Read the paper here
"},{"location":"projects/harvest/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var togo_cropland_binary = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/togo_cropland_binary\");\nvar togo_cropland_probability = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/togo_cropland_probability\");\nHannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe,Brian Barker, Blake Munshell,\nMadhava Paliyam, and Mehdi Hosseini. 2020.Rapid Response Crop Maps in Data Sparse Regions.\nKDD \u201920: ACMSIGKDDConference on Knowledge Discovery and Data Mining Workshops, August22\u201327, 2020, San Diego, CA.\nSample code:https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/NASA-HARVEST-CROPLAND
"},{"location":"projects/harvest/#annual-and-in-season-mapping-of-cropland-at-field-scale-with-sparse-labels","title":"Annual and in-season mapping of cropland at field scale with sparse labels","text":"Previously, we developed a method for binary classification of cropland that learns from sparse local labels and abundant global labels using a multi-headed LSTM and timeseries multispectral satellite inputs over one year. In this work, we present a new method that uses an autoregressive LSTM to classify cropland during the growing season (i.e., partially-observed time series). We used these methods to produce publicly-available 10m-resolution cropland maps in Kenya for the 2019-2020 and 2020-2021 growing seasons. These are the highest-resolution and most recent cropland maps publicly available for Kenya. These methods and associated maps are critical for scientific studies and decision-making at the intersection of food security and climate change. Read the paper here
"},{"location":"projects/harvest/#earth-engine-snippet_1","title":"Earth Engine Snippet","text":"var kenya_cropland_binary = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/kenya_cropland_binary\");\nvar kenya_cropland_probability = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/kenya_2019_cropland_probability\");\nvar busia_cropland_probability = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/busia_cropland_probability\");\nvar busia_cropland_binary = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/busia_cropland_binary\");\nTseng, Gabriel, Hannah Kerner, Catherine Nakalembe, and Inbal Becker-Reshef.\n\"Annual and in-season mapping of cropland at field scale with sparse labels.\"\nSample code:https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/NASA-HARVEST-CROPLAND
"},{"location":"projects/harvest/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: agriculture, Africa, Togo, crops, crop classification, food security, satellite data, Earth observation, GIS
Last updated: 2021-04-25
"},{"location":"projects/health_sites/","title":"Global Healthsites Mapping Project","text":"Healthsites.io and the Global Healthsites Mapping Project's mission is to help supply governments, NGOs, and the private sector with accurate and up-to-date health facility information. Health facility registers are the building blocks of a well-functioning health information system within a country. Accurate and up-to-date data provides the basic data that helps drive activities like service availability planning, monitoring and evaluation, and disaster risk preparedness.
The data is shared on both https://healthsites.io and expected a monthly updated on the Humanitarian Data Exchange. Expected update Frequency for now is every month. Read the Healthsites concept note http://bit.ly/2ocL2KY
The healthsites.io datasets are served as nodes (defining points in space) and ways (defining linear features and area boundaries) based on open street map object relations.
"},{"location":"projects/health_sites/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var node = ee.FeatureCollection(\"projects/sat-io/open-datasets/health-site-node\");\nvar way = ee.FeatureCollection(\"projects/sat-io/open-datasets/health-site-way\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-HEALTHSITES-MAPPING-PROJECT
"},{"location":"projects/health_sites/#license","title":"License","text":"Open Database License (ODbL)
You are free to
Conditions
Compiled by : Global Healthsites Mapping Project
Curated by: Samapriya Roy
Keywords: :\"Global Healthsites Mapping Project, Healthsites, Health, GLC\"
Last updated: 2024-01-16
"},{"location":"projects/heat-hazard/","title":"Global Extreme Heat Hazard","text":"Published by the World Bank (2017), this is a global data layer for extreme heat hazard, which is classified based on an existing and widely accepted heat stress indicator, the Wet Bulb Globe Temperature (WBGT, in \u00b0C) \u2013 more specifically the daily maximum WGBT. The WBGT has an obvious relevance for human health, but it is relevant in all kinds of projects and sectors, including infrastructure related, as heat stress affects personnel and stakeholders, and therefore the design of buildings and infrastructure. Heat stress studies in the scientific literature that make use of the WBGT apply thresholds of 28\u00b0C and 32\u00b0C to categorise heat stress risk. The damaging intensity thresholds are applied following this definition of slight/low (<28\u00b0C), moderate/high (28-32\u00b0C) and severe/very high (>32\u00b0C) heat stress. This dataset is licensed under Creative Commons Attribution 4.0. You can download the report here. Point of contact: sfraser@worldbank.org
Extra Info: There are three global GeoTIFF files in total, which can be combined into one single collection 5 year, 20 year and 100 year return period.
"},{"location":"projects/heat-hazard/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var global_extreme_heat_hazard = ee.ImageCollection('projects/sat-io/open-datasets/WORLD-BANK/global-ext-heat-hazard');\nSample code: : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-EXTREME-HEAT-HAZARD
"},{"location":"projects/heat-hazard/#license","title":"License","text":"This dataset is classified as Public under the Access to Information Classification Policy. Users inside and outside the Bank can access this dataset under a Creative Commons Attribution 4.0
Curated by: Koen De Ridder, Dirk Lauwaet, Hans Hooyberghs and Filip Lefebre from VITO (Author)
Keywords: Hazard Assessment, Extreme Heat, Climatology, Climate Change
Last updated: Mar 1, 2017
"},{"location":"projects/hihydro_soil/","title":"HiHydroSoil v2.0 layers","text":"In May 2020, ISRIC has released the latest version (v2.0) of its Soilgrids250m product. This release has made it possible for FutureWater to update its HiHydroSoil v1.2 database with newer, more precise and with a higher resolution soil data, which resulted in the development and release of HiHydroSoil v2.0.
Soil information is the basis for all environmental studies. Since local soil maps of good quality are often not available, global soil maps with a low resolution are used. Furthermore, soil maps do not include information about soil hydraulic properties, which are of importance in, for example, hydrological modeling, erosion assessment and crop yield modelling. HiHydroSoil v2.0 can fill this data gap. HiHydroSoil v2.0 includes the following data and additional information along with links to download the data can be found here
The Hydrologic Soil Group (HSG) determines the Runoff Curve Number which is often used in hydrological modelling to estimate the direct runoff from rainfall. Four hydrologic soil groups and three dual hydrologic soil groups. The data layers originally consisting of float data type were multiplied by a factor of 10,000 and subsequently converted to integer type. It is therefore required to translate the data to the proper units by multiplying with 0.0001.
"},{"location":"projects/hihydro_soil/#citation-related-publications","title":"Citation & Related Publications","text":"Simons, G.W.H., R. Koster, P. Droogers. 2020. HiHydroSoil v2.0 - A high resolution soil map of global hydraulic properties.\nFutureWater Report 213.\nYou can download the report here
Variable Unit Description Range Assets on GEE Organic Matter Content (ORMC) % Soil organic matter\u00a0(SOM) is the\u00a0organic matter\u00a0component of\u00a0soil, consisting of plant and animal\u00a0detritus\u00a0at various stages of\u00a0decomposition, cells and tissues of\u00a0soil microbes, and substances that soil microbes synthesize. 0 - 50 ormc Soil Texture Class (STC) O (Organic), VF\u00a0(Very\u00a0Fine), F\u00a0(Fine), MF (Medium Fine), C\u00a0(Coarse), M (Medium) Soil texture\u00a0is a\u00a0classification\u00a0instrument used both in the field and laboratory to determine\u00a0soil\u00a0classes based on their physical texture. 1- 6 (see column Unit) stc Alpha parameter for Mualem Van Genuchten Equation (ALPHA) 1/cm The shape of water retention curves can be characterized by several models, one of them known as the van Genuchten model. The Alpha parameter in this model is related to the inverse of the air entry suction. 0 - 0.2 alpha N parameter for Mualem Van Genuchten Equation (N) - The shape of water retention curves can be characterized by several models, one of them known as the van Genuchten model. The N parameter in this model is a measure of the pore-size distribution. 1 - 2.3 N Saturated Water Content (Wcsat) m3/m3 Saturated water content is\u00a0the maximum amount of water a soil can store and which is equivalent to the porosity of the soil. 0.25 - 0.85 wcsat Residual Water Content (Wcres) m3/m3 The residual volumetric water content\u00a0\u00a0represents the volumetric water content of a soil where a further increase in negative pore-water\u00a0pressure does not produce significant changes in water content. 0 - 0.02 wcres Saturated Hydraulic Conductivity (Ksat) cm/d Saturated hydraulic conductivity is a quantitative measure of a saturated soil's ability to transmit water when subjected to a hydraulic gradient. It can be thought of as the ease with which pores of a saturated soil permit water movement. 0 - 1500 ksat Water content at pF2 (field capacity) (WCpF2) m3/m3 Field Capacity is the amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. It's the upper limit of the rapidly available water for plants at a matric potential of -100 cm or pF2. 0 - 0.8 wcpf2 Water content at pF3 (critical point) (WCpF3) m3/m3 Critical point: lower limit of rapidly available water for plants. Upper limit of slowly available water for plants. This is at a matric potential of -1000 cm or pF3. 0 - 0.7 wcpf3 Water content at pF4.2 (permanent wilting point) (WCpF4.2) m3/m3 Plants can - on average - produce a suction till 16 x atmospheric pressure before a plants starts to permanently wilt. This atmosperic pressures is similar to a matrix potentential of -16000 cm, or pF 4.2. 0 - 0.7 wcpf4-2 Available water content (Wcavail) m3/m3 The amount of water between field capacity (pF2) and permanent wilting point (pF4.2). This value should be used with caution. First, plants will start wilting with subsequent yield losses well before the permanent wilting point. Secondly, plant available soil water is replenished by capillary rise, rainfall and irrigation water. 0 - 0.6 wcavail Water content between saturation point and field capacity (pF2) (SAT_FIELD) m3/m3 Water content between saturation point and field capacity (pF2) \u2026 sat-field Water content between field capacity (pF2) and critical point (pF3) (FIELD-CRIT) m3/m3 Water content between field capacity (pF2) and critical point (pF3) 0 - 0.4 field-crit Water content between critical point (pF3) and permanent wilting point (pF4.2) (CRIT-WILT) m3/m3 Water content between critical point (pF3) and permanent wilting point (pF4.2) 0 - 0.25 crit-wilt Hydrologic Soil Group A (low runoff potential), A/D, B\u00a0(moderately low runoff potential), B/D, C (moderately high runoff potential), C/D, D\u00a0(high runoff potential) Along with land use, land management practices and soil hydrologic conditions the Hydrologic Soil Group (HSG) determines the Runoff Curve Number which is often used in hydrological modelling to estimate the direct runoff from rainfall. Four hydrologic soil groups and three dual hydrologic soil groups are described by the USDA (2009) hydrologic-soil-group"},{"location":"projects/hihydro_soil/#earth-engine-snippet-hihydro-layers-hydrologic_soil_group_250m","title":"Earth Engine Snippet: HiHydro Layers (Hydrologic_Soil_Group_250m)","text":"var hydrologic_soil_group = ee.Image('projects/sat-io/open-datasets/HiHydroSoilv2_0/Hydrologic_Soil_Group_250m');\nSample Code: https://code.earthengine.google.com/4da512c4c0785ef2767f159028579fc6
"},{"location":"projects/hihydro_soil/#earth-engine-snippet-hihydro-additional-layers","title":"Earth Engine Snippet: HiHydro Additional Layers","text":"var ksat = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/ksat\");\nvar satfield = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/sat-field\");\nvar N = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/N\");\nvar alpha = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/alpha\");\nvar crit_wilt = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/crit-wilt\"),\nvar field_cirt = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/field-crit\");\nvar ormc = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/ormc\");\nvar stc = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/stc\");\nvar wcavail = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcavail\");\nvar wcpf2 = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcpf2\");\nvar wcpf3 = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcpf3\");\nvar wcpf4_2 = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcpf4-2\");\nvar wcres = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcres\");\nvar wcsat = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcsat\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/HiHYDRO-SOIL-LAYERS
"},{"location":"projects/hihydro_soil/#license-information","title":"License Information","text":"HiHydroSoil v2.0 can be used freely and redistributed with attribution. No additional information made available by authors.
Curated by: William Ouellette and Samapriya Roy
Keywords: Global Hydrologic Soil Group, Hydrology, Hydrological, Soil, Hydraulic, Conductivity, Runoff, Run-off, Water, Water Cycle
Last updated dataset: October 2020 Last curated: 2021-05-05
"},{"location":"projects/histarfm/","title":"HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) database","text":"The HISTARFM database is a high spatial resolution monthly reflectance temporal series corrected from cloud data gaps. The dataset was created at 30 meters resolution through the fusion of the Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) temporal series. The method involves using two estimators that work together to eliminate random noise and minimize the bias of Landsat spectral reflectances. The first estimator is an optimal interpolator that generates Landsat reflectance estimates using Landsat historical data and fused MODIS and Landsat reflectances from the nearest overpasses. The fusion process employs a linear regression model at a pixel level. The second estimator is a Kalman filter that corrects any bias in the reflectance produced by the first estimator. HISTARFM provides improved reflectance values and a unique and useful side product, the reflectance uncertainties, which is helpful for realistic error calculation (e.g., computing error bars of Vegetation Indexes or biophysical variables). For a more detailed explanation of the HISTARFM algorithm, please refer to the Moreno-Martinez et al. 2020 manuscript.
Example of a mosaic ofHIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) database the HISTARFM data [right bottom], the red band uncertainty [left bottom], and the derivate leaf area index [top] over a large area in continental East Asia for a given year (2021).
"},{"location":"projects/histarfm/#dataset-access","title":"Dataset Access","text":"To access the HISTARFM dataset, you need to join the HISTARFM google group once you have access to the google group you can access the dataset using the code snippets and paths below. The method to find and add yourself to the group is fairly simple. Go to groups.google.com use the drop down to select all groups rather than my groups and search for keyword HISTARFM collection then click on join group and follow along. The steps are also captured below
"},{"location":"projects/histarfm/#citation","title":"Citation","text":"Moreno-Mart\u00ednez, \u00c1lvaro, Emma Izquierdo-Verdiguier, Marco P. Maneta, Gustau Camps-Valls, Nathaniel Robinson, Jordi Mu\u00f1oz-Mar\u00ed, Fernando Sedano,\nNicholas Clinton, and Steven W. Running. \"Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud.\" Remote Sensing of\nEnvironment 247 (2020): 111901.\nDifferent versions and study areas are already processes:
var histarfm_conus = ee.ImageCollection(\"projects/KalmanGFwork/GFLandsat_V1\")\nvar histarfm_ic = ee.ImageCollection(\"projects/ee-kalman-gap-filled/assets/histarfm_v5\")\nSample code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/HISTARFM-V5-EXAMPLE
For more information about how to work with HISTARFM and some examples of how to improve your research and applications with the HISTARFM database, visit the tutorial here.
The HISTARFM database was used in the following papers
Mart\u00ednez-Ferrer, L., et al. \"Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning.\" Remote Sensing of Environment 280 (2022): 113199.
Salerno, L., et al. \"Satellite Analyses Unravel the Multi-Decadal Impact of Dam Management on Tropical Floodplain Vegetation.\" Frontiers in Environmental Science (2022): 357.
Kushal, K. C., and Sami Khanal. \"Agricultural productivity and water quality tradeoffs of winter cover crops at a landscape scale through the lens of remote sensing.\" Journal of Environmental Management 330 (2023): 117212.
The dataset is licensed under a Creative Commons Attribution NonCommercial 4.0 International license.
Curated by: \u00c1lvaro Moreno-Mart\u00ednez, Emma Izquierdo-Verdiguier, Jordi Mu\u00f1oz-Mar\u00ed and Nicolas Clinton.
Keywords: MODIS, Landsat, Land reflectance images, gap-filled temporal series, vegetation
Last updated: 06-03-2023
"},{"location":"projects/historical_us/","title":"USGS Historical Imagery Western US","text":"This dataset contains an imagery base layer representing conditions from the mid-1950s across the western United States. We sourced the imagery from over 160,000 aerial images in the USGS EROS Archive taken between 1940 and 1970, with the median acquisition date being 1954. The imagery provides complete coverage for 17 western U.S. states: Arizona, California, Colorado, Idaho, Kansas, Montana, North Dakota, Nebraska, New Mexico, Nevada, Oklahoma, Oregon, South Dakota, Texas, Utah, Washington, and Wyoming. Explore the dataset visually through our easy-to-use web map application at LandscapeExplorer.org.
Find alternative methods of downloading and utilizing the imagery on our data download page.
We preprocessed the imagery in MATLAB to reduce image vignetting and improve image contrast. Orthorectification was performed in Metashape. The compiled imagery had varying Ground Sampling Distance values, ranging from 0.6 to 1.7 meters. The GEE dataset was written out at 1 meter GSD. Dive deeper into our data processing methods at our LandscapeExplorer.org development page.
"},{"location":"projects/historical_us/#additional-preprocessing","title":"Additional Preprocessing","text":"The feature collection had Dates in different formats everything from simply
Format YYYY YYYY-MM/dd YYYYs YYYY-MM-dd
An approach was taken to convert and standardize these dates and add the corresponding dates as epoch system:time_start to features in the overall feature collection. This was then merged back into a feature collection with additional properties system:time_start, year, standardized_date. Based on the year metadata you can now get counts across multiple time periods I am summarizing a 5 year range distribution
Year Range Data Total 1935-1939 2175 1940-1944 4380 1945-1949 31176 1950-1954 29966 1955-1959 25657 1960-1964 12347 1965-1969 12360 1970-1974 19623 1975-1979 14290 1980-1984 675 1985-1989 67
"},{"location":"projects/historical_us/#citation","title":"Citation","text":"Morford, S.L., Allred, B.W., Jensen, E.R., Maestas, J.D., Mueller, K.R., Pacholski, C.L., Smith, J.T., Tack, J.D., Tackett, K.N. and Naugle, D.E.\n(2023), Mapping tree cover expansion in Montana, U.S.A. rangelands using high-resolution historical aerial imagery. Remote Sens Ecol Conserv.\n[https://doi.org/10.1002/rse2.357]( https://doi.org/10.1002/rse2.357)\nvar conusWest_imagery = ee.ImageCollection(\"projects/wlfw-um/assets/historical-imagery/conus-west\");\nvar conusWest_metadata = ee.FeatureCollection(\"projects/wlfw-um/assets/historical-imagery/conus-west-seamlines\");\nvar conusWest_metadata_with_date = ee.FeatureCollection(\"projects/sat-io/open-datasets/wlfm-um-extra/wlfm-um-seamlines\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/USGS-HISTORICAL-AERIAL-IMAGERY
Sample app: https://sat-io.earthengine.app/view/landscape-explorer
"},{"location":"projects/historical_us/#license","title":"License","text":"These datasets are available under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Keywords: Aerial imagery, United States, Great Plains, Great Basin, Historical, photogrammetry
Provided by: University of Montana, Lands for Wildlife, Montana NRCS and Intermountain West Joint Venture
Curated in GEE by: University of Montana, Lands for Wildlife, Montana NRCS and Intermountain West Joint Venture
Last updated: 2023-09-24
"},{"location":"projects/hitisae/","title":"High-spatial-resolution Thermal-stress Indices over South and East Asia (HiTiSAE)","text":"This newly developed dataset is a high-spatial-resolution (0.1\u00b0\u00d70.1\u00b0) gridded product that contains the daily values of the indoor, outdoor shaded and outdoor unshaded UTCI, the MRT and eight other widely adopted human thermal-stress indices (ESI, HI, Humidex, WBGT, WBT, WCT, AT, NET), derived from the newly available ECMWF ERA5-Land and ERA5 reanalysis products, over South and East Asia from Jan 3, 1981 to Dec 31, 2019. You can read the complete article here. This high-spatial-resolution database of human thermal stress indices over South and East Asia (HiTiSEA), which contains the daily mean, maximum, and minimum values of UTCI, MRT, and eight other widely adopted indices, is suitable for both indoor and outdoor applications and allows researchers and practitioners to investigate the spatial and temporal evolution of human thermal stress and its impacts on densely populated regions over South and East Asia at a fner scale. The dataset is available for download via a Figshare collection which can be found here
"},{"location":"projects/hitisae/#paper-citation","title":"Paper Citation","text":"Yan, Yechao, Yangyang Xu, and Shuping Yue. \"A high-spatial-resolution dataset of human thermal stress indices over South and East Asia.\"\nScientific Data 8, no. 1 (2021): 1-14.\nYan, Yechao; Xu, Yangyang; Yue, Shuping (2021): A High-spatial-resolution Dataset of Human Thermal Stress Indices over South and East Asia.\nfigshare. Collection. https://doi.org/10.6084/m9.figshare.c.5196296\nThe dataset contains 14242 daily NetCDF files which are archived by month and compressed into tar.gz files with a total volume of 450 GB. The netcdf files for each subvariable was converted into Geotifs with Minimum, Mean and Maximum value for each parameter. To reduce the overall index size, a band order was constructed with b1, b2,b3 for each variable corresponding to Min, Mean and Maximum value for the same parameter.
For example HiTiSea_1981-01-03_AT contains 3 bands with b1 with AT_min, b2 as AT_mean and b3 as AT_max
Included indices, names and GEE Variable are included in the table below
Termal Indices Full Name of the Indices GEE Variable Variable Stats UTCI universal thermal climate index UTCI Min,Mean, Max indoor UTCI (UTCI2) UTCI for indoor environment UTCI2 Min,Mean, Max outdoor shaded(UTCI3) UTCI UTCI for outdoor shaded space UTCI3 Min,Mean, Max MRT mean radiant temperature MRT Min,Mean, Max ESI environment stress index ESI Min,Mean, Max HI heat index HI Min,Mean, Max Humidex humidity index Humidex Min,Mean, Max WBGT \u00a0wet-bulb globe temperature WBGT Min,Mean, Max WBT wet bulb temperature WBT Min,Mean, Max WCT wind chill temperature WCT Min,Mean, Max AT apparent temperature AT Min,Mean, Max NET net effective temperature NET Min,Mean, Max "},{"location":"projects/hitisae/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var AT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/AT\");\nvar ESI = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/ESI\");\nvar MRT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/MRT\");\nvar UTCI = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/UTCI\");\nvar UTCI2 = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/UTCI2\");\nvar UTCI3 = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/UTCI3\");\nvar HI = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/HI\");\nvar Humidex = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/Humidex\");\nvar WBGT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/WBGT\");\nvar WBT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/WBT\");\nvar WCT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/WCT\");\nvar NET = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/NET\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/HIGHRES-THERMAL-STRESS-INDICES
"},{"location":"projects/hitisae/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by: Yechao Yan; Yangyang Xu; Shuping Yue
Preprocessed and Curated in GEE by : Samapriya Roy
Keywords: thermal-stress indices, south and southeast asia, heat index, humidity index, wind chill, apparent temperature
Last updated: 2021-05-20
Last updated on GEE: 2021-11-08
"},{"location":"projects/hntl/","title":"Harmonized Global Night Time Lights (1992-2021)","text":"In this study, the authors generated an integrated and consistent NTL dataset at the global scale by harmonizing the inter-calibrated NTL observations from the DMSP data and the simulated DMSP-like NTL observations from the VIIRS data. The generated global DMSP NTL time-series data (1992\u20132018) show consistent temporal trends. This temporally extended DMSP NTL dataset provides valuable support for various studies related to human activities such as electricity consumption and urban extent dynamics. The dataset contains
Spatial resolution: 30 arc-seconds (~1km)
The authors suggest using pixels with DN values greater than 7.
You can read the paper here
You can download the datasets here
"},{"location":"projects/hntl/#data-citation","title":"Data Citation","text":"Li, Xuecao; Zhou, Yuyu; zhao, Min; Zhao, Xia (2020): Harmonization of DMSP and VIIRS nighttime\nlight data from 1992-2020 at the global scale. figshare. Dataset.\nhttps://doi.org/10.6084/m9.figshare.9828827.v5\nLi, Xuecao, Yuyu Zhou, Min Zhao, and Xia Zhao. \"A harmonized global nighttime light dataset 1992\u20132018.\" Scientific data 7, no. 1 (2020): 1-9.\nvar dmsp = ee.ImageCollection(\"projects/sat-io/open-datasets/Harmonized_NTL/dmsp\");\nvar viirs = ee.ImageCollection(\"projects/sat-io/open-datasets/Harmonized_NTL/viirs\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/HARMONIZED-GLOBAL-NTL
"},{"location":"projects/hntl/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Xuecao Li et al
Curated by: Samapriya Roy
Keywords: : DMSP/OLS data, VIIRS, nighttime light, calibration, consistent, global
"},{"location":"projects/hntl/#changelog","title":"Changelog","text":"Last updated: 2023-09-22
"},{"location":"projects/hrdem/","title":"Canada High Resolution Digital Elevation Model (HRDEM)","text":"NoteThis dataset is currently only available to those in the insiders program
The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps.
The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. You can find more information here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or providers of the dataset and their works.
"},{"location":"projects/hrdem/#preprocessing","title":"Preprocessing","text":"Most of the 2m resolution datasets are generated from ArcticDEM project and as such not ingested in this effort and only 1m resolution tiles were ingested. Since tile edges were not matched and datasets were from various sources and dates a simple approach was used for tiles with same names where the largest file size replaced any file of that name. This was a decision to help deconflict tiles with similar names and was done programatically.
"},{"location":"projects/hrdem/#citation","title":"Citation","text":"openCanada.ca; High Resolution Digital Elevation Model (HRDEM) - CanElevation Series : Last accessed date\nvar dsm = ee.ImageCollection(\"projects/sat-io/open-datasets/OPEN-CANADA/CAN_ELV/HRDEM_1M_DSM\");\nvar dtm = ee.ImageCollection(\"projects/sat-io/open-datasets/OPEN-CANADA/CAN_ELV/HRDEM_1M_DTM\");\nvar footprint = ee.FeatureCollection(\"projects/sat-io/open-datasets/OPEN-CANADA/CAN_ELV/dataset_footprints\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/OPEN-CANADA-HRDEM
"},{"location":"projects/hrdem/#license","title":"License","text":"This work is licensed under a Open Government Licence - Canada.
Created by: CanElevation Series, Gov of Canada
Curated in GEE by: Samapriya Roy
keywords: digital terrain model, digital surface model, bare-earth, terrain, remote sensing, lidar,dsm,dtm
Last updated in GEE: 2022-12-27
"},{"location":"projects/hrdpa/","title":"High Resolution Deterministic Precipitation Analysis (HRDPA)","text":"The High Resolution Deterministic Precipitation Analysis (HRDPA) is a best estimate of 6 and 24 hour precipitation amounts. This objective estimate integrates data from in situ precipitation gauge measurements, radar QPEs and a trial field generated by a numerical weather prediction system. CaPA produces four analyses of 6 hour amounts per day, valid at synoptic hours (00, 06, 12 and 18 UTC) and two 24 hour analyses valid at 06 and 12 UTC. HRDPA is provided by the Meterological Service of Canada (MSC), a part of Environment and Climate Change Canada (ECCC). The MSC provides weather forecasts and warnings 24 hours a day, 365 days a year. MSC also provides federal department, agencies and other levels of government with information to support emergency preparedness and response to events such as storms, floods, wildfires and other weather-related emergencies. The model is based on the Canadian Precipitation Analysis (CaPA) system. You can find additional information here and information about the dataset can also be found on climate engine org data page here.
"},{"location":"projects/hrdpa/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Canada Spatial resolution 2.5 km grid (1/24 deg) Temporal resolution Daily Time span 2018-03-01 to present Update frequency Updated daily with 1 day lag timeVariables
Variable Details Precipitation ('Precipitation') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/hrdpa/#citation","title":"Citation","text":"- [Canadian Precipitation Analysis (CaPA)](https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/capa_information_leaflet_20141118_en.pdf) Methodology system\n\n- Fortin, V., Roy, G., Stadnyk, T., Koenig, K., Gasset, N., & Mahidjiba, A. (2018). Ten years of science based on the Canadian precipitation analysis: A CaPA system overview and literature review. Atmosphere-Ocean, 56(3), 178-196.\n// Read in Image Collections and get single image\nvar hrdpa_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-hrdpa-daily')\nvar hrdpa_i = hrdpa_ic.first()\n\n// Print single image to see bands\nprint(hrdpa_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(hrdpa_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CE-HRDPA-DAILY
"},{"location":"projects/hrdpa/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: precipitation, Canada, near real-time, daily, climate
Dataset provided by: Environment and Climate Change Canada
Dataset curated in GEE by: Climate Engine Org
"},{"location":"projects/hrdps/","title":"High Resolution Deterministic Prediction System (HRDPS)","text":"The High Resolution Deterministic Prediction System (HRDPS) provides useful numerical simulations of temperature over large areas. Climate Engine is ingesting only the band containing temperature at 2m above ground level, but HRDPS also produces bands for precipitation, cloud cover, wind speed and direction, humidity, and others. These numerical simulations can be used for air quality modeling and forecasting, climate and wildfire modeling, and extreme weather forecasting. Users who will benefit most from using these new data are those for whom a detailed forecast of surface temperatures and winds is important. The 2.5 km forecasts could add much value especially during the change of seasons and in wintertime when rapid changes in temperature and winds cause phase transitions of precipitation (freezing rain to snow to rain for example). HRDPS is the high resolution counterpart to the RDPS dataset. You can additional information here and on the climate engine org dataset page.
"},{"location":"projects/hrdps/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Canada Spatial resolution 2.5 km grid (1/24 deg) Temporal resolution Daily Time span 2015-04-23 to present Update frequency Updated daily with 1 day lag timeVariables
Variable Details Mean temperature ('Tavg') - Units: Degrees Celsius - Scale factor: 1.0"},{"location":"projects/hrdps/#citation","title":"Citation","text":"Milbrandt, J. A., B\u00e9lair, S., Faucher, M., Vall\u00e9e, M., Carrera, M. L., & Glazer, A. (2016). The pan-Canadian high resolution (2.5 km) deterministic\nprediction system. Weather and Forecasting, 31(6), 1791-1816.\n// Read in Image Collection and get first image\nvar hrdps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-hrdps-daily')\nvar hrdps_i = hrdps_ic.first()\n\n// Print first image to see bands\nprint(hrdps_i)\n\n// Visualize temperature from first image\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(hrdps_i.select('Tavg'), {min: -10, max: 20, palette: temp_palette}, 'Tavg')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CE-HRDPS-DAILY
"},{"location":"projects/hrdps/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: climate, temperature, daily, Canada, near real-time
Dataset Provider: Environment and Climate Change Canada
Curated in GEE by: Climate Engine Org
"},{"location":"projects/hrsl/","title":"High Resolution Settlement Layer","text":"In partnership with the Center for International Earth Science Information Network (CIESIN) at Columbia University, Facebook used state-of-the-art computer vision techniques to identify buildings from publicly accessible mapping services to create the world's most accurate population datasets. You can read about their project here. These are the datasets available for download on the Humanitarian Data Exchange for nearly every country in the world:
To reference this data, please use the following citation:
Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL Copyright 2016 DigitalGlobe. Accessed DAY MONTH YEAR. Data shared under: Creative Commons Attribution International.\nYou can get methodology here:
https://dataforgood.fb.com/docs/methodology-high-resolution-population-density-maps-demographic-estimates/
and step by step download here
https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/
License: Creative Commons Attribution International
"},{"location":"projects/hrsl/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var HRSL = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrslpop\");\nvar HRSL_men = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_men\");\nvar HRSL_women = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_women\");\nvar HRSL_youth = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_youth\");\nvar HRSL_children_under_five = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_children_under_five\");\nvar HRSL_women_reproductive_age = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_women_reproductive_age\");\nvar HRSL_elderly_over_sixty = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_elderly_over_sixty\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/FACEBOOK-HRSL-30m
Extra Info: Medium Article here
Download Tool/Code snippets if any: hdxpop
Curated by: Samapriya Roy
Keywords: High Density Population, Population, Facebook
"},{"location":"projects/hrsl/#changelog","title":"Changelog","text":"Last updated: 2022-08-27
"},{"location":"projects/hwsd/","title":"Harmonized World Soil Database (HWSD) version 2.0","text":"The Harmonized World Soil Database version 2.0 (HWSD v2.0) is a unique global soil inventory providing information on the morphological, chemical and physical properties of soils at approximately 1 km resolution. Its main objective is to serve as a basis for prospective studies on agro-ecological zoning, food security and climate change.
This updated version (HWSD v2.0) is built on the previous versions of HWSD with several improvements on (i) the data source that now includes several national soil databases, (ii) an enhanced number of soil attributes available for seven soil depth layers, instead of two in HWSD v1.2, and (iii) a common soil reference for all soil units (FAO1990 and the World Reference Base for Soil Resources). This contributes to a further harmonization of the database.
You can download the files and tutorials for the datasets here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hwsd/#dataset-preprocessing","title":"Dataset preprocessing","text":"A multiband image containing all the attributes of the FAO Harmonized World Soil Database v2.0 Soil Mapping Units (SMUs). The dataset was processed with special thanks to William Ouellette.
The band order is the following:
HWSD2_ID (numeric)WISE30s_ID (string converted to numeric in ascending alphanumeric order)COVERAGE (numeric)SHARE (numeric) - unit: %WRB4 (string converted to numeric in ascending alphanumeric order)WRB_PHASES (string converted to numeric in ascending alphanumeric order)WRB2_CODE (numeric) -- WRB2 is skipped, as it is redundant with WRB2_CODEFAO90 (string converted to numeric in ascending alphanumeric order)KOPPEN (string converted to numeric in ascending alphanumeric order)TEXTURE_USDA (numeric)REF_BULK_DENSITY (numeric) - unit: g/cm\u00b3BULK_DENSITY (numeric) - unit: g/cm\u00b3DRAINAGE (numeric)ROOT_DEPTH (numeric)AWC (numeric) - unit: mm/mPHASE1 (numeric)PHASE2 (numeric)ROOTS (numeric)IL (numeric)ADD_PROP (numeric)FAO & IIASA. 2023. Harmonized World Soil Database version 2.0. Rome and Laxenburg. https://doi.org/10.4060/cc3823en\nvar hwsd2 = ee.Image(\"projects/sat-io/open-datasets/FAO/HWSD_V2_SMU\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/HWSD-V2-SMU
"},{"location":"projects/hwsd/#license","title":"License","text":"This dataset is made available under Attribution-NonCommercial-ShareAlike 3.0 International (CC BY-NC-SA 3.0)
Created by: FAO & IIASA
Curated in GEE by: William Ouellette & Samapriya Roy
Keywords: Soil, World Soil properties, Texture, USDA, FAO, IIASA
Last updated in GEE: 23/03/2023
"},{"location":"projects/hydra_water/","title":"Tensor Flow Hydra Flood Models","text":"This dataset is a surface water output image from the Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods) system utilizing a Deep Learning TensorFlow approach. Specifically, this Joint Research Centre (JRC) Adjusted Learning Rate Binary Cross-Entropy (BCE) Dice model and methodology are discussed in detail in the recent Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine publication.
"},{"location":"projects/hydra_water/#citation","title":"Citation","text":"Mayer, T., Poortinga, A., Bhandari, B., Nicolau, A.P., Markert, K., Thwal, N.S., Markert, A., Haag, A., Kilbride, J., Chishtie, F. and Wadhwa, A.,\n2021. Deep Learning approach for Sentinel-1 Surface Water Mapping leveraging Google Earth Engine. ISPRS Open Journal of Photogrammetry and Remote\nSensing, p.100005.\nFor greater detail on the HYDRAFloods open-source Python application for downloading, processing, and delivering surface water maps derived from remote sensing data. Please see the HYDRAFloods Documentation.
"},{"location":"projects/hydra_water/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var HYDRAFloods = ee.Image(\"users/tjm0042/Hydrafloods_Outputs/TensorFlow_Surface_Water_Model_Mosaic\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/TENSORFLOW-HYDRA-FLOOD-MODELS
"},{"location":"projects/hydra_water/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Tim Mayer, Kel Markert, Biplov Bhandari, Ate Poortinga
Keywords: Surface Water Mapping, Floods, Deep Learning TensorFlow, SERVIR
Last updated: 2021-10-20
"},{"location":"projects/hydro90/","title":"Hydrography 90m Layers","text":"The Hydrography 90 layers uses the MERIT Hydro digital elevation model at 3 arcsec (\u223c 90 m at the Equator) to derive a globally seamless, standardised hydrographic network, the \"Hydrography90m\", with corresponding stream topographic and topological information. A central feature of the network is the minimal upstream contributing area, i.e. flow accumulation, of 0.05 km2 (or 5 ha) to initiate a stream channel, which allowed us to extract headwater stream channels in great detail.
The data validation procedures confirmed Hydrography90m as a more accurate representation of stream networks compared to HydroRIVERS, GRWL, and MERIT Hydro\u2013Vector. Improved accuracy was achieved principally by employing a higher resolution DEM, the MD8 flow routing algorithm, and a markedly smaller flow accumulation threshold to initiate stream channels. With these characteristics, Hydrography90m provides a valuable basis for supporting a variety of freshwater-related research disciplines. Find additional details in the paper here. The datasets can be downloaded here. This is one of the highest resolution global hydrography datasets and has multiple applications.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hydro90/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The hydrography datasets can downloaded here. The folders were renamed based on the descriptions of the papers and are included in the tables below. The parameter name were kept consistent and additional information is provided as need along with units for said parameters.
"},{"location":"projects/hydro90/#paper-citation","title":"Paper Citation","text":"Amatulli, Giuseppe, Jaime Garcia Marquez, Tushar Sethi, Jens Kiesel, Afroditi Grigoropoulou, Maria M. \u00dcblacker, Longzhu Q. Shen, and Sami Domisch.\n\"Hydrography90m: A new high-resolution global hydrographic dataset.\" Earth System Science Data 14, no. 10 (2022): 4525-4550.\nBase and network layers of Hydrography90m: flow accumulation, flow direction, drainage basins, outlets, stream segments, subcatchments, regional units, and depression
Output map description Unit GEE Collection Name Flow accumulation (raster) km2 accumulation=acc flow_accumulation Flow direction (raster) NE\u2013N\u2013NW\u2013W\u2013SW\u2013S\u2013SE\u2013E correspond to 1\u20132\u20133\u20134\u20135\u20136\u20137\u20138 flow__direction Drainage basin (raster) IDs from 1 to 1 676 628 drainage_basin Outlets (raster) ID=1 stream_vector=stream threshold=0.05; v.to.rast input=stream outlet Depression (raster) ID = 1 depression Stream segment (raster) IDs from 1 to 726 723 221 segment Sub-catchment (raster) IDs from basins=sub_catchment sub_catchment Regional unit (raster) IDs from 1 to 116 IDs from 150 to 200 regional_unit "},{"location":"projects/hydro90/#earth-engine-snippet-basin-network-layers","title":"Earth Engine snippet: basin-network-layers","text":"var flow_accumulation = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/flow_accumulation\");\nvar flow_direction = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/flow_direction\");\nvar depression = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/depression\");\nvar drainage_basin = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/drainage_basin\");\nvar outlet = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/outlet\");\nvar regional_unit = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/regional_unit\");\nvar segment = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/segment\");\nvar sub_catchment = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/sub_catchment\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-BASE-NETWORK
"},{"location":"projects/hydro90/#data-structure-stream-slope","title":"Data structure: stream-slope","text":"Curvature, gradient (elevation difference divided by distance), and elevation difference raster maps.
Output raster map description Unit GEE Collection Name Maximum curvature between highest upstream cell, focal cell, and downstream cell m^-1 (scale factor 10^6) slope_curv_max_dw_cel Minimum curvature between highest upstream cell, focal cell, and downstream cell m^-1 (scale factor 10^6) slope_curv_min_dw_cel Elevation difference between focal cell and downstream cell m slope_elv_dw_cel Focal cell gradient Unitless (scale factor 10^6) slope_grad_dw_cel "},{"location":"projects/hydro90/#earth-engine-snippet-stream-slope","title":"Earth Engine Snippet: stream-slope","text":"var slope_curv_max_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-slope/slope_curv_max_dw_cel\");\nvar slope_curv_min_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-slope/slope_curv_min_dw_cel\");\nvar slope_elv_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-slope/slope_elv_dw_cel\");\nvar slope_grad_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-slope/slope_grad_dw_cel\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-STREAM-SLOPE
"},{"location":"projects/hydro90/#data-structure-stream-outlet-distance","title":"Data structure: stream-outlet-distance","text":"Stream or outlet distance and elevation difference raster maps.
Output raster map description Unit GEE collection name Shortest upstream distance between focal grid cell and the nearest sub-catchment drainage divide m stream_dist_up_near Longest upstream distance between focal grid cell and the nearest sub-catchment drainage divide m stream_dist_up_farth Distance between focal grid cell and its nearest downstream stream grid cell m stream_dist_dw_near Distance between focal grid cell and the outlet grid cell in the network m outlet_dist_dw_basin Distance between focal grid cell and the downstream stream node grid cell m outlet_dist_dw_scatch Euclidean distance between focal grid cell and the stream network m stream_dist_proximity Elevation difference of the shortest path from focal grid cell to the sub-catchment drainage divide m stream_diff_up_near Elevation difference of the longest path from focal grid cell to the sub-catchment drainage divide m stream_diff_up_farth Elevation difference between focal grid cell and its nearest downstream stream pixel m stream_diff_dw_near Elevation difference between focal grid cell and the outlet grid cell in the network m outlet_diff_dw_basin Elevation difference between focal grid cell and the downstream stream node grid cell m outlet_diff_dw_scatch "},{"location":"projects/hydro90/#earth-engine-snippet-stream-outlet-distance","title":"Earth Engine snippet: stream-outlet-distance","text":"var outlet_diff_dw_basin = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/outlet_diff_dw_basin\");\nvar outlet_diff_dw_scatch = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/outlet_diff_dw_scatch\");\nvar outlet_dist_dw_basin = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/outlet_dist_dw_basin\");\nvar outlet_dist_dw_scatch = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/outlet_dist_dw_scatch\");\nvar stream_diff_dw_near = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_diff_dw_near\");\nvar stream_diff_dw_far = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_diff_up_farth\");\nvar stream_diff_up_near = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_diff_up_near\");\nvar stream_dist_dw_near = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_dist_dw_near\");\nvar stream_dist_proximity = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_dist_proximity\");\nvar stream_dist_up_farth = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_dist_up_farth\");\nvar stream_dist_up_near = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_dist_up_near\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-STREAM-OUTLET-DIST
"},{"location":"projects/hydro90/#data-structure-flow-index","title":"Data structure: flow-index","text":"The compound topographic index (cti), stream power index (spi), and stream transportation index (sti) derived from flow accumu- lation (\u03b1) and terrain slope (\u03b2)
Output raster map description Unit GEE collection name Stream power index (spi) Unitless (scale factor 10^3) spi Stream transportation index (sti) Unitless (scale factor 10^3) sti Compound topographic index (cti) Unitless (scale factor 10^8) cti "},{"location":"projects/hydro90/#earth-engine-snippet-flow-index","title":"Earth Engine Snippet: flow-index","text":"var cti = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/flow_index/cti\");\nvar spi = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/flow_index/spi\");\nvar sti = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/flow_index/sti\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-FLOW-INDEX
"},{"location":"projects/hydro90/#data-structurestream-order","title":"Data structure:stream-order","text":"Stream order rasters
Output map description GEE collection name Strahler\u2019s stream order (raster) order_strahler Shreve\u2019s stream magnitude (raster) order_shreve Horton\u2019s stream order (raster) order_horton Hack\u2019s stream order (raster) order_hack Topological dimension of streams (raster) order_topo "},{"location":"projects/hydro90/#earth-engine-snippet-stream-order","title":"Earth Engine snippet: stream-order","text":"var order_hack = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_hack\");\nvar order_horton = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_horton\");\nvar order_shreve = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_shreve\");\nvar order_strahler = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_strahler\");\nvar order_topo = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_topo\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-STREAM-ORDER
"},{"location":"projects/hydro90/#data-structurecurvature-gradient","title":"Data structure:curvature-gradient","text":"Curvature, gradient (elevation difference divided by distance), and elevation change raster maps.
Output raster map description Unit GEE collection name Segment downstream mean gradient between focal cell and the node or outlet Unitless (scale factor 10^6) channel_grad_dw_seg Segment upstream mean gradient between focal cell and the init or node Unitless (scale factor 10^6) channel_grad_up_seg Upstream gradient between focal cell and the next cell Unitless (scale factor 10^6) channel_grad_up_cel Cell stream course curvature of the focal cell m^-1 (scale factor 10^6) channel_curv_cel Segment downstream elevation difference between focal cell and the node or outlet m channel_elv_dw_seg Segment upstream elevation difference between focal cell and the init or node m channel_elv_up_seg Upstream elevation difference between focal cell and the next cell m (outlet cell value = 99 999) channel_elv_up_cel Downstream elevation difference between focal cell and the next cell m channel_elv_dw_cel Segment downstream distance between focal cell and the node or outlet m channel_dist_dw_seg Segment upstream distance between focal cell and the init or node m channel_dist_up_seg Upstream distance between focal cell and next cell m channel_dist_up_cel "},{"location":"projects/hydro90/#earth-engine-snippet-curvature-gradient","title":"Earth Engine Snippet: curvature-gradient","text":"var channel_curv_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_curv_cel\");\nvar channel_dist_dw_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_dist_dw_seg\");\nvar channel_dist_up_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_dist_up_cel\");\nvar channel_dist_up_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_dist_up_seg\");\nvar channel_elv_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_elv_dw_cel\");\nvar channel_elv_dw_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_elv_dw_seg\");\nvar channel_elv_up_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_elv_up_cel\");\nvar channel_elv_up_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_elv_up_seg\");\nvar channel_grad_dw_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_grad_dw_seg\");\nvar channel_grad_up_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_grad_up_cel\");\nvar channel_grad_up_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_grad_up_seg\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-STREAM-CHANNEL
"},{"location":"projects/hydro90/#license","title":"License","text":"The dataset is licensed under a Creative Commons \"CC BY-NC 4.0\" license.
Created by: Amatulli, Giuseppe, Jaime Garcia Marquez, Tushar Sethi, Jens Kiesel, Afroditi Grigoropoulou, Maria M. \u00dcblacker, Longzhu Q. Shen, and Sami Domisch
Curated by: Samapriya Roy
Keywords: Hydrology, Hydrography, Slope, Channel, Stream, Runoff, Flow accumulation, Flow direction
Last updated in GEE: 2022-11-14
"},{"location":"projects/hydroatlas/","title":"HydroATLAS v1.0","text":"HydroATLAS offers a global compendium of hydro-environmental characteristics for all sub-basins of HydroBASINS, all river reaches of HydroRIVERS, and all lake polygons of HydroLAKES. The HydroATLAS database is divided into three distinct sub-datasets: BasinATLAS, RiverATLAS, and LakeATLAS which represent sub-basin delineations (polygons), the river network (lines), and lake shorelines (polygons), respectively. In total, HydroATLAS contains 1.0 million sub-basins, 8.5 million river reaches, and 1.4 million lakes.
HydroATLAS has been created by compiling and re-formatting a wide range of hydro-environmental attributes derived from existing global datasets in a consistent and organized manner. The resulting data compendium offers attributes grouped in seven categories: hydrology; physiography; climate; land cover & use; soils & geology; and anthropogenic influences. For each of the three sub-datasets, HydroATLAS contains 56 hydro-environmental variables, partitioned into 281 individual attributes. You can download the files here
The HydroATLAS database is distributed in large file sizes due to the enriched attribute information. Users who only need geometric information and digital vector maps of sub-basin boundaries, river network lines, and lake shorelines may prefer to download the HydroBASINS, HydroRIVERS, or HydroLAKES products instead.
"},{"location":"projects/hydroatlas/#technical-documentation","title":"Technical Documentation","text":"For more information on HydroATLAS please refer to hydrosheds page on hydroatlas and the HydroATLAS Technical Documentation.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hydroatlas/#citations","title":"Citations","text":"The development of BasinATLAS and RiverATLAS is fully described in Linke et al. (2019) and should be cited as:
Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V.,\nMaxwell, S., Moidu, H., Tan, F., Thieme, M. (2019). Global hydro-environmental sub-basin and river reach\ncharacteristics at high spatial resolution. Scientific Data 6: 283. doi: https://doi.org/10.1038/s41597-019-0300-6\nLehner, B., Messager, M.L., Korver, M.C., Linke, S. (2022). Global hydro-environmental lake characteristics at\nhigh spatial resolution. Scientific Data 9: 351. doi: https://doi.org/10.1038/s41597-022-01425-z\nThe HydroATLAS database is licensed under a Creative Commons Attribution (CC-BY) 4.0 International License. Please also refer to the HydroATLAS Technical Documentation for more details on the license and requested citations. By downloading and using the data the user agrees to the terms and conditions of this license.
ou can read the paper here : https://www.nature.com/articles/ncomms13603?origin=ppub
"},{"location":"projects/hydroatlas/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var riveratlas = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/RiverAtlas_v10\");\nvar lakeatlas_pt = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/LakeAtlas/LakeAtlas_v10_point\");\nvar lakeatlas_poly = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/LakeAtlas/LakeAtlas_v10_polygon\");\nvar basin_l01 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev01\");\nvar basin_l02 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev02\");\nvar basin_l03 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev03\");\nvar basin_l04 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev04\");\nvar basin_l05 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev05\");\nvar basin_l06 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev06\");\nvar basin_l07 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev07\");\nvar basin_l08 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev08\");\nvar basin_l09 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev09\");\nvar basin_l10 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev10\");\nvar basin_l11 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev11\");\nvar basin_l12 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev12\");\nCreated by: Linke et al and Lehner et al
Curated by: Samapriya Roy
Keywords: water,hydrology, lakes, global lake surface, discharge, depth, volume, area, hydrolakes, hydrobasins, hydrorivers
Last updated: 2022-07-10
"},{"location":"projects/hydrolakes/","title":"HydroLAKES v1.0","text":"Lakes are key components of biogeochemical and ecological processes, thus knowledge about their distribution, volume and residence time is crucial in understanding their properties and interactions within the Earth system. However, global information is scarce and inconsistent across spatial scales and regions. Here we develop a geo-statistical model to estimate the volume of global lakes with a surface area of at least 10\u2009ha based on the surrounding terrain information.
The HydroLAKES database was designed as a digital map repository to include all lakes with a surface area of at least 10 ha with a total surface area of 2.67 \u00d7 106\u2009km2 (1.8% of global land area), a total shoreline length of 7.2 \u00d7 106\u2009km (about four times longer than the world\u2019s ocean coastline) and a total volume of 181.9 \u00d7 103\u2009km3 (0.8% of total global non-frozen terrestrial water stocks). HydroLAKES aims to be as comprehensive and consistent as possible at a global scale and contains both freshwater and saline lakes, including the Caspian Sea, as well as human-made reservoirs and regulated lakes.
HydroLAKES is publicly available for download at http://www.hydrosheds.org and is free for scientific, educational, and other uses.
"},{"location":"projects/hydrolakes/#datasets-used-for-creation-of-hydrolakes","title":"Datasets used for creation of HydroLAKES","text":"Original dataset Region Original format and resolution Reference Number of lakes Canadian hydrographic dataset (CanVec) Canada (entire country) Vector; 1:50,000 Natural Resources Canada (2013) 863550 Shuttle Radar Topographic Mission (SRTM) Water Body Data (SWBD) 56\u00b0 South to 60\u00b0 North Raster; 1 arc-second (~30 m at the equator); vectorized and smoothed Slater et al. (2006) 282571 MODerate resolution Imaging Spectro-radiometer (MODIS) MOD44W water mask Russia above 60\u00b0 North Raster; 250 m; vectorized and smoothed Carroll et al. (2009) 167435 US National Hydrography Dataset (NHD) Alaska (entire state) Vector; 1:24:000 U.S. Geological Survey (2013) 58496 European Catchments and Rivers Network System (ECRINS) Europe above 60\u00b0 North and entire Norway Vector; varying resolutions (~1:250,000) European Environment Agency (2012) 50699 Global Lakes and Wetlands Database (GLWD) World Vector; 1:1 million Lehner and D\u00f6ll (2004) 3023 Global Reservoir and Dam database (GRanD) World Vector; varying resolutions (1:1 million or better) Lehner et al. (2011) 1133 Other (own mapping) World Vector; varying resolutions (1:1 million or better) n/a 781 Total 1427688"},{"location":"projects/hydrolakes/#paper-citation","title":"Paper Citation","text":"Messager, Mathis Lo\u00efc, Bernhard Lehner, G\u00fcnther Grill, Irena Nedeva, and Oliver Schmitt. \"Estimating the volume and\nage of water stored in global lakes using a geo-statistical approach.\"\nNature communications 7, no. 1 (2016): 1-11.\nYou can read the paper here : https://www.nature.com/articles/ncomms13603?origin=ppub
"},{"location":"projects/hydrolakes/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var lake_poly = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroLakes/lake_poly_v10\");\nvar lake_points = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroLakes/lake_points_v10\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROLAKES
"},{"location":"projects/hydrolakes/#attribute-table-of-hydrolakes-polygon-and-point-layers","title":"Attribute table of HydroLAKES polygon and point layers","text":"Property Description Hylak_id Unique lake identifier. Values range from 1 to 1,427,688. Lake_name Name of lake or reservoir. This field is currently only populated for lakes with an area of at least 500 km2; for large reservoirs where a name was available in the GRanD database; and for smaller lakes where a name was available in the GLWD database. Country Country that the lake (or reservoir) is located in. International or transboundary lakes are assigned to the country in which its corresponding lake pour point is located and may be arbitrary for pour points that fall on country boundaries. Continent Continent that the lake (or reservoir) is located in. Geographic continent: Africa, Asia, Europe, North America, South America, or Oceania (Australia and Pacific Islands) Poly_src Source of original lake polygon: CanVec; SWBD; MODIS; NHD; ECRINS; GLWD; GRanD; or Other More information on these data sources can be found in Table 1. Lake_type Indicator for lake type: 1: Lake 2: Reservoir 3: Lake control (i.e. natural lake with regulation structure) Note that the default value for all water bodies is 1, and only those water bodies explicitly identified as other types (mostly based on information from the GRanD database) have other values; hence the type \u2018Lake\u2019 also includes all unidentified smaller human-made reservoirs and regulated lakes. Grand_id ID of the corresponding reservoir in the GRanD database, or value 0 for no corresponding GRanD record. This field can be used to join additional attributes from the GRanD database. Lake_area Lake surface area (i.e. polygon area), in square kilometers. Shore_len Length of shoreline (i.e. polygon outline), in kilometers. Shore_dev Shoreline development, measured as the ratio between shoreline length and the circumference of a circle with the same area. A lake with the shape of a perfect circle has a shoreline development of 1, while higher values indicate increasing shoreline complexity. Vol_total Total lake or reservoir volume, in million cubic meters (1 mcm = 0.001 km3). For most polygons, this value represents the total lake volume as estimated using the geostatistical modeling approach by Messager et al. (2016). However, where either a reported lake volume (for lakes \u2265 500 km2) or a reported reservoir volume (from GRanD database) existed, the total volume represents this reported value. In cases of regulated lakes, the total volume represents the larger value between reported reservoir and modeled or reported lake volume. Column \u2018Vol_src\u2019 provides additional information regarding these distinctions. Vol_res Reported reservoir volume, or storage volume of added lake regulation, in million cubic meters (1 mcm = 0.001 km3). 0: no reservoir volume Vol_src 1: \u2018Vol_total\u2019 is the reported total lake volume from literature 2: \u2018Vol_total\u2019 is the reported total reservoir volume from GRanD or literature 3: \u2018Vol_total\u2019 is the estimated total lake volume using the geostatistical modeling approach by Messager et al. (2016) Depth_avg Average lake depth, in meters. Average lake depth is defined as the ratio between total lake volume (\u2018Vol_total\u2019) and lake area (\u2018Lake_area\u2019). Dis_avg Average long-term discharge flowing through the lake, in cubic meters per second. This value is derived from modeled runoff and discharge estimates provided by the global hydrological model WaterGAP, downscaled to the 15 arc-second resolution of HydroSHEDS (see section 2.2 for more details) and is extracted at the location of the lake pour point. Note that these model estimates contain considerable uncertainty, in particular for very low flows. -9999: no data as lake pour point is not on HydroSHEDS landmask Res_time Average residence time of the lake water, in days. The average residence time is calculated as the ratio between total lake volume (\u2018Vol_total\u2019) and average long-term discharge (\u2018Dis_avg\u2019). Values below 0.1 are rounded up to 0.1 as shorter residence times seem implausible (and likely indicate model errors). -1: cannot be calculated as \u2018Dis_avg\u2019 is 0 -9999: no data as lake pour point is not on HydroSHEDS landmask Elevation Elevation of lake surface, in meters above sea level. This value was primarily derived from the EarthEnv-DEM90 digital elevation model at 90 m pixel resolution by calculating the majority pixel elevation found within the lake boundaries. To remove some artefacts inherent in this DEM for northern latitudes, all lake values that showed negative elevation for the area north of 60\u00b0N were substituted with results using the coarser GTOPO30 DEM of USGS at 1 km pixel resolution, which ensures land surfaces \u22650 in this region. Note that due to the remaining uncertainties in the EarthEnv-DEM90 some small negative values occur along the global ocean coastline south of 60\u00b0N which may or may not be correct. Slope_100 Average slope within a 100 meter buffer around the lake polygon, in degrees. This value is derived from the EarthEnv-DEM90 digital elevation model at 90 m pixel resolution. Slopes for each pixel were computed with latitudinal corrections for the distortion in the XY spacing of geographic coordinates by approximating the geodesic distance between cell centers. For 12 lakes located above the northern limit of the EarthEnv-DEM90 digital elevation model (83\u00b0N), slopes were computed from the GTOPO30 DEM of USGS at 1 km pixel resolution. -1: slope values were not calculated for the largest lakes (polygon area \u2265 500 km2) Wshd_area Area of the watershed associated with the lake, in square kilometers. The watershed area is calculated by deriving and measuring the upstream contribution area to the lake pour point using the HydroSHEDS drainage network map at 15 arc-second resolution. -9999: no data as lake pour point is not on HydroSHEDS landmask Pour_long Longitude of the lake pour point, in decimal degrees. Pour_lat Latitude of the lake pour point, in decimal degrees."},{"location":"projects/hydrolakes/#license","title":"License","text":"The data is licensed under a Creative Commons Attribution 4.0 International License (see section 4). By downloading and using the data the user agrees to the terms and conditions of this license.
Created by: Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O
Curated by: Samapriya Roy
Keywords: water,hydrology, lakes, global lake surface, discharge, depth, volume, area, hydrolakes
Last updated: 2021-09-05
"},{"location":"projects/hydrowaste/","title":"HydroWASTE v1.0","text":"HydroWASTE is a spatially explicit global database of 58,502 wastewater treatment plants (WWTPs) and their characteristics. This database was developed by combining national and regional datasets with auxiliary information to derive or complete missing characteristics, including the amount of people served, the flow rate of effluents, and the level of treatment of processed wastewater. The HydroSHEDS river network with streamflow estimates was used to geo-reference plant outfall locations and to assess the distribution of wastewaters at a global scale. All wastewater treatment plants are co-registered to the global river network of the HydroRIVERS database via their estimated outfall locations. You can find the datasets page here
For more information on HydroATLAS please refer to hydrosheds page on hydroatlas and technical information is included in the paper
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hydrowaste/#citations","title":"Citations","text":"The development of BasinATLAS and RiverATLAS is fully described in Linke et al. (2019) and should be cited as:
Ehalt Macedo, H., Lehner, B., Nicell, J., Grill, G., Li, J., Limtong, A., Shakya, R. (2022). Distribution and characteristics of wastewater\ntreatment plants within the global river network. Earth System Science Data, 14(2): 559\u2013577. https://doi.org/10.5194/essd-14-559-2022\nvar hydrowaste = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroWaste/HydroWASTE_v10\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROWASTE
"},{"location":"projects/hydrowaste/#license","title":"License","text":"The data is licensed under a Creative Commons Attribution 4.0 International License (see section 4). By downloading and using the data the user agrees to the terms and conditions of this license.
Created by: Ehalt Macedo, H., Lehner, B., Nicell, J., Grill, G., Li, J., Limtong, A., Shakya, R.
Curated in GEE by: Samapriya Roy
Keywords: water,hydrology, rivers, discharge, depth, volume, area, hydrowaste, wastewater
Last updated: 2022-07-09
"},{"location":"projects/hyspecnet/","title":"HySpecNet-11K Hyperspectral Benchmark dataset","text":"The HySpecNet-11k dataset is a large-scale hyperspectral benchmark dataset constructed by the Remote Sensing Image Analysis (RSiM) group at TU Berlin and the Big Data Analytics in Earth Observation group at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). It consists of 11,483 nonoverlapping image patches acquired by the EnMAP satellite, with each patch being a portion of 128 \u00d7 128 pixels and containing 224 spectral bands. These patches have a ground sample distance of 30 m. The dataset was constructed using a total of 250 EnMAP tiles acquired between 2 November 2022 and 9 November 2022, during the routine operation phase. Only tiles with less than 10% cloud and snow cover were considered. These tiles underwent radiometric, geometric, and atmospheric corrections to generate the L2A water & land product. Subsequently, the tiles were divided into nonoverlapping image patches, eliminating the cropped patches at the tile borders. This process resulted in more than 45 patches per tile, totaling 11,483 patches for the complete HySpecNet-11k dataset. You can read details in the paper here and find information on the dataset and more here.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hyspecnet/#dataset-preprocessing","title":"Dataset preprocessing","text":"The datset was split into multiple patch files including the Spectral images and the quality files. Single band quality files were selected and added as bands to the original spectral file with 224 bands. As suggested by the authors 22 bands were invalid and an example cope is included to remove the invalid bands. The single bands QL files and their values are included below. A custom manifest was used to achieve the band names of choice and to make sure the pyramiding schema for QL and spectral bands were Mode and Mean accordingly.
Quality Layer 0 1 2 3 QL_QUALITY_CIRRUS.TIF None Thin Medium Thick QL_QUALITY_CLASSES.TIF None Land Water Background QL_QUALITY_CLOUD.TIF None Cloud QL_QUALITY_CLOUDSHADOW.TIF None Cloud Shadow QL_QUALITY_HAZE.TIF None Haze QL_QUALITY_SNOW.TIF None Snow"},{"location":"projects/hyspecnet/#citation","title":"Citation","text":"Fuchs, Martin Hermann Paul, and Beg\u00fcm Demir. \"HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image\nCompression Methods.\" arXiv preprint arXiv:2306.00385 (2023).\nvar hyspecnet = ee.ImageCollection(\"projects/sat-io/open-datasets/HySpecNet/HYSPECNET-11K\");\nprint(hyspecnet.size())\n\n//Remove invalid bands\nvar invalid_bands = ['B126', 'B127', 'B128', 'B129', 'B130', 'B131', 'B132', 'B133', 'B134', 'B135', 'B136', 'B137', 'B138', 'B139', 'B140', 'B160', 'B161', 'B162', 'B163', 'B164', 'B165', 'B166']\n\n//Select an image\nvar image = hyspecnet.sort('system:time_start',false).first()\nimage = image.select(image.bandNames().removeAll(invalid_bands))\nprint('Resolution',image.select(['B1']).projection().nominalScale())\nprint('Band Names',image.bandNames())\n\n//Add image as layer\nMap.centerObject(image,12)\nMap.addLayer(image,{\"opacity\":1,\"bands\":[\"B3\",\"B2\",\"B1\"],\"min\":-154,\"max\":934,\"gamma\":1},'Sample HYSPECNET Image Chip')\nSample code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/HYSPECNET-11K
"},{"location":"projects/hyspecnet/#license","title":"License","text":"This dataset is available under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Curated by: Fuchs, Martin Hermann Paul, and Beg\u00fcm Demir
Keywords: Hyperspectral, Enmap, Benchmark, Tile
Last updated: June 29, 2023
"},{"location":"projects/iceland_dem/","title":"\u00cdslandsDEM v1.0 10m","text":"Since 2015, elevation data from the Arctic (north of 60\u00b0N, including Iceland) started to be openly available through the ArcticDEM project, led by the Polar Geospatial Center, University of Minnesota (https://www.pgc.umn.edu/data/arcticdem/).
This data consists of a large amount of Digital Elevation Models (DEMs) repeatedly acquired (multitemporal), typically from 2012-present, and the oldest data reaching back to 2008. The DEMs are derived from satellite sub-meter stereo imagery, particularly from WorldView 1-3 and GeoEye-1. The processing of the DEMs was done using SETSM, an open-source digital photogrammetric software, in the Bluewaters supercomputer (University of Ilinois). Each DEM has 2x2m resolution and a footprint of ~18x100km.
In a collaborative effort between the National Land Survey of Iceland, the Icelandic Meteorological Office and the Polar Geospatial Center, we developed methods to handle and process a large amount of data available for Iceland. The methods developed consisted of
Spatial adjustment of all the available DEMs, for homogeneity and consistency in the location of each individual DEM.
Robust mosaicking into one single DEM of Iceland, by taking advantage of the multi-temporal coverage of DEMs. Each pixel of the mosaic corresponds to a median elevation value from the possible elevations available from the ArcticDEM. More details on the dataset available here. This DEM is resampled for 10x10m resolution.
var DEM_10m_isn93 = ee.Image(\"projects/ee-landmaelingar/assets/IslandsDEMv1_10m_isn93\")\nProjection used: EPSG 3057 (ISN93/Lambert 1993)
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/ICELAND-DEM-10m
"},{"location":"projects/iceland_dem/#license","title":"License","text":"The datasets are distributed under a Attribution 4.0 International (CC BY 4.0) license.
Produced by : National Land Survey of Iceland & PGC
Curated in GEE by : National Land Survey of Iceland
Keywords: : Elevation, DEM, ArticDEM, Iceland, Geophysical
Last updated on GEE: 2022-03-29
"},{"location":"projects/india_river_trends/","title":"Temporal trends of Surface water across Indian Rivers & Basins","text":"This dataset quantifies the extent and rate of annual change in surface water area (SWA) across India's rivers and basins over 30 years from 1991 to 2020. It does so by season (annual dry, wet and permanent water, based on India's seasons) and at two spatial scales: the river basin scale (1516 level-7 basins from HydroBASINS) and the finer river reach scale (68,367 reaches). This dataset is derived from the historical time series of monthly surface water occurrence by JRC's Global Surface Water Explorer. You can read additional details about the dataset in the paper and access the dataset here.
The authors have also provided a dataset page and an earth engine app to analyze the dataset further.
These are available as the following GEE assets
Annual rate of change of surface water area, by season
projects/sat-io/open-datasets/indian_rivers/riverchanges/txsTrendsprojects/sat-io/open-datasets/indian_rivers/riverchanges/basinsTrendsAttribute Description HYBAS_ID or txId Feature's unique identifier.
Time series of annual surface water area, by season
projects/sat-io/open-datasets/indian_rivers/riverchanges/mainlandIndia_areasTs_txsprojects/sat-io/open-datasets/indian_rivers/riverchanges/mainlandIndia_areasTs_basinsL7Attribute Description HYBAS_ID or txId Feature's unique identifier. - HYBAS_ID is for basins. It is the basin's identifier HYBAS_ID in the HydroBASINS dataset. - txId is for transects. It is the '' concatenated string derived from the longitude and latitude values, truncated to 4 decimals, of the transect's median point. Specifically, it is \"_xx.xxxx_yy.yyyy\" where xx.xxxx and yy.yyyy are the median's longitude and latitude values truncated to 4 decimals. season Denotes the season, in \"sss_mmm\" format. - \"sss\" denotes the season: \"dry\" for dry, \"wet\" for wet, and \"prm\" for permanent. - \"mmm\" denotes the span of the season in calendar months: \"fma\" is for the dry season of February-March-April, \"ond\" is for the wet (post-monsoon) season of October-November-December, and \"DnW\" is for permanent which is dry AND wet season. year Year. water_ha Area of water pixels in the feature, in hectares. notwater_ha Area of notwater pixels in the feature, in hectares. nodata_ha Area of nodata pixels in the feature, in hectares. nodataFrac Proportion of the feature's area with nodata pixels. system:index GEE system-generated unique identifier.
projects/sat-io/open-datasets/indian_rivers/riverchanges/waterOccSeasComps Bands Description drySeasCompos_fma Each pixel in these bands have one of 3 integer values (following the convention in the JRC water dataset, Pekel et al. 2016) wetSeasCompos_ond * 2: a pixel with valid data and containing water (denoting a \"water\" pixel) prmSeasCompos_DnW * 1: a pixel with valid data and not containing water (denoting a \"notwater\" pixel) * 0: a pixel with no valid data (denoting a \"nodata\" pixel) Expand to show attributes for Time series of annual surface water image collection
Properties Description year year of the image. monsoonYearStartMonth Number (between 1-12) of the month when monsoon (or, hydrological) year starts. It is 6, indicating June, and is the same for all images. A year is taken to be June to May in this analysis. drySeasMonthsOffset Number of months after monsoonYearStartMonth when dry season starts. It is 8, indicating February. drySeasMonthsTag Suffix tag, in names of image bands, table columns, etc., indicating the 3 months of the dry season. wetSeasMonthsOffset Number of months after monsoonYearStartMonth when wet season starts. It is 4, indicating October. wetSeasMonthsTag Suffix tag, in names of image bands, table columns, etc., indicating the 3 months of the wet season.
More details and resources:
Published data repository (excluding the time series of annual surface water occurrence) https://doi.org/10.5281/zenodo.7803903 Published Earth Engine code behind the analysis https://doi.org/10.5281/zenodo.7839588 Published data description https://doi.org/10.1016/j.dib.2023.109991 Interactive visualization, and more https://sites.google.com/view/surface-water-trends-india/"},{"location":"projects/india_river_trends/#citation","title":"Citation","text":"Koulgi P, Jumani S. Dataset of temporal trends of surface water area across India's rivers and basins. Data Brief. 2023 Dec 19;52:109991.\ndoi: 10.1016/j.dib.2023.109991. PMID: 38235174; PMCID: PMC10792741.\nvar reachTrends = ee.FeatureCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/txsTrends');\nvar reachAreaTimeseries = ee.FeatureCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/mainlandIndia_areasTs_txs');\nvar basTrends = ee.FeatureCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/basinsTrends');\nvar basAreaTimeseries = ee.FeatureCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/mainlandIndia_areasTs_basinsL7');\nvar annualWaterOccSeasComps = ee.ImageCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/waterOccSeasComps');\n\nvar brewer7ClPuOr = ['b35806', 'f1a340', 'fee0b6', 'f7f7f7', 'd8daeb', '998ec3', '542788'];\nvar empty = ee.Image().byte();\n\nvar reachTrendsDrySeason = reachTrends.filter(ee.Filter.eq('season', 'dry_fma'));\nvar fillsreach = empty.paint(reachTrendsDrySeason, 'sl_perYr');\nMap.addLayer(fillsreach, {palette: brewer7ClPuOr, min: -0.02, max: 0.02}, 'dry_fma_reach');\nMap.setCenter(79.49959, 16.63471, 14);\n\nvar basTrendDrySeason = basTrends.filter(ee.Filter.and(ee.Filter.eq('HYBAS_ID', 4071092530), ee.Filter.eq('season', 'dry_fma')));\nvar fillsBas = empty.paint(basTrendDrySeason, 'sl_perYr');\nMap.addLayer(fillsBas, {palette: brewer7ClPuOr, min: -75, max: 75}, 'dry_fma_bas', false);\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/TEMPORAL-TRENDS-INDIAN-RIVERS-BASINS
Earth Engine app: Access the Earth Engine app here and the data page here
"},{"location":"projects/india_river_trends/#license","title":"License","text":"These datasets are provided under a CC-BY-4.0 license.
Provided by: Koulgi and Jumani 2023
Curated in GEE by: Pradeep Koulgi and Samapriya Roy
Keywords : surface water, river reaches, river basins, time series,india
Last updated on GEE: 2024-02-16
"},{"location":"projects/irc/","title":"Irrecoverable carbon in Earth\u2019s ecosystems","text":"These datasets provide global maps of carbon density (aboveground, belowground biomass carbon and soil organic carbon stocks) for the year 2010 and 2018 at ~300-m spatial resolution in Mg ha-1 (Coordinate System: WGS 1984, float format). Input maps were collected from published literature, and where necessary, updated to cover the focal time period. These updates were applied to the manageable carbon, vulnerable carbon and irrecoverable carbon maps. Manageable carbon is carbon in terrestrial and coastal ecosystems that could experience an anthropogenic land-use conversion event . Vulnerable carbon is the carbon that would be that would be released in a typical land-use conversion. Irrecoverable carbon is the carbon that, if lost, would not recover by mid-century. Datasets are disaggregated for carbon density in biomass or soils. To view these datasets, go to: https://irrecoverable.resilienceatlas.org/map. You can read the open sourced paper here
"},{"location":"projects/irc/#preprocessing","title":"Preprocessing","text":"All datasets tif files were ingested in Google Earth Engine, Ecosystem layers were ingested after removing the no data value to avoid conflict with a -128 no data value. The ecosystem categorical layers were also ingested with a mode sampling as recommended by GEE.
"},{"location":"projects/irc/#paper-citation","title":"Paper Citation","text":"Noon, M.L., Goldstein, A., Ledezma, J.C. et al. Mapping the irrecoverable carbon in Earth\u2019s ecosystems.\nNat Sustain (2021). https://doi.org/10.1038/s41893-021-00803-6\nNoon, Monica, Goldstein, Allie, Ledezma, Juan Carlos, Roehrdanz, Patrick, Cook-Patton, Susan C., Spawn-Lee, Seth A., Wright, Timothy Maxwell,\nGonzalez-Roglich, Mariano, Hole, David G., Rockstr\u00f6m, Johan, & Turner, Will R. (2021). Mapping the irrecoverable carbon in Earth's ecosystems\n(1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4091029\nvar irrecoverable_carbon_total = ee.ImageCollection(\"projects/sat-io/open-datasets/irrecoverable_carbon/carbon_total\");\nvar irrecoverable_carbon_soil = ee.ImageCollection(\"projects/sat-io/open-datasets/irrecoverable_carbon/carbon_soil\");\nvar irrecoverable_carbon_biomass = ee.ImageCollection(\"projects/sat-io/open-datasets/irrecoverable_carbon/carbon_biomass\");\nvar vulnerable_carbon_total = ee.ImageCollection(\"projects/sat-io/open-datasets/vulnerable_carbon/carbon_total\");\nvar vulnerable_carbon_soil = ee.ImageCollection(\"projects/sat-io/open-datasets/vulnerable_carbon/carbon_soil\");\nvar vulnerable_carbon_biomass = ee.ImageCollection(\"projects/sat-io/open-datasets/vulnerable_carbon/carbon_biomass\");\nvar manageable_carbon_total = ee.ImageCollection(\"projects/sat-io/open-datasets/manageable_carbon/carbon_total\");\nvar manageable_carbon_soil = ee.ImageCollection(\"projects/sat-io/open-datasets/manageable_carbon/carbon_soil\");\nvar manageable_carbon_biomass = ee.ImageCollection(\"projects/sat-io/open-datasets/manageable_carbon/carbon_biomass\");\nvar ecosystem_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/ecosystem_extent\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GLOBAL-IRRECOVERABLE-CARBON
"},{"location":"projects/irc/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Noon et al
Curated by: Samapriya Roy
Keywords: : irrecoverable carbon, vulnerable carbon, manageable carbon, global map, ecosystem
Last updated: 2021-12-14
"},{"location":"projects/isccp_hxg/","title":"International Satellite Cloud Climatology Project: HXG Cloud Cover","text":"The data consists of one variable 'cloud' from the ISCCP HXG, dataset as retrieved from the NCEI in September of 2021. Quoting from the ISCCP website (isccp.giss.nasa.gov) :
The International Satellite Cloud Climatology Project (ISCCP) was established in 1982 as part of the World Climate Research Program (WCRP) to collect weather satellite radiance measurements and to analyze them to infer the global distribution of clouds, their properties, and their diurnal, seasonal and interannual variations. The resulting datasets and analysis products are being used to study the role of clouds in climate, both their effects on radiative energy exchanges and their role in the global water cycle.
The \"H\" series of data products is a high spatial resolution (0.1 degree) version of the ISCCCP dataset which is documented at: https://data.amerigeoss.org/es/dataset/97db2b39-9602-4501-a3de-421ab2375027
The ISCCP H-Series Climate Data Record consists of several parts
ISCCP H Series data The full ISCCP dataset consists of netCDF files containing various derived cloud parameters. The H-Series data includes several products. These include: HXS (H-series pixel level single satellite - not in netcdf), HXG (H-series pixel level gridded), HGG (H-series Gridded Global), HGH (H-series gridded monthly by hour), and * HGM ( H-series Gridded Monthly). The netCDF files are not structured with CF-standard names. Data variables are unitless and rely on data tables that are needed to represent each geophysical variable. Keeping ISCCP H-Series in this native format ensures that existing \"power users\" will be able to continue using the data.ISCCP Basic H Series ISCCP Basic files contains a subset of the cloud variables and products available in the full ISCCP dataset. It consists of remapped, calibrated, and subsetted variables following CF-conventions. In addition, the netCDF files follow full netCDF CF and ACDD Conventions. These files are intended to be use by new and/or less advanced users that may want to use cloud data, but do not need the full ISCCP dataset. These were converted to Geotiff files for use from the netCDF files.
The values in the file are as follows: 0 (no cloud) 1 (cloud) and 255 (NoData)
"},{"location":"projects/isccp_hxg/#citation","title":"Citation","text":"Rossow, WB., RA Schiffer, 1999: Advances in understanding clouds from ISCCP. BULLETIN OF THE AMERICAN\nMETEOROLOGICAL SOCIETY, 80, 2261-2287.\nvar isccp = ee.ImageCollection('projects/sat-io/open-datasets/isccp/hxg');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/HXG-CLOUD-COVER
"},{"location":"projects/isccp_hxg/#license","title":"License","text":"Public Domain/No restrictions (CC0): Under the terms of this license you are free to use the material for any purpose without any restrictions.
Preprocessed by: Michael Lefsky
Curated by: Samapriya Roy & Michael Lefsky
Keywords: ISCCP, Clouds, International Satellite Cloud Climatology Project, WCRP, World Climate Research Program
"},{"location":"projects/isric/","title":"Soil Grids 250m v2.0","text":"SoilGrids is designed as a globally consistent, data-driven system that predicts soil properties and classes using global covariates and globally fitted models. If you are looking for soil information on national and/or local levels we advise to compare SoilGrids predictions with soil maps derived from national and local soil geographical databases. National soil maps are usually based on more detailed input soil information and therefore are often more accurate than SoilGrids (within the local coverage area). For an overview of national and regional soil databases, please refer to the Soil Geographic Databases compendium. The \u2018mean\u2019 and \u2018median (0.5 quantile)\u2019 may both be used as predictions of the soil property for a given cell. The mean represents the \u2018expected value\u2019 and provides an unbiased prediction of the soil property.
Name Description Mapped units Conversion factor Conventional units Assets on GEE bdod Bulk density of the fine earth fraction cg/cm\u00b3 100 kg/dm\u00b3 bdod_mean cec Cation Exchange Capacity of the soil mmol\u00a9/kg 10 cmol\u00a9/kg cec_mean cfvo Volumetric fraction of coarse fragments (> 2 mm) cm3/dm3 (vol\u2030) 10 cm3/100cm3 (vol%) cfvo_mean clay Proportion of clay particles (< 0.002 mm) in the fine earth fraction g/kg 10 g/100g (%) clay_mean nitrogen Total nitrogen (N) cg/kg 100 g/kg nitrogen_mean phh2o Soil pH pHx10 10 pH phh2o_mean sand Proportion of sand particles (> 0.05 mm) in the fine earth fraction g/kg 10 g/100g (%) sand_mean silt Proportion of silt particles (\u2265 0.002 mm and \u2264 0.05 mm) in the fine earth fraction g/kg 10 g/100g (%) silt_mean soc Soil organic carbon content in the fine earth fraction dg/kg 10 g/kg soc_mean ocd Organic carbon density hg/dm\u00b3 10 kg/dm\u00b3 ocd_mean ocs Organic carbon stocks t/ha 10 kg/m\u00b2 ocs_mean"},{"location":"projects/isric/#citation","title":"Citation","text":"Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217\u2013240, https://doi.org/10.5194/soil-7-217-2021, 2021.\nvar isric_bdod_mean = ee.Image(\"projects/soilgrids-isric/bdod_mean\");\nvar isric_cec = ee.Image(\"projects/soilgrids-isric/cec_mean\");\nvar isric_cfvo = ee.Image(\"projects/soilgrids-isric/cfvo_mean\");\nvar isric_clay = ee.Image(\"projects/soilgrids-isric/clay_mean\");\nvar isric_sand = ee.Image(\"projects/soilgrids-isric/sand_mean\");\nvar isric_silt = ee.Image(\"projects/soilgrids-isric/silt_mean\");\nvar isric_nitrogen = ee.Image(\"projects/soilgrids-isric/nitrogen_mean\");\nvar isric_phh20 = ee.Image(\"projects/soilgrids-isric/phh2o_mean\");\nvar isric_soc = ee.Image(\"projects/soilgrids-isric/soc_mean\");\nvar isric_ocd = ee.Image(\"projects/soilgrids-isric/ocd_mean\");\nvar isric_ocs = ee.Image(\"projects/soilgrids-isric/ocs_mean\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/ISRIC-SOIL-GRID-250
"},{"location":"projects/isric/#data-available-from","title":"Data available from","text":"www.soilgrids.org.
"},{"location":"projects/isric/#publication-date","title":"Publication date","text":"2020-05-04
"},{"location":"projects/isric/#period","title":"Period","text":"Fri Mar 31 1905 19:00:00 GMT-0500 Mon Jul 04 2016 20:00:00 GMT-0400
"},{"location":"projects/isric/#provided-by","title":"Provided by :","text":"International Soil Reference and Information Centre (ISRIC)
"},{"location":"projects/isric/#license","title":"License","text":"Attribution 4.0 International (CC BY 4.0)
"},{"location":"projects/isric/#doi","title":"DOI","text":"https://doi.org/10.17027/isric-soilgrids.713396fa-1687-11ea-a7c0-a0481ca9e724
Created and Curated by: International Soil Reference and Information Centre (ISRIC)
Keywords: For example Global Soilgrid, Sandy Soil, ISRIC
Last updated: 2020-10-20
"},{"location":"projects/japan_eq2024/","title":"Emergency Observation Data for the 2024 Sea of Japan Earthquake","text":"The 2024 Sea of Japan earthquake occurred on January 1, 2024, after 4:00 PM (Japan time), resulting in significant damage, including building collapses, landslides, and fires at various locations. In response to requests from domestic disaster prevention agencies, JAXA conducted emergency observations using ALOS-2 from the night of the disaster. The released data includes Level 2.1 (GeoTIFF) and archive data, facilitating interference analysis and change detection to contribute to disaster reduction and prevention. Notably, this publicly released data is intended for non-commercial purposes, including government and local authority use, as well as research by universities.
"},{"location":"projects/japan_eq2024/#dataset-preprocessing","title":"Dataset preprocessing","text":"Additional metadata was added to the images in the collection. Field names such as system:time_start and system:time_end were added to make the collection filterable in Google Earth Engine. Custom code was written for ingest into Google Earth Engine and a no data value of 0 was used for masking.
"},{"location":"projects/japan_eq2024/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var notoPeninsula = ee.ImageCollection(\"projects/sat-io/open-datasets/disaster/japan-earthquake-2024_ALOS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/SEA-OF-JAPAN-EQ-2024
"},{"location":"projects/japan_eq2024/#license","title":"License","text":"This publicly released data is intended for non-commercial purposes, including government and local authority use, as well as research by universities.
Please note the terms of use: https://global.jaxa.jp/policy.html
Provided by: Japan Aerospace Exploration Agency (JAXA)
Year: 2024
URL: https://www.eorc.jaxa.jp/ALOS/jp/dataset/alos_open_and_free_j.htm#Noto2024
For citation details, please refer to the above URL
Curated in GEE by Samapriya Roy and Keiko Nomura
Keywords: Emergency Data, ALOS, JAPAN, Earthquake
Last updated on: 2024-01-06
"},{"location":"projects/land_subsidence/","title":"Global Land subsidence mapping","text":"This dataset centers on the creation of a global land subsidence dataset through the use of advanced geospatial and modeling techniques. The study investigates the relationships between groundwater stress, aquifer depletion, and land subsidence on a worldwide scale. Employing remote sensing data and model-based datasets, a machine learning model has been developed to predict land subsidence at a remarkably high spatial resolution of approximately 2 kilometers. The outcomes of this study include a comprehensive estimation of global land subsidence magnitude, a first-order assessment of aquifer storage loss due to consolidation, and the quantification of key factors driving subsidence. Notably, a significant portion of the observed subsidence is concentrated in cropland and urban areas, underscoring the urgency of adopting sustainable groundwater management practices in these regions. This dataset is invaluable for understanding the spatial distribution of subsidence in both known and previously unidentified groundwater-stressed areas worldwide.
The global land subsidence dataset is a pioneering effort in characterizing the complex interplay between groundwater dynamics, land subsidence, and aquifer storage loss. By leveraging machine learning and comprehensive datasets, this study contributes to a deeper understanding of the environmental challenges posed by excessive groundwater pumping and highlights the need for proactive measures to safeguard water resources and mitigate land subsidence impacts, particularly in regions facing water scarcity and population growth. You can read the full paper here. You can find additional information in this GitHub repository.
"},{"location":"projects/land_subsidence/#citation","title":"Citation","text":"Hasan, M.F., Smith, R., Vajedian, S. et al. Global land subsidence mapping reveals widespread loss of aquifer storage capacity.\nNat Commun 14, 6180 (2023). https://doi.org/10.1038/s41467-023-41933-z\nHasan, M. F., R. Smith, S. Vajedian, R. Pommerenke, S. Majumdar (2023). Global Land Subsidence Mapping Reveals Widespread Loss of Aquifer Storage\nCapacity Datasets, HydroShare, https://doi.org/10.4211/hs.dc7c5bfb3a86479b889d3b30ab0e4ef7\nvar subsidence_prediction_probability = ee.Image(\"projects/sat-io/open-datasets/global_subsidence/Final_subsidence_proba_greater_1cm_2013_2019_recoded\");\nvar subsidence_prediction_recoded = ee.Image(\"projects/sat-io/open-datasets/global_subsidence/Final_subsidence_prediction_recoded\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GLOBAL-LAND-SUBSIDENCE
"},{"location":"projects/land_subsidence/#license","title":"License","text":"This resource is shared under the Creative Commons Attribution CC BY
Created by: Hasan, M. F., R. Smith, S. Vajedian, R. Pommerenke, S. Majumdar
Curated in GEE by: Samapriya Roy
Keywords: machine learning,global groundwater,groundwater monitoring,land subsidence, InSAR
Last updated: 2023-11-08
"},{"location":"projects/landfire/","title":"Landfire Mosaics LF","text":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture's Forest Service, U.S. Department of the Interior's Geological Survey, and The Nature Conservancy.
LANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees. You can read about the Landfire 2022 updates v2.3.0 here
The LANDFIRE (LF) 2022 Update represents another step in moving towards an annual update. This update is the first time in LANDFIRE history in which disturbances from the year before are represented in current year products. LF 2022 includes adjustments to vegetation and fuels in disturbed areas for disturbances recorded in 2021 and 2022. LF 2022 disturbance layers contain comprehensive polygon treatment data (disturbance events) obtained from national and local sources and fire program data including:
Disturbances are also identified with LF's remote sensing of landscape change (RSLC), which identifies spectral change in vegetation using automated algorithms and image analyst review of the entire country.
Both vegetation cover and height, as well as fuels, will be 2023 capable in disturbed areas. This means that in mapped disturbances, vegetation and fuels represent current year conditions. Transition rulesets for vegetation account for disturbances from 2017 to 2022 since they were designed to use LF 2016 Remap vegetation data as inputs. Fuel updates utilize 2013\u20132022 disturbances because fuels transition rules encompass ten years of disturbance and can use pre-disturbance fuel inputs.
Important changes featured in the LF 2022 update include:
Currently included layers are
"},{"location":"projects/landfire/#earth-engine-snippet-fire-regime-v230","title":"Earth Engine Snippet: Fire Regime v2.3.0","text":"var sclass = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fire-regime/sclass\");\nvar vcc = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fire-regime/vcc\");\nvar vdep = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fire-regime/vdep\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-FIRE-REGIME
"},{"location":"projects/landfire/#earth-engine-snippet-disturbance-230","title":"Earth Engine Snippet: Disturbance 2.3.0","text":"var fdist = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/disturbance/FDIST\");\nvar hdist = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/disturbance/HDIST\");\nvar distyear = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/disturbance/DISTYEAR\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-DISTURBANCE
"},{"location":"projects/landfire/#earth-engine-snippet-topographic-220","title":"Earth Engine Snippet: Topographic 2.2.0","text":"var elevation = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/topographic/ELEV\");\nvar aspect = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/topographic/ASP\");\nvar slope_degrees = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/topographic/SLP\");\nvar slope_perc_rise = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/topographic/SlpP\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-TOPOGRAPHIC
"},{"location":"projects/landfire/#earth-engine-snippet-fuel-230","title":"Earth Engine Snippet: Fuel 2.3.0","text":"var cbd = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CBD\");\nvar cbh = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CBH\");\nvar cc = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CC\");\nvar cffdrs = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CFFDRS\");\nvar ch = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CH\");\nvar fbfm13 = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FBFM13\");\nvar fbfm40 = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FBFM40\");\nvar fvc = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FVC\");\nvar fvh = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FVH\");\nvar fvt = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FVT\");\nvar fccs = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FCCS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-FUEL
"},{"location":"projects/landfire/#earth-engine-snippet-vegetation-230","title":"Earth Engine Snippet: Vegetation 2.3.0","text":"var evc = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/vegetation/EVC\");\nvar evh = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/vegetation/EVH\");\nvar evt = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/vegetation/EVT\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-VEGETATION
"},{"location":"projects/landfire/#earth-engine-snippet-transportation-220","title":"Earth Engine Snippet: Transportation 2.2.0","text":"var roads = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/transportation/ROADS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-TRANSPORTATION
Resolution: approx 30m
"},{"location":"projects/landfire/#citation","title":"Citation","text":"LANDFIRE spatial data products
Homepage title: Data product.(Last update). Agency. [Online].Available: URL [Access date].
LANDFIRE: LANDFIRE Existing Vegetation Type layer.(2013, June - last update). U.S. Department of Interior, Geological Survey.[Online]. Available: http://landfire.cr.usgs.gov/viewer/ [2013,May 8].\nLANDFIRE data are public domain data with no use restrictions, though if modifications or derivatives of the product(s) are created, then please add some descriptive modifier to the data set to avoid confusion.
Curated in GEE by: Samapriya Roy
Keywords: doi, fire, landfire, nature-conservancy, usda, usgs, vegetation, wildfire
Last updated: 2024-01-14
"},{"location":"projects/landfire/#changelog","title":"Changelog","text":"The LandScan Program was initiated at Oak Ridge National Laboratory (ORNL) in 1997 to address the need for improved estimates of population for consequence assessment. For example, natural and manmade disasters across the globe place vast populations at risk, often with little or no advance warning. It was critical to develop highly resolved estimates so that they were useful to evaluate to events across multiple geographic scales. This has been an annual product since 1998.
Building on the modeling approach developed for LandScan Global, and taking advantage of higher quality data available for the U.S., we improved on both the spatial and the temporal resolution with our first version of LandScan USA in 2004. LandScan USA was created with the goal of capturing the diurnal variation of population that is critical for a variety of analyses and actions including emergency preparedness and response. In 2016, the original LandScan USA model was re-engineered to incoroporate advances in geospatial technology, machine learning approaches, and new input data sources. Since then, we have made annual improvements to the underlying model and released a new version of the dataset each year.
Around the time LandScan USA was first initiated, ORNL was also pioneering work in machine learning and computer vision, specifically to identify anthropogenic signals apparent in overhead imagery. This work ultimately enabled rapid, large-scale detection of human settlements from high resolution imagery and became the basis for early efforts to develop an improved resolution population distribution outside the U.S. known as Landscan HD. LandScan HD model employs a mixture of multi-modal data fusion, spatial data science, big data resources, and satellite imagery exploitation. The first country-scale LandScan HD dataset was created in 2014 and a continuous stream of new country-scale datasets have been developed ever since.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/landscan/#paper-citation","title":"Paper Citation","text":"LandScan Global 2021 for other years find citation information here
Sims, K., Reith, A., Bright, E., McKee, J., & Rose, A. (2022). LandScan Global 2021 [Data set]. Oak Ridge National Laboratory. https://doi.org/10.\n48690/1527702\nLandScan USA 2021 for other years find citation information here
Weber, E., Moehl, J., Weston, S., Rose, A., Brelsford, C., & Hauser, T. (2022). LandScan USA 2021 [Data set]. Oak Ridge National Laboratory. https://\ndoi.org/10.48690/1527701\nLandScan HD 2021 for find citation information for each country here
"},{"location":"projects/landscan/#earth-engine-snippet-landscan-global","title":"Earth Engine Snippet: LANDSCAN GLOBAL","text":"var landscan_global = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/LANDSCAN-GLOBAL
"},{"location":"projects/landscan/#earth-engine-snippet-landscan-usa","title":"Earth Engine Snippet: LANDSCAN USA","text":"var landscan_usa_night = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_USA_NIGHT\");\nvar landscan_usa_day = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_USA_DAY\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/LANDSCAN-USA
"},{"location":"projects/landscan/#earth-engine-snippet-landscan-hd","title":"Earth Engine Snippet: LANDSCAN HD","text":"var landscan_hd = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_HD\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/LANDSCAN-HD
"},{"location":"projects/landscan/#user-contributed-code","title":"User Contributed Code","text":"The code snippet shows how to use the Landscan Global population dataset to plot a time series chart comparing changes in the yearly population of two regions from 2000-2020.
Code link: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/LANDSCAN-POPULATION-COMPARE
Code Attribution Source: Ujaval Gandhi
"},{"location":"projects/landscan/#license","title":"License","text":"These datasets are offered under the Creative Commons Attribution 4.0 International License. Users are free to use, copy, distribute, transmit, and adapt the data for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
"},{"location":"projects/landscan/#changelog","title":"Changelog","text":"Created by: Oakridge National Laboratory
Curated in GEE by : Samapriya Roy
keywords: Global Population, Population count, Diurnal population, remote sensing, machine learning
Last modified: 2023-07-05
Last updated on GEE: 2023-07-20
"},{"location":"projects/landslide/","title":"Global Landslide Catalog :NASA Goddard (1970-2019)","text":"The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This is a unique data set with the ID tag \u201cGLC\u201d in the landslide editor.
You can find information about the project here and find source information for the dataset here
"},{"location":"projects/landslide/#citation","title":"Citation","text":"Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog\nfor hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561\u2013575.\ndoi:10.1007/s11069-009-9401-4.\n\nKirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global\nLandslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016.\nvar glc = ee.FeatureCollection(\"projects/sat-io/open-datasets/events/global_landslide_1970-2019\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/GLOBAL-LANDSLIDE-CATALOG
"},{"location":"projects/landslide/#license","title":"License","text":"This dataset is intended for public access and use.
Compiled by : NASA Goddard Space Flight Center
Curated by: Samapriya Roy
Keywords: :\"landslide, rainfall, NASA, GLC\"
Last updated: 2021-05-01
"},{"location":"projects/lcmap/","title":"Land Change Monitoring, Assessment, and Projection (LCMAP) v1.3","text":"Land Change Monitoring, Assessment, and Projection (LCMAP) represents a new generation of land cover mapping and change monitoring from the U.S. Geological Survey\u2019s Earth Resources Observation and Science (EROS) Center. LCMAP answers a need for higher quality results at greater frequency with additional land cover and change variables than previous efforts. LCMAP Collection 1.3 products were released in August 2022, including LCMAP products for 1985-2021
LCMAP science product documentation contain details, descriptions and caveats for the products and it can be downloaded here. Collection 1.0 for Hawaii was made available Jan 19 and more information can be found here
Additional Resources Links LCMAP website https://www.usgs.gov/core-science-systems/eros/lcmap Algorithm description document https://www.usgs.gov/media/files/lcmap-ccdc-add RSS Feed https://www.usgs.gov/core-science-systems/eros/lcmap/news Validation (these are tables) https://www.sciencebase.gov/catalog/item/5f86f28682cebef40f170771 Reference data (these are points) https://www.sciencebase.gov/catalog/item/5e42e54be4b0edb47be84535 USGS LCMAP Publications https://www.usgs.gov/core-science-systems/eros/lcmap/publications
"},{"location":"projects/lcmap/#lcmap-products","title":"LCMAP Products","text":"LCMAP provides 10 science products based on the USGS implementation of CCDC. The science products provide annual products for the years 1985-2019 for each CONUS ARD tile and CONUS level downloads are available which is used for the GEE collection. Land surface change products, denoted by the \u201cSC\u201d prefix in the short name, are produced directly from CCDC time series models. Land cover products, denoted by the \u201cLC\u201d prefix in the short name, are produced by the classification of the time series models. Note to optimize size GeoTiff files were run through a lossless LZW compression.
Product Name Short Name Product Description Time of Spectral Change SCTIME Indicator of a spectral change in the current year and the specific timing (day-of-year - DOY) within the year. Change Magnitude SCMAG Indicator of a spectral change in the current year and degree of change. Spectral Stability Period SCSTAB Time, in days, that the spectral time series has been in its current state. Time Since Last Change SCLAST Time, in days, since the last identified Spectral Change (SCTIME). Spectral Model Quality SCMQA Information regarding the type of time series model applied on July 1 of the current year. Primary Land Cover LCPRI The most likely Level 1 land cover class on July 1 of the current year Primary Land Cover Confidence LCPCONF Measure of confidence that the Primary Land Cover label matches the training data. Secondary Land Cover LCSEC The second most likely Level 1 land cover class on July 1 of the current year Secondary Land Cover Confidence LCSCONF Measure of confidence that the Secondary Land Cover label matches the training data. Annual Land Cover Change LCACHG Synthesis of Primary Land Cover of current and previous year identifying changes in land cover class."},{"location":"projects/lcmap/#product-specifications","title":"Product Specifications","text":"The product specifications allows for understanding categorical vs continuous datasets and informed pyramid policy for ingest into Google Earth Engine.
Short Name Data Type Units Range Valid Range Fill Value SCTIME UINT16 DOY 0-65535 0-366 0 SCMAG FLOAT32 Unitless -3.4e+38 - +3.4e+38 0 to +3.4e+38 0 SCSTAB UINT16 Days 0-65535 0-65535 0 SCLAST UINT16 Days 0-65535 0-65535 0 SCMQA UINT8 Discrete 0-255 0- 4,6,8,14,24,44,54 0 LCPRI UINT8 Discrete 0-255 0-8 0 LCPCONF UINT8 0-255 0-255 0 LCSEC UINT8 Discrete 0-255 0-8 0 LCSCONF UINT8 0-255 0-255 0 LCACHG UINT8 Discrete 0-255 0-87 0"},{"location":"projects/lcmap/#lcmap-level-1-land-cover-classes","title":"LCMAP Level 1 Land Cover Classes","text":"For classification of thematic land cover, LCMAP implements a Level 1 classification schema similar to an Anderson Level 1 (Anderson\u00a0et\u00a0al.,\u00a01976) representing dominant land cover classes most relevant to remotely monitoring land change.
Land Cover Class Description Developed Areas of intensive use with much of the land covered with structures (e.g., high-density residential, commercial, industrial, mining, or transportation), or less intensive uses where the land cover matrix includes vegetation, bare ground, and structures (e.g., low-density residential, recreational facilities, cemeteries, transportation/utility corridors, etc.), including any land functionality related to the developed or built-up activity. Cropland Land in either a vegetated or unvegetated state used in production of food, fiber, and fuels. This includes cultivated and uncultivated croplands, hay lands, orchards, vineyards, and confined livestock operations. Forest plantations are considered as forests or woodlands (Tree Cover class) regardless of the use of the wood products. Grass/Shrub Land predominantly covered with shrubs and perennial or annual natural and domesticated grasses (e.g., pasture), forbs, or other forms of herbaceous vegetation. The grass and shrub cover must comprise at least 10% of the area and tree cover is less than 10% of the area. Tree Cover Tree-covered land where the tree cover density is greater than 10%. Cleared or harvested trees (i.e., clearcuts) will be mapped according to current cover (e.g., Barren, Grass/Shrub). Water Areas covered with water, such as streams, canals, lakes, reservoirs, bays, or oceans. Wetland Lands where water saturation is the determining factor in soil characteristics, vegetation types, and animal communities. Wetlands are composed of mosaics of water, bare soil, and herbaceous or wooded vegetated cover. Ice/Snow Land where accumulated snow and ice does not completely melt during the summer period (i.e., perennial ice/snow). Barren Land comprised of natural occurrences of soils, sand, or rocks where less than 10% of the area is vegetated."},{"location":"projects/lcmap/#citation","title":"Citation","text":"There are no restrictions on the use of the LCMAP Reference Data Products. It is not a requirement of data use, but the following citation may be used in publication or presentation materials to acknowledge the USGS as a data source and to credit the original research.
LCMAP Reference Data products courtesy of the U.S. Geological Survey Earth Resources Observation and Science Center.
Brown, J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E., Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z.,\nPengra, B.W., Smith, K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant, A.L., Zelenak, D., Reker, R.R., and Rover, J.,\n2020 Lessons learned implementing an operational continuous United States national land change monitoring capability\u2014The Land Change Monitoring,\nAssessment, and Projection (LCMAP) approach: Remote Sensing of Environment, v. 238, article 111356, at https://doi.org/10.1016/j.rse.2019.111356.\n\nZhu, Z., and Woodcock, C.E., 2014, Continuous change detection and classification of land cover using all available Landsat data: Remote Sensing\nof Environment, v. 144, p. 152\u2013171, at https://doi.org/10.1016/j.rse.2014.01.011.\nvar lcachg = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCACHG\");\nvar lcpconf = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCPCONF\");\nvar lcpri = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCPRI\");\nvar lcsconf = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCSCONF\");\nvar lcsec = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCSEC\");\nvar sclast = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCLAST\");\nvar scmag = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCMAG\");\nvar scmqa = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCMQA\");\nvar scstab = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCSTAB\");\nvar sctime = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCTIME\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/LCMAP
I have also added the reference dataset to be used with the other 10 LCMAP products, which is about 25000 plot level datasets.
var reference = ee.FeatureCollection(\"projects/sat-io/open-datasets/LCMAP/LCMAP_CU_20200414_V01_REF\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/LCMAP-REFERENCE
"},{"location":"projects/lcmap/#reference-publications-find-additional-publications-here","title":"Reference Publications: Find additional publications here","text":"LCMAP data are freely available to the public (similar to a CC0 license) and are generated by leveraging other national programs including the Landsat satellite program
Created by: U.S. Geological Survey Center for Earth Resources Observation and Science (EROS)
Curated by: Samapriya Roy
Keywords: Landsat, ARD, Land Cover, Spectral Change, USGS, EROS
Last updated: 2023-04-04
"},{"location":"projects/lcmap/#changelog","title":"Changelog","text":"Updated v1.3
Collection 1.3 extends LCMAP\u2019s record to 37 years, characterizing the landscapes across CONUS at 30-meter resolution from 1985-2021. The 10-product\nsuite can be used to capture the dynamics of wetlands in growth or decline, characterize the ephemeral impacts of hurricanes or rapidly-shifting\nmining operations, track the pace of coastal erosion or urban growth, observe the progression of fires, monitor recovery from mudslides and\nwildfires, and much more. LCMAP products can also serve as a complement to other USGS Landsat-based mapping efforts, such as the National Land Cover\nDatabase, LANDFIRE, Monitoring Trends in Burn Severity, and others.\nUSGS posted a notification for v1.2, so datasets were reprocessed and ingested
A processing issue was found in LCMAP Conterminous United States (CONUS) Collection 1.2 product mosaics in March 2022. The CONUS Collection 1.2\nmosaics have been reprocessed as of April 14, 2022, and users who downloaded CONUS Collection 1.2 mosaics before that date are encouraged to\nredownload the reprocessed data. CONUS Collection 1.2 tiled data were unaffected and are available on EarthExplorer. LCMAP Conterminous United\nStates (CONUS) Collection 1.2 products are available on EarthExplorer, the LCMAP Web Viewer, and the LCMAP Mosaic Download website as of December\n2021. CONUS Collection 1.2 includes LCMAP products for 1985-2020. Previous LCMAP collections will remain available on EarthExplorer; however, user\nare encouraged to use the most recent release. LCMAP Hawaii (HI) Collection 1.0 products are also available on EarthExplorer, the LCMAP Web Viewer,\nand the LCMAP Mosaic Download website as of January 2022. HI Collection 1.0 includes LCMAP products for 2000-2020.\nLandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. Image chips of 256 x 256 pixels in LandCoverNet spanning across multiple tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.
One of the strongest feature of this dataset is Consensus labeling where each image chip was validated by three independent users. The accuracy of each user was assessed using chips that were separately labeled by experts from Radiant Earth\u2019s team. To generate the consensus label for each pixel a Bayesian model averaging approach was implemented taking into account the accuracy of each user. The resulting labels are accompanied by a \u201cconsensus score\u201d between 0 and 100 which indicates the degree of agreement among the three users. This forms b2 for the dataset while b1 is the class value.
You can read a sample detailed methodology here and you can go to the sample dataset page here. You can read about the approach in the paper here
Tutorials on this can be further accessed here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/lcnet/#citation","title":"Citation","text":"Alemohammad, Hamed, and Kevin Booth. \"LandCoverNet: A global benchmark land cover classification training dataset.\"\narXiv preprint arXiv:2012.03111 (2020).\nAlemohammad S.H., Ballantyne A., Bromberg Gaber Y., Booth K., Nakanuku-Diggs L., & Miglarese A.H. (2020) \"LandCoverNet Africa: A Geographically\nDiverse Land Cover Classification Training Dataset\", Version 1.0, Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.d2ce8i\nThe datasets are provided as raster chips with 256 x 256 pixel resolution meaning a total of 65,536 pixels. Overall dataset distribution per region is summarized below
Abbreviation Image Chip Count Ref ID Region Proportion Global AU 600 ref_landcovernet_au_v1_labels Australia 6.72 AS 2753 ref_landcovernet_as_v1_labels Asia 30.81 AF 1980 ref_landcovernet_af_v1_labels Africa 22.16 EU 840 ref_landcovernet_eu_v1_labels Europe 9.4 NA 1561 ref_landcovernet_na_v1_labels North America 17.47 SA 1200 ref_landcovernet_sa_v1_labels South America 13.43The datasets do consist of a STAC representation and while the command line tool is the way to access this data, I wrote some custom script for parsing the properties in STAC metadata as well as to download the raster objects and the source imagery CSVs for use as Google Earth Engine assset level property.
Retained metadata includes date which is used for both start and end date.
"},{"location":"projects/lcnet/#additional-metadata-fields","title":"Additional Metadata fields","text":"var au = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_au_v1_labels\");\nvar af = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_af_v1_labels\");\nvar as = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_as_v1_labels\");\nvar eu = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_eu_v1_labels\");\nvar na = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_na_v1_labels\");\nvar sa = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_sa_v1_labels\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/LANDCOVERNET_V1
"},{"location":"projects/lcnet/#license","title":"License","text":"The dataset is released under (CC BY 4.0) license. You can find license summary here
Produced by: Radiant Earth Foundation
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Remote Sensing, landsat-8, sentinel-1, sentinel-2, segmentation
Last updated on GEE: 2022-07-17
"},{"location":"projects/lghap/","title":"Long-term Gap-free High-resolution Air Pollutants (LGHAP)","text":"A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), the 21-year-long (2000\u20132020) gap free AOD, PM2.5 and PM10 grids with a 1-km resolution covering the land area of China was provided. You can read the accepted preprint here
Specifically, data gaps in daily AOD imageries from MODIS aboard Terra were reconstructed based on a set of AOD data tensors acquired from satellites, numerical analysis, and in situ air quality data via integrative efforts of spatial pattern recognition for high dimensional gridded image analysis and knowledge transfer in statistical data mining.
"},{"location":"projects/lghap/#citation","title":"Citation","text":"Bai, K., Li, K., Ma, M., Li, K., Li, Z., Guo, J., Chang, N.-B., Tan, Z., and Han, D.: LGHAP: a Long-term Gap-free High-resolution Air Pollutants concentration dataset derived\nvia tensor flow based multimodal data fusion, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-404, in review, 2021.\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Daily 1-km gap-free AOD grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5652257\n\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Daily 1-km gap-free PM2.5 grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5652265\n\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Daily 1-km gap-free PM10 grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5652263\n\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Monthly averaged 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5655797\n\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Annual mean 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5655807\nAll datasets were provided as netCDF file formats and the authors did provide some code for conversation to geotiff. Their code was modified to support multithreaded batch processing along with the addition of LZW compression. Overall uncompressed size was approximately 4 TB which post ingestion across all assets was brought down to 246.65 GB. The code was also adjusted to handle tiling for optimizing output files. Additionally date were added to the GEE assets for quick filter and sorting.
Collection Name Collection Path AOD_daily projects/sat-io/open-datasets/LGHAP/AOD_daily AOD_monthly_avg projects/sat-io/open-datasets/LGHAP/AOD_monthly_avg AOD_yearly_avg projects/sat-io/open-datasets/LGHAP/AOD_yearly_avg PM10_daily projects/sat-io/open-datasets/LGHAP/PM10_daily PM10_monthly_avg projects/sat-io/open-datasets/LGHAP/PM10_monthly_avg PM10_yearly_avg projects/sat-io/open-datasets/LGHAP/PM10_yearly_avg PM25_daily projects/sat-io/open-datasets/LGHAP/PM25_daily PM25_monthly_avg projects/sat-io/open-datasets/LGHAP/PM25_monthly_avg PM25_yearly_avg projects/sat-io/open-datasets/LGHAP/PM25_yearly_avg
"},{"location":"projects/lghap/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aod_daily = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/AOD_daily\");\nvar aod_monthly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/AOD_monthly_avg\");\nvar aod_yearly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/AOD_yearly_avg\");\nvar pm10_daily = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM10_daily\");\nvar pm10_monthly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM10_monthly_avg\");\nvar pm10_yearly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM10_yearly_avg\");\nvar pm25_daily = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM25_daily\");\nvar pm25_monthly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM25_monthly_avg\");\nvar pm25_yearly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM25_yearly_avg\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/LONG-TERM-HIGHRES-AIR-POLLUTANTS
"},{"location":"projects/lghap/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created by: Kaixu Bai; Ke Li; Zhuo Tan; Di Han; Jianping Guo
Curated by: Samapriya Roy
Keywords: AOD, PM2.5, PM10, Gap free
"},{"location":"projects/lgrip30/","title":"GFSAD Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP)","text":"The Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP) provides high resolution, global cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data (GFSAD) project, LGRIP maps the world\u2019s agricultural lands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas for every country in the world. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2014-2017 time period to create a nominal 2015 product.
Each LGRIP 30 meter resolution GeoTIFF file contains a contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also available.
The datasets are coded as follows and are an enhacement to the GFSAD GCEP30 dataset which does not differentiate between Irrigated and rainfed croplands. You can find the dataset and links to download here
Class Label Name Description 0 Ocean Ocean and Water bodies 1 Non-croplands Land with other land use 2 Irrigated croplands Agricultural croplands that are irrigated 3 Rainfed croplands Agricultural croplands that are rainfedDisclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/lgrip30/#citation","title":"Citation","text":"Teluguntla, P., Thenkabail, P., Oliphant, A., Gumma, M., Aneece, I., Foley, D., and Mccormick,\nR. (2023). The GFSAD Landsat-derived Global Rainfed and Irrigated-Cropland Product at nominal 30m of the World (GFSADLGRIP30WORLD). NASA EOSDIS Land\nProcesses DAAC. IP148728. DOI: https://doi.org/10.5067/Community/LGRIP/LGRIP30.001\nvar lgrip30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GFSAD/LGRIP30\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LGRIP-30-CROPLAND-EXTENT
"},{"location":"projects/lgrip30/#license","title":"License","text":"GFSAD LGRIP 30 data are freely available to the public (similar to a CC0 license) and are generated by leveraging other national programs including the Landsat satellite program.
Created by: U.S. Geological Survey Center for Earth Resources Observation and Science (EROS)
Curated by: Samapriya Roy
Keywords: Landsat, Global Food, Cropland Extent, GEE, USGS, EROS
Last updated in GEE: 2023-03-01
"},{"location":"projects/ltrait/","title":"Global Leaf trait estimates for land modelling","text":"At the organismal level, plant traits, which are measurable morphological, anatomical, physiological, and phenological characteristics, can influence individuals' establishment, fitness, and survival. These measurable characteristics provide essential information to explain long-term (e.g., annual) patterns underlying carbon, water, energy fluxes, and biodiversity globally. We provide the only global remotely sensed-based maps of leaf traits at 1km spatial resolution. In particular, we present global maps of specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen content per dry mass (LNC), and leaf phosphorus content per dry mass (LPC). The methodology combines MODIS and Landsat data, climatological data (Worldclim), the largest traits database (TRY), and machine learning algorithms.
The following figure shows a flowchart of the methodology for providing our traits estimates. The numbered boxes indicate the three main components of the methods: (1) gap filling the traits database; (2) calculating the community weighted mean (CWM) trait values at the canopy level for MODIS pixels with nearby trait observations; and (3) spatialization of CWMs to global trait maps at 500\u202fm resolution.
The full information about the methodology can be found here. The users can also explore the dataset in GEE with the following app.
The data is also available at two spatial resolutions, 3km and 1km. It can be downloaded from these links 1, 2.
"},{"location":"projects/ltrait/#additional-information-about-v3","title":"Additional Information about v3","text":"Version 3.0 of the processing chain prevents extrapolation and uses an updated categorical trait table. To prevent extrapolations, this updated version of the processing chain uses the random forest algorithm (RF) with surrogates for the estimation of trait values. RF with surrogates allows obtaining an ensemble of models inside the convex hull of the input data for the predictions. Additionally, the use of an updated and more extensive categorical trait table allowed increasing the amount of training samples to produce the maps.
"},{"location":"projects/ltrait/#citation","title":"Citation","text":"Moreno-Mart\u00ednez, \u00c1., Camps-Valls, G., Kattge, J., Robinson, N., Reichstein, M., Bodegom, P. V., Kramer, K., Cornelissen, J. H. C., Reich, P. B.,\nBahn, M., Niinemets, \u00dc., Pe\u00f1uelas, J., Craine, J., Cerabolini, B., Minden, V., Laughlin, D. C., Sack, L., Allred, B., Baraloto, C., Byun, C.,\nSoudzilovskaia, N. A., Running, S. W. (2018). A methodology to derive global maps of leaf traits using remote sensing and climate data.\nRemote Sensing of Environment, 218, 69-88. [doi](https://doi.org/10.1016/j.rse.2018.09.006)\n// SLA (mm2/g)\nvar SLA=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/SLA_1km_v3').select([0],['SLA']);\nvar SLA_SD = ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/SLA_sd_1km_v3').select([0],['SLA_sd']);\n\n// LNC (mg/g)\nvar LNC=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LNC_1km_v3').select([0],['LNC']);\nvar LNC_SD = ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LNC_sd_1km_v3').select([0],['LNC_sd']);\n\n// LPC (mg/g)\nvar LPC=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LPC_1km_v3').select([0],['LPC']);\nvar LPC_SD=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LPC_sd_1km_v3').select([0],['LPC_sd']);\n\n// Leaf dry matter content LDMC (g/g)\nvar LDMC=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LDMC_1km_v3').select([0],['LDMC']);\nvar LDMC_SD = ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LDMC_sd_1km_v3').select([0],['LDMC_sd']);\n\n//let's mask unprocessed data (Positive values correspond with natural vegetated areas)\nSLA = SLA.mask(SLA.gt(0));\nLNC = LNC.mask(LNC.gt(0));\nLPC = LPC.mask(LPC.gt(0));\nLDMC = LDMC.mask(LDMC.gt(0));\n\nvar vis_vi = {min:7 , max: 22, palette: [\"ffffd9\", \"edf8b1\", \"c7e9b4\", \"7fcdbb\", \"41b6c4\", \"1d91c0\", \"225ea8\", \"253494\", \"081d58\"]};\nMap.addLayer(SLA, vis_vi, 'SLA (mm2 / g)',true)\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-LEAF-TRAITS
"},{"location":"projects/ltrait/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
"},{"location":"projects/ltrait/#contact-information","title":"Contact information","text":"If you have any further questions or doubts, please don't hesitate to contact us.
Curated by: Alvaro Moreno-Mart\u00ednez, Gustau Camps-Valls, Jens Kattge, Nathaniel Robinson, Markus Reichstein, Peter van Bodegom, Josep Pe\u00f1uelas, Brady Allred, Steve W. Running
Curated copy in GEE by: Samapriya Roy
Keywords: Plant traits, Machine learning, Remote sensing, Plant ecology, Climate, MODIS, Landsat
Last updated: Nov 2021
Last updated in GEE: 2022-12-18
"},{"location":"projects/mangrove/","title":"Global Mangrove Watch","text":"This study has used L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA) for 11 epochs from 1996 to 2020 to develop a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. This approach was applied between all image-date pairs producing 10 maps for each epoch, which were summarised to produce the global mangrove time-series. The resulting mangrove extent maps had an estimated accuracy of 87.4 % (95th conf. int.: 86.2 - 88.6 %), although the accuracies of the individual gain and loss change classes were lower at 58.1 % (52.4 - 63.9 %) and 60.6 % (56.1 - 64.8 %), respectively.
Sources of error included a mis-registration in the SAR mosaic datasets, which could only be partially corrected for, but also confusion in fragmented areas of mangroves, such as around aquaculture ponds. Overall, 152,604 km2 (133,996 - 176,910) of mangroves were identified for 1996, with this decreasing by -5,245 km2 (-13,587 - 3686) resulting in a total extent of 147,359 km2 (127,925 - 168,895) in 2020, and representing an estimated loss of 3.4 % over the 24-year time period. The Global Mangrove Watch Version 3.0 represents the most comprehensive record of global mangrove change achieved to date and is expected to support a wide range of activities, including the ongoing monitoring of the global coastal environment, defining and assessments of progress towards conservation targets, protected area planning and risk assessments of mangrove ecosystems worldwide.
You can download the dataset here and read the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/mangrove/#preprocessing","title":"Preprocessing","text":"Raster tiles were mosaiced so that all extents and allied rasters can fit into single collections. Date ranges were added later to the raster and the vector layers.
"},{"location":"projects/mangrove/#citation","title":"Citation:","text":"Bunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R.M.; Thomas, T.; Tadono, T.; Worthington, T.A.; Spalding, M.; Murray, N.J.; Rebelo, L-M. Global\nMangrove Extent Change 1996 \u2013 2020: Global Mangrove Watch Version 3.0. Remote Sensing. 2022\nBunting, Pete, Rosenqvist, Ake, Hilarides, Lammert, Lucas, Richard, Thomas, Nathan, Tadono , Takeo, Worthington, Thomas, Spalding , Mark, Murray,\nNicholas, & Rebelo, Lisa-Maria. (2022). Global Mangrove Watch (1996 - 2020) Version 3.0 Dataset (3.0) [Data set]. Zenodo. https://doi.org/10.5281/\nzenodo.6894273\nvar extent_raster = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/GMW_V3\");\nvar extent_1996 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_1996_vec\");\nvar extent_2007 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2007_vec\");\nvar extent_2008 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2008_vec\");\nvar extent_2009 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2009_vec\");\nvar extent_2010 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2010_vec\");\nvar extent_2015 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2015_vec\");\nvar extent_2016 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2016_vec\");\nvar extent_2017 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2017_vec\");\nvar extent_2018 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2018_vec\");\nvar extent_2019 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2019_vec\");\nvar extent_2020 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2020_vec\");\nvar change_f1996_raster = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/change_f1996\");\nvar change_f1996_2007 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2007_vec\");\nvar change_f1996_2008 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2008_vec\");\nvar change_f1996_2009 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2009_vec\");\nvar change_f1996_2010 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2010_vec\");\nvar change_f1996_2015 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2015_vec\");\nvar change_f1996_2016 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2016_vec\");\nvar change_f1996_2017 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2017_vec\");\nvar change_f1996_2018 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2018_vec\");\nvar change_f1996_2019 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2019_vec\");\nvar change_f1996_2020 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2020_vec\");\nSingle layer of pixels which were mangroves at any date in the time series
var gmw_union_raster = ee.Image(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/union/gmw_v3_mng_union\");\nvar gmw_union_vector = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/union/gmw_v3_union_vec\");\nSingle layer of pixels which were mangroves at all dates within the time series
var gmw_core_raster = ee.Image(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/core/gmw_v3_mng_core\");\nvar gmw_core_vector = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/core/gmw_v3_core_vec\");\nVector layer with the 1x1 degree tiles used for the analysis
var tiles = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/gmw_v3_tiles\");\nResolution: approx 30m
"},{"location":"projects/mangrove/#global-mangrove-watch-annual-mangrove-extent-4019","title":"Global Mangrove Watch: Annual Mangrove Extent 4.0.19","text":"To improve the resolution and local relevance of the Global Mangrove Watch (GMW) baseline, a new layer has been created for 2020. Using Copernicus Sentinel-2 satellite imagery, processed to a pixel resolution of 10 m, the mangrove extent has been completely remapped and revised with many areas which were not previously mapped now included within the new map. This has increased the spatial resolution of the mapping from a pixel resolution of 25 m to 10 m, allowing finer features to be mapped, such as fringing and riverine mangroves.
"},{"location":"projects/mangrove/#earth-engine-snippet-sentinel-raster-and-vector-baseline-v4019","title":"Earth Engine Snippet: Sentinel Raster and Vector Baseline v4.0.19","text":"//Extent v4.0.19\nvar raster_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/GMW/annual-extent/GMW_MNG_2020\");\nvar vector_extent = ee.FeatureCollection(\"projects/sat-io/open-datasets/GMW/annual-extent/GMW_MNG_VEC_2020\");\nMap.addLayer(raster_extent.median(),{\"opacity\":1,\"bands\":[\"b1\"],\"min\":1,\"max\":1,\"palette\":[\"228B22\"]},'GMW Raster Extent 2020 v4.0.19')\nMap.addLayer(ee.Image().paint(vector_extent,0,3), {\"palette\":[\"red\"]}, 'GMW Vector Extent 2020 v4.0.19')\nResolution: approx 10m
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-MANGROVE-WATCH
"},{"location":"projects/mangrove/#license-usage","title":"License & Usage","text":"Attribution 4.0 International CC BY 4.0.
Curated in GEE by: Samapriya Roy
Keywords: Global, Mangrove, GMW, 1996, 2020
Last updated: 2024-09-08
"},{"location":"projects/mangrove/#changelog","title":"Changelog","text":""},{"location":"projects/mangrove/#updated-2024-09-08","title":"Updated 2024-09-08","text":"This paper is currently in review with Nature Scientific Data and the citation will be updated once the paper has been published. Please keep this into consideration while using this dataset
This dataset provides global mangrove canopy height maps for 2015 at a 12-meter resolution. Canopy height estimates were derived from the TanDEM-X digital surface models, calibrated and validated with GEDI Lidar data. The dataset covers a circum-equatorial band from 34 degrees north to 39 degrees south latitude, encompassing the majority of mangrove ecosystems globally. The dataset includes 1443 GeoTIFF files containing global mangrove canopy height maps organized into 1\u00b0 by 1\u00b0 tiles. Each GeoTIFF file represents a single tile and is named as follows: TDM1_DEM__10_Y##X###_DEM_EGM08_GMW314_2015_WM_hcap_cal.tif, where Y##X### represents the latitude (Y = \"N\" or \"S\") and longitude (X = \"W\" or \"E\") coordinates of the tile's southwest corner.
These canopy height maps are useful for assessing local-scale geophysical and environmental conditions that regulate forest structure and carbon cycle dynamics in mangrove ecosystems. These canopy height maps are instrumental for assessing local-scale geophysical and environmental conditions that regulate forest structure and carbon cycle dynamics in mangrove ecosystems. You can find the dataset on ORNAL DAAC here
"},{"location":"projects/mangrove_ht_tandemx/#data-acquisition-and-materials","title":"Data acquisition and materials","text":"For data acquisition and materials, the sources included Digital Elevation Model (DEM) data from the German Aerospace Agency (DLR) TanDEM-X mission, as reported by Rizzoli et al. in 2017. The Global Mangrove Watch (GMW) map provided a global mangrove extent map, as detailed by Bunting et al. in 2022. Additionally, Global Ecosystems Dynamics Investigation (GEDI) L2A data was used, as documented by Dubayah et al. in 2020.
"},{"location":"projects/mangrove_ht_tandemx/#citation","title":"Citation","text":"Simard, M., L. Fatoyinbo, N.M. Thomas, A.E. Stovall, A. Parra, A. Barenblitt, P. Bunting, and I. Hajnsek. 2024. A new global mangrove height map with a 12-meter spatial resolution. In review 2024, Nature Scientific Data.\nSimard, M., L. Fatoyinbo, N. Thomas, A. Stovall, A. Parra, M.W. Denbina, D. Lagomasino, and I. Hajnsek. 2024. CMS: Global Mangrove Canopy Height\nMaps Derived from TanDEM-X, 2015. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2251\nThe GEDI_MANGROVE_HT, layer contains mangrove heights for individual GEDI L2A tiles used to generate the GeoTIFF files.
Variable GEDI L2A variable name Units Description GEDI_file_name - - Name of the GEDI file beam Beam ID - Beam number (0-11) delta_time delta_time YYYY-MM-DD HH:MM:SS.SSSSSS+00:00 Transmit time of the shot shot_number shot_number 1 Unique shot ID lat_lowestmode lat_lowestmode degrees Latitude of center of lowest mode lon_lowestmode lon_lowestmode degrees Longitude of center of lowest mode channel channel 1 Channel number (0-7) degrade_flag degrade_flag flag Non-zero values indicate the shot occurred during a degraded period digital_elevation_model digital_elevation_model m Digital elevation model height above the WGS84 ellipsoid. Interpolated at latitude_bin0 and longitude_bin0 from the TandemX 90m product. digital_elevation_model_srtm digital_elevation_model_srtm m Shuttle Radar Topography Mission (SRTM) elevation at GEDI footprint location elev_highestreturn elev_highestreturn m Elevation of highest detected return relative to reference ellipsoid elev_lowestmode elev_lowestmode m Elevation of lowest mode elevation_bias_flag elevation_bias_flag flag Elevations potentially affected by 4bin (~60 cm) ranging error energy_total energy_total 1 Integrated counts in the return waveform relative to the mean nise level landsat_treecover landsat_treecover percent Tree cover in the year 2010, defined as canopy closure for all L2A vegetation taller than 5 m in height (Hansen et al., 2013). Encoded as a percentage per output grid cell. landsat_water_persistence landsat_water_persistence percent The percent UMD GLAD Landsat observations with classified Derived surface water between 2018 and 2019. Values >80 usually represent permanent water, while values <10 represent permanent land. urban_proportion urban_proportion percent The percentage proportion of land area within a focal area surrounding each shot that is urban land cover. Urban land cover is derived from the DLR 12 m resolution TanDEM-X Global Urban Footprint Product. mean_sea_surface mean_sea_surface m Mean sea surface height above the WGS84 ellipsoid, includes the geoid. Interpolated at latitude_bin0 and longitude_bin0 from DTU15. num_detectedmodes num_detectedmodes 1 Number of detected modes in rxwaveform quality_flag quality_flag flag Flag simplifying selection of most useful data rh rh m Relative height metrics at 98% interval rx_energy rx_energy 1 total energy f rxwaveform, mean noise removed selected_algorithm selected_algorithm - ID of algorithm selected as identifying the lowest non-noise mode sensitivity sensitivity degrees Maximum canopy cover that can be penetrated considering th NR of the waveform solar_elevation solar_elevation degrees The elevation of the sun position vector from the laser bounce point position in the local ENU frame. The angle is measured from the East-North plane and is positive Up. surface_flag surface_flag flag Indicates elev_lowestmode is within 300 m of DEM or MSS egm_08 m Elevation over the EGM 2008 geoid tdx_max m Maximum TanDEM-X DEM value from the pixels overlapping the GEDI footprint tdx_std m Standard deviation of TanDEM-X DEM values from the pixels overlapping the GEDI footprint tdx_mean m Mean TanDEM-X DEM value from the pixels overlapping the GEDI footprint tdx_min m Minimum TanDEM-X DEM value from the pixels overlapping the GEDI footprint pixel_count 1 Number of TanDEM-X pixels overlapping the GEDI footprint
"},{"location":"projects/mangrove_ht_tandemx/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var mangrove_tandemx_12 = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL_MANGROVE_HT_TANDEMX\");\nvar mangrove_gedi = ee.FeatureCollection(\"projects/space-geographer/assets/GEDI_MANGROVE_HT\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-MANGROVE-CANOPY-HT-TANDEMX
"},{"location":"projects/mangrove_ht_tandemx/#license","title":"License","text":"Data hosted by the ORNL DAAC is openly shared, without restriction, in accordance with NASA's Earth Science program Data and Information Policy
Provided by: Simard et al 2024
Curated in GEE by: Samapriya Roy
Keywords: Mangrove, Tandem-X, Canopy Height, GEDI
Last updated : 2024-07-26
"},{"location":"projects/mapbiomas/","title":"Mapbiomas Annual land cover and use maps","text":"The Brazilian Annual Land Use and Land Cover Mapping Project is an initiative that involves a collaborative network of biomes, land use, remote sensing, GIS and computer science experts that rely on Google Earth Engine platform and its cloud processing and automated classifiers capabilities to generate Brazil\u2019s annual land use and land cover time series. MapBiomas Project - is a multi-institutional initiative to generate annual land cover and use maps using automatic classification processes applied to satellite images. The complete description of the project can be found here.
Other regions such as Pan Amazonia, Indonesia, Bolivia , Peru and others were spun out of the work in Mapbiomas Brazil and as such these are also included in the current release
Scale: 30 m to 10m Data Type: Multiple raster datasets and types
"},{"location":"projects/mapbiomas/#citation","title":"Citation","text":"Souza at. al. (2020) - Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine -\nRemote Sensing, Volume 12, Issue 17, 10.3390/rs12172735 doi: 10.3390/rs12172735 https://doi.org/10.3390 /rs12172735\n\"Project MapBiomas - Collection [version] of [region] Land Cover & Use Map Series, accessed on [date] through the link: [LINK]\"\n//From collection 8\nassets: {\n integration: 'projects/mapbiomas-workspace/public/collection8/mapbiomas_collection80_integration_v1',\n transitions: 'projects/mapbiomas-workspace/public/collection8/mapbiomas_collection80_transitions_v1',\n vectors: [\n 'projects/mapbiomas-workspace/AUXILIAR/areas-protegidas',\n 'projects/mapbiomas-workspace/AUXILIAR/municipios-2016',\n 'projects/mapbiomas-workspace/AUXILIAR/estados-2017',\n 'projects/mapbiomas-workspace/AUXILIAR/biomas-2019',\n 'projects/mapbiomas-workspace/AUXILIAR/bacias-nivel-1',\n 'projects/mapbiomas-workspace/AUXILIAR/bacias-nivel-2',\n ]\n},\nAdd repo link: https://code.earthengine.google.com/?accept_repo=users/mapbiomas/user-toolkit
Extra Info: GitHub Tutorial
Curated by: MapBiomas
"},{"location":"projects/mapbiomas/#license","title":"License","text":"All these datasets are shared under Creative Commons Attribution-Share Alike 4.0 International License
Keywords: Mapbiomas, Land Use, Land Cover
Last updated: Refer to webpage
Last updated on GEE community datasets: 2023-10-14
"},{"location":"projects/mapbiomas/#changelog","title":"Changelog","text":"Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, monitoring of staging areas and emergency response, damage assessment, and recovery. These images are generated using the Maxar ARD pipeline, tiled on an organized grid in analysis-ready cloud-optimized formats. New data is released in response to activations. Older data may be migrated to the ARD format as needed. You can find additional details and datasets here
"},{"location":"projects/maxar_opendata/#preprocessing","title":"Preprocessing","text":"The metadata tags were parsed from the existing metadata json files and available properties were parsed to confirm to property names for GEE. The datetime property was added as system:time_start for easy filtering of datasets. Not all datasets have been added and this will be continuously updated to include additional datasets/disasters
"},{"location":"projects/maxar_opendata/#citation","title":"Citation","text":"Maxar Open Data Program was accessed on DATE from [url].\nvar afghanistan_earthquake_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/afghanistan_earthquake_2022\");\nvar gambia_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/gambia_flooding_2022\");\nvar hurricane_fiona_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/hurricane_fiona_2022\");\nvar hurriance_ian_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/hurricane_ian_2022\");\nvar kentucky_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/kentucky_flooding_2022\");\nvar pakistan_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/pakistan_flooding_2022\");\nvar southafrica_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/southafrica_flooding_2022\");\nvar sudan_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/sudan_flooding_2022\");\nvar turkey_earthquake_2023 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/earthquake_turkey_2023\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/MAXAR-OPENDATA-MS
"},{"location":"projects/maxar_opendata/#license","title":"License","text":"This work is licensed under Creative Commons Attribution Non Commercial 4.0.
Data provided by: MAXAR
Curated in GEE by : Samapriya Roy
Keywords: disaster, maxar, high resolution, flooding, hurriance, earthquake
Last updated on GEE: 2022-10-30
"},{"location":"projects/merrav2/","title":"Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2)","text":"NASAs Global Modeling and Assimilation Office (GMAO) produces the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) which is a 30+ year global climate reanalysis dataset.This dataset complements existing MERRA2 Earth Engine assets: https://developers.google.com/earth-engine/datasets/tags/merra. You can find additional information on this dataset here and read more about the dataset in the climate org data page
"},{"location":"projects/merrav2/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Global Spatial resolution ~50-km (0.5-deg x 0.625-deg) Temporal resolution Daily Time span 1980-04-02 to present Update frequency Updated every 1-2 monthsVariables
Variable Details Minimum temperature, 2m ('T2MMIN') - Units: Degrees Kelvin - Scale factor: 1.0 Maximum temperature, 2m ('T2MMAX') - Units: Degrees Kelvin - Scale factor: 1.0 Precipitation ('PRECTOTCORR') - Units: Millimeters - Scale factor: 1.0 Wind speed, 10m ('WIND2M') - Units: Meters/second - Scale factor: 1.0 - NOTE: Windspeed outputs are based on the standard 10m measurement height, despite the erroneous \u20182M\u2019 suffix. ASCE Grass Reference Evapotranspiration - Units: Millimeters ('ETo_ASCE') - Scale factor: 1.0 ASCE Alfalfa Reference Evapotranspiration - Units: Millimeters ('ETr_ASCE') - Scale factor: 1.0 Specific humidity, 2m ('QV2M') - Units: kg kg-1 - Scale factor: 1.0 Surface pressure ('PS') - Units: Pa - Scale factor: 1.0 Surface incoming shortwave flux ('SWGDN') - Units: W m-2 - Scale factor: 1.0 Surface incoming shortwave flux assuming - Units: W m-2 clear sky ('SWGDNCLR') - Scale factor: 1.0"},{"location":"projects/merrav2/#citation","title":"Citation","text":"MERRA-2 Overview: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Ronald Gelaro, et al., 2017, J. Clim.,\ndoi: 10.1175/JCLI-D-16-0758.1\n// Read in Image Collection and get first image\nvar merra2_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/climate-engine/merra2/daily')\nvar merra2_i = merra2_ic.first()\n\n// Print first image to see bands\nprint(merra2_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nvar eto_palette = [\"#ffffb2\", \"#fed976\", \"#feb24c\", \"#fd8d3c\", \"#fc4e2a\", \"#e31a1c\", \"#b10026\"]\nMap.addLayer(merra2_i.select('PRECTOTCORR'), {min: 0, max: 10, palette: prec_palette}, 'PRECTOTCORR')\nMap.addLayer(merra2_i.select('T2MMAX').subtract(273.15), {min: -10, max: 30, palette: temp_palette}, 'T2MMAX')\nMap.addLayer(merra2_i.select('T2MMIN').subtract(273.15), {min: -10, max: 30, palette: temp_palette}, 'T2MMIN')\nMap.addLayer(merra2_i.select('ETo_ASCE'), {min: 0, max: 10, palette: eto_palette}, 'ETo_ASCE')\nMap.addLayer(merra2_i.select('ETr_ASCE'), {min: 0, max: 10, palette: eto_palette}, 'ETr_ASCE')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/MERRA-2
"},{"location":"projects/merrav2/#license","title":"License","text":"NASA promotes the full and open sharing of all data with research and applications communities, private industry, academia, and the general public.
Keywords: MERRA2, NASA, global, climate, reanalysis, temperature, precipitation, evapotranspiration, evaporative demand, wind
Datasets provided by: NASA
Dataset curated in GEE by: Climate Engine Org
"},{"location":"projects/meta_trees/","title":"High Resolution 1m Global Canopy Height Maps","text":"The Global Canopy Height Maps dataset offers comprehensive insights into tree canopy heights worldwide, providing an overview of tree canopy presence and height for the analysed period (2009-2020), with eighty per cent of the data obtained from imagery acquired between 2018 and 2020. This baseline can be used as a reference for supplementing field-based measurements of carbon in carbon credit monitoring and verification schema. When newer imagery is available, the publicly shared model can be used to detect changes in canopy heights. Developed through a collaboration between Meta and the World Resources Institute, this dataset stands as a cornerstone for understanding forest structure and dynamics. This dataset achieves an unparalleled level of detail through the fusion of state-of-the-art satellite imagery and advanced artificial intelligence techniques. By analyzing satellite imagery spanning from 2009 to 2020, with a focus on data from 2018 to 2020, it provides extensive temporal coverage for tracking changes in canopy height over time across the entire landmass of the planet. Using AI models such as DiNOv2, the dataset enables precise prediction of canopy height with a mean absolute error of 2.8 meters, empowering accurate assessment of carbon stocks and the effectiveness of mitigation strategies.
Moreover, its integration into conservation initiatives, carbon credit monitoring, and climate agreements underscores its significance in guiding sustainable forest management practices, afforestation, reforestation efforts, and biodiversity conservation. Complemented by the accessibility of the AI model used to generate the data on GitHub, this dataset catalyzes further research and development in forest monitoring and carbon sequestration, contributing to global efforts to combat climate change. You can read the blogpost from meta here and the associated paper here.
"},{"location":"projects/meta_trees/#citation","title":"Citation","text":"Tolan, J., Yang, H.I., Nosarzewski, B., Couairon, G., Vo, H.V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J. and Moutakanni, T.,\n2024. Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial\nlidar. Remote Sensing of Environment, 300, p.113888.\nHigh Resolution Canopy Height Maps by WRI and Meta was accessed on DATE from Google Earth Engine. Meta and World Resources Institude (WRI) - 2023.\nHigh Resolution Canopy Height Maps (CHM). Source imagery for CHM \u00a9 2016 Maxar. Accessed DAY MONTH YEAR.\nvar canopy_ht = ee.ImageCollection(\"projects/meta-forest-monitoring-okw37/assets/CanopyHeight\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-1m-CANOPY-HEIGHT
GEE app link: https://meta-forest-monitoring-okw37.projects.earthengine.app/view/canopyheight
"},{"location":"projects/meta_trees/#license","title":"License","text":"This dataset is made available under a Creative Commons Attribution 4.0 International License
Dataset provider: Meta and WRI, Tolan et al 2023
Curated in GEE by: Meta & WRI
Keywords: DiNOv2, Maxar, Self Supervised Learning (SSL), Canopy height, Global dataset, Meta, WRI
Last updated on GEE: 2024-04-13
"},{"location":"projects/mlab_extracts/","title":"Measurement Lab Network Extracts (M-Lab)","text":"Measurement Lab (M-Lab) is the largest open source Internet measurement effort in the world. The M-Lab Network Diagnostic Tool (NDT) dataset is a valuable resource for researchers and network engineers interested in understanding internet performance. It consists of a massive collection of test results gathered from users running the NDT tool. These tests measure various aspects of a user's internet connection, including download and upload bandwidth, latency (signal delay), and packet loss.
This is a very small sample extract for 15,000 download and upload extracts from a single days worth of extract 2024-06-01
The unique aspect of the NDT dataset lies in its user-initiated nature. Unlike traditional network monitoring conducted by service providers, the NDT data reflects real-world user experiences. Users often run the NDT tool when they encounter internet issues, resulting in a surge of tests during network outages or periods of poor performance. This makes the NDT dataset a rich source for analyzing trends in internet health, identifying bottlenecks, and understanding how network problems manifest for end-users. By studying the characteristics of NDT tests, researchers can gain valuable insights into the overall quality and performance of the internet.
"},{"location":"projects/mlab_extracts/#citation","title":"Citation","text":"Measurement Lab. \"The M-Lab NDT Data Set.\" (February 11, 2009 -- December 21, 2015).\nAccessed July 2, 2024. https://measurementlab.net/tests/ndt.\nvar mlab_download_extract = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/MLAB/mlab_download_extract\");\nvar mlab_upload_extract = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/MLAB/mlab_upload_extract\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/MLAB-EXTRACTS-NETWORK-SPEED
"},{"location":"projects/mlab_extracts/#license_1","title":"License","text":"All data collected by M-Lab tests are available to the public without restriction under a No Rights Reserved Creative Commons Zero Waiver.
Dataset accessed: 2024-07-01
Dataset provided by: Measurement Lab (M-Lab)
Curated in GEE by: Samapriya Roy
Keywords: : analytics,network speed,cities,civic,infrastructure,internet,network traffic, telecommunications,isp
Last updated: 2024-07-02
"},{"location":"projects/modis_8day_snow/","title":"MOD10A2061 Snow Cover 8-Day L3 Global 500m","text":"MOD10A2 is a snow cover data set from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. The data set consists of 1200 km by 1200 km tiles of 500 m resolution data gridded in a sinusoidal map projection. The data set reports the maximum snow cover extent during an eight-day period. The snow cover algorithm identifies snow-covered land and snow-covered ice on inland water. The algorithm uses a Normalized Difference Snow Index (NDSI) and other criteria tests. The eight-day compositing period was chosen because that is the exact ground track repeat period of the Terra and Aqua platforms.
Parameter Description Values Maximum_Snow_Extent Maximum snow extent observed over an eight-day period. 0: missing data1: no decision11: night25: no snow37: lake39: ocean50: cloud100: lake ice200: snow254: detector saturated255: fill"},{"location":"projects/modis_8day_snow/#dataset-details","title":"Dataset details","text":"Title: MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid Author: Hall, D. K. and G. A. Riggs. Publisher: NASA NSIDC DAAC: NASA National Snow and Ice Data Center Distributed Active Archive Center Publication date: 2021-03-30T12:00:00Z Publication place: Boulder, Colorado USA Series: MOD10A2 Edition: 61 DOI: 10.5067/MODIS/MOD10A2.061 URL: https://doi.org/10.5067/MODIS/MOD10A2.061
"},{"location":"projects/modis_8day_snow/#citation","title":"Citation","text":"Hall, D. K., V. V. Salomonson, and G. A. Riggs. \"MODIS/Terra snow cover 8-day l3 global 500m grid, version 5.\" Tile h12v12]. Boulder, Colorado USA:\nNational Snow and Ice Data Center (2006).\nHall, D. K. and G. A. Riggs. 2021. MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid, Version 61. [Indicate subset used]. Boulder, Colorado USA.\nNASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD10A2.061. [Date Accessed]\nvar MOD10A261 = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS/MOD10A261\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-MOD10A261-Snow-Cover-8-Day
"},{"location":"projects/modis_8day_snow/#license","title":"License","text":"You may download and use photographs, imagery, or text from the NSIDC web site, unless limitations for its use are specifically stated. For more information on usage and citing NSIDC datasets, please visit the NSIDC Use and Copyright page.
Curated in GEE by: Michael Lefsky and Samapriya Roy
Keywords: albedo, eight-day, 8-day, geophysical, global, modis, nasa, nsidc, snow, terra, mod10a2
Last updated: Last date the dataset was updated (if known)
"},{"location":"projects/monoculture/","title":"Aboveground carbon accumulation in global monoculture plantation forests","text":"Restoring forest cover is a key action for mitigating climate change. Although monoculture plantations dominate existing commitments to restore forest cover, we lack a synthetic view of how carbon accumulates in these systems. Here, we assemble a global database of 4756 field-plot measurements from monoculture plantations across all forested continents. With these data, we model carbon accumulation in aboveground live tree biomass and examine the biological, environmental, and human drivers that influence this growth.
This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). You can read the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/monoculture/#dataset-preprocessing","title":"Dataset Preprocessing","text":"To connect site id lat long to studies a code was written to iterate through all sites and studies and link with lat long. This was then ingested as a combined table.
"},{"location":"projects/monoculture/#paper-citation","title":"Paper Citation","text":"Bukoski, J.J., Cook-Patton, S.C., Melikov, C. et al. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests.\nNat Commun 13, 4206 (2022). https://doi.org/10.1038/s41467-022-31380-7\nBukoski, Jacob, Cook-Patton, Susan C., Melikov, Cyril, Ban, Hongyi, Chen, Jessica Liu, Goldman, Elizabeth D., Harris, Nancy L., & Potts, Matthew D.\n(2022). Global Plantation Forest Carbon database (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6555216\nvar global_fertilizer_use = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-monoculture-plantations\");\nSample code: https://code.earthengine.google.com/243774c7f7cdbec21c3450c4fa8a64fb
"},{"location":"projects/monoculture/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International license.
Created by: Bukoski, Jacob et al 2022
Curated in GEE by : Samapriya Roy
keywords: Forests, Aboveground carbon stocks, Climate change, Reforestation, Plantations, Aboveground biomass
Last modified: 2022-05-16
Last updated on GEE: 2022-09-05
"},{"location":"projects/mpw/","title":"Mismanaged Plastic Waste Dataset in Rivers","text":"This dataset shows the exposure of global rivers to mismanaged plastic waste (MPW) in 2015 and its projected impact in 2060 based on three scenarios. The different scenarios for 2060 are A: business as usual, B: improved plastic recycling, and C:improved plastic recycling and reduced plastic use projection.
Four global datasets are available that include
Datasets are described in further detailed in the paper published in Science of the Total Environment, at the Zenodo data repository or using the interactive map available here.
Mismanaged Plastic Waste Dataset
Values for MPW datasets
Values for FFPW datasets
River Type Dataset
River Migration Dataset
River Impact Dataset
Nyberg, Bj\u00f6rn, Peter T. Harris, Ian Kane, and Thomas Maes. \"Leaving a plastic legacy: Current and future scenarios for mismanaged plastic waste in\nrivers.\" Science of the Total Environment 869 (2023): 161821.\nNyberg, Bjorn. (2022). Legacy of MPW in Rivers (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6894684\nvar change = ee.Image('projects/sat-io/open-datasets/MPW/changeMap'); //River Change\nvar ffr = ee.Image('projects/sat-io/open-datasets/MPW/riverImpact'); //Free flowing rivers\nvar env = ee.Image('projects/sat-io/open-datasets/MPW/Plastics_Env'); //River Types\nvar mpw = ee.Image('projects/sat-io/open-datasets/MPW/MPW_data'); // Mismanaged plastic waste\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/MISMANAGED-PLASTIC-WASTE
"},{"location":"projects/mpw/#license","title":"License","text":"The dataset is made available under the terms of the Creative Commons Attribution 4.0 International license
Curated by: Bj\u00f6rn Nyberg
Keywords: Rivers, Plastic, Mismanaged plastic waste
Last updated: July 24th 2022
"},{"location":"projects/msbuildings/","title":"Global ML Building Footprints","text":"Bing Maps is releasing open building footprints around the world. We have detected 777M buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. For the sake of completeness datasets from earlier releases were included in this dataset and included. You can find the Github repo and more information about the methodology here. Datasets are zipped and available as GeoJSON and GeoJSONL files from different regions. Additional information on preprocessing and some more context is available on the blog here
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/msbuildings/#data-preprocessing","title":"Data preprocessing","text":"The MSBuildings dataset that I have ingested into Google Earth Engine includes earlier releases apart from the 777 Million Global building footprints from Microsoft and in its final state stands at 1 Billion+ footprint (1,069,059,359). There are some interesting performance behaviors across Ingest.\u00a0
All datasets are in the format
var country = ee.FeatureCollection('projects/sat-io/open-datasets/MSBuildings/{country_name}');\nfor a list of all countries and assets use this
var ee_folder = ee.data.listAssets(\"projects/sat-io/open-datasets/MSBuildings\");\nHere are some example setups for two countries
var australia = ee.FeatureCollection('projects/sat-io/open-datasets/MSBuildings/Australia');\nvar chile = ee.FeatureCollection('projects/sat-io/open-datasets/MSBuildings/Chile')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GLOBAL-ML-BUILDINGS
"},{"location":"projects/msbuildings/#license","title":"License","text":"The datasets are released under the Open Data Commons Open Database License.
Created by: Microsoft
Curated in GEE by: Samapriya Roy
Keywords: building footprint, machine learning, remote sensing, global
Last updated in GEE: 2022-05-30
"},{"location":"projects/msroads/","title":"Microsoft Bing Global Mined Roads","text":"NoteThis dataset is currently only available to those in the insiders program
Bing Maps is releasing mined roads around the world. We have detected 47.8M km of all roads and 1165K km of roads missing from OSM. Mining is performed with Bing Maps imagery between 2020 and 2022 including Maxar and Airbus. Datasets were provided in tsv formats and additional steps were used to convert them into GEE ready formatting.
"},{"location":"projects/msroads/#data-generation-details","title":"Data generation details","text":"The road extraction is done in four stages (full drop went through two stages and OSM missing set went through all four)
You can find additional information here.
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/msroads/#data-preprocessing","title":"Data preprocessing","text":"To get the datasets ready the TSV files were converted to GeoJSON format and then to shapefile splitting up large GeoJSON that would exceed the 4 GB limit for shapefiles. To allow for consistency checks checks were performed to exclude the point datasets that were part of the US data extract but encoded as line strings.The larger datasets like Europe and US were then merged back, flattened and exported into a single GEE asset for ease of use.
"},{"location":"projects/msroads/#citation","title":"Citation","text":"Microsoft Road Detection - Mined Roads : Last accessed date\nvar africa_center = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaCenter\");\nvar africa_east = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaEast\");\nvar africa_north = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaNorth\");\nvar africa_south = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaSouth\");\nvar africa_west = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaWest\");\nvar america_center = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/AmericaCenter\");\nvar asia_center = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Asia/AsiaCenter\");\nvar asia_north = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Asia/AsiaNorth\");\nvar asia_south = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Asia/AsiaSouth\");\nvar asia_southeast = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Asia/AsiaSouthEast\");\nvar canada = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Canada\");\nvar caribbean_islands = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/CaribbeanIslands\");\nvar eu = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/EU\");\nvar middle_east = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/MiddleEast\");\nvar oceania = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Oceania\");\nvar south_america = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/SouthAmerica\");\nvar united_states = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/US\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/MS-GLOBAL-ROADS
"},{"location":"projects/msroads/#license","title":"License","text":"The datasets are released under the Open Data Commons Open Database License.
Created by: Microsoft
Curated in GEE by: Samapriya Roy
Keywords: Mined Roads, Machine Learning, Classification, linestring, Global roads, OSM
Last updated in GEE: 2022-12-30
"},{"location":"projects/mtbs/","title":"Monitoring Trends in Burn Severity (MTBS) 1984-2019","text":"Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map the burn severity and extent of large fires across all lands of the United States from 1984 to present. This includes all fires 1000 acres or greater in the western United States and 500 acres or greater in the eastern Unites States. The extent of coverage includes the continental U.S., Alaska, Hawaii and Puerto Rico.
The program is conducted by the U.S. Geological Survey Center for Earth Resources Observation and Science (EROS) and the USDA Forest Service Geospatial Technology and Applications Center (GTAC).
The two datasets included in this package include
The fire occurrence location dataset is a vector point ESRI shapefile of the centroids of all currently completed MTBS fires occurring in the continental United States, Alaska, Hawaii and Puerto Rico.
The burned area boundaries dataset is a vector polygon ESRI shapefile of the extent of the burned areas of all currently completed MTBS fires for the continental United States, Alaska, Hawaii and Puerto Rico.
You can read the MTBS overview paper here
In the MTBS project (from the FAQ page ), \"burn severity\" refers specifically to fire effects on above-ground biomass. The definition is drawn from the reference: NWCG Glossary of Wildland Fire Terms and is based on the term Fire Severity, which is defined as: \"Degree to which a site has been altered or disrupted by fire; loosely, a product of fire intensity and residence time.\"
The following additional statements further clarify the nature of the products developed by this project:
The area occurrence layer is now part of official GEE catalog offering, you can find it here
"},{"location":"projects/mtbs/#paper-citation","title":"Paper citation","text":"Eidenshink, Jeff, Brian Schwind, Ken Brewer, Zhi-Liang Zhu, Brad Quayle, and Stephen Howard. \"\nA project for monitoring trends in burn severity.\" Fire ecology 3, no. 1 (2007): 3-21.\nvar area_boundaries = ee.FeatureCollection(\"projects/sat-io/open-datasets/MTBS/burned_area_boundaries\");\nvar fire_occurrence = ee.FeatureCollection(\"projects/sat-io/open-datasets/MTBS/fire_occurrence\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/MONITORING-TRENDS-BURN-SEVERITY
"},{"location":"projects/mtbs/#license","title":"License","text":"MTBS data are freely available to the public (similar to a CC0 license) and are generated by leveraging other national programs including the Landsat satellite program, jointly developed and managed by the USGS and NASA. Landsat data are analyzed through a standardized and consistent methodology, generating products at a 30 meter resolution dating back to 1984.
Created by: U.S. Geological Survey Center for Earth Resources Observation and Science (EROS) and the USDA Forest Service Geospatial Technology and Applications Center (GTAC)
Curated by: Samapriya Roy
Keywords: burned area,fire occurrence, fire area, burn severity,MTBS, EROS, GTAC, USGS, USDA
Last updated: 2021-09-05
"},{"location":"projects/nadm/","title":"North American Drought Monitor (NADM)","text":"The North American Drought Monitor (NADM) raster dataset is produced by the National Centers for Environmental Information (NCEI) and the National Oceanic and Atmospheric Administration's (NOAA) National Integrated Drought Information System (NIDIS). This dataset is a gridded version of the North American Drought Monitor (NADM) produced by Canadian, Mexican and US authors where for each 2.5-km gridcell, the value is given by the current NADM drought classification for that region is:
Drought categories are coded as the following values in the images:
Additional details can be found here and information about this dataset is also available at climate engine org.
"},{"location":"projects/nadm/#dataset-details","title":"Dataset details","text":"Spatial extent North America Spatial resolution 2.5-km (0.025 deg) Temporal resolution Monthly Time span 2001-11-01 to present Update frequency Updated Monthly
Variables
Variable Drought category ('nadm') Units Drought classification Scale factor 1.0
"},{"location":"projects/nadm/#citation","title":"Citation","text":"Heim, Jr., R. R., 2002. A review of Twentieth-Century drought indices used in the United States. Bulletin of the American Meteorological Society, 83, 1149-1165.\n\nLawrimore, J., et al., 2002. Beginning a new era of drought monitoring across North America. Bulletin of the American Meteorological Society, 83, 1191-1192.\n\nLott, N., and T. Ross, 2000. NCDC Technical Report 2000-02, A Climatology of Recent Extreme Weather and Climate Events. [Asheville, N.C.]: National Climatic Data Center.\n\nSvoboda, M., et al., 2002. The Drought Monitor. Bulletin of the American Meteorological Society, 83, 1181-1190.\n// Read in Image Collection and mosaic to single image\nvar nadm_ic = ee.ImageCollection('projects/climate-engine/nadm/monthly')\nvar nadm_i = nadm_ic.first()\n\n// Print image to see bands\nprint(nadm_i)\n\n// Visualize a single image\nvar nadm_palette = [\"#ffffff\", \"#ffff00\", \"#fcd37f\", \"#ffaa00\", \"#e60000\", \"#730000\"]\nMap.addLayer(nadm_i, {min:-1, max:4, palette: nadm_palette}, 'nadm_i')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/NADM-MONTHLY
"},{"location":"projects/nadm/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: drought, NADM, North America, United States, Canada, Mexico
Created & provided by: NOAA, NIDIS, NCEI
Curated by: Climate Engine Org
"},{"location":"projects/nafd/","title":"NAFD Forest Disturbance History 1986-2010","text":"The North American Forest Dynamics (NAFD) products provided in this data set consist of 25 annual and two time-integrated forest disturbance maps for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. Each annual map has classified pixels showing water, no forest cover, forest cover, no data available (data gaps) in present year, and forest disturbances that occurred in that year. The time-integrated maps are similarly classified, but over the entire 1986-2010 period with the first and last forest disturbance years identified and provided as separate maps.
Maps have a nominal spatial resolution of 30 m, with forest disturbances mapped at an annual time step . These products are referred to as the NAFD-NEX data set to acknowledge the collaboration with the supercomputing facilities at the NASA Ames Research Center provided by the NASA Earth Exchange (NEX: Nemani et al. 2011) to process the large volume of Landsat imagery used in this study. You can find details about the dataset including the Vegetation Change Tracker (VCT) algorithm here
"},{"location":"projects/nafd/#data-structure","title":"Data structure","text":"The North American Forest Dynamics (NAFD) products provided in this data set consist of 25 annual and two time-integrated forest disturbance maps for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. Each annual map has classified pixels showing water, no forest cover, forest cover, no data available (data gaps) in present year, and forest disturbances that occurred in that year. The time-integrated maps are similarly classified, but over the entire 1986-2010 period with the first and last forest disturbance years identified and provided as separate maps.
Maps have a nominal spatial resolution of 30 m, with forest disturbances mapped at an annual time step (Fig. 1). These products are referred to as the NAFD-NEX data set to acknowledge the collaboration with the supercomputing facilities at the NASA Ames Research Center provided by the NASA Earth Exchange (NEX: Nemani et al. 2011) to process the large volume of Landsat imagery used in this study.
"},{"location":"projects/nafd/#data-file-naming","title":"Data File Naming","text":"The annual forest disturbance map GeoTIFF files are named as follows: VCT_Annual_30m_YYYY.tif
The two time-integrated forest disturbance GeoTIFF maps are named VCT_30m_first.tif and VCT_30m_last.tif.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/nafd/#data-citation","title":"Data Citation","text":"Goward, S.N., C. Huang, F. Zhao, K. Schleeweis, K. Rishmawi, M. Lindsey, J.L. Dungan, and A. Michaelis. 2016. NACP NAFD Project:\nForest Disturbance History from Landsat, 1986-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1290\nvar nafd_annual = ee.ImageCollection(\"projects/sat-io/open-datasets/NAFD/vct_annual\");\nvar nafd_first = ee.Image(\"projects/sat-io/open-datasets/NAFD/VCT_30m_first\");\nvar nafd_last = ee.Image(\"projects/sat-io/open-datasets/NAFD/VCT_30m_last\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/NAFD-FOREST-DISTURBANCE
"},{"location":"projects/nafd/#license","title":"License","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
Created by: Goward, S.N., C. Huang, F. Zhao, K. Schleeweis, K. Rishmawi, M. Lindsey, J.L. Dungan, and A. Michaelis
Curated in GEE by : Samapriya Roy
keywords: NAFD, Forest Dynamics, NAFD-NEX, Landsat
Last modified: 2016-03-03
Last updated on GEE: 2022-09-05
"},{"location":"projects/native/","title":"Native Land (Indigenous Land Maps)","text":"Land acknowledgements are a way that people insert an awareness of Indigenous presence and land rights in everyday life. This is often done at the beginning of ceremonies, lectures, or, in this case, education guides. It can be an explicit yet limited way to recognize the history of colonialism and first nations as well as a need for change in settler-colonial societies. In this context, we\u2019re looking to acknowledge the existence of Indigenous bodies in geography and how they occupy land. You can visit the actual map here https://native-land.ca/
Native-Land.ca offers an online platform where users can interact with maps of Indigenous territories, treaties, and languages, and locate themselves and their favorite places on the map. Fundamentally, the maps aim to visualize the complexity and diversity of Indigenous peoples, nations, and cultures across the Americas, Australia, and increasingly the world, so that nonIndigenous and Indigenous people alike can increase their understanding and knowledge of the breadth and depth of Indigenous history in these places. Some of the studies in the systematic review describe Indigenous populations within administrative boundaries (i.e. states and countries), for which data is relatively easy to obtain as it is often available through government sources. Other studies described Indigenous groups, lands and territories, for which data isn\u2019t readily available for various reasons (colonial legacies and land tenure and governance, with factors such as changing boundaries and non-digitized records).
"},{"location":"projects/native/#native-land-disclaimer","title":"Native Land Disclaimer","text":"This map does not represent or intend to represent official or legal boundaries of any Indigenous nations. To learn about definitive boundaries, contact the nations in question. Also, this map is not perfect -- it is a work in progress with tons of contributions from the community. Please send us fixes if you find errors. If you would like to read more about the ideas behind Native Land or where we are going, check out the blog. You can also see the roadmap. Also something to keep in mind
(dataset) Native Land Territories map. (2021). Native Land CA. https://native-land.ca/. Accessed 2021-09-19.
"},{"location":"projects/native/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var territories = ee.FeatureCollection(\"projects/sat-io/open-datasets/native-land/indigenousTerritories\");\nvar languages = ee.FeatureCollection(\"projects/sat-io/open-datasets/native-land/indigenousLanguages\");\nvar treaties = ee.FeatureCollection(\"projects/sat-io/open-datasets/native-land/indigenousTreaties\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/INDIGENOUS-LAND-MAPS
"},{"location":"projects/native/#license","title":"License","text":"The Native Land Maps are under a Creative Commons International Attribution License and the datasets are publicly available resource.
Created by: Native Land CA
Curated by: Samapriya Roy
Keywords: native lands, indigenousTreaties, indigenousLanguages, indigenousTerritories, Indigenous, land rights
Last updated: 2023-12-14
"},{"location":"projects/nawbd/","title":"High-Res water body dataset for tundra and boreal forests North America","text":"This represents a water body dataset for the North American high latitudes (WBD-NAHL). Nearly 6.5 million water bodies were identified, with approximately 6 million (\u223c90\u2009%) of them smaller than 0.1\u2009km2. The dataset provides area and morphological attributes for every water body. During this study, we developed an automated approach for detecting surface water extent and identifying water bodies in the 10\u2009m resolution Sentinel-2 multispectral satellite data to enhance the capability of delineating small water bodies and their morphological attributes. The approach was applied to the Sentinel-2 data acquired in 2019 to produce the water body dataset for the entire tundra and boreal forests in North America. The dataset provided a more complete representation of the region than existing regional datasets for North America, e.g., Permafrost Region Pond and Lake (PeRL). The total accuracy of the detected water extent by the WBD-NAHL dataset was 96.36\u2009% through comparison to interpreted data for locations randomly sampled across the region. The original data source is from the National Tibetan Plateau/Third Pole Environment Data Center.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/nawbd/#citation","title":"Citation","text":"Sui, Yijie, Min Feng, Chunling Wang, and Xin Li. \"A high-resolution inland surface water body dataset for the tundra and boreal forests of North\nAmerica.\" Earth System Science Data 14, no. 7 (2022): 3349-3363.\nvar wbd = ee.FeatureCollection(\"projects/sat-io/open-datasets/HYDRO/wbd_fixed_geoms\");\nSample Code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HRES-INLAND-WB-NA
"},{"location":"projects/nawbd/#license-and-usage","title":"License and Usage","text":"This dataset is shared under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.
Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).
Curated by: Ethan D. Kyzivat & Samapriya Roy
Keywords: Hydrology, Boreal , Tundra, water body
Last updated: 2022-02-21
Last updated in GEE: 2023-02-22
"},{"location":"projects/nbac/","title":"Canada National Burned Area Composite (NBAC)","text":"The National Burned Area Composite (NBAC) is a GIS database and system that calculates the area of forest burned on a national scale for each year since 1986. The data are used to help estimate carbon emissions in Canada. The burned area is determined by evaluating a number of available sources of data, which use different techniques to map any given fire. The system chooses the best available source of data for each burned area and builds a national composite picture.
The NBAC is part of the Fire Monitoring, Accounting and Reporting System (FireMARS), jointly developed by the Canada Centre for Mapping and Earth Observation (formerly the Canada Centre for Remote Sensing) of Natural Resources Canada and the Canadian Forest Service. Initially, FireMARS was developed with funding support from the Canadian Space Agency Government Related Initiatives Program through a collaboration of those in fire research, forest carbon accounting and remote sensing.
Data are provided for NBAC from: \u2022 Natural Resources Canada, and \u2022 Provincial, Territorial, and Parks Canada agencies.
The NBAC can be used for spatial and temporal analyses of landscape-scale fire impacts. You can download the datasets here
"},{"location":"projects/nbac/#supplemental-information","title":"Supplemental Information","text":"NBAC is a national product compiled annually since 1986 by the FireMARS system which tracks forest fires for annual estimates of carbon emissions and to help identify National Forest Inventory plots that may have been disturbed by fire. See the FireMARS website at (http://www.nrcan.gc.ca/forests/fire/13159) and carbon accounting - disturbance monitoring website (http://www.nrcan.gc.ca/forests/climate-change/13109) for additional information.
When using these data for mapping activities and analysis, research, evaluation or display, please acknowledged the source using the following citation:
Canadian Forest Service. National Burned Area Composite (NBAC). Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta. https://cwfis.cfs.nrcan.gc.ca/.
"},{"location":"projects/nbac/#citation","title":"Citation","text":"Skakun, R.; Castilla, G.; Metsaranta, J.; Whitman, E.; Rodrigue, S.; Little, J.; Groenewegen, K.; Coyle, M. (2022). Extending the National Burned\nArea Composite Time Series of Wildfires in Canada. Remote Sensing, 14, 3050. DOI: https://doi.org/10.3390/rs14133050\n\nSkakun, R.S.; Whitman, E.; Little, J.M.; and Parisien, M.-A. (2021). Area burned adjustments to historical wildland fires in Canada. Environmental\nResearch Letters 16 064014. DOI: https://doi.org/10.1088/1748-9326/abfb2c\n\nHall, R.J.; Skakun, R.S.; Metsaranta, J.M.; Landry, R.; Fraser, R.H.; Raymond, D.A.; Gartrell, J.M.; Decker, V. and Little, J.M. (2020). Generating\nannual estimates of forest fire disturbance in Canada: the National Burned Area Composite. International Journal of Wildland Fire. 10.1071/WF19201.\nDOI: https://doi.org/10.1071/WF19201\nvar nbac_raster8622 = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/NBAC_MRB_1986_to_2022\");\nvar nbac8622 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/nbac_1986_to_2022_20230630\");\nvar nbac_1986_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1986_r9_20210810\");\nvar nbac_1987_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1987_r9_20210810\");\nvar nbac_1988_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1988_r9_20210810\");\nvar nbac_1989_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1989_r9_20210810\");\nvar nbac_1990_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1990_r9_20210810\");\nvar nbac_1991_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1991_r9_20210810\");\nvar nbac_1992_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1992_r9_20210810\");\nvar nbac_1993_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1993_r9_20210810\");\nvar nbac_1994_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1994_r9_20210810\");\nvar nbac_1995_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1995_r9_20210810\");\nvar nbac_1996_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1996_r9_20210810\");\nvar nbac_1997_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1997_r9_20210810\");\nvar nbac_1998_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1998_r9_20210810\");\nvar nbac_1999_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1999_r9_20210810\");\nvar nbac_2000_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2000_r9_20210810\");\nvar nbac_2001_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2001_r9_20210810\");\nvar nbac_2002_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2002_r9_20210810\");\nvar nbac_2003_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2003_r9_20210810\");\nvar nbac_2004_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2004_r9_20210810\");\nvar nbac_2005_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2005_r9_20210810\");\nvar nbac_2006_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2006_r9_20210810\");\nvar nbac_2007_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2007_r9_20210810\");\nvar nbac_2008_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2008_r9_20210810\");\nvar nbac_2009_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2009_r9_20210810\");\nvar nbac_2010_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2010_r9_20210810\");\nvar nbac_2011_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2011_r9_20210810\");\nvar nbac_2012_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2012_r9_20210810\");\nvar nbac_2013_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2013_r9_20210810\");\nvar nbac_2014_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2014_r9_20210810\");\nvar nbac_2015_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2015_r9_20210810\");\nvar nbac_2016_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2016_r9_20210810\");\nvar nbac_2017_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2017_r9_20210810\");\nvar nbac_2018_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2018_r9_20210810\");\nvar nbac_2019_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2019_r9_20210810\");\nvar nbac_2020_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2020_r9_20210810\");\nvar nbac_2021_r9_20220624 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2021_r9_20220624\");\nvar nbac_2022_r12_20230630 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2022_r12_20230630\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-NATIONAL-BURNED-AREA-COMPOSITE
"},{"location":"projects/nbac/#license","title":"License","text":"Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada). When using these data for mapping activities and analysis, research, evaluation or display, please acknowledged the source using the following citation: Canadian Forest Service. National Burned Area Composite (NBAC). Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta. https://cwfis.cfs.nrcan.gc.ca/.
Created by: Natural Resources Canada,Canadian Wildland Fire Information System
Curated in GEE by : Samapriya Roy
Keywords: canada,burned area,forestry,forest fire,wildfire
Last updated in GEE: 2024-04-02
"},{"location":"projects/nclim_grid/","title":"NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid)","text":"The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) dataset is available as either a daily (NClimGrid-d) or a monthly (NClimGrid-m) dataset. The datasets contain gridded fields and area averages of maximum, minimum, and mean temperature and precipitation amounts for the contiguous United States. NClimGrid consists of gridded fields covering the land area between approximately 24\u00b0N and 49\u00b0N and between 67\u00b0W and 125\u00b0W at a resolution of 1/24 of a degree (0.041667\u00b0). The primary purpose of these products is to support applications such as drought monitoring that require time series of spatially and/or temporally aggregated gridpoint values. Reliance on single-day values and individual points is discouraged due to the significant uncertainty that is inherent in such a product, as a result of the spatial distribution of the underlying observations, differences in observation time between neighboring stations, and interpolation errors. Spatial and temporal averaging tends to reduce the effect of these uncertainties, and time series of such aggregated values can be shown to be suitable for climatological applications. You can find addtional informationabout the dataset here and climate engine org page here.
"},{"location":"projects/nclim_grid/#dataset-description","title":"Dataset description","text":"Spatial Information
Parameter Value Spatial extent Conterminous United States Spatial resolution 4.6-km (1/24-deg x 1/24-deg) Temporal resolution Daily and monthly Time span 1951-01-01 to present (daily data); 1895-01-01 to present (monthly data) Update frequency Updated daily with 3 day lag (daily data); Updated monthly with 1 month lag (monthly data)Variables
Variable Details Minimum temperature, 2m - Units: Degrees Celsius - Scale factor: 1.0 Maximum temperature, 2m - Units: Degrees Celsius ('tmin') - Scale factor: 1.0 Mean temperature ('tavg') - Units: Degrees Celsius - Scale factor: 1.0 Precipitation ('precip') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/nclim_grid/#citation","title":"Citation","text":"Vose, Russell S., Applequist, Scott, Squires, Mike, Durre, Imke, Menne, Matthew J., Williams, Claude N. Jr., Fenimore, Chris, Gleason, Karin, and\nArndt, Derek (2014): NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid), Version 1. [indicate subset used]. NOAA National Centers for\nEnvironmental Information. DOI:10.7289/V5SX6B56 [access date].\n// Read in daily and monthly Image Collections and get single image from each collection\nvar nclimgrid_m_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-ncei-nclimgrid/monthly')\nvar nclimgrid_m_i = nclimgrid_m_ic.first()\nvar nclimgrid_d_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-ncei-nclimgrid/daily')\nvar nclimgrid_d_i = nclimgrid_d_ic.first()\n\n// Print each single image to see bands\nprint(nclimgrid_m_i)\nprint(nclimgrid_d_i)\n\n// Visualize each band from each single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(nclimgrid_m_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip, monthly')\nMap.addLayer(nclimgrid_m_i.select('tmin'), {min: -20, max: 20, palette: temp_palette}, 'tmin, monthly')\nMap.addLayer(nclimgrid_m_i.select('tmax'), {min: -20, max: 20, palette: temp_palette}, 'tmax, monthly')\nMap.addLayer(nclimgrid_m_i.select('tavg'), {min: -20, max: 20, palette: temp_palette}, 'tavg, monthly')\nMap.addLayer(nclimgrid_d_i.select('precip'), {min: 0, max: 10, palette: prec_palette}, 'precip, daily')\nMap.addLayer(nclimgrid_d_i.select('tmin'), {min: -20, max: 20, palette: temp_palette}, 'tmin, daily')\nMap.addLayer(nclimgrid_d_i.select('tmax'), {min: -20, max: 20, palette: temp_palette}, 'tmax, daily')\nMap.addLayer(nclimgrid_d_i.select('tavg'), {min: -20, max: 20, palette: temp_palette}, 'tavg, daily')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/NOAA-NCLIM-GRID
"},{"location":"projects/nclim_grid/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site. NClimGrid Data Use And Access Constraints: https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332#Constraints
Keywords: NOAA, nclim, CONUS, United Stated, daily, near real-time, temperature, precipitation
Provider: NOAA
Curated in Earth Engine by: Climate Engine Org
"},{"location":"projects/nhd/","title":"National Hydrography Dataset (NHD)","text":"The National Hydrography Dataset (NHD) represents the water drainage network of the United States with features such as rivers, streams, canals, lakes, ponds, coastline, dams, and streamgages. The National Hydrography Dataset (NHD) is mapped at 1:24,000 or larger scale (1:63,360 or larger scale in Alaska). These data are updated and maintained through Stewardship partnerships with states and other collaborative bodies (source)
The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all. You can find the dataset and additional links here
"},{"location":"projects/nhd/#data-citation","title":"Data Citation","text":"You can find citation information here. The USGS recommends the user to follow guidelines from the journal in question.
Citation example
U.S. Geological Survey, 2022, National Hydrography Dataset (ver. USGS National Hydrography Dataset Best Resolution (NHD) for Hydrologic Unit (HU) 4 - 2001 (published 20191002)), accessed April 29, 2022 at URL https://www.usgs.gov/national-hydrography/access-national-hydrography-products\nindividual shapefiles pertaining to each dataset within a state is ingested to Google Earth Engine. It seems that the flowline features were split for large areas and as such to avoid the use having to combine multiple collections and flatten, a flattened joined NHDFlowline layer has been provided for each state. Each state has it's own sub folder and as such we recommend using the state abbreviations to get to the state of choice. Overall list of all states can be generated by simply using the earthengine command line
earthengine ls \"projects/sat-io/open-datasets/NHD\"\nThe datasets are arranged by state abbreviations, so to get to a specific state simply replace state abbreviation
Example Path: projects/sat-io/open-datasets/NHD/NHD_AK
"},{"location":"projects/nhd/#earth-engine-snippet","title":"Earth Engine Snippet","text":"Sample example for state Missouri with state abbreviation MO
var nhd_area = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDArea\");\nvar nhd_flowline = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDFlowline\");\nvar nhd_line = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDLine\");\nvar nhd_point = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDPoint\");\nvar nhd_point_event_fc = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDPointEventFC\");\nvar nhd_waterbody = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDWaterbody\");\nvar nhd_wbdhu10 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU10\");\nvar nhd_wbdhu12 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU12\");\nvar nhd_wbdhu14 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU14\");\nvar nhd_wbdhu2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU2\");\nvar nhd_wbdhu4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU4\");\nvar nhd_wbdhu6 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU6\");\nvar nhd_wbdhu8 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU8\");\nvar nhd_wbdline = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDLine\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-HYDROGRAPHY-DATASET
"},{"location":"projects/nhd/#license","title":"License","text":"The NHD data is distributed under a license similar to Public domain license and distributed by United States Geological Survey (USGS)
Created by: United States Geological Survey (USGS)
Curated in GEE by : Samapriya Roy
Keywords: Hydrography, Hydrologic, United States, USGS, NHD, National Hydrography Dataset
Last updated on GEE: 2022-05-04
"},{"location":"projects/nhd/#data-changelog","title":"Data changelog","text":"The ACIS Climate Maps are produced daily using data from the Applied Climate Information System (ACIS) at a 5-km (0.04-deg x 0.04-deg) spatial resolution from 1951-present, updated every 1-2 weeks.You can find additiona information here and the Climate Engine org dataset page. Station data in ACIS primarily come from the following networks:
Note: All near-real-time data are considered preliminary and subject to change.
"},{"location":"projects/noaa_acis/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Conterminous United States Spatial resolution 5-km (0.04-deg x 0.04-deg) Temporal resolution Daily Time span 1951-01-01 to present Update frequency 1-2 weeksVariables
Variable Details Minimum temperature, 2m ('tmax') - Units: Degrees Fahrenheit - Scale factor: 1.0 Maximum temperature, 2m ('tmin') - Units: Degrees Fahrenheit - Scale factor: 1.0 Precipitation ('precip') - Units: Inches - Scale factor: 1.0"},{"location":"projects/noaa_acis/#earth-engine-snippet-if-dataset-already-in-gee","title":"Earth Engine Snippet if dataset already in GEE","text":"// Read in Image Collection and get first image\nvar acis_nrcc_nn_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-nrcc-acis-nn/daily')\nvar acis_nrcc_nn_i = acis_nrcc_nn_ic.first()\n\n// Print first image to see bands\nprint(acis_nrcc_nn_i)\n\n// Visualize each band from first image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(acis_nrcc_nn_i.select('precip'), {min: 0, max: 0.5, palette: prec_palette}, 'precip')\nMap.addLayer(acis_nrcc_nn_i.select('tmin'), {min: -10, max: 50, palette: temp_palette}, 'tmin')\nMap.addLayer(acis_nrcc_nn_i.select('tmax'), {min: -10, max: 50, palette: temp_palette}, 'tmax')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/NOAA-NRCC-ACIS
"},{"location":"projects/noaa_acis/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: climate, precipitation, temperature, NOAA, reanalysis, CONUS, daily, near real-time
Dataset provider: NOAA
Curated in GEE by: Climate Engine Org
"},{"location":"projects/npp_viirs_ntl/","title":"Global NPP-VIIRS-like nighttime light (2000-2022)","text":"The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process.
The extended data NPP-VIIRS-like NTL data (2000\u20132022) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000\u20132018) of nighttime light data.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/npp_viirs_ntl/#citation","title":"Citation","text":"Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., and Wu, J.: An extended time series (2000\u20132018) of global NPP-VIIRS-like nighttime\nlight data from a cross-sensor calibration, Earth Syst. Sci. Data, 13, 889\u2013906, https://doi.org/10.5194/essd-13-889-2021, 2021.\nvar viirs_ntl = ee.ImageCollection(\"projects/sat-io/open-datasets/npp-viirs-ntl\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-NPP-VIIRS-LIKE-NTL
"},{"location":"projects/npp_viirs_ntl/#license","title":"License","text":"These datasets are made available under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information below.
Created by: Chen, Zuoqi et al
Curated in GEE by : Samapriya Roy
keywords: Nighttime light, VIIRS, NTL, NPP-VIIRS
Last updated on GEE: 2023-08-13
"},{"location":"projects/nsi/","title":"National Structures Inventory","text":"NoteThese will be made available primarily in the insiders only dataset before being made generally available to all users of the community catalog
The National Structure Inventory (NSI) is a system of databases containing structure inventories of varying quality and spatial coverage. The purpose of the NSI databases is to facilitate storage and sharing of point-based structure inventories used in the assessment and analysis of natural hazards. Flood risk is the primary usage, but sufficient data exists on each structure to compute damages and life safety risk due to other hazard types. This document describes the NSI data structure and the processes utilized to produce the 2022 NSI base data.
The National Structure Inventory Base layer was created and is maintained by the U.S. Army Corps of Engineers (USACE). The USACE base data layer was created to simplify the GIS pre-processing workflow for the USACE Modeling Mapping and Consequence center, but the data has gone on to see use in a variety of USACE, FEMA, and other agency applications. The NSI is a repository of point structure inventories with a structured RESTful API service, and the inventory contains a series of required attributes or fields that describe each point in the inventory.
"},{"location":"projects/nsi/#table-of-public-fields","title":"Table of Public Fields","text":"The NSI attributes available to the general public areField Name Description Attribute Type Limits fd_id A number that should be unique for all structures. Integer bid A building ID, represented as the centroid (in grid reference system format) and four cardinal extents. String x X coordinate of each structure in the Geographic Coordinate System (GCS) WGS84. Double y Y coordinate of each structure in GCS WGS84. Double cbfips Census Block that contains the structure. Currently, the NSI refers to 2010 census blocks. String 15 Characters st_damcat Damage category of the structure. Aggregated categories include Residential, Commercial, Industrial, or Public. String occtype Damage Function or Occupancy Type of the structure, related to depth-damage relationships. String bldgtype Building type of the structure (e.g., Masonry, Wood, Manufactured, Steel). String source The source of the initial iteration of the structure (e.g., Parcel, ESRI, HIFLD). String sqft Estimated square footage of the structure. Double ftprntid Identifier of the building footprint record used for estimating fields such as sqft and num_story. String ftprntsrc The source of the utilized footprint (e.g., Bing, Oak Ridge National Labs, NGA). String found_type Type of foundation (e.g., Crawl, Basement, Slab, Pier). String found_ht Foundation height of the structure in feet from the ground elevation. Double num_story The number of stories of the structure. Double val_struct Value in dollars of the structure. The base NSI estimates depreciated replacement value. Double val_cont Value in dollars of the contents of the structure. Double val_vehic Value in dollars of the cars at the structure. Double med_yr_blt Median year built of structures within the Census Block. Integer pop2amu65 Population at night for people under the age of 65. Integer pop2amo65 Population at night for people over the age of 65. Integer pop2pmu65 Population during the day for people under the age of 65. Integer pop2pmo65 Population during the day for people over the age of 65. Integer students Number of students attending the school as estimated by NCES data. Integer o65disable Percent of the county population over the age of 65 with an ambulatory disability. Double u65siable Percent of the county population under the age of 65 with an ambulatory disability. Double firmzone Estimated 2021 flood zone for the structure. String grnd_elv_m Ground elevation (in meters, NAVD88) at the structure. Double ground_elv Ground elevation (in feet, NAVD88) at the structure. Double
"},{"location":"projects/nsi/#structure-processing-and-valuation","title":"Structure Processing and Valuation","text":"The National Structure Inventory (NSI) involves several key processes for managing and analyzing structures:
Structure Placement and Aggregation: Initially, structure locations are based on source data such as parcel centroids or business addresses. The NSI Generator refines these locations by aligning structures with building footprints within the same parcel. Commercial structures outside their original parcel are relocated based on distance and use code similarity. Structures are placed in unpaired footprints until all footprints are matched. Stacked structures are partially or completely merged based on their occupancy type, with residential units combined as multi-family structures if stacked. Commercial structures are merged only if they share the same occupancy type and similar characteristics, like number of stories and construction material.
Population Distribution: The NSI-2022 estimates population levels for 2020 using data from 2010 block information and 2020 county data. This population is distributed to structures according to housing units and block-level estimates. For commercial structures, worker population estimates are derived from the U.S. Census Bureau\u2019s LEHD database.
Structure Valuation: Depreciated replacement values for structures are estimated based on a dollar per square foot metric, with depreciation at 1% per year for the first 20 years. Content values are calculated using specific ratios tied to occupancy types.
Occupancy types help determine structure valuation and damage criteria. They are based on FEMA definitions with further classification.
The table below shows occupancy types and their content-to-structure value ratiosDamage Category Occupancy Type Name Description Content to Structure Value Ratio Residential RES1-1SNB Single Family Residential, 1 story, no basement 0.5 Residential RES1-1SWB Single Family Residential, 1 story, with basement 0.5 Residential RES1-2SNB Single Family Residential, 2 story, no basement 0.5 Residential RES1-2SWB Single Family Residential, 2 story, with basement 0.5 Residential RES1-3SNB Single Family Residential, 3 story, no basement 0.5 Residential RES1-3SWB Single Family Residential, 3 story, with basement 0.5 Residential RES1-SLNB Single Family Residential, split-level, no basement 0.5 Residential RES1-SLWB Single Family Residential, split-level, with basement 0.5 Residential RES2 Manufactured Home 0.5 Residential RES3A Multi-Family housing 2 units 0.5 Residential RES3B Multi-Family housing 3-4 units 0.5 Residential RES3C Multi-Family housing 5-10 units 0.5 Residential RES3D Multi-Family housing 10-19 units 0.5 Residential RES3E Multi-Family housing 20-50 units 0.5 Residential RES3F Multi-Family housing 50 plus units 0.5 Commercial COM1 Light Commercial, Office, Retail 1.0 Commercial COM2 General Commercial 1.0 Commercial COM3 Heavy Commercial, Manufacturing 1.0 Commercial COM4 Industrial 1.0 Public PUB1 Institutional 1.0 Public PUB2 Education 1.0 Public PUB3 Healthcare 1.0 Public PUB4 Government 1.0
"},{"location":"projects/nsi/#main-data-sources","title":"Main Data Sources","text":"The table contains main data sources of dataSource Database Dataset Description HAZUS Bndrygbs.mdb hzCensusBlock Provides the structure building schemes and block type. flSchemeCoastal, flSchemeRiverine, flSchemeGLakes Provides information on foundation type and height. MSH.mdb flGenBldgScheme Provides the construction type distributions and NFIP entry year for structures. USACE NSI 2015 Base layer Used in any Census Block that lacks ESRI or CoreLogic data. Homeland Infrastructure Foundation-Level Data Lightbox County Level Data Parcel polygons and associated data tables; used for initial spatial location and occupancy type, and may influence structure attributes (square feet, foundation type, etc) of single-family structures. Nursing Home Point data indicating the presence of a nursing home and its number of beds. Hospital Point data indicating the presence of a hospital and its number of beds. Mobile Home Point data indicating the presence of a mobile home park and the number of units associated with the park (either exact units, or a range). Map Building Layer Nationwide building footprint parcel. Largely restricted to central business districts. Often indicating the height of the building to the nearest meter. Used to improve structure locations, square foot estimates and number of stories estimates. Esri Business Layer InfoGroup Provides initial structure location; NAICS code informs occupancy type and the number of employee field influences population weighting and square footage estimates. Microsoft Building Footprints State level polygons Paired with parcel polygons to improve structure location and to inform structure aggregation and square footage estimates. FEMA Geospatial Resource Center USA Structures State level polygons Includes both ORNL and NGA generated footprint polygons. Paired with parcel polygons to improve structure location and to inform structure aggregation and square footage estimates. NGA based footprints include heights in meters and help inform number of stories estimates. U.S. Census Bureau American Community Survey Population, Demographics Informs population growth estimates, disability rates, and age distribution. Characteristics of New Housing Annual, Various Provides structure characteristic data such as number of stories and square feet. Longitudinal Employer-Household Dynamic Database Population Data Contains worker counts by origin and destination census blocks. Used to decrease residential populations (primarily in the day) and to create a population pool for commercial workers. NCES Schools Database School Data Contains the locations of schools, number of teachers and students per school. U.S. Geological Survey National Elevation Dataset 10 Meter Dataset Provides raster ground elevation data.
"},{"location":"projects/nsi/#citation","title":"Citation","text":"U.S. Army Corps of Engineers (Year). National Structure Inventory (NSI) Base Data. U.S. Army Corps of Engineers. URL or DOI.\nThe datasets are available by state using the two alphabet state abbreviation for example, for WYOMING (WY)
var nsi_wy = ee.FeatureCollection('projects/sat-io/open-datasets/NSI/nsi_2022_WY');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/NSI
"},{"location":"projects/nsi/#license","title":"License","text":"The National Structure Inventory (NSI) from the U.S. Army Corps of Engineers (USACE) has a licensing structure that allows for public access to its primary fields, as these fields are curated to have less restrictive license agreements.
Provided by: USACE
Curated in GEE by: Samapriya Roy
Keywords: Buildings, Structures Inventory, US
Last updated: 2024-08-07
"},{"location":"projects/nwi/","title":"National Wetland Inventory (Surface Water and Wetlands)","text":"The US Fish and Wildlife Service (FWS) is the principal US Federal agency tasked with providing information to the public on the status and trends of our Nation's wetlands. Wetlands provide a multitude of ecological, economic and social benefits. They provide habitat for fish, wildlife and plants - many of which have a commercial or recreational value - recharge groundwater, reduce flooding, provide clean drinking water, offer food and fiber, and support cultural and recreational activities. Unfortunately, over half of America\u2019s wetlands have been lost since 1780, and wetland losses continue today. This highlights the urgent need for geospatial information on wetland extent, type, and change. The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US wetlands. NWI data are used by natural resource managers, within the US FWS and throughout the Nation, to promote the understanding, conservation and restoration of wetlands. You can download the dataset here.
"},{"location":"projects/nwi/#dataset-and-gee-reference","title":"Dataset and GEE reference","text":"Layer Name GEE_Folder_Prefix Description NWI Historic Wetlands historic_wetlands This data set represents the extent and approximate location of historic wetland habitats in certain areas of the conterminous United States NWI Historic Wetlands Project Metadata hwpm This data set represents the extent, status, and location of current NWI historic wetland mapping projects. NWI Wetlands wetlands This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories NWI Wetlands Project Metadata wpm This data set represents the extent, status, and location of National Wetland Inventory wetland mapping projects for NWI Version 2, Surface Waters and Wetlands NWI Riparian Areas riparian This data set represents the extent, approximate location and type of riparian habitats in the western United States. These data delineate the areal extent of riparian habitats as defined by a System For Mapping Riparian Areas in the United States (USFWS 2009) NWI Riparian Project Metadata rpm This data set represents the extent, status, and location of current NWI riparian mapping projects NWI Wetlands Historic Map Info hmi This data set represents the extent and location of historic wetland map reports generated by the U.S. Fish and Wildlife Service, cooperators, and contractors"},{"location":"projects/nwi/#data-preprocessing","title":"Data Preprocessing","text":"The datasets are provided by states and some states are split into multiparts. The shapefile consists of multiple geometry types including but not limited to points an linestring apart from polygons and multipolygons. Attempt was made to combine multiple parts from each state into a single feature collection within earthengine, since GEE will not work with a zero area object during a featurecollection export, a filter was created to tag each feature type and calculate area. Also zero area features were then excluded. Currently the wetlands datasets is the only one where this transformation was applied.
Currently these wetlands files are complete and are present in the folder with naming State-Abbreviation_Wetlands so for example for Florida , FL_Wetlands and so on. Other datasets are not present for all states and you can get a list of assets by simply running an earthengine ls on the objects or by using the catalog. Since this is an immensely large dataset collection, no attempt was made to create a country wide composite. A single JSON file is also created to allow you to asses which datasets contain which state as an easy reference, you can find it here.
(dataset) U.S. Fish & Wildlife Service. (2018). National Wetlands Inventory. U.S. Fish & Wildlife Service. https://data.nal.usda.gov/dataset/national-wetlands-inventory. Accessed 2021-09-19.
"},{"location":"projects/nwi/#earth-engine-snippet","title":"Earth Engine Snippet","text":"The dataset templates underneath can be simply replaced by the state code/territory code to get to the state/region of interest.
var wetlands = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/wetlands/FL_Wetlands\");\nvar historic_wetland = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/historic_wetlands/FL_Historic_Wetlands\");\nvar historic_wetland_project_metadata = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/hwpm/FL_Historic_Wetlands_Project_Metadata\");\nvar historic_map_info = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/hmi/FL_Wetlands_Historic_Map_Info\");\nvar co_riparian = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/riparian/CO_Riparian\");\nvar co_riparian_metadata = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/rpm/CO_Riparian_Project_Metadata\");\nvar wetlands_metadata = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/wpm/FL_Wetlands_Project_Metadata\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-WETLANDS-INVENTORY
"},{"location":"projects/nwi/#wetlands-layer-legend","title":"Wetlands Layer Legend","text":"Wetland types are displayed on the Wetlands Mapper in groups of similar classifications (e.g. all freshwater emergent wetlands are displayed as a single color category). The display categories are shown in the table below. Display color codes are provided for those looking to create their own maps using the Mapper color scheme.
#008837 Freshwater- Forested and Shrub wetland #7FC31C Freshwater Emergent wetland #688CC0 Freshwater pond #66C2A5 Estuarine and Marine wetland #0190BF Riverine #13007C Lakes #007C88 Estuarine and Marine Deepwater #B28653 Other Freshwater wetland
"},{"location":"projects/nwi/#license","title":"License","text":"The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US. NWI datasets are freely available to the public (similar to a CC0 license) and the U.S. Public Domain license.
Created by: U.S. Fish and Wildlife Service
Curated by: Samapriya Roy
Keywords: wetlands, conservation areas, habitats, fish, wildlife, drinking water, recreation, U.S. Fish and Wildlife Service
Last updated: 2021-09-19
"},{"location":"projects/oam/","title":"Open Aerial Map Subset","text":"OpenAerialMap (OAM) was created as a set of tools and portal for searching, sharing, and using openly licensed satellite and unmanned aerial vehicle (UAV) imagery. Built on top of the Open Imagery Network (OIN), OAM is an open service that provides search and access to this imagery. While Open Aerial Map is excellent and with plug and play capability coming in the future this will only evolve as a resouce for most users the goal of bringing a subset in Google Earth Engine was to explore current capabilities of the platform with ultra high resolution datasets and to allow for labeling classification and potential use of the collection as teaching and training tool via plugins like Collect Earth online and other providers who might be interested in leveraging this further including applications like Machine learning models.
While a lot of preprocessing steps were applied the datasets are initially housed withing the analysis ready datasets snippets to reflect these images are often post processed by providers and are effectively ready to use to some extent.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/oam/#data-preprocessing","title":"Data preprocessing","text":"For creating a collection subset a automated script was created to fetch all images with a valid link and those that are not related to the platform satellite. This was to keep the overall platforms limited to other forms of platforms. The datasets were upload into a GCS bucket before ingest and while the uncompressed size for the overall collection was only 1.9 TB , GEE uncompressed quota usage exceed over 7+ TB. I am hoping to do more event specific updates in the future to allow for exploration across different use cases. Since a lot of the no data values for 8 bit imagery were coded incorrectly 0 and 255 were chosen and default nodata values list but results will vary depending on the individual images.
"},{"location":"projects/oam/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var oam_subset = ee.ImageCollection(\"projects/sat-io/open-datasets/open-aerial-map\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/OPEN-AERIAL-MAP
"},{"location":"projects/oam/#license","title":"License","text":"All imagery is publicly licensed and made available through the Humanitarian OpenStreetMap Team's Open Imagery Network (OIN) Node. All imagery contained in OIN is licensed CC-BY 4.0, with attribution as contributors of Open Imagery Network. Each imagery has their own license type and it is included in the image metadata.
Provided by: Open Aerial Map Community providers
Curated in GEE by : Samapriya Roy
keywords: Open Aerial Map, OAM, HOTOSM, Drones, UAV, High resolution
Last updated on GEE: 2023-04-20
"},{"location":"projects/ogim/","title":"Oil and Gas Infrastructure Mapping (OGIM) database","text":"The Oil and Gas Infrastructure Mapping (OGIM) dataset, available in the Awesome Google Earth Engine (GEE) Community Catalog, is a comprehensive global repository of spatially explicit information on oil and gas infrastructure. Developed by the Environmental Defense Fund (EDF) (www.edf.org), the OGIM database is meticulously curated to facilitate the quantification and characterization of methane emissions from oil and gas sources.
This dataset is a result of extensive efforts at EDF, involving the acquisition, analysis, curation, integration, and quality assurance of public-domain datasets sourced from official government reports, industry publications, academic studies, and various non-government entities. The primary objective of the OGIM database is to provide valuable support for research and analysis related to oil and gas methane emissions. The dataset spans across the globe, offering a comprehensive view of oil and gas infrastructure.It provides detailed information on the locations and attributes of various oil and gas infrastructure types known to be significant sources of methane emissions. This includes data on oil and gas production wells, offshore production platforms, natural gas compressor stations, processing facilities, liquefied natural gas facilities, crude oil refineries, pipelines, and more.
You can read more about the OGIM dataset here and you can find the dataset here.
"},{"location":"projects/ogim/#citation","title":"Citation","text":"Omara, Mark, Ritesh Gautam, Madeleine O'Brien, Anthony Himmelberger, Alex Franco, Kelsey Meisenhelder, Grace Hauser et al.\n\"Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at\nhigh resolution.\" Earth System Science Data Discussions 2023 (2023): 1-35.\nRitesh Gautam. (2023). Oil and Gas Infrastructure Mapping (OGIM) database (OGIM_v1.1) [Data set]. Zenodo.\nhttps://doi.org/10.5281/zenodo.7922117\nOGIM geospatial data layer Additional information Geometry type Oil and natural gas wells Includes active, inactive, and plugged and abandoned oil and natural gas wells. POINT Natural gas compressor stations Facilities for natural gas compression in the gathering, transmission, and distribution sector. POINT Gathering and processing facilities Includes natural gas processing plants, natural gas dehydration and other treatment facilities, and oil gathering and processing facilities. POINT Tank battery Can be collocated with well sites; typical equipment includes oil and natural gas separation equipment and an arrangement of storage tanks. POINT Offshore platforms Oil and natural gas drilling, production, and processing platforms in offshore areas. POINT LNG facilities Includes both liquefaction and regasification facilities. POINT Crude oil refineries - POINT Petroleum terminals Includes tank farms and petroleum bulk storage tanks and terminals. POINT Injection, disposal, and underground storage facilities - POINT Stations - Other Includes metering and regulating stations and POL (petroleum, oil, and lubricants) pumping stations. POINT Equipment and components Includes point locations for dehydrators, separators, tanks, and valves. POINT Oil and natural gas production Includes reported well-level, facility-level, and field-level oil and natural gas production, as reported for 2021. POINT Natural gas flaring detections Based on VIIRS natural gas flaring detections in 2021. POINT
"},{"location":"projects/ogim/#earth-engine-snippet","title":"Earth Engine Snippet","text":"
var crude_oil_refineries = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/crude_oil_refineries\");\nvar equipment_and_components = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/equipment_and_components\");\nvar gathering_and_processing = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/gathering_and_processing\");\nvar injection_disposal_and_underground_storage = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/injection_disposal_and_underground_storage\");\nvar lng_facilities = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/lng_facilities\");\nvar natural_gas_compressor_stations = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/natural_gas_compressor_stations\");\nvar natural_gas_flaring_detections = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/natural_gas_flaring_detections\");\nvar offshore_platforms = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/offshore_platforms\");\nvar oil_and_natural_gas_production = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/oil_and_natural_gas_production\");\nvar oil_and_natural_gas_wells = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/oil_and_natural_gas_wells\");\nvar petroleum_terminals = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/petroleum_terminals\");\nvar stations_other = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/stations_other\");\nvar tank_battery = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/tank_battery\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/OGIM
"},{"location":"projects/ogim/#license","title":"License","text":"These datasets are provided under a Creative Commons Attribution 4.0 International Public License, unless otherwise noted.
Created by: Omara et al
Curated in GEE by: Samapriya Roy
Keywords: OGIM dataset, Methane emissions, Environmental Defense Fund (EDF), infrastructure types, oil and gas wells, offshore platforms, Compressor stations, Liquefied natural gas facilities, Crude oil refineries
Last updated: 2024-01-19
"},{"location":"projects/oil-palm/","title":"Oil Palm Plantation Layers","text":""},{"location":"projects/oil-palm/#1-oil-palm-plantation-indonesia-malaysia-thailand-1984-2017","title":"1) Oil Palm Plantation (Indonesia, Malaysia, Thailand) 1984-2017","text":"This paper used a landsat time series approach coupled with other datasets to determine the year in which the oil palm plantations are first detected, at which point they are 2 to 3 years of age. From this, the approximate age of the oil palm plantation in 2017 was generated.
Read the paper here
"},{"location":"projects/oil-palm/#data-records","title":"Data Records","text":"The data set is publicly accessible for download from the permanent DARE repository housed by the International Institute for Applied Systems Analysis (IIASA) (http://dare.iiasa.ac.at/85/)33. It consists of a 16-bit GeoTIFF at a resolution of 30\u2009m with a single attribute value, i.e., the year in which the oil palm plantation was first detected. At this point, the plantation is 2 to 3 years of age. The data values range from 0 to 37 where 0 is the No Data value. Values 1 to 3 are not present and a value of 4 corresponds to the year 1984, the first year oil palm was detected, and each consecutive number represents the next year, i.e., 5 is 1985, while the maximum value of 37 corresponds to 2017.
Use the following credit when these datasets or paper is cited:
Danylo, O., Pirker, J., Lemoine, G. et al. A map of the extent and year of detection of oil palm plantations in Indonesia\nMalaysia and Thailand. Sci Data 8, 96 (2021). https://doi.org/10.1038/s41597-021-00867-1\nSince the dataset was categorical the no data value was use for masking and a Mode pyramiding policy was applied for ingestion into Google Earth Engine.
var oil_palm = ee.ImageCollection(\"projects/sat-io/open-datasets/landcover/oil-palm-plantation-1984_2017\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/OIL-PALM-PLANTATION-LAYERS
App Website: App link here
Source Code to App: https://code.earthengine.google.com/b569003eec6dc5d60dd6a187a9213f06
"},{"location":"projects/oil-palm/#shared-license","title":"Shared License","text":"This work is licensed under a Creative Commons Attribution 3.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Olga Danylo, et al, International Institute for Applied Systems Analysis
Curated in GEE by: Samapriya Roy
Keywords: Oil palm plantations, Indonesia, Malaysia, Thailand, Landsat, Sentinel-1
Last updated: 2021-06-19
"},{"location":"projects/oil-palm/#2-high-resolution-global-industrial-and-smallholder-oil-palm-map-for-2019","title":"2) High resolution global industrial and smallholder oil palm map for 2019","text":"The dataset contains 634 100x100 km tiles, covering areas where oil palm plantations were detected. The classified images (\u2018oil_palm_map\u2019 folder, in geotiff format) are the output of the convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. The images have a spatial resolution of 10 meters and contain three classes: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm.
You can find the paper here and download the datasets here
Use the following credit when these datasets or paper is cited:
Descals, Adri\u00e0, Serge Wich, Erik Meijaard, David LA Gaveau, Stephen Peedell, and Zoltan Szantoi.\n\"High-resolution global map of smallholder and industrial closed-canopy oil palm plantations.\"\nEarth System Science Data 13, no. 3 (2021): 1211-1231.\nCite the Data using
Adri\u00e0, Descals, Serge, Wich, Erik, Meijaard, David, Gaveau, Stephen, Peedell, & Zoltan, Szantoi. (2021).\nHigh resolution global industrial and smallholder oil palm map for 2019 (Version v1) [Data set].\nZenodo. http://doi.org/10.5281/zenodo.4473715\nSince the dataset was categorical the no data value was use for masking and a Mode pyramiding policy was applied for ingestion into Google Earth Engine.
var oil_palm = ee.ImageCollection(\"projects/sat-io/open-datasets/landcover/oil_palm_industrial_smallholder_2019\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/GLOBAL-INDUSTRIAL-SMALLHOLDER-OIL-PALM
"},{"location":"projects/oil-palm/#shared-license_1","title":"Shared License","text":"This work is licensed under a Creative Commons Attribution 4.0 International. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Adri\u00e0 Descals et al 2021
Curated in GEE by: Samapriya Roy
Keywords: industrial, smallholder, oil palm, deep learning, global, remote sensing, Sentinel-1, Sentinel-2, convolutional neural network
Last updated: 2021-06-19
"},{"location":"projects/ookla_5g/","title":"Ookla 5G Map Data","text":"The Ookla 5G Map\u2122 was launched in May 2019 to provide a comprehensive view of the global expansion of 5G technology. At its inception, the map highlighted 300 deployments across 17 countries, illustrating the initial rollout of this transformative technology. Over time, the map has grown significantly, now encompassing data from 233 providers with over 145,000 deployments in 142 countries. This extensive coverage underscores the rapid adoption and widespread implementation of 5G networks worldwide.
As 5G technology has become prevalent in many regions, the Ookla 5G Map\u2122 is evolving its focus. With the widespread presence of 5G, the emphasis is shifting towards analyzing the latest emerging technologies and identifying markets where connectivity is lagging. This pivot aims to spotlight new technological advancements and address the digital divide, ensuring that connectivity improvements continue to progress globally. The Ookla 5G Map\u2122 remains an essential tool for understanding the current landscape and future trends of global connectivity.
"},{"location":"projects/ookla_5g/#citation","title":"Citation","text":"\"Ookla\u00ae 5G Map Data was provided by Ookla and accessed on [DAY MONTH YEAR] from [https://www.speedtest.net/ookla-5g-map].\nBased on [LICENSEE\u2019S] analysis of Ookla\u00ae 5G Map Data. Ookla trademarks used under license and reprinted with permission.\"\nvar ookla_5g_map = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/ookla_5g_map\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/OOKLA-5G-MAP
"},{"location":"projects/ookla_5g/#license","title":"License","text":"The dataset is made available under a CC BY-NC-SA 4.0
Dataset provided by: Ookla
Curated in GEE by: Samapriya Roy
Keywords: : analytics,operator,cities,status,civic,infrastructure,internet,telecommunications
Last updated: 2024-07-01
"},{"location":"projects/osm_water/","title":"OSM Water Layer Surface Waters in OpenStreetMap","text":"OSM Water Layers is a global surface water data, generated by extracting surface water features from OpenStreetMap. The OSM water layer rasterized map is referenced to WGS84. The data is prepared as 5 degree x 5 degree tiles (6000 pixel x 6000 pixel). Filename represents the center of the lower left pixel of the data domain; e.g. the file \"n30w120.tif\" is for the domain N30-N35, W120-W115. (more accurately, N29.99958333-N34.99958333,W120.0004167-W115.0004167)
Scale: 90m
"},{"location":"projects/osm_water/#raster-values","title":"Raster Values","text":"Citation
Yamazaki, Dai, Daiki Ikeshima, Jeison Sosa, Paul D. Bates, George H. Allen, and Tamlin M. Pavelsky. \"MERIT Hydro: a high\u2010resolution global hydrography map based on latest topography dataset.\" Water Resources Research 55, no. 6 (2019): 5053-5073.\nLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)
"},{"location":"projects/osm_water/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var mainlands = ee.ImageCollection(\"projects/sat-io/open-datasets/OSM_waterLayer\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/OSM-WATER-SURFACE
Extra Info: Go to the OSM Water Layer webpage
Curated by: Samapriya Roy and Erin Trochim
Keywords: Global water layer, Open Street Map, OSM
Last updated: 2020-04-28
"},{"location":"projects/overture_buildings/","title":"Overture Foundation Building Footprints","text":"NoteThis dataset will be updated in batches owing to the extent and size of the feature collections. These will be made available primarily in the insiders only dataset while they are ingested and tested. Once all areas have been ingested this will be made available to all users of the community catalog
The Overture Foundation's building dataset is part of the 2024-07-22.0 data release and v1.0.0 of the schema, now available. The base, buildings, divisions, and places themes have reached General Availability (GA). The transportation theme remains in beta, and users can anticipate additional breaking changes to the transportation schema. Currently, the dataset only includes data extracted for the CONUS region.
"},{"location":"projects/overture_buildings/#overview","title":"Overview","text":"The Overture Maps buildings theme describes human-made structures with roofs or interior spaces that are permanently or semi-permanently in one place (source: OSM building definition). The theme includes two feature types:
has_parts that describes whether there are any associated building parts. (Currently only building layers are being added to the catalog)building_id.The Overture buildings dataset is a combination of the following open building datasets:
Source Type Conflation Priority Count OpenStreetMap Community-contributed 1 ~600 Million Esri Community Maps Community-contributed 2 ~14 Million Google Open Buildings ML-derived roofprints (>90% precision) 3 ~400 Million Microsoft ML-derived roofprints 4 ~600 Million Google Open Buildings ML-derived roofprints (<90% precision) 5 ~700 Million "},{"location":"projects/overture_buildings/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var buildings = ee.FeatureCollection('projects/sat-io/open-datasets/OVERTURE/BUILDINGS/CONUS-EXTRACT');\nMap.centerObject(buildings.first(),12)\nMap.addLayer(buildings, {}, 'Buildings CONUS Extract');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/OVERTURE-BUILDINGS-EXTRACT
"},{"location":"projects/overture_buildings/#license","title":"License","text":"The data from the Overture Foundation Building Dataset is available under the Open Data Commons Open Database License (ODbL).
Provided by: Overture Foundation
Curated in GEE by: Samapriya Roy
Keywords: Building Data, Overture Foundation, OpenStreetMap, Esri, Google, Microsoft
Dataset release date: 2024-07-22
Last updated in GEE: 2024-08-01
"},{"location":"projects/peatland/","title":"Global Peatland Database","text":"The Global Peatland Map 2.0, launched by the Global Peatlands Initiative partners at the Global Peatland Pavilion during UNFCCC COP26, enhances our understanding of the location and extent of peatlands worldwide. This dataset integrates data on location, extent, and drainage status of peatlands and organic soils globally, covering 268 countries and regions. It combines external data with mapping contributions from the Greifswald Mire Centre, providing a composite map of global peatlands, organic soils, and proxy data. The dataset supports map production, statistics, and analysis for the Global Peatland Assessment 2022.
You can download Global Peatland Map 2.0 here and additional information about about Global Peatland Database can be found here
"},{"location":"projects/peatland/#dataset-characteristics","title":"Dataset Characteristics","text":"Property Value Format GeoTiff Projection WGS 84 Pixel Values 1 = peat dominated, 2 = peat in soil mosaic Spatial Resolution 1x1 km"},{"location":"projects/peatland/#citation","title":"Citation","text":"Greifswald Mire Centre (2022). Global Peatland Map 2.0. Underlying dataset of the UNEP Global Peatland Assessment - The State of the World\u2019s Peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands, Global Peatlands Initiative, United Nations Environment Programme, Nairobi.\n// Load administrative boundaries for Indonesia\nvar admin1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM1\");\nvar geometry = admin1.filter(ee.Filter.eq('shapeGroup', 'IDN'));\n\nMap.centerObject(geometry, 4);\nMap.setOptions(\"Hybrid\");\n\nvar peat = ee.Image(\"projects/sat-io/open-datasets/GLOBAL-PEATLAND-DATABASE\")\n .clip(geometry)\n .unmask();\n\n// Display the results\nMap.addLayer(peat.clip(geometry),\n {min: 0, max: 1, palette: ['#f7fcf5', '#c7e9c0', '#74c476', '#238b45', '#00441b']},\n 'Peatland Distribution', true\n );\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-PEATLAND-FRACTIONAL-COVER
"},{"location":"projects/peatland/#license","title":"License","text":"This dataset is made available under a Creative Commons NonCommercial-ShareAlike 4.0 International.
"},{"location":"projects/peatland/#keywords","title":"Keywords","text":"peatland, wetland, organic soil, soil carbon, ecosystem
Provided by: Greifswald Mire Centre (2022)
Curated in GEE by: Ka Hei and Samapriya Roy
Last updated in GEE: 2024-07-14
"},{"location":"projects/peatland_ml/","title":"Global Peatland Fractional Coverage","text":"Peatlands, as waterlogged terrestrial wetland ecosystems, store vast amounts of soil carbon and freshwater, playing a crucial role in the global carbon and hydrologic cycles. The Peat-ML dataset is a spatially continuous global map of peatland fractional coverage generated using machine learning models trained with climate, geomorphological, soil data, and remotely-sensed vegetation indices. Available peatland coverage maps from 14 regions, along with non-peatland ecoregions, were used to develop a statistical model with an average r-squared of 0.73 and errors of 9.11% (root mean square) and -0.36% (bias). The dataset is available in NetCDF format and published in 2021. For more information, you can access the associated research paper here.
The original datasets are available as NetCDF with a model accuracy with R\u00b2 = 0.73, RMSE = 9.11%, MBE = -0.36%. You can download the Peat-ML Dataset (2021) here. Additional details are available in the paper Melton et al., 2022
Workflow for dataset generation (Joe R. Melton et al., 2022)
Example data visualization of peatland distribution in Indonesia
"},{"location":"projects/peatland_ml/#citation","title":"Citation","text":"Melton, J. R., Chan, E., Millard, K., Fortier, M., Winton, R. S., Mart\u00edn-L\u00f3pez, J. M., Cadillo-Quiroz, H., Kidd, D., and Verchot, L. V.: A map of\nglobal peatland extent created using machine learning (Peat-ML), Geosci. Model Dev., 15, 4709\u20134738, https://doi.org/10.5194/gmd-15-4709-2022, 2022.\nMelton, J. R., Chan, E., Millard, K., Fortier, M., Winton, R. S., Mart\u00edn-L\u00f3pez, J. M., Cadillo-Quiroz, H., Kidd, D., & Verchot, L. V. (2021). A map\nof global peatland extent created using machine learning (Peat-ML) [Data set]. In Geoscientific Model Development (1.0).\nZenodo. https://doi.org/10.5281/zenodo.7352284\n// Load administrative boundaries for Indonesia\nvar admin1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM1\");\nvar geometry = admin1.filter(ee.Filter.eq('shapeGroup', 'IDN'));\n\nMap.centerObject(geometry, 4);\nMap.setOptions(\"Hybrid\");\n\nvar peat = ee.Image(\"projects/sat-io/open-datasets/ML-GLOBAL-PEATLAND-EXTENT\")\n .clip(geometry)\n .unmask();\n\n// Display the results\nMap.addLayer(peat.clip(geometry),\n {min: 0, max: 100, palette: ['#f7fcf5', '#c7e9c0', '#74c476', '#238b45', '#00441b']},\n 'Peatland Distribution', true\n );\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-PEATLAND-FRACTIONAL-COVER
"},{"location":"projects/peatland_ml/#license","title":"License","text":"These datasets are provided under a Creative Commons Attribution 4.0.
"},{"location":"projects/peatland_ml/#keywords","title":"Keywords","text":"peatland, soil carbon, wetland, ecosystem
Provided by: Melton et al 2022
Curated in GEE by: Samapriya Roy
Last updated in GEE: 2024-07-14
"},{"location":"projects/piscoeo/","title":"Reference ET gridded database based on FAO Penman-Monteith for Peru (PISCOeo_pm)","text":"PISCOeo_pm has been developed for the 1981\u20132016 period at ~1 km (0.01\u00b0) spatial resolution for the entire continental Peruvian territory. The framework for the development of PISCOeo_pm is based on previously generated gridded data of meteorological subvariables such as air temperature (maximum and minimum), sunshine duration, dew point temperature, and wind speed.
Different steps, i.e., (i) quality control, (ii) gap-filling, (iii) homogenization, and (iv) spatial interpolation, were applied to the subvariables. PISCOeo_pm is useful for better understanding the terrestrial water and energy balances in Peru as well as for its application in fields such as climatology, hydrology, and agronomy, among others. Read the full paper here
"},{"location":"projects/piscoeo/#citation","title":"Citation","text":"Huerta, A., Bonnesoeur, V., Cuadros-Adriazola, J., Gutierrez, L. F., Ochoa-Tocachi, B. F., Rom\u00e1n-Da\u00f1obeytia, F., & Lavado-Casimiro, W.. (2022). PISCOeo_pm, a\nreference evapotranspiration gridded database based on FAO Penman-Monteith in Peru. Nature Scientific Data. https://doi.org/10.1038/s41597-022-01373-8\nHuerta, A., Bonnesoeur, V., Cuadros-Adriazola, J., Gutierrez, L. F., Ochoa-Tocachi, B. F., Rom\u00e1n-Da\u00f1obeytia, F., & Lavado-Casimiro, W.. (2022). Reference\nevapotranspiration gridded database based on FAO Penman-Monteith for Peru (PISCOeo_pm) V.1.0. SENAMHI-Per\u00fa. https://doi.org/10.6084/m9.figshare.c.5633182.v3\nCurrently included layers are:
"},{"location":"projects/piscoeo/#earth-engine-snippet-yearly-mean-data","title":"Earth Engine Snippet: Yearly mean data","text":"var PISCOeo_pm_yearly = ee.ImageCollection('users/lgutierrezlf/PISCOeo_pm/yearly')\nvar PISCOeo_pm_climatology = ee.ImageCollection('users/lgutierrezlf/PISCOeo_pm/climatology')\nvar PISCOeo_pm_monthly = ee.ImageCollection('users/lgutierrezlf/PISCOeo_pm/monthly')\nvar PISCOeo_pm_daily = ee.ImageCollection('users/lgutierrezlf/PISCOeo_pm/daily')\nhttps://lgutierrezlf.users.earthengine.app/view/piscoeopmts
Resolution: 0.01\u00b0 (or roughly 1km x 1km)
app code : https://code.earthehttps://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/REFERENCE-ET-GRIDDED-PERU
"},{"location":"projects/piscoeo/#license","title":"License:","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Gutierrez Leonardo & Samapriya Roy
Keywords: reference evapotranspiration, FAO Penman Monteith, Peru, hydrology, satellite data, Earth observation, GIS.
Last updated: 27/06/2022
"},{"location":"projects/pk_nssed/","title":"National-Scale Soil Erosion Dataset for Pakistan (2005 and 2015)","text":"This dataset offers a comprehensive assessment of soil erosion dynamics in Pakistan from 2005 to 2015, utilizing the Revised Universal Soil Loss Equation (RUSLE) and considering six key influencing factors: rainfall erosivity (R), soil erodibility (K), slope-length (L), slope-steepness (S), cover management (C), and conservation practice (P). Soil erosion maps, categorized into four classes ranging from low to very high erosion rates, provide insights into the spatial distribution and changes in soil erosion patterns over the study period. Transition analyses among erosion classes reveal shifts in erosion intensity, while spatial patterns and dynamics are evaluated across seven administrative units of Pakistan. The dataset highlights a national-scale increase in soil erosion from 1.79 \u00b1 11.52 ton ha\u207b\u00b9 yr\u207b\u00b9 in 2005 to 2.47 \u00b1 18.14 ton ha\u207b\u00b9 yr\u207b\u00b9 in 2015, driven by land cover and land use changes induced by population growth, infrastructural development, and natural resource exploitation. Comprehensive assessment of soil erosion dynamics in Pakistan for 2005 and 2015 at 1 km\u00b2 spatial resolution using the Revised Universal Soil Loss Equation (RUSLE) model and six influencing factors. Soil erosion maps are categorized into four classes: low, medium, high, and very high, revealing an increase from 1.79 to 2.47 ton ha\u207b\u00b9 yr\u207b\u00b9 on the national level. You can read the full paper here
The national-scale soil erosion dataset for Pakistan (2005 and 2025) at 1km spatial resolution data is available here
"},{"location":"projects/pk_nssed/#citation","title":"Citation","text":"Gilani, H., Ahmad, A., Younes, I., & Abbas, S. (2021). Impact assessment of land cover and land use changes on soil erosion changes (2005\u20132015) in\nPakistan. Land Degradation & Development, 33(1):204\u2013217. [doi.org/10.1002/ldr.4138](https://doi.org/10.1002/ldr.4138)\nGilani, Hammad, Ahmad, Adeel, Younes, Isma, & Abbas, Sawaid. (2021). National-scale soil erosion dataset for Pakistan (2005 and 2025) at 1km spatial resolution (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10694225\nvar pk_soil_erosion_2005 = ee.Image('projects/sat-io/open-datasets/NSSED-PAKISTAN/Pakistan_soil_erosion_2005_1km');\nvar pk_soil_erosion_2015 = ee.Image('projects/sat-io/open-datasets/NSSED-PAKISTAN/Pakistan_soil_erosion_2015_1km');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/NATIONAL-SOIL-ERODABILITY-DATASET-PK
"},{"location":"projects/pk_nssed/#license","title":"License","text":"The datasets are licensed under a Creative Commons Attribution (CC-BY) 4.0 International License.
Created by: Gilani et al. 2021
Curated in GEE by : Adeel Ahmad and Samapriya Roy
Keywords: soil erosion, soil conservation, RUSLE, Pakistan, temporal soil erosion
Last updated in GEE: 2024-02-20
"},{"location":"projects/plastic/","title":"Plastic Inputs from Rivers into Oceans","text":"This dataset shows a global estimate of plastic inputs from rivers into the oceans for 2010, expressed in kilograms per year. The authors used data on waste management, population density, and hydrological information to create this model. The dataset includes information on 40,760 watersheds and 182 different countries. The data is presented in a vector format.
Plastic pollution in our oceans and on our coastlines have become a major threat to ocean health worldwide. A better understanding and quantification of marine plastic sources can help in implementing mitigation strategies to alleviate the issue. The dataset can help in identifying places that require higher attention in terms of plastic waste monitoring and mitigation plans. This data can also be used as a baseline measurement for ocean plastic mass balance exercises.
This data was developed by researchers funded by The Ocean Cleanup Foundation.
"},{"location":"projects/plastic/#methodology","title":"Methodology","text":"The amount of plastic inputs from rivers into the oceans was estimated by using data on mismanaged plastic waste production (MPW) per country, population density, topographic elevation, and location of artificial barriers (weirs and dams).
For each catchment area mismanaged plastic waste production (MPW) rates per day were calculated by combining data on waste generation by inhabitant per day and population density for the area. This data was combined with water flow per river catchment area to provide a final value for the mass of plastic released at the river\u2019s mouth. This data was extrapolated using seasonal variations in water flow to create a year dataset. Data on population density was derived from the dataset Global 15 x 15 Minute Grids of the Downscaled Population by the Socioeconomic Data and Applications Center (SEDAC) for 182 countries. Data used to calculate MPW rates were collected from seven peer reviewed studies. Topographic information was taken from Global Land Data Assimilation System (GLDAS) hydrological model for surface/subsurface runoff and location of artificial barriers was taken from AquaStat and Global Reservoir and Dam Database (GRanD).
In total the dataset includes information for 40,760 watersheds worldwide. For the full documentation, please see the source methodology.
"},{"location":"projects/plastic/#citation","title":"Citation","text":"lebreton, laurent; Reisser, Julia (2018): Supplementary data for 'River plastic emissions to the world's oceans'. figshare. Dataset. https://doi.org/10.6084/m9.figshare.4725541\nvar plastic = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/river_plastic_emissions\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/PLASTIC-INPUT-RIVERS
"},{"location":"projects/plastic/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Ocean Cleanup Foundation
Curated by: Samapriya Roy
Keywords: : Pollution, Society, Coral Reefs, SDG 14, Life below Water, Cities, Reefs Water, Oceans Waste, hydrology, waste management, marine plastic
Last updated: 2022-01-05
"},{"location":"projects/polaris/","title":"Polaris 30m Probabilistic Soil Properties US","text":"Probabilistic Remapping of SSURGO (POLARIS) soil properties\u2014a database of 30-m probabilistic soil property maps over the contiguous United States (CONUS). The mapped variables over CONUS include soil texture, organic matter, pH, saturated hydraulic conductivity, Brooks-Corey and Van Genuchten water retention curve parameters, bulk density, and saturated water content.
Variable Description Units silt silt percentage % sand sand percentage % clay clay percentage % bd bulk density g/cm3 theta_s saturated soil water content m3/m3 theta_r residual soil water content m3/m3 ksat saturated hydraulic conductivity log10(cm/hr) ph soil pH in H20 N/A om organic matter log10(%) lambda pore size distribution index (brooks corey) N/A hb bubbling pressure (brooks corey) log10(kPa) n measure of the pore size distribution (van genuchten) N/A alpha scale parameter inversely proportional to mean pore diameter (van genuchten) log10(kPa-1)"},{"location":"projects/polaris/#citation-related-publications","title":"Citation & Related Publications","text":"Read the original paper here and cite the work using
Chaney, Nathaniel W., Budiman Minasny, Jonathan D. Herman, Travis W. Nauman, Colby W. Brungard, Cristine LS Morgan\nAlexander B. McBratney, Eric F. Wood, and Yohannes Yimam. \"POLARIS soil properties: 30\u2010m probabilistic maps of soil properties\nover the contiguous United States.\" Water Resources Research 55, no. 4 (2019): 2916-2938.\nPOLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons.
The dataset is available at varying depth from surface, while the statistics provided include mean, mode , median and percentile values, only median values have been included as part of the collection created.
Depth from Surface 0-5 cm 5-15 cm 15-30 cm 30-60 cm 60-100 cm 100-200 cm
Overall datasets include processing approximately 80,000 files which have been converted into individual images within a collection per property at varying depth. So for example collection bd_mean includes bd_0_5 and represents a single image for contiguous US with bd value at surface depth of 0-5 cm from surface.
"},{"location":"projects/polaris/#notes-from-data-providers","title":"Notes from Data providers","text":"05/01/2019 - The variables hb, alpha, ksat, om are in log10 space.
05/01/2019 - Due to file size constraints, the 1 arcsec database is split into 1x1 degree tiffs. Each variable/layer/statistic has its own virtual raster that acts as the \"glue\" of all the smaller 1x1 degree chunks. For more information on virtual rasters see https://www.gdal.org/gdal_vrttut.html.
06/02/2019 - The variables hb and alpha were originally reported to have the units of log10(cm) and log10(cm-1) respectively. This was a typo. The correct units are log10(kPa) and log10(kPa-1) respectively.
var bd_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/bd_mean');\nvar clay_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/clay_mean');\nvar ksat_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/ksat_mean');\nvar n_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/n_mean');\nvar om_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/om_mean');\nvar ph_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/ph_mean');\nvar sand_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/sand_mean');\nvar silt_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/silt_mean');\nvar theta_r_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/theta_r_mean');\nvar theta_s_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/theta_s_mean');\nvar lambda_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/lambda_mean');\nvar hb_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/hb_mean');\nvar alpha_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/alpha_mean');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/POLARIS-PROBABILISTIC-SOIL-PROPERTIES-30
You can download the datasets here: http://hydrology.cee.duke.edu/POLARIS/
"},{"location":"projects/polaris/#license","title":"License","text":"POLARIS is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Curated by: Samapriya Roy
Keywords: Digital soil mapping, Soil, Environmental modeling, High performance computing
Last updated dataset: 2019-05-04
Last curated: 2022-03-05
"},{"location":"projects/pomelo/","title":"POMELO Model Population Density Maps","text":"POMELO is a deep learning model addressing the need for fine-grained population maps in urban planning, environmental monitoring, public health, and humanitarian operations. It uses coarse census data and open geospatial data to create high-resolution population maps with a 100-meter ground sampling distance. POMELO can estimate populations even in regions lacking census data, achieving accuracy in sub-Saharan Africa experiments. It evaluates performance in three scenarios: coarse supervision, fine supervision, and transfer tasks, highlighting the practicality of fine supervision. POMELO estimates building occupancy rates and computes populations by multiplying them with building counts. It relies on free sources like the Open Buildings dataset but acknowledges potential errors. Dependence on high-resolution images and data availability is a concern. Crowdsourced data is essential in data-scarce regions. POMELO uses various geospatial data layers, with nightlight and settlement layers being predictive. Future improvements may include additional covariates from open geospatial sources, but handling incomplete and biased data remains a challenge. This dataset presents a fine-grained population map of Tanzania, Mozambique, Uganda, Zambia, and Rwanda with a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details. Each pixel contains a floating point number specifying the number of inhabitants of the respective pixel (i.e. People/100m).
Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.
"},{"location":"projects/pomelo/#key-features","title":"Key Features","text":"Metzger, Nando, John E. Vargas-Mu\u00f1oz, Rodrigo C. Daudt, Benjamin Kellenberger, Thao Ton-That Whelan, Ferda Ofli, Muhammad Imran, Konrad Schindler,\nand Devis Tuia. \"Fine-grained population mapping from coarse census counts and open geodata.\" Scientific Reports 12, no. 1 (2022): 20085.\n// load the population density\nvar popDensity = ee.Image(\"projects/sat-io/open-datasets/POMELO/POMELOv1\");\n\n// Define the inferno color palette\nvar infernoPalette = [\n '#000004', '#1b0c41', '#4a0c6b', '#781c81', '#a52c7a', '#cf4446',\n '#ed721c', '#fb9b06', '#f7d03c', '#fcffa4'\n];\n\n// Define visualization parameters.\nvar visParams = {\n min: 0,\n max: 450,\n palette: infernoPalette,\n opacity: 0.7 // 70% transparent\n};\n\n// Add the population density layer to the map.\nMap.addLayer(popDensity, visParams, 'Population Density');\n\n// Center map\nMap.setCenter(39.2026, -6.1659, 12);\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/POMELO-POP-DENSITY
"},{"location":"projects/pomelo/#license","title":"License","text":"Creative Commons Attribution 4.0 International (CC-BY-4.0)
Keywords: population mapping, developing countires, population density, humanitarian actions
Provided by: Metzger et al 2022
Curated in GEE by: Metzger et al 2022 and Samapriya Roy
"},{"location":"projects/popcorn/","title":"POPCORN Scalable Population Mapping with Sentinel-1 & Sentinel-2","text":"Popcorn (POPulation from COaRrse census Numbers) is a population mapping method designed to address the challenges of generating accurate population maps, especially in data-scarce regions. By using only free, globally available satellite imagery from Sentinel-1 and Sentinel-2, along with a small number of aggregate census counts, Popcorn surpasses the accuracy of many traditional population mapping approaches that rely on high-resolution building footprints. For example, Popcorn produced 100m resolution population maps for Rwanda with fewer than 400 regional census counts, achieving an accuracy score of 66% in Kigali with an average error of just 10 inhabitants per hectare.
Popcorn's method retrieves explicit maps of built-up areas and local building occupancy rates, providing additional insights into the distribution of unpopulated built-up areas, such as industrial warehouses. This makes the method interpretable and practical for urban planning and humanitarian efforts. Popcorn aims to democratize access to high-resolution population maps, making them available to regions without the resources for extensive census campaigns. You can find the full paper here and find a lot more information about the model and files here on Popcorn Population Mapping Project page
"},{"location":"projects/popcorn/#data-characteristics","title":"Data Characteristics","text":"Category Details Data Inputs - Satellite imagery from Sentinel-1 and Sentinel-2 - Coarse regional population counts Key Features - 100m ground sampling distance (GSD) - Built-up area and building occupancy rate maps - Scalable and timely mapping for urban planning and humanitarian use Example Use Case - Rwanda population mapping: 66% accuracy in Kigali with minimal census data"},{"location":"projects/popcorn/#citation","title":"Citation","text":"Metzger, Nando, Rodrigo Caye Daudt, Devis Tuia, and Konrad Schindler. \"High-resolution population maps derived from Sentinel-1 and Sentinel-2.\"\nRemote Sensing of Environment 314 (2024): 114383.\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/15/subtle-grayscale\", \"Greyscale\");\n\n\n// load the population density\nvar popDensity = ee.Image(\"projects/sat-io/open-datasets/POPCORN/POPCORNv1\");\n\n// Define the inferno color palette\nvar infernoPalette = [\n '#000004', '#1b0c41', '#4a0c6b', '#781c81', '#a52c7a', '#cf4446',\n '#ed721c', '#fb9b06', '#f7d03c', '#fcffa4'\n];\n\n// Define visualization parameters.\nvar visParams = {\n min: 0,\n max: 4,\n palette: infernoPalette,\n opacity: 0.8 // 70% transparent\n};\n\n\n// Mask out the lowest values (e.g., less than a certain threshold)\nvar threshold = 0.08;\nvar maskedPopDensity = popDensity.updateMask(popDensity.gt(threshold));\n\n// Add the masked population density layer to the map.\nMap.addLayer(maskedPopDensity, visParams, 'Population Density');\n\n// Create a legend\nvar legend = ui.Panel({\n style: {\n position: 'bottom-right',\n padding: '8px 15px',\n }\n});\n\n// Create legend title\nvar legendTitle1 = ui.Label({\n value: 'POPCORN',\n style: {\n fontWeight: 'bold',\n fontSize: '32px',\n margin: '0 0 4px 0',\n padding: '0'\n }\n});\n\nlegend.add(legendTitle1);\n\n// Create another legend title\nvar legendTitle2 = ui.Label({\n value: 'Population Density [People/ha]',\n style: {\n fontWeight: 'bold',\n fontSize: '18px',\n margin: '0 0 4px 0',\n padding: '0'\n }\n});\n\nlegend.add(legendTitle2);\n\n// Create a continuous color legend\nvar legendImage = ui.Thumbnail({\n image: ee.Image.pixelLonLat().select(0),\n params: {\n bbox: [0, 0, 1, 0.1],\n dimensions: '300x15',\n format: 'png',\n min: 0,\n max: 1,\n palette: infernoPalette,\n },\n style: { margin: '0 0 4px 0' },\n});\n\nlegend.add(legendImage);\n\n// Create labels for min and max values\nvar minLabel = ui.Label(visParams.min.toString(), { margin: '0 269px 4px 0' });\nvar maxLabel = ui.Label(visParams.max.toString(), { margin: '0 0 4px 0' });\n\n// Add labels to the legend\nvar labelsPanel = ui.Panel([minLabel, maxLabel], ui.Panel.Layout.flow('horizontal'));\nlegend.add(labelsPanel);\nMap.setControlVisibility({all: false});\n\n// Add the legend to the map\nMap.add(legend);\n\n// Center map for the rwanda/DRC boarder scene\nMap.setCenter(29.244453536522037, -1.6857641047022471, 13); // The third parameter is the zoom level.\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/POPCORN-POPULATION-DENSITY
Earth Engine App: https://ee-nandometzger.projects.earthengine.app/view/popcornv1-rwa
"},{"location":"projects/popcorn/#license","title":"License","text":"Creative Commons Attribution 4.0 International (CC-BY-4.0)
Keywords: population mapping, developing countries, population density, humanitarian actions, machine learning models
Provided by: Metzger et al 2024
Curated in GEE by: Metzger et al 2024 and Samapriya Roy
Last updated: 2024-09-08
"},{"location":"projects/pwplants/","title":"Global Power Plant Database","text":""},{"location":"projects/pwplants/#release-version-13-release-date-2021-06-02","title":"release version: 1.3, release date: 2021-06-02","text":"The Global Power Plant Database is an open-source open-access dataset of grid-scale (1 MW and greater) electricity generating facilities operating across the world.
The Database currently contains nearly 35000 power plants in 167 countries, representing about 72% of the world's capacity. Entries are at the facility level only, generally defined as a single transmission grid connection point. Generation unit-level information is not currently available.
You can find the dataset and related details at https://datasets.wri.org/dataset/globalpowerplantdatabase
"},{"location":"projects/pwplants/#citation","title":"Citation","text":"Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia,\nWorld Resources Institute. 2019. Global Power Plant Database.\nPublished on Resource Watch and Google Earth Engine. http://resourcewatch.org/ https://earthengine.google.com/\nsource field 14 geolocation_source geo_source attribution for geolocation information 15 wepp_id wepp_id a reference to a unique plant identifier in the widely-used PLATTS-WEPP database. 16 year_of_capacity_data yr_capacity year the capacity information was reported 17 generation_gwh_2013 gen_gwh2013 electricity generation in gigawatt-hours reported for the year 2013 18 generation_gwh_2014 gen_gwh2014 electricity generation in gigawatt-hours reported for the year 2014 19 generation_gwh_2015 gen_gwh2015 electricity generation in gigawatt-hours reported for the year 2015 20 generation_gwh_2016 gen_gwh2016 electricity generation in gigawatt-hours reported for the year 2016 21 generation_gwh_2017 gen_gwh2017 electricity generation in gigawatt-hours reported for the year 2017 22 generation_gwh_2018 gen_gwh2018 electricity generation in gigawatt-hours reported for the year 2018 23 generation_gwh_2019 gen_gwh2019 electricity generation in gigawatt-hours reported for the year 2019 24 generation_data_source gen_dat_src attribution for the reported generation information 25 estimated_generation_gwh_2013 est_gen_gwh2013 estimated electricity generation in gigawatt-hours for the year 2013 (see [2]) 26 estimated_generation_gwh_2014 est_gen_gwh2014 estimated electricity generation in gigawatt-hours for the year 2014 (see [2]) 27 estimated_generation_gwh_2015 est_gen_gwh2015 estimated electricity generation in gigawatt-hours for the year 2015 (see [2]) 28 estimated_generation_gwh_2016 est_gen_gwh2016 estimated electricity generation in gigawatt-hours for the year 2016 (see [2]) 29 estimated_generation_gwh_2017 est_gen_gwh2017 estimated electricity generation in gigawatt-hours for the year 2017 (see [2]) 30 estimated_generation_note_2013 est_gen_nt2013 label of the model/method used to estimate generation for the year 2013 (see section on this field below) 31 estimated_generation_note_2014 est_gen_nt2014 label of the model/method used to estimate generation for the year 2014 (see section on this field below) 32 estimated_generation_note_2015 est_gen_nt2015 label of the model/method used to estimate generation for the year 2015 (see section on this field below) 33 estimated_generation_note_2016 est_gen_nt2016 label of the model/method used to estimate generation for the year 2016 (see section on this field below) 34 estimated_generation_note_2017 est_gen_nt2017 label of the model/method used to estimate generation for the year 2017 (see section on this field below)"},{"location":"projects/pwplants/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var global_power_plants = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_power_plant_DB_1-3\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-POWERPLANT-DATABASE
"},{"location":"projects/pwplants/#license","title":"License","text":"The Global Power Planet Database is available under a CC BY 4.0 license
Data download page: Download v1.3 from here
Dataset created by: World Resources Institute
Curated in GEE by: Samapriya Roy
Keywords: : infrastructure, energy, climate, power, power-plants, wri
Last updated: 2021-07-16
Note: Older version of this dataset is available in GEE and might be updated to reflect in the public catalog
"},{"location":"projects/qdann/","title":"QDANN 30m Yield Map for Corn, Soy, and Winter Wheat in the U.S","text":"This dataset presents a novel scale transfer framework for estimating crop yields at subfield levels using satellite remote sensing and machine learning techniques. The framework, known as Quantile Loss Domain Adversarial Neural Networks (QDANN), utilizes knowledge from county-level datasets to accurately map yields at finer spatial resolutions, addressing the limitations posed by the scarcity of ground truth data for model training and evaluation.While broader scale yield mapping (e.g., state or county level) has become standard, finer-scale mapping has faced challenges due to the lack of subfield yield information. QDANN employs an unsupervised domain adaptation strategy, training on labeled county-level data while leveraging unlabeled subfield data, thus eliminating the need for yield information at the subfield level.
The dataset is based on Landsat imagery and Gridmet weather data, focusing on maize, soybean, and winter wheat fields across the United States. It is validated using yield monitor records from approximately one million field-year observations. QDANN's performance is benchmarked against various process-based and machine learning methods that utilize simulated yield records or county-level data.
Key results include: - R\u00b2 scores (RMSE) for maize, soybean, and winter wheat were 48% (2.29 t/ha), 32% (0.85 t/ha), and 39% (1.40 t/ha) respectively, demonstrating superior accuracy compared to benchmark methods. - When yields were aggregated to the county level, QDANN's performance improved significantly, achieving R\u00b2 scores (RMSE) of 78% (0.98 t/ha) for maize, 62% (0.37 t/ha) for soybean, and 53% (1.00 t/ha) for winter wheat.
This study illustrates the efficacy of the QDANN framework for reliable yield mapping at subfield levels, even in the absence of fine-scale yield data. The dataset includes publicly available 30-meter annual yield maps for major crop-producing states in the U.S., generated since 2008 with units kg/ha. You can find additional details in the paper here.
"},{"location":"projects/qdann/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The datasets were originally ingested by the authors as images into folders with State abbreviation and year to get to a specific image. These were now moved into two separate collections for corn-soybean and winter-wheat. State abbreviations were added as a property called \"state_abbv\" and dates are added to represent the time period. This allows for easy filtering of the collection by state and date.
Raster Band Info Unit Corn & Soybean b1: corn, b2: soybean kg/ha Winter Wheat b1: winter wheat kg/ha
"},{"location":"projects/qdann/#citation","title":"Citation","text":"Ma, Yuchi, Sang-Zi Liang, D. Brenton Myers, Anu Swatantran, and David B. Lobell. \"Subfield-level crop yield mapping without ground truth data:\nA scale transfer framework.\" Remote Sensing of Environment 315 (2024): 114427.\nvar corn_soybean = ee.ImageCollection(\"projects/sat-io/open-datasets/lobell-lab/VAE_QDANN_YIELD_MAP/CORN_SOY_MAP\");\nvar winter_wheat = ee.ImageCollection(\"projects/sat-io/open-datasets/lobell-lab/VAE_QDANN_YIELD_MAP/WINTER_WHEAT_MAP\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/QDANN-30M-YIELD-MAPS
"},{"location":"projects/qdann/#license","title":"License","text":"QDANN Yield Maps follow CC-BY-NC-SA 4.0. Thus, those compounds are freely available for academic purposes or individual research, but restricted for commercial use.
Created by: Ma,Yuchi et al. 2024, Lobell Lab
Curated in GEE by: Yuchi Ma & Samapriya Roy
Keywords : corn,soybean,winter wheat,yield
Last updated in GEE: 2024-09-22
"},{"location":"projects/radd/","title":"RADD Forest Disturbance Alert","text":"RADD - RAdar for Detecting Deforestation - Near real-time disturbances in humid tropical forest based on Sentinel-1 at 10m spatial scale. Primary humid tropical forest of South America (13 countries), Central America (6 countries), Africa (25 countries), insular Southeast Asia (5 countries) and Pacific (1 country). You can find more information here at University of Wageningen
The RADD (RAdar for Detecting Deforestation) alerts contribute to the World Resources Institute\u2019s Global Forest Watch initiative in providing timely and accurate information to support a wide range of stakeholders in sustainable forest management and law enforcement activities against illegal deforestation. The RADD alerts are implemented in Google Earth Engine. This dataset is also available on Global Forest Watch - Open Data Portal, Sepal.io and EarthMap
"},{"location":"projects/radd/#dataset-citation","title":"Dataset Citation","text":"Reiche J, Mullissa A, Slagter B, Gou Y, Tsendbazar N, Odongo-Braun C, Vollrath A, Weisse M, Stolle F, Pickens A, Donchyts G, Clinton N, Gorelick N &\nHerold M, (2021), Forest disturbance alerts for the Congo Basin using Sentinel-1, Environmental Research Letters\nhttps://doi.org/10.1088/1748-9326/abd0a8\nvar radd = ee.ImageCollection('projects/radar-wur/raddalert/v1');\nvar geography = 'sa';\n\n// forest baseline mask\nvar forest_baseline = ee.Image(radd.filterMetadata('layer','contains','forest_baseline')\n .filterMetadata('geography','equals',geography).first())\n\nMap.addLayer(forest_baseline, {palette:['black'], opacity: 0.3},'Forest baseline')\n\nvar latest_radd_alert = ee.Image(radd.filterMetadata('layer','contains','alert')\n .filterMetadata('geography','equals',geography)\n .sort('system:time_end', false).first());\n\nMap.addLayer(latest_radd_alert.select('Alert'), {min:2,max:3,palette:['blue','coral']}, 'RADD alert')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/RADD-FOREST-ALERT
Sample code to download RADD Alerts as GeoTIFF to Google Drive
"},{"location":"projects/radd/#license","title":"License","text":"The data is licensed under a Creative Commons Attribution 4.0 International License and may be used by anyone, anywhere, anytime without permission or royalty payment.
Curated by: J Reiche, J Balling, M Herold, B Slagter, NE Tsendbazar
Keywords: Forest, Deforestation, Alerts, NBS, Sentinel-1, Radar
Last updated: 2023-01-08 (updates with S1)
"},{"location":"projects/rai/","title":"Rural Access Index (RAI)","text":"The Rural Access Index (RAI) is one of the most important global development indicators in the transport sector. It is currently the only indicator for the SDGs that directly measures rural accessibility, and it does so by assessing rural populations\u2019 access to all-season roads. Following its adoption as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, the indicator received a new methodology taking advantage of geospatial techniques, published under the \u201cMeasuring rural access using new technologies\u201d report in 2016 (World Bank, 2016). The World Bank has since endorsed an additional Research for Community Access Partnership (ReCAP) funded project led by the Transport Research Laboratory (TRL)\u2014the RAI Supplemental Guidelines (Workman and McPherson, 2019)\u2014which provided detailed guidance for calculating the RAI, notably with an alternative approach to the all-season aspect of RAI, focusing on the changing accessibility profile of road networks rather than relying on road surface quality alone or scarce physical measurements for road conditions. Nevertheless, neither the 2016 nor the 2019 methodologies were implemented globally, with official implementations published by the World Bank being restricted to more in-depth studies for selected countries mostly in Africa and the Middle East (World Bank, 2023a) due to data source restrictions.
Here the SDG Transformation Center, part of the UN Sustainable Development Solutions Network (UN SDSN), seeks to fill in this gap by implementing the most up-to-date methodology endorsed by the World Bank\u2019s (World Bank\u2019s 2016 methodology supplemented by TRL\u2019s 2019 guidelines) at global scale with free remotely sensed datasets with global coverage. This dataset was produced by UN SDSN\u2019s SDG Transformation Center and is, to date, the only publicly available application of this particular method at a global scale.
The complete methodology is available here
"},{"location":"projects/rai/#citation","title":"Citation","text":"Iablonovski G, Drumm E, Fuller G and Lafortune G (2024) A global implementation of the rural access index.\nFront. Remote Sens. 5:1375476. doi: 10.3389/frsen.2024.1375476\n//Use the inaccessibility index to multiply your gridded rural population dataset to obtain the\n//distribution of rural population with access to all-season roads\nvar inaccessibilityindex = ee.Image('projects/sat-io/open-datasets/RAI/raimultiplier');\nMap.addLayer(inaccessibilityindex,{min:0, max:1, 'palette': ['EFC2B3','ECB176','E9BD3A','E6E600','63C600','00A600']}, 'Inaccessibility index');\n\n//In order to get the Rural Access Index for any given set of boundaries, get zonal statistics\n//for the total rural population and the rural population with access to all-season roads\n\nvar ruralpopulation = ee.Image('projects/sat-io/open-datasets/RAI/ruralpop');\nMap.addLayer(ruralpopulation, {min:0, max:100,'palette': ['FFFFFF', 'ff0000']},'Rural Population');\n\nvar ruralpopulationwithaccess = ee.Image('projects/sat-io/open-datasets/RAI/ruralpopaccess');\nMap.addLayer(ruralpopulationwithaccess,{min:0, max:100,'palette': ['00A600','63C600','E6E600','E9BD3A','ECB176','EFC2B3']},'Rural Pop w/ Access');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/RURAL-ACCESS-INDEX
"},{"location":"projects/rai/#license","title":"License","text":"Creative Commons Attribution Noncommercial Share Alike license (CC BY-NC-SA-4.0) Most of our work is available in open source. Copyrights and licensing conditions for commercial reuse may vary across reports and studies. Should you have any questions on licensing and reuse of our work please reach out to: privacy@unsdsn.org .
Provided by: United Nations Sustainable Development Solutions Network
Curated in GEE by: UNSDSN and Samapriya Roy
Keywords: sdg, accessibility, rural
Last updated: 2024-06-15
"},{"location":"projects/rap/","title":"Rangeland Analysis Platform layers","text":"Rangeland Analysis Platform data products are available as GEE assets and are made publicly available for analysis through the web application at rangelands.app
Vegetation Cover: Vegetation Cover: Rangeland Analysis Platform (RAP) Vegetation Cover, version 3.0 consists of gridded fractional estimates of plant functional groups for rangelands in the continental United States. The estimates are produced at 30-meter spatial resolution for each year between 1984\u2013present. The six plant functional groups are Annual Forbs and Grasses, Perennial Forbs and Grasses, Shrubs, Trees, Litter, and Bare Ground. Cover values are reported as percentages on a pixel-by-pixel basis. The estimates were produced using a temporal convolutional network using field measures of plant functional groups collected by the Natural Resources Conservation Service Natural Resources Inventory (NRI) program, the Bureau of Land Management Assessment, Inventory, and Monitoring (AIM) program, and the National Park Service Northern Colorado Plateau Network (NCPN) alongside spatially continuous earth observations from Landsat TM, ETM+, and OLI Collection 2.
Rangeland Production: : Rangeland Analysis Platform (RAP) Rangeland Production, version 3.0 consists of gridded estimates of herbaceous aboveground biomass, partitioned into vegetation types for annual forbs and grasses and perennial forbs and grasses. The estimates are produced at 30m spatial resolution from 1986-present. Estimates are provided annually and at 16-day intervals. Values are reported in terms of net primary productivity which can be converted to pounds per acre of new growth of aboveground biomass using the function in the Google Earth Engine script below\u2013 estimates do not reflect standing biomass from previous years. Estimates are calculated using a light use efficiency model (to estimate net primary production in terms of carbon) which is then allocated to aboveground and belowground pools (based on mean annual temperature) and further converted to biomass using a carbon-to-dry matter ratio.
Dataset was updated based on specifications provided in changelog below. Updated validation statistics provided here: https://rangelands.app/products/rapV3/
"},{"location":"projects/rap/#earth-engine-asset-snippets","title":"Earth Engine Asset Snippets","text":"// Vegetation Cover\nvar RAP_veg = ee.ImageCollection(\"projects/rap-data-365417/assets/vegetation-cover-v3\")\n\n// Net Primary Production (annual)\nvar RAP_npp = ee.ImageCollection(\"projects/rap-data-365417/assets/npp-partitioned-v3\")\nCode Snippets: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/RANGELAND-ANALYSIS-PLATFORM-EXAMPLE
"},{"location":"projects/rap/#citation","title":"Citation","text":"Jones, M.O., N.P. Robinson, D.E. Naugle, J.D. Maestas, M.C. Reeves, R.W.\nLankston, and B.W. Allred. 2020. Annual and 16-day rangeland production\nestimates for the western United States. bioRxiv 2020.11.06.343038.\nhttp://dx.doi.org/10.1101/2020.11.06.343038\n\nRobinson, N. P., M. O. Jones, A. Moreno, T. A. Erickson, D. E. Naugle, and B. W.\nAllred. 2019. Rangeland productivity partitioned to sub-pixel plant functional\ntypes. Remote Sensing 11:1427. http://dx.doi.org/10.3390/rs11121427\n\nAllred, B. W., B. T. Bestelmeyer, C. S. Boyd, C. Brown, K. W. Davies, L. M.\nEllsworth, T. A. Erickson, S. D. Fuhlendorf, T. V. Griffiths, V. Jansen, M. O.\nJones, J. Karl, J. D. Maestas, J. J. Maynard, S. E. McCord, D. E. Naugle, H. D.\nStarns, D. Twidwell, and D. R. Uden. 2020. Improving Landsat predictions of\nrangeland fractional cover with multitask learning and uncertainty.\nbioRxiv:2020.06.10.142489. http://dx.doi.org/10.1101/2020.06.10.142489\nSample scripts are available on the RAP Support Site.
Extra Info: See any of the three herbaceous biomass scripts for the function to convert from net primary production to biomass.
Download Tool/Code snippets if any: Analysis can be performed on these datasets for your regions of interest through the GUI at rangelands.app
"},{"location":"projects/rap/#license-information","title":"License Information","text":"Public Domain-CC0
"},{"location":"projects/rap/#curated-by","title":"Curated by","text":"Sarah McCord, Point of Contact, and Jeb Williamson, Agricultural Research Service, U.S. Department of Agriculture
Keywords: rangelands, vegetation, time-series, machine learning, landsat
Last updated: 2022-01-06
"},{"location":"projects/rap/#changelog","title":"Changelog","text":"RAP v3 was released on 2022-01-01
Primary changes include:
The Regional Deterministic Precipitation Analysis (RDPA) based on the Canadian Precipitation Analysis (CaPA) system is on a domain that corresponds to that of the operational regional model, i.e. the Regional Deterministic Prediction System (RDPS-LAM3D) except for areas over the Pacific Ocean where the western limit of the RDPA domain is slightly shifted eastward with respect to the regional model domain. The resolution of the RDPA analysis is identical to the resolution of the operational regional system RDPS LAM3D. The fields in the RDPA GRIB2 dataset are on a polar-stereographic (PS) grid covering North America and adjacent waters with a 10 km resolution at 60 degrees north, 2003-present. You can find additional information on the dataset here, and also here apart from the climate engine org page.
Spatial extent Spatial resolution Temporal resolution Time span Update frequency United States and Canada 10.0 km grid (1/11 deg) Daily 2003-01-01 to present Updated daily with 5 month lag timeVariables
Variable Units Scale factor Precipitation ('precip') Millimeters 1.0"},{"location":"projects/rdpa/#citation","title":"Citation","text":"[Canadian Precipitation Analysis (CaPA)](https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/capa_information_leaflet_20141118_en.pdf) Methodology system\n\nFillion, Luc, Monique Tanguay, Ervig Lapalme, Bertrand Denis, Michel Desgagne, Vivian Lee, Nils Ek et al. \"The Canadian regional data assimilation and forecasting system.\" Weather and Forecasting 25, no. 6 (2010): 1645-1669.\n\nEnvironment and Climate Change Canada. (2023). Regional Deterministic Precipitation Analysis (RDPA) dataset [Version 2.0]. [Dataset]. Retrieved from https://open.canada.ca/data/en/dataset/fdd3446a-dc20-5bad-9755-0855e3ec9b19\n// Read in Image Collections and get single image\nvar rdpa_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-rdpa-daily')\nvar rdpa_i = rdpa_ic.first()\n\n// Print single image to see bands\nprint(rdpa_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(rdpa_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CE-RDPA-DATASETS
"},{"location":"projects/rdpa/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: climate, precipitation, Canada, United States, daily
Provided by: Environment and Climate Change Canada
Curated in GEE by: Climate Engine Org
"},{"location":"projects/rdps/","title":"Regional Deterministic Prediction System (RDPS)","text":"The Regional Deterministic Prediction System (RDPS) carries out physics calculations to arrive at deterministic predictions of atmospheric elements from the current day out to 48 hours into the future at a 10.0 km grid (1/11 deg) spatial resolution. The data for mean temperature covers North America and is provided by the Meterological Service of Canada (MSC), a part of Environment and Climate Change Canada (ECCC). The MSC provides weather forecasts and warnings 24 hours a day, 365 days a year. MSC also provides federal department, agencies and other levels of government with information to support emergency preparedness and response to events such as storms, floods, wildfires and other weather-related emergencies. You can find additional information here and also on the climate org data page.
"},{"location":"projects/rdps/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent United States and Canada Spatial resolution 10.0 km grid (1/11 deg) Temporal resolution Daily Time span 2010-11-01 to present Update frequency Updated daily with 1 day lag timeVariables
Variable Details Mean temperature ('Tavg') - Units: Degrees Celsius - Scale factor: 1.0"},{"location":"projects/rdps/#citation","title":"Citation","text":"Fillion, L., Tanguay, M., Lapalme, E., Denis, B., Desgagne, M., Lee, V., ... & Pag\u00e9, C. (2010). The Canadian regional data assimilation and\nforecasting system. Weather and Forecasting, 25(6), 1645-1669.\n// Read in Image Collection and get first image\nvar rdps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-rdps-daily')\nvar rdps_i = rdps_ic.first()\n\n// Print first image to see bands\nprint(rdps_i)\n\n// Visualize temperature from first image\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(rdps_i.select('Tavg'), {min: -10, max: 20, palette: temp_palette}, 'Tavg')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CE-RDPS-DAILY
"},{"location":"projects/rdps/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: climate, daily, United States, Canada, daily, near real-time
Dataset provided by: Environment and Climate Change Canada
Dataset curated in GEE by: Climate Org
"},{"location":"projects/realsat/","title":"RealSAT Global Dataset of Reservoir and Lake Surface Area","text":"RealSAT presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. The static subset of the dataset was ingested rather than timeseries since there was transformation issues however the dataset can be downloaded here.. A reattempt might be made in the future to ingest the timeseries files.
You can read the paper here : https://www.nature.com/articles/s41597-022-01449-5 and a viewer is made available to look at an online copy of the data
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/realsat/#citations","title":"Citations","text":"Khandelwal, A., Karpatne, A., Ravirathinam, P. et al. ReaLSAT, a global dataset of reservoir and lake surface area variations. Sci Data 9, 356\n(2022). https://doi.org/10.1038/s41597-022-01449-5\nvar realsat = ee.FeatureCollection(\"projects/sat-io/open-datasets/ReaLSAT/ReaLSAT-1_4\");\nThe database is licensed under a Creative Commons Attribution (CC-BY) 4.0 International License.
Created by: Khandelwal, A., Karpatne, A., Ravirathinam, P. et al.
Curated in GEE by: Samapriya Roy
Keywords: water,hydrology, lakes, global lake surface, ReaLSAT, Surface water monitoring, Lakes and reservoirs, Hydrology, Landsat
Last updated: 2022-07-10
"},{"location":"projects/rgi/","title":"Randolph Glacier Inventory","text":"The Randolph Glacier Inventory RGI is a globally complete inventory of glacier outlines (excluding the ice sheets in Greenland and Antarctica). It is a subset of the database compiled by the Global Land Ice Measurements from Space GLIMS initiative. While GLIMS is a multi-temporal database with an extensive set of attributes, the RGI is intended to be a snapshot of the world\u2019s glaciers at a specific target date, which in RGI 7.0 and all previous versions has been set as close as possible to the year 2000 (although in fact its range of dates can still be substantial in some regions). The RGI includes outlines of all glaciers larger than 0.01 km\u00b2, which is the recommended minimum of the World Glacier Inventory. You can read more about the dataset in the user guide here
"},{"location":"projects/rgi/#dataset-citation","title":"Dataset citation","text":"RGI Consortium, . (2023). Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 7 [Data Set]. Boulder,\nColorado USA. National Snow and Ice Data Center. https://doi.org/10.5067/F6JMOVY5NAVZ. Date Accessed 01-04-2024.\nvar glacier_ft = ee.FeatureCollection(\"projects/sat-io/open-datasets/RGI/RGI_VECTOR_MERGED_V7\");\nMap.centerObject(glacier_ft.first())\n\n// print a feature\nprint('first glacier feature', glacier_ft.first());\n\n// Make a raster image out of the land area attribute.\nvar glacier_img = glacier_ft.reduceToImage({\nproperties: ['area_km2'],\nreducer: ee.Reducer.first()\n});\n\n// Make a binary mask\nvar glacier_binary = glacier_img.gt(0).unmask();\n\n//Add layers\nMap.addLayer(glacier_binary, {min:0, max:1}, 'Glacier raster mask',false);\nMap.addLayer(glacier_ft,{color:'#368BC1'},'Glacier Features')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/RANDOLPH-GLACIER-INVENTORY
"},{"location":"projects/rgi/#license","title":"License","text":"The RGI is licensed for distribution under a CC-BY-4.0 license.
Curated in GEE by: Hendrik Wulf and Samapriya Roy
Keywords: glacier outlines, RGI, GLIMS
Last updated: 2024-01-04
"},{"location":"projects/river_deltas/","title":"Global River Deltas and vulnerability","text":"Global river delta dataset combines 2174 delta locations with polygons that define delta area. We define delta area as the extent of geomorphic activity created by deltaic channel movement, and delta progradation. We focus on channel network activity because it defines the most flood-prone zone and creates the resources and natural infrastructures that make deltas attractive sites for habitation. We define deltaic polygons with five points that encompass deltaic activity. These five points mark visible traces of deltaic activity with two points capturing the lateral extent of deposition along the shoreline, and with three points enclosing the up and downstream extent of deposition. The convex hull around these five points defines a delta polygon. You can read the open source paper here and you can download the data used to create the feature collection from the supplementary material here.
"},{"location":"projects/river_deltas/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var convex_hull = ee.FeatureCollection(\"projects/sat-io/open-datasets/delta/delta-convex-hull\");\nvar convex_hull_bound = ee.FeatureCollection(\"projects/sat-io/open-datasets/delta/delta-convex-bounds\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-DELTAS-VULNERABILITY
"},{"location":"projects/river_deltas/#citation","title":"Citation","text":"Edmonds, Douglas A., Rebecca L. Caldwell, Eduardo S. Brondizio, and Sacha MO Siani.\n\"Coastal flooding will disproportionately impact people on river deltas.\"\nNature communications 11, no. 1 (2020): 1-8.\nThis work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: : Fluvial Geomorphology, Hydrology, Rivers, Coastal Rivers, Tidal, River Mouth, Vulnerability, Poverty
Last updated: 2021-04-24
"},{"location":"projects/rivermouth/","title":"Global coastal rivers and environmental variables","text":"A global dataset of 5399 coastal rivers and data on eight environmental variables. Of these rivers, 40\u2009% (n=2174) have geomorphic deltas defined either by a protrusion from the regional shoreline, a distributary channel network, or both. Globally, coastlines average one delta for every \u223c300\u2009km of shoreline, but there are hotspots of delta formation, for example in Southeast Asia where there is one delta per 100\u2009km of shoreline. Our analysis shows that the likelihood of a river to form a delta increases with increasing water discharge, sediment discharge, and drainage basin area. On the other hand, delta likelihood decreases with increasing wave height and tidal range. Delta likelihood has a non-monotonic relationship with receiving-basin slope: it decreases with steeper slopes, but for slopes\u2009>0.006 delta likelihood increases. This reflects different controls on delta formation on active versus passive margins.
"},{"location":"projects/rivermouth/#earth-engine-snippet","title":"Earth Engine Snippet:","text":"var global_costal_rivers = ee.FeatureCollection(\"projects/sat-io/open-datasets/delta/global-costal-rivers-points\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-COASTAL-RIVERS-ENV-VARIABLES
"},{"location":"projects/rivermouth/#citation","title":"Citation","text":"Caldwell, R. L., Edmonds, D. A., Baumgardner, S., Paola, C., Roy, S., and Nienhuis, J. H.: A global delta dataset and the environmental variables that predict delta formation on marine coastlines, Earth Surf. Dynam., 7, 773\u2013787, https://doi.org/10.5194/esurf-7-773-2019, 2019.\nAdditional information
Property Match from Supplement
Properties Reference Property ID ID DL_Binary Delta Presence or Absence Region Region Latitude RM_Lat Longitude RM_Lon MF_matches M&F_matches MF_IDs M&F_ID(s) WV_HT_Hw_m Wave_Height_Hw (m) WV_HT_m Tidal_Range_Ht (m) Bslope_RM Bathymetric_Slope_from_RM_Sbr SLC_mm Sea_Level_Change (mm yr^-1)Region Code and Description Tables
Region Code Region Description AFR mainland Africa AUS Australia, New Zealand, New Guinea BLS Black Sea, Sea of Azov CAM Central America EAS East Asia EUR Europe MAD Madagascar MED Mediterranean MID Middle East NAM North America RUS Russia SAM South America SAS South Asia SEA Southeast Asia"},{"location":"projects/rivermouth/#license","title":"License","text":"Shared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: :\"Fluvial Geomorphology, Hydrology, Rivers, Coastal Rivers, Tidal, River Mouth\"
Last updated: 2021-04-17
"},{"location":"projects/rwi/","title":"Relative Wealth Index (RWI)","text":"The Relative Wealth Index predicts the relative standard of living within countries using de-identified connectivity data, satellite imagery and other nontraditional data sources. This index is built using nontraditional data sources, including satellite imagery and de-identified Facebook connectivity data. The index is validated using ground truth measurements from the Demographic and Health Surveys. The data is provided for 93 low and middle-income countries at 2.4km resolution.
"},{"location":"projects/rwi/#extra-processing","title":"Extra processing","text":"The datasets are provided as CSV files with lat long Relative Wealth Index (RWI) and error. The CSV files are converted to Shapefiles and ingested as tables for each of the countries. A master feature collection is then created to combine all feature collections across low and middle-income countries (LMICs) countries. Currently only 92 countries are made available from Facebook. You can download the updated dataset from the Humanitarian Data Exchange website or from Facebook's data for Good website.
"},{"location":"projects/rwi/#steps","title":"Steps","text":"var rwi = ee.FeatureCollection(\"projects/sat-io/open-datasets/facebook/relative_wealth_index\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/RELATIVE-WEALTH-INDEX(RWI)
Interactive Map: http://beta.povertymaps.net/
"},{"location":"projects/rwi/#license","title":"License","text":"Public Domain/No restrictions (CC0): Under the terms of this license you are free to use the material for any purpose without any restrictions.
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: :\"Relative Wealth Index, RWI, Facebook, CIESIN, gridded\"
Last updated: 2021-04-18
"},{"location":"projects/s1gbm/","title":"Normalized Sentinel-1 Global Backscatter Model Land Surface","text":"This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation, within a dedicated project by the European Space Agency (ESA). The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016-17 by the mean C-band radar cross section in VV- and VH-polarization at a 10 m sampling, giving a high-quality impression on surface- structures and -patterns. The TU Wein center processed 0.5 million Sentinel-1 scenes totaling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects.
The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Supporting the design and verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states, as well as water body mapping. You can read the full paper which is open source here. The authors further introduce the use of Equi7Grid for distribution of the dataset which is a high resolution optimized global grid for distribution of data.
"},{"location":"projects/s1gbm/#citation","title":"Citation","text":"Bauer-Marschallinger, Bernhard, Senmao Cao, Claudio Navacchi, Vahid Freeman, Felix Reu\u00df, Dirk Geudtner, Bj\u00f6rn Rommen et al. \"The normalised\nSentinel-1 Global Backscatter Model, mapping Earth\u2019s land surface with C-band microwaves.\" Scientific Data 8, no. 1 (2021): 1-18.\nThe VV and VH mosaics are sampled at 10 m pixel spacing, georeferenced to the Equi7Grid and divided into six continental zones (Africa, Asia, Europe, North America, Oceania, South America), which are further divided into square tiles of 100 km extent (\"T1\"-tiles). With this setup, the S1GBM consists of 16071 tiles over six continents, for VV and VH each, totaling to a compressed data volume of 2.67 TB. The files were distributed as aggregated zipfiles with a total of 12 zip files.
The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.
"},{"location":"projects/s1gbm/#gee-pre-processing","title":"GEE Pre Processing","text":"The main file rather than the preview files are ingested and file name is used to create the complete metadata structure for each of these tiled images. While all attempts were made for completeness of tiles, the extremely large volume of each zipfile caused multiple failed attempts and broken link issues. However attempts were made to retry for failures at both download and ingest stages into GEE.
A filename of one tile of a mosaic may be for example:
M20160104_20171230_TMENSIG38_S1-IWGRDH1VH-_\u2014\u2014_B0104_NA010M_E064N036T1.tif\nIt defines the following:
\"M\" for the actual main data, or \u201cQ\u201d for the quicklook-file (for preview, see below).
start- and end-time of input data to this mosaic tile, in the format YYYYMMDD
the aggregated statistical parameter; for Version 1.0 this is always \u201cTMENSIG38\u201d, i.e. mean of backscatter normalised to 38\u00b0
relating to the input data, the satellite and sensor mode identifier \u201cS1-IWGRDH1\u201d, abbreviating Sentinel-1 Interferometric Wide Swath mode that is Ground Range Detected at High-resolution
the backscatter polarisation; so \"VV\" or \"VH\"
the version of TU Wien\u2019s internal processing engine, i.e. \"B0104\"
the identifier for Equi7Grid\u2019s continental grid, with pixel sampling in meters, e.g., \"NA010M\" for North America and 10 m pixel size
the identifier for Equi7Grid\u2019s tile within the continent, defined by the lower left coordinate, and the tile extent; e.g. \"E064N036\" for 6400\u2009km easting and 3600\u2009km northing, and \"T1\" for 100 km tile extent to the east and north
var AF_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_AF_GEOG_TILE_T1\");\nvar AN_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_AN_GEOG_TILE_T1\");\nvar AS_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_AS_GEOG_TILE_T1\");\nvar EU_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_EU_GEOG_TILE_T1\");\nvar NA_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_NA_GEOG_TILE_T1\");\nvar OC_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_OC_GEOG_TILE_T1\");\nvar SA_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_SA_GEOG_TILE_T1\");\nSample Code Equi7Grid: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/EQUI7-GRID
"},{"location":"projects/s1gbm/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var VH = ee.ImageCollection(\"projects/sat-io/open-datasets/S1GBM/normalized_s1_backscatter_VH\");\nvar VV = ee.ImageCollection(\"projects/sat-io/open-datasets/S1GBM/normalized_s1_backscatter_VV\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/S1-GLOBAL-BACKSCATTER
"},{"location":"projects/s1gbm/#web-based-data-viewer","title":"Web-Based Data Viewer","text":"The layer is also made available for visualization from Earth Observation Data Centre (EODC) under http://s1map.eodc.eu/.
"},{"location":"projects/s1gbm/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Bernhard et al, European Space Agency
Keywords: Mosaic, Sentinel-1, Backscatter, Normalized, VV, VH, polarization, S1GBM, European Space Agency, ESA
Last data update: 2021-10-26
Last updated on GEE: 2021-11-07
"},{"location":"projects/s2hswiss/","title":"S2 SR HARMONIZED SWITZERLAND","text":"Sentinel-2 (ESA) optical satellite data provides complete coverage of Switzerland approximately every three days. The effectiveness of this data relies significantly on meteorological factors like cloud cover, atmospheric correction, data registration, and delivery methods (projection). We've enhanced existing processing procedures and incorporated additional post-processing techniques to produce analysis-ready surface reflectance data specifically tailored for Switzerland. You can find additional information in geocat.ch - the swiss geographic catalogue and metadata information can be found here. Processing code can be found in the SATROMO GitHub Repository.
"},{"location":"projects/s2hswiss/#processing","title":"Processing","text":"Each overpass is mosaiced and there are two assets per overpass for the 10m and 20m spatial resolution.
Note from Data Providers
Please note that we are in commissioning mode until 01.01.2025. For 2024, we will have real-time data,\nand we will reprocess data from 2017 onward\nAsset Resolution Bands 10m - B2 (Blue) - B3 (Green) - B4 (Red) - B8 (NIR) - terrainShadowMask (binary mask for terrain shadow) - cloudAndCloudShadowMask (binary mask for clouds and cloud shadows) - reg_dx (offset in x direction from the co-registration) - reg_dy (offset in y direction from the co-registration) - reg_confidence (displacement confidence from the co-registration) - cloudProbability (percentage of cloud probability) 20m - B8A (NIR 2) - B11 (SWIR 1) - B5 (Red Edge 1)
"},{"location":"projects/s2hswiss/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var s2_sr_harmonized_swiss = ee.ImageCollection(\"projects/sat-io/open-datasets/SWISSTOPO/S2_SR_HARMONIZED_SWISS\");\nThe free geodata and geoservices of swisstopo may be used, distributed and made accessible. Furthermore, they may be enriched and processed and also used commercially. Terms of use for free geodata and geoservices(OGD) from swisstopo. Contains modified Copernicus Sentinel data.
The use of free geodata and geoservices from swisstopo is governed by the following legal bases
A reference to the source is mandatory. In the case of digital or analogue representations and publications, as well as in the case of dissemination, one of the following source references must be attached in any case:
Keywords: BGDI, optical satellite imagery, Sentinel-2, surface reflectance, Analysis ready data
Created and provided by: Federal Office of Topography swisstopo
Curated in GEE by: swisstopo and Samapriya Roy
Last updated: 2024-04-24
"},{"location":"projects/sa_nlc/","title":"South African National Land Cover (SANLC)","text":"The South African National Land Cover (SANLC) datasets are a series of land cover classification datasets for South Africa. The datasets are based on the gazetted land-cover classification standard (SANS 19144-2) and have 73 classes of information. New updates includes the 2020 SANLC apart from the 2018 dataset included earlier.The South African National Land-Cover 2018 dataset has been generated from 20 meter multi-seasonal Sentinel 2 satellite imagery. The imagery used represents the full temporal range of available imagery acquired by Sentinel 2 during the period 01 January 2018 to 31 December 2018. The SANLC 2018 dataset is based primarily on the new gazetted land-cover classification standard (SANS 19144-2) with 73 classes of information and is comparable, with the previous 1990 and 2013-14 South African National Land-Cover (SANLC) datasets. The previous land cover classes are also included for comparisons.
The SANLC 2018 data was launched on the 1st October 2019 and is now available for download from the E-GIS website, download link: https://egis.environment.gov.za/gis_data_downloads.
"},{"location":"projects/sa_nlc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var sa_nlc2018 = ee.Image('projects/sat-io/open-datasets/landcover/SA_NLC_1990');\nvar sa_nlc2018 = ee.Image('projects/sat-io/open-datasets/landcover/SA_NLC_2018');\nvar sa_nlc2013_2014 = ee.Image('projects/sat-io/open-datasets/landcover/SA_NLC_2013_2014');\nvar sa_nlc_2020 = ee.Image('projects/sat-io/open-datasets/landcover/SA_NLC_2020');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/SOUTH-AFRICA-LULC
No. Legend Colour 2018 NLC Class Name 1 #F2F2F2 Contiguous (indigenous) Forest (combined very high, high, medium) 2 #065106 Contiguous Low Forest & Thicket (combined classes) 3 #005F00 Dense Forest & Woodland (35 - 75% cc) 4 #008500 Open Woodland (10 - 35% cc) 5 #F74006 Contiguous & Dense Planted Forest (combined classes) 6 #F9764D Open & Sparse Planted Forest 7 #F9906C Temporary Unplanted Forest 8 #B8ABD1 Low Shrubland (other regions) 9 #8FAB39 Low Shrubland (Fynbos) 10 #AC92C5 Low Shrubland (Succulent Karoo) 11 #AC9CDA Low Shrubland (Nama Karoo) 12 #85D285 Sparsely Wooded Grassland (5 - 10% cc) 13 #D2B485 Natural Grassland 14 #00009F Natural Rivers 15 #041FA7 Natural Estuaries & Lagoons 16 #0639AB Natural Ocean, Coastal 17 #0D50AC Natural Lakes 18 #125FAC Natural Pans (flooded @ obsv time) 19 #1373B4 Artificial Dams (incl. canals) 20 #1D81B6 Artificial Sewage Ponds 21 #1F8EB8 Artificial Flooded Mine Pits 22 #06DEDC Herbaceous Wetlands (currently mapped) 23 #06E0D0 Herbaceous Wetlands (previous mapped extent) 24 #9F1FEC Mangrove Wetlands 25 #ffffe0 Natural Rock Surfaces 26 #DCDAC5 Dry Pans 27 #F9E0E0 Eroded Lands 28 #F9F9C5 Sand Dunes (terrestrial) 29 #F9F9A7 Coastal Sand Dunes & Beach Sand 30 #CDD2E0 Bare Riverbed Material 31 #ffffe0 Other Bare 32 #A62C39 Cultivated Commercial Permanent Orchards 33 #B31F5C Cultivated Commercial Permanent Vines 34 #DB0000 Cultivated Commercial Sugarcane Pivot Irrigated 35 #9F3978 Commercial Permanent Pineapples 36 #FF0000 Cultivated Commercial Sugarcane Non-Pivot (all other) 37 #F64D6C Cultivated Emerging Farmer Sugarcane Non-Pivot (all other) 38 #381A12 Commercial Annuals Pivot Irrigated 39 #521F1C Commercial Annuals Non-Pivot Irrigated 40 #85402C Commercial Annuals Crops Rain-Fed / Dryland / Non-Irrigated 41 #C5735F Subsistence / Small-Scale Annual Crops 42 #C1436C Fallow Land & Old Fields (Trees) 43 #C55E82 Fallow Land & Old Fields (Bush) 44 #D27592 Fallow Land & Old Fields (Grass) 45 #E0AAB8 Fallow Land & Old Fields (Bare) 46 #DB90A9 Fallow Land & Old Fields (Low Shrub) 47 #ECDB0F Residential Formal (Tree) 48 #F6EC13 Residential Formal (Bush) 49 #F9F81F Residential Formal (low veg / grass) 50 #FFFF29 Residential Formal (Bare) 51 #EC82EC Residential Informal (Tree) 52 #F691E0 Residential Informal (Bush) 53 #F99FCF Residential Informal (low veg / grass) 54 #FFC5CF Residential Informal (Bare) 55 #ECC500 Village Scattered (bare only) 56 #FFD91F Village Dense (bare only) 57 #AC7879 Smallholdings (Tree) 58 #B89192 Smallholdings (Bush) 59 #C49C9E Smallholdings (low veg / grass) 60 #D2B8B8 Smallholdings (Bare) 61 #BFFF00 Urban Recreational Fields (Tree) 62 #33FF33 Urban Recreational Fields (Bush) 63 #66FF66 Urban Recreational Fields (Grass) 64 #99FF99 Urban Recreational Fields (Bare) 65 #C49F0D Commercial 66 #8F8506 Industrial 67 #F9DD03 Roads & Rail (Major Linear) 68 #FFFF00 Mines: Surface Infrastructure 69 #B30606 Mines: Extraction Sites: Open Cast & Quarries combined 70 #C50606 Mines: Extraction Sites: Salt Mines 71 #D21D1A Mines: Waste (Tailings) & Resource Dumps 72 #F95479 Land-fills 73 #6CE7DC Fallow Land & Old Fields (wetlands)"},{"location":"projects/sa_nlc/#license","title":"License","text":"The South African National Land-Cover 2018 dataset is available on an open licence agreement.
Created by: Department of Forestry, Fisheries and the Environment, Republic of South Africa
Curated by: Geethen Singh & Samapriya Roy
Keywords: : land use, South Africa, land cover, Sentinel-2, copernicus, sentinel, satellite
Last updated: 2023-09-07
"},{"location":"projects/salinity/","title":"Global Surface water and groundwater salinity measurements (1980-2019)","text":"This data contains a new global salinity database, compiled from electrical conductivity (EC) monitoring data of both surface water (rivers, lakes/reservoirs) and groundwater locations over the period 1980-2019. The database includes information from over 16.3 million samples, from 45,103 surface water locations and 208,550 groundwater locations around the world.
The database consists of three categories; 1. River Data 2. Lake/Reservoir Data 3. Groundwater Data
Each category have two associated data files in csv-format, one containing the full salinity data, and one summary file currently the full salinity datasets are ingested. You can download the dataset here. You can read the article here
"},{"location":"projects/salinity/#data-citation","title":"Data Citation","text":"Thorslund, Josefin; van Vliet, Michelle T H (2020): A global salinity dataset of surface water\nand groundwater measurements from 1980-2019. PANGAEA, https://doi.org/10.1594/PANGAEA.913939\nThorslund, Josefin, and Michelle TH van Vliet. \"a global dataset of surface water and groundwater\nsalinity measurements from 1980\u20132019.\" Scientific Data 7, no. 1 (2020): 1-11.\nvar groundwater = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_water_salinity/groundwaters_database\");\nvar rivers = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_water_salinity/rivers_database\");\nvar lakes_reservoir = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_water_salinity/lakes_reservoirs_database\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GLOBAL-WATER-SALINITY
"},{"location":"projects/salinity/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Thorslund et al
Curated by: Samapriya Roy
Keywords: : electrical conductivity, groundwater monitoring, Salinity, surface water, lakes, reservoirs
Last updated: 2021-11-25
"},{"location":"projects/sarl/","title":"Surface Area of Rivers and Lakes (SARL)","text":"The Surface Area of Rivers and Lakes (SARL) dataset, developed by Nyberg et al. (2024), provides a comprehensive analysis of water surface area changes in rivers and lakes over a 38-year period (1984-2022). This global dataset, at a 30-meter resolution, offers valuable insights into the dynamics of surface water, particularly highlighting the increasing role of seasonality.
The SARL dataset categorizes water bodies into seven classes: - 0: Background Value: Represents areas without water. - 1: Permanent River: Areas with continuous water presence throughout the year. - 2: Permanent Lake: Areas with continuous water presence throughout the year. - 3: Seasonal River: Areas with water present for at least one month during the year. - 4: Seasonal Lake: Areas with water present for at least one month during the year. - 5: No Data Lakes: Areas with missing data for lakes. - 6: No Data Rivers: Areas with missing data for rivers.
The study, reveals that while the total permanent surface area of both rivers and lakes has remained relatively constant, the areas experiencing intermittent seasonal coverage have significantly increased. Specifically, seasonal river coverage has risen by 12%, and seasonal lake coverage has increased by 27%. These trends are statistically significant across over 84% of global water catchments. The open access article published in Hydrology and Earth System Sciences can be found here. In addition, the datasets are archived in a Zenodo repository available at this url.
"},{"location":"projects/sarl/#citation","title":"Citation","text":"Nyberg, B., Sayre, R., and Luijendijk, E.: Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022,\nHydrol. Earth Syst. Sci., 28, 1653\u20131663, 2024.\nNyberg, B. (2023). Surface Area of River and Lakes (SARL) (1.0) [Data set]. Zenodo.\nhttps://doi.org/10.5281/zenodo.6895820\nvar sarl = ee.Image(\"projects/sat-io/open-datasets/SARL\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/SURFACE-AREA-RIVER-LAKES
Earth Engine App: https://bjornburrnyberg.users.earthengine.app/view/waterchange
App Source Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/SARL-APP
"},{"location":"projects/sarl/#license","title":"License","text":"Creative Commons Attribution 4.0 International
Created by: Nyberg et al 2024
Curated in GEE by: Nyberg and Samapriya Roy
Keywords: water, water change, rivers, lakes
"},{"location":"projects/sci/","title":"Social Connectedness Index (SCI)","text":"The Social Connectedness Index measures the strength of connectedness between two geographic areas as represented by Facebook friendship ties. These connections can reveal important insights about economic opportunities, social mobility, trade and more. We use aggregated friendship connections on Facebook to measure social connectedness between geographies. Locations are assigned to users based on information they provide, connection information, and location services they have opted into (to learn more about how Facebook uses location data and how to control location privacy see Location Privacy Basics). We use these friendships to estimate the probability a pair of users in these countries are Facebook friends (we rescale based on the population of two regions) and map this to an index score called the Social Connectedness Index (SCI). If the SCI is twice as large between two pairs of regions, it means the users in the first region-pair. are about twice as likely to be connected than the second region-pair. More details on the methodology can be found by clicking Learn More below, or in the paper Social Connectedness: Measurement, Determinants, and Effects published in the Journal of Economic Perspectives.
"},{"location":"projects/sci/#features","title":"Features","text":"Friendship Data The Social Connectedness Index offers a new type of data to the research and non-profit community, measuring the frequency and density of friendship and social ties around the world, a type of data that has rarely been made available to those interested in understanding how relationships affect social outcomes.
Global Reach With over 2.5 billion active users globally, the Social Connectedness Index from Facebook provides the first comprehensive measure of social networks at an international level.
Privacy The index provides researchers with connectedness scores, but not the number of links between two places or any of the underlying data. The data set uses sampling, differential privacy noise, and normalization to protect privacy.
"},{"location":"projects/sci/#extra-processing","title":"Extra processing","text":"The datasets are provided as TSV files with lat long Relative Wealth Index (RWI) and error. The TSV files are then converted to spatial by joining with country boundary units and ingested as tables for each of the countries. For now only Country to Country SCIs are processed. Two datasets are created one using the User or First Location and the second using the FR_LOC or Second Location. You can download the source data here to process it as needed.
"},{"location":"projects/sci/#citation","title":"Citation","text":"M. Bailey, R. Cao, T. Kuchler, J. Stroebel, and A. Wong. Social connectedness: Measurements,\ndeterminants, and effects. Journal of Economic Perspectives, 32(3):259\u201380, 2018b.and the Facebook Data for Good Program, Social Connectedness Index (SCI). https://dataforgood.fb.com/, Accessed DAY MONTH YEAR.\"\nEach dataset has three columns
Column Name Description user_loc First Location fr_loc Second Location scaled_sci Scaled SCI"},{"location":"projects/sci/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var sci_user_loc = ee.FeatureCollection(\"projects/sat-io/open-datasets/facebook/sci_user_loc\");\nvar sci_fr_loc = ee.FeatureCollection(\"projects/sat-io/open-datasets/facebook/sci_fr_loc\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/SOCIAL-CONNECTEDNESS-INDEX(SCI)
Interactive Map: http://beta.povertymaps.net/
"},{"location":"projects/sci/#license","title":"License","text":"Public Domain/No restrictions (CC0): Under the terms of this license you are free to use the material for any purpose without any restrictions.
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: : \"Social Connectedness Index, SCI, Facebook, CIESIN, country,first location, second location\"
Last updated: 2021-04-18
"},{"location":"projects/scs/","title":"Soil carbon storage in terrestrial ecosystems of Canada","text":"This collection contains datasets with the spatial distribution of carbon stock in soil and plants of Canada and canopy heights. It is being made public to act as supplementary data for the publication 'Large soil carbon storage in terrestrial ecosystems of Canada', currently under review. The maps were produced in the Remote Sensing Lab, McMaster University, between January and December 2020. This research project was made possible by a grant from the World Wildlife Fund (WWF)- Canada. This project aimed to produce the first wall-to-wall estimate of carbon stocks in plants and soils of Canada at 250 m spatial resolution using multisource satellite, climate and topographic data and a machine-learning algorithm.
You can read the paper here and download the datasets:
Canopy Height Map The canopy height maps were built to be included as covariates in the model to predict AGB (and carbon stock) in forest areas of Canada. We created wall-to-wall height metrics using ATL08 LiDAR products from the ICESat-2 satellite. The data was download for one-year period (October 2018 to October 2019). Points were filtered regarding solar background noise and atmospheric scattering, totaling 49,959 points distributed over the entire Canada. These points were associated with 10 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, annual PALSAR-2 HH and HV polarization, annual clumping index, and also the MODIS NDVI summer season. Afterwards, the random forest algorithm was used to extrapolate ATL08 parameters and develop regression models with the abovementioned spatially continuous variables.
Soil Carbon Concentration To generate the soil carbon concentration maps, we used 6,533 ground soil samples, long-term climate data, multisource remote sensing data, topografic information, soil type, depth, and a 3D random forest regression model.
Forest Carbon This dataset contains the map with total carbon stored in plants of forested areas in Canada (AGB, BGB and dead plants) and carbon stock uncertainty. To estimate the carbon stored in plants of forest areas, we used 47,967 ground measurements of AGB and 58 covariates mainly composed of optical data, terrain parameters, structural parameters (e.g., SAR data, clump index, canopy heights \u2013 generated from satellite LiDAR- included in the other dataset), soil type map and radiation flux data. We used a random forest model for spatial prediction of AGB in forest areas while 1st and 3rd quantiles of RF quantile regression were used to build the uncertainty map. After generating the AGB map, the root biomass of forest areas was computed by its relationship to AGB according to forest type. The dead plant materials were computed by a linear regression between live and dead AGB defined with ground measurements. Ultimately, the AGB as well as dead plant materials and BGB were multiplied by 0.5 to provide the carbon maps. includes carbon stored in above and belowground biomass and dead plant materials
Soil Carbon Stock Map To generate the soil carbon stock map, we used 6,490 ground samples of soil organic carbon concentration (g/kg) and 2,973 ground samples of bulk density (kg/dm3), long-term climate data, multisource remote sensing data, topografic information, soil type, depth, and a 3D random forest regression model. The uncertainty map was generated using the random forest quantile regression approach difference between 95th and 5th quantiles (90% confidence interval) of soil organic carbon and bulk density predictions. Water and ice/snow areas were masked based on the 2015 Land Cover of Canada and SOC stock in permafrost areas was discounted according to ice abundance using the 'Ground ice map of Canada' (O'Neill et al., 2020).
GEE asset Variable name projects/sat-io/open-datasets/carbon_stocks_ca/ch Canopy Height projects/sat-io/open-datasets/carbon_stocks_ca/fc Forest Carbon projects/sat-io/open-datasets/carbon_stocks_ca/sc Soil Carbon Stock projects/sat-io/open-datasets/carbon_stocks_ca/scc Soil Carbon Concentration"},{"location":"projects/scs/#data-citation","title":"Data Citation","text":"Sothe, C., Gonsamo, A., Arabian, J., Kurz, W. A., Finkelstein, S. A., & Snider, J. (2022). Large soil carbon storage in terrestrial ecosystems of Canada. Global Biogeochemical Cycles, 36, e2021GB007213. https://doi.org/10.1029/2021GB007213\nvar ch = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon_stocks_ca/ch\");\nvar fc = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon_stocks_ca/fc\");\nvar sc = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon_stocks_ca/sc\");\nvar scc = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon_stocks_ca/scc\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/SOIL-CARBON-STOCKS-CANADA
"},{"location":"projects/scs/#license","title":"License","text":"This work is licensed under and freely available to the public (similar to a CC0 license).
Created by: Sothe et al 2022
Curated in GEE by : Samapriya Roy
keywords: soil carbon density, soil carbon stock estimate, soil carbon storage, terrestrial ecosystem models, machine Learning Methods Enabled Predictive Modeling
Last updated on GEE: 2021-11-14
"},{"location":"projects/secondary_forest/","title":"Benchmark maps of 33 years of secondary forest age for Brazil v3 (1986-2019)","text":"The restoration and reforestation of 12 million hectares of forests by 2030 are amongst the leading mitigation strategies for reducing carbon emissions within the Brazilian Nationally Determined Contribution targets assumed under the Paris Agreement. Understanding the dynamics of forest cover, which steeply decreased between 1985 and 2018 throughout Brazil, is essential for estimating the global carbon balance and quantifying the provision of ecosystem services. To know the long-term increment, extent, and age of secondary forests is crucial; however, these variables are yet poorly quantified. Here we developed a 30-m spatial resolution dataset of the annual increment, extent, and age of secondary forests for Brazil over the 1986\u20132018 period. Land-use and land-cover maps from MapBiomas Project were used as input data for our algorithm, implemented in the Google Earth Engine platform. This dataset provides critical spatially explicit information for supporting carbon emissions reduction, biodiversity, and restoration policies, enabling environmental science applications, territorial planning, and subsidizing environmental law enforcement. Read the dataset paper and details here
"},{"location":"projects/secondary_forest/#citation","title":"Citation:","text":"Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G., Broggio, I.S., Rosan, T.M., Doblas, J.,\nAnderson, L.O., Rousseau, G.X., Shimabukuro, Y.E., Silva, C.A., House, J.I., Arag\u00e3o, L.E.O.C.\nBenchmark maps of 33 years of secondary forest age for Brazil. Scientific Data (2020).\nhttps://doi.org/10.1038/s41597-020-00600-4\nYou can access the dataset here: https://doi.org/10.5281/zenodo.3928660
The updated v3 of the dataset is available directly as GEE collections
"},{"location":"projects/secondary_forest/#dataset-citation","title":"Dataset Citation","text":"Celso H. L. Silva Junior, Viola H. A. Heinrich, Ana T. G. Freire, Igor S. Broggio, Thais M. Rosan, Juan Doblas,\nLuiz E. O. C. Arag\u00e3o. (2020). Benchmark maps of 33 years of secondary forest age for Brazil (Version v2.0.0)\n[Data set]. Scientific Data. Zenodo. http://doi.org/10.5281/zenodo.3928660\nvar forest_age = ee.Image('users/celsohlsj/public/secondary_forest_age_collection5_v3');\nvar forest_extent = ee.Image('users/celsohlsj/public/secondary_forest_extent_collection5_v3');\nvar forest_increment = ee.Image('users/celsohlsj/public/secondary_forest_increment_collection5_v3');\nvar forest_loss = ee.Image('users/celsohlsj/public/secondary_forest_loss_collection5_v3');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/BRAZIL-SECONDARY-FOREST-AGE
"},{"location":"projects/secondary_forest/#technical-validation","title":"Technical Validation","text":"This dataset was based on the Collection 4.1 of MapBiomas Project (Annual Land-Use and Land-Cover Maps of Brazil)1; thus, the accuracy of the secondary forest increment, extension and age maps presented here is anchored to the accuracy of the MapBiomas land-use and land-cover dataset. The MapBiomas analyses of accuracy were performed using the Pontius Jr and Millones (2011) method23. For the entire Brazil24, the MapBiomas dataset has an average of 86.40\u2009\u00b1\u20090.46% of overall accuracy, 11.06\u2009\u00b1\u20090.67% of allocation disagreement, and 2.5\u2009\u00b1\u20090.29% of area disagreement between 1985 and 2018, considering the land-use and land-cover classes from the legend level with the greatest detail (level 3).The accuracy assessment for the Brazilian biomes can be found in the MapBiomas accuracy statistics web page (https://mapbiomas.org/en/accuracy-analysis).
"},{"location":"projects/secondary_forest/#license-usage","title":"License & Usage","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Github Page: https://github.com/celsohlsj/gee_brazil_sv
Export Page for App: https://code.earthengine.google.com/13bfcedb77ac7bac9ea1fb962b587a54?hideCode=true
Zenodo Data download page: https://zenodo.org/record/3928660
Created and Curated by: Celso H. L. Silva Junior
Keywords: Deforestation, MapBiomas, Climate Change, Forest Restoration, Carbon Sequestration
Last updated: 2021-03-05
"},{"location":"projects/shd_sun_gpp/","title":"Global Sunlit and Shaded GPP for vegetation canopies (1992-2020)","text":"Gross primary production (GPP) is a vital component of the terrestrial carbon budget and plays a prominent role in the global carbon cycle. Accurate estimation of terrestrial GPP is critical for understanding the interaction between the terrestrial biosphere and the atmosphere in the context of global climate change projecting future change, and informing climate policy decisions. GPP is closely related to vegetation types meteorological factors, soil moisture, and other factors. In particular, GPP is affected by vegetation canopy structures e.g., sunlit and shaded leaves. Sunlit leaves can absorb direct and diffuse radiation simultaneously, and light saturation is easy to occur when the radiation is high, while shaded leaves can only absorb diffuse radiation and the absorbed radiation intensity is generally between the light compensation point and the light saturation point.
Here we produce a global 0.05\u00b0, 8-day dataset for GPP, GPPshade and GPPsun over 1992\u20132020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Such products can support exploring the similarities and differences of sunlit and shaded leaves contributing to GPP or SIF (Sun induced chlorophyll fluorescence), to further excavate the interior ecological mechanism of different carbon cycle processes and advance carbon cycle modelling.
You can download the dataset here and read the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/shd_sun_gpp/#usage-notes-from-dataset-page","title":"Usage notes from Dataset page","text":"The units of three temporal resolutions (8-day, monthly, annual) are gC m-2 8day-1, gC m-2 month-1 and gC m-2 a-1, respectively. And the scale factor of the monthly data is 0.1, that of the 8-day data is 0.01. In the dataset, in order to ensure the authenticity, we did not delete or modify a small number of abnormally high values (caused by LAI). Therefore, when using this dataset, you can set thresholds to remove the anomalies.
"},{"location":"projects/shd_sun_gpp/#citation","title":"Citation:","text":"Bi, W., He, W., Zhou, Y. et al. A global 0.05\u00b0 dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020. Sci\nData 9, 213 (2022). https://doi.org/10.1038/s41597-022-01309-2\nWenjun, Bi; Yanlian, Zhou (2022), A global 0.05\u00b0 dataset for gross primary production of sunlit and shaded vegetation canopies (1992\u20132020), Dryad,\nDataset, https://doi.org/10.5061/dryad.dfn2z352k\nvar gpp_annual = ee.Image(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/gpp_yearly/GPP_v21_2020\");\nvar shaded_annual = ee.Image(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/shaded_yearly/Shade_GPP_v21_2020\");\nvar sunlit_annual = ee.Image(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/sunlit_yearly/Sun_GPP_v21_2020\");\nvar gpp_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/gpp_monthly\");\nvar shaded_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/shaded_monthly\");\nvar sunlit_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/sunlit_monthly\");\nvar gpp_8day = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/gpp_8day\");\nvar shaded_8day = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/shaded_8day\");\nvar sunlit_8day = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/sunlit_8day\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-SUNLIT-SHADED-GPP-VEG-CANOPIES
"},{"location":"projects/shd_sun_gpp/#license-usage","title":"License & Usage","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
Curated in GEE by: Samapriya Roy
Keywords: carbon flux, global changes, long-time series, shaded GPP, sunlit GPP
Last updated: 2022-09-16
"},{"location":"projects/shoreline/","title":"Global Shoreline Dataset","text":"A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1\u2005km2 (21,818), and islands smaller than 1\u2005km2 (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.
Publication URL: https://pubs.er.usgs.gov/publication/70202401
Scale: 30m
Please use Citation:
Sayre, R., S. Noble, S. Hamann, R. Smith, D. Wright, S. Breyer, K. Butler, K. Van Graafeiland, C. Frye, D. Karagulle, D. Hopkins, D. Stephens, K. Kelly, Z. Basher, D. Burton, J. Cress, K. Atkins, D. Van Sistine, B. Friesen, R. Allee, T. Allen, P. Aniello, I. Asaad, M. Costello, K. Goodin, P. Harris, M. Kavanaugh, H. Lillis, E. Manca, F. Muller-Karger, B. Nyberg, R. Parsons, J. Saarinen, J. Steiner, and A. Reed. 2019. A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units. Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: 10.1080/1755876X.2018.1529714\nShared Under: Creative Commons Attribution-Share Alike 4.0 International License
"},{"location":"projects/shoreline/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var mainlands = ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/mainlands');\nvar big_islands = ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/big_islands');\nvar small_islands = ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/small_islands');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL_SHORELINES
Extra Info: Over 100 Million+ vertices
Curated by: Samapriya Roy
Keywords: Global Shoreline, Shoreline, Oceans
Last updated: 2020-05-08
"},{"location":"projects/slrdem/","title":"NOAA Sea-Level Rise Digital Elevation Models (DEMs)","text":"The NOAA Coastal Services Center has developed high-resolution digital elevation models (DEMs) for use in the Center's Sea Level Rise and Coastal Flooding Impacts internet mapping application. These DEMs serve as source datasets used to derive data to visualize the impacts of inundation resulting from sea level rise along the coastal United States and its territories.
These data were created as part of the National Oceanic and Atmospheric Administration Coastal Services Center's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise (slr) and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed here
URL(s) of dataset description can be found here and the dataset can be downloaded here
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/slrdem/#preprocessing","title":"Preprocessing","text":"While the datasets were collected and made available from NOAA different collects do have varying nominal resolutions , different CRS and different no data values. While GEE collections will allow for variable values for all of those, the nominal resolution and native CRS was left intact was no data value was reprocessed to -9999 by simply using gdalwarp. I have added a function onto the example script which allows you to add the nominal scale as a property to the collection in case the user would like to split and apply different methods on top.
"},{"location":"projects/slrdem/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var slrdem = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/SLR_DEM\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/NOAA-SLR-DEM
"},{"location":"projects/slrdem/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. There are no restrictions on the use of data received from the U.S. Geological Survey's Earth Resources Observation and Science (EROS) Center or NASA's Land Processes Distributed Active Archive Center (LP DAAC), unless expressly identified prior to or at the time of receipt. Depending on the product source, we request that the following statements be used when citing, copying, or reprinting data: Data available from the U.S. Geological Survey.
Provider: NOAA
Curated by: Samapriya Roy
Keywords: Elevation, topography, topobathymetric, bathymetry, SLR, DEM, sea level rise
Last updated on GEE: 2022-02-27
"},{"location":"projects/snodas/","title":"Snow Data Assimilation System (SNODAS)","text":"The Snow Data Assimilation System (SNODAS) represents a comprehensive modeling and data assimilation system meticulously developed by the National Operational Hydrologic Remote Sensing Center (NOHRSC). Its primary objective is to provide highly accurate estimations of snow cover and associated parameters, serving as a crucial resource for hydrologic modeling and analysis. SNODAS achieves this by assimilating data from a diverse array of sources, including satellite observations, ground-based measurements, and numerical weather prediction models. These disparate data streams undergo thorough processing within a snow mass and energy balance model, ultimately yielding estimations of snow water equivalent (SWE), snow depth, snow cover extent, and snow albedo.
The SNODAS dataset boasts a spatial resolution of 1 km and a temporal resolution of 24 hours, ensuring precise and timely insights. Updated daily, the dataset encompasses the continental United States, Alaska, and Hawaii, offering comprehensive coverage for users across a spectrum of applications. SNODAS data caters to a wide-ranging audience, including water resource managers, emergency responders, and climate scientists. These invaluable data play a pivotal role in diverse applications, including estimating snowmelt runoff, forecasting snow avalanches, monitoring snowpack conditions for drought and flood management, and conducting studies on the influence of climate change on snow dynamics. SNODAS data is freely accessible through the National Snow and Ice Data Center (NSIDC), further enhancing its accessibility and utility for a broad user base.
This dataset description provides a comprehensive overview of SNODAS, emphasizing its significance in supporting hydrologic research and decision-making across various domains. You can find additional information here and you can also find link to the dataset in climate engine org here
"},{"location":"projects/snodas/#dataset-details","title":"Dataset details","text":"Spatial extent Conterminous US Spatial resolution 1000 m (1/120-deg) Temporal resolution Daily Time span 2003-10-01 to present Update frequency Updated daily with 1 day lag
Variables
Variable Units Scale Factor Snow Water Equivalent Meters 1.0 Snow Depth Meters 1.0
"},{"location":"projects/snodas/#citation","title":"Citation","text":"Barrett, Andrew. 2003. National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS) Products at NSIDC. NSIDC Special\nReport 11. Boulder, CO USA: National Snow and Ice Data Center. 19 pp.\n\nBarrett, A. P., R. L. Armstrong, and J. L. Smith. 2001. The Snow Data Assimilation System (SNODAS): An overview.\nJournal of Hydrometeorology 2(3):288-306.\n// Read in Image Collection and get image\nvar snodas_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/climate-engine/snodas/daily')\nvar snodas_i = snodas_ic.filterDate('2022-01-01', '2022-01-05').first()\n\n// Print first image to see bands\nprint(snodas_i)\n\n// Visualize select bands from first image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(snodas_i.select('Snow_Depth'), {min: 0, max: 1, palette: prec_palette}, 'Snow_Depth')\nMap.addLayer(snodas_i.select('SWE'), {min: 0, max: 1, palette: prec_palette}, 'SWE')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/SNODAS-DAILY
"},{"location":"projects/snodas/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: snow, climate, near real-time, CONUS, United States, NOAA, daily
Created & provided by: NOAA, NSIDC
Curated by: Climate Engine Org
"},{"location":"projects/snow_cover/","title":"Global MODIS-based snow cover monthly values (2000-2020)","text":"The Global monthly snow cover repository contains multiple products (based on the MODIS/Terra MOD10A2), the description along with the datasets can be found here
Quantiles (probability either 0.05, 0.5, 0.9 and/or 0.95) have been derived by matching dates in the filenames (daily or weekly values). After deriving quantiles, gaps were filled using temporal neighbors (e.g. missing values for year 2002 were filled using average of values between year 2001 and 2003). The gaps were especially large for months of November, December, January and February, northern Hemisphere. Important note: maps still contain some artifacts due to high reflections of white-sands e.g. Salar de Uyuni desert in Bolivia and similar. Processing steps are available here. Antarctica is not included.
To access and visualize global datasets use: https://openlandmap.org
If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:
Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues All files provided as Cloud-Optimized GeoTIFFs / internally compressed using \"COMPRESS=DEFLATE\" creation option in GDAL. File naming convention:
Hengl, T. (2021). Global MODIS-based snow cover monthly long-term (2000-2012) at 500 m, and aggregated monthly values (2000-2020)\nat 1 km (v1.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5774954\nvar lt_p90 = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/monthly_lt_p90\");\nvar lt_sd = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/monthly_lt_sd\");\nvar lt_snow_quantile = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/monthly_snow_quantile\");\nvar tmax_geom = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/midmonth_geom_tmax\");\nvar tmin_geom = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/midmonth_geom_tmin\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-MODIS-SNOWCOVER
"},{"location":"projects/snow_cover/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Hengl 2021
Curated by: Samapriya Roy
Keywords: : snow cover, global, openlandmap
Last updated: 2021-12-18
"},{"location":"projects/soc/","title":"Soil Organic Carbon Stocks & Trends South Africa","text":"Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details
"},{"location":"projects/soc/#citation","title":"Citation","text":"Venter, Zander S., Heidi-Jayne Hawkins, Michael D. Cramer, and Anthony J. Mills. \"Mapping soil organic\ncarbon stocks and trends with satellite-driven high resolution maps over South Africa.\" Science of The\nTotal Environment 771 (2021): 145384.\nvar SOC30_mean = ee.ImageCollection(\"projects/sat-io/open-datasets/NINA/SOC30_SA_mean\");\nvar SOC30_trend = ee.ImageCollection(\"projects/sat-io/open-datasets/NINA/SOC30_SA_trend\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/SOIL-ORGANIC-CARBON-SA
"},{"location":"projects/soc/#dataset-details","title":"Dataset Details","text":"Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are:
NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.
"},{"location":"projects/soc/#dataset-citation","title":"Dataset Citation","text":"Venter, Zander S, Hawkins, Heidi-Jayne, Cramer, Michael D, & Mills, Anthony J. (2020). Soil organic\ncarbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas\nof South Africa (Version 01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4384692\nCreative Commons Attribution-Share Alike 4.0 International License
Created by: Venter, Zander S, Hawkins, Heidi-Jayne, Cramer, Michael D, & Mills, Anthony J
Curated by: Samapriya Roy
Keywords: : carbon stocks, land degradation, natural climate solutions, remote sensing, soil mapping, spatial prediction, soil carbon, carbon sequestration
Last updated: 2021-04-29
"},{"location":"projects/soil_bioclim/","title":"Global Soil bioclimatic variables","text":"Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts).
"},{"location":"projects/soil_bioclim/#citation","title":"Citation","text":"Lembrechts, Jonas J., Johan van den Hoogen, Juha Aalto, Michael B. Ashcroft, Pieter De Frenne, Julia Kemppinen, Martin Kopeck\u00fd et al. \"Global maps\nof soil temperature.\" Global Change Biology 28, no. 9 (2022): 3110-3144.\nvar SBIO_0_5cm = ee.Image(\"projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm\")\nvar SBIO_5_15cm = ee.Image(\"projects/crowtherlab/soil_bioclim/SBIO_v2_5_15cm\")\n// Load image\nvar SBIO_0_5cm = ee.Image('projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm')\n\n// Print bandNames\nprint(SBIO_0_5cm.bandNames())\n\n// Add to map\nMap.addLayer(SBIO_0_5cm.select('SBIO1_Annual_Mean_Temperature'),\n {min: -10, max: 30, palette: [\"2166AC\", \"4393C3\", \"92C5DE\", \"D1E5F0\", \"FDDBC7\", \"F4A582\", \"D6604D\", \"B2182B\"]},\n 'SBIO1_Annual_Mean_Temperature')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/SOIL-BIOCLIM
Extra Info: Each of the 11 soil bioclims is available for two soil depths: 0-5 cm and 5-15cm.
"},{"location":"projects/soil_bioclim/#license","title":"License","text":"Creative Commons Attribution 4.0 International License
Curated by: Jonas Lembrechts & Johan van den Hoogen
Keywords: Bioclim, soil, soil temperature, climate
Last updated: 2022-10-02
"},{"location":"projects/soil_nematode/","title":"Soil nematode abundance & functional group composition","text":"As the most abundant animals on earth, nematodes are a dominant component of the soil community. They play critical roles in regulating biogeochemical cycles and vegetation dynamics within and across landscapes and are an indicator of soil biological activity. Here, we present a comprehensive global dataset of soil nematode abundance and functional group composition. This dataset includes 6,825 georeferenced soil samples from all continents and biomes. For geospatial mapping purposes these samples are aggregated into 1,933 unique 1-km pixels, each of which is linked to 73 global environmental covariate data layers. This study uses direct measurements of soil nematode abundance from 6,825 georeferenced locations around the world, covering all continents and all terrestrial biomes. You can read the paper here
"},{"location":"projects/soil_nematode/#data-citation","title":"Data Citation","text":"Hoogen, Johan van den; Geisen, Stefan; Wall, Diana H.; Wardle, David A.; Traunspurger, Walter; Goede, Ron G. M. de;\net al. (2020): A global database of soil nematode abundance and functional group composition. figshare. Collection.\nhttps://doi.org/10.6084/m9.figshare.c.4718003.v1\nvan den Hoogen, J., Geisen, S., Wall, D.H. et al. A global database of soil nematode abundance and functional group\ncomposition. Sci Data 7, 103 (2020). https://doi.org/10.1038/s41597-020-0437-3\nvar nematode= ee.FeatureCollection(\"projects/sat-io/open-datasets/global-nematode\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/SOIL-NEMATODE-ABUNDANCE
Property Name GEE_Property Description Units Source Sample_ID \ufeffSample_ID Unique sample ID Bacterivores Bacterivores Number of bacterivorous nematodes individuals per 100g dry soil Fungivores Fungivores Number of fungivorous nematodes individuals per 100g dry soil Herbivores Herbivores Number of herbivorous nematodes individuals per 100g dry soil Omnivores Omnivores Number of omnivorous nematodes individuals per 100g dry soil Predators Predators Number of predatory nematodes individuals per 100g dry soil Unidentified Unidentified Number of unidentified nematodes individuals per 100g dry soil Total_Number Total_Number Total number of nematodes individuals per 100g dry soil Latitude Pixel_Lat Sample latitude Decimal degree in WGS84 Longitude Pixel_Long Sample longitude Decimal degree in WGS84 WWF_Biome WWF_Biome WWF Biome https://www.worldwildlife.org/biomes sampling method sampling method: Nematode extraction method sampling_ref sampling_ref Nematode extraction method, summarised sampling depth sampling depth Sampling Depth cm DOI/URL DOI/URL Reference to original publication, where applicable Data_provider Data_provider Name of co-author(s) who supplied data"},{"location":"projects/soil_nematode/#license","title":"License","text":"Creative Commons Attribution-Share Alike 4.0 International License
Created by: Hoogen et al
Curated by: Samapriya Roy
Keywords: : nematode,soil ecology,biogeographic studies,soil biotic community
Last updated: 2021-08-16
"},{"location":"projects/soilprop/","title":"Soil Properties 800m","text":"The data shown here were obtained by aggregating current USDA-NCSS soil survey data (SSURGO back-filled with STATSGO where SSURGO is not available) within 800m\u00b2 grid cells. This data aggregation technique results in maps that may not match the original data at any given point, and is intended to depict regional trends in soil properties at the statewide scale.
This app was developed by the California Soil Resource Lab at UC Davis and UC-ANR in collaboration with the USDA Natural Resources Conservation Service. Please use the following citation for this website and gridded data products:
"},{"location":"projects/soilprop/#citation","title":"Citation","text":"Walkinshaw, Mike, A.T. O'Geen, D.E. Beaudette. \"Soil Properties.\" California Soil Resource Lab, 1 Oct. 2020,\ncasoilresource.lawr.ucdavis.edu/soil-properties/.\nFor layers with depth profiles a _profile collection is created with a min_depth and max_depth property,this can then be used for filtering an allows the varying profiles to stay in single image collection for a single property like sand and sand_profile for example.
You can download the data layers here along with lookup tables for layers that are categorical instead of continuous. Also mode pyramding policy is applied to all categorical layers and you can find the lookup table for all categorical variable in the download page.
"},{"location":"projects/soilprop/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var caco3 = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/caco3');\nvar cec = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/cec');\nvar cec_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/cec_profile');\nvar ec = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/ec');\nvar ec_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/ec_profile');\nvar ph = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/ph');\nvar ph_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/ph_profile');\nvar sar = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/sar');\nvar som = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/som');\nvar som_max = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/som_max');\nvar hydrologic_group = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/hydrologic_group');\nvar kw_025 = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/kw_025');\nvar lcc_i = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/lcc_i');\nvar lcc_ni = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/lcc_ni');\nvar resdept = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/resdept');\nvar soil_depth = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/soil_depth');\nvar soil_order = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/soil_order');\nvar soil_temp_regime = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/soil_temp_regime');\nvar survey_type = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/survey_type');\nvar wind_erodibility_group = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/wind_erodibility_group');\nvar wind_erodibility_index = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/wind_erodibility_index');\nvar bulk_density = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/bulk_density');\nvar clay = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/clay');\nvar clay_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/clay_profile');\nvar drainage_class = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/drainage_class');\nvar ksat_05 = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/ksat_05');\nvar ksat_max = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/ksat_max');\nvar ksat_mean = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/ksat_mean');\nvar ksat_min = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/ksat_min');\nvar rf_025 = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/rf_025');\nvar sand = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/sand');\nvar sand_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/sand_profile');\nvar silt = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/silt');\nvar silt_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/silt_profile');\nvar soil_texture_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/soil_texture_profile');\nvar water_storage = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/water_storage');\nvar water_storage_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/water_storage_profile');\nvar soil_color = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/soil_color');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/CRSL-SOIL-PROPERTIES-800
"},{"location":"projects/soilprop/#soil-texture-palette","title":"Soil Texture Palette","text":"Palettes have been created for variable types and a palette for Soil Texture is included below. Some palettes include rgb values and can be converted in hex codes for each variable type. These can be extracted from the app page and a few are included in the sample code.
#BEBEBE Sand #FDFD9E Loamy Sand #ebd834 Sandy Loam #307431 Loam #CD94EA Silt Loam #546BC3 Silt #92C158 Sandy Clay Loam #EA6996 Clay Loam #6D94E5 Silty Clay Loam #4C5323 Sandy Clay #E93F4A Silty Clay #AF4732 Clay
Sample Code with Soil Texture Class:https://code.earthengine.google.com/bb16ef5adbd5711d9fcb77ce7705618e
"},{"location":"projects/soilprop/#shared-license","title":"Shared License","text":"This work is licensed under a Creative Commons Attribution 4.0 International and is an open license unless otherwise indicated. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : California Soil Resource Lab at UC Davis and UC-ANR in collaboration with the USDA Natural Resources Conservation Service
Curated in GEE by: Samapriya Roy
Keywords: Soil grid, USDA, CSRL, UCANR,USDA, Soil, Coarse
Last updated: 2021-07-22
"},{"location":"projects/speedtest/","title":"Global fixed broadband and mobile (cellular) network performance","text":"Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy.
Available years of datasets: 2019,2020,2021,2022,2023 and 2024.Find the GitHub project and datasets here: https://github.com/teamookla/ookla-open-data You can also download the datasets from AWS Open data registry: https://registry.opendata.aws/speedtest-global-performance/
"},{"location":"projects/speedtest/#tiles","title":"Tiles","text":"Hundreds of millions of Speedtests are taken on the Ookla platform each month. In order to create a manageable dataset, we aggregate raw data into tiles. The size of a data tile is defined as a function of \"zoom level\" (or \"z\"). At z=0, the size of a tile is the size of the whole world. At z=1, the tile is split in half vertically and horizontally, creating 4 tiles that cover the globe. This tile-splitting continues as zoom level increases, causing tiles to become exponentially smaller as we zoom into a given region. By this definition, tile sizes are actually some fraction of the width/height of Earth according to Web Mercator projection (EPSG:3857). As such, tile size varies slightly depending on latitude, but tile sizes can be estimated in meters.
For the purposes of these layers, a zoom level of 16 (z=16) is used for the tiling. This equates to a tile that is approximately 610.8 meters by 610.8 meters at the equator (18 arcsecond blocks). The geometry of each tile is represented in WGS 84 (EPSG:4326) in the tile field.
Each tile contains the following adjoining attributes:
Field Name Type Description avg_d_kbps Integer The average download speed of all tests performed in the tile, represented in kilobits per second. avg_u_kbps Integer The average upload speed of all tests performed in the tile, represented in kilobits per second. avg_lat_ms Integer The average latency of all tests performed in the tile, represented in milliseconds tests Integer The number of tests taken in the tile. devices Integer The number of unique devices contributing tests in the tile. quadkey Text The quadkey representing the tile."},{"location":"projects/speedtest/#quadkeys","title":"Quadkeys","text":"Quadkeys can act as a unique identifier for the tile. This can be useful for joining data spatially from multiple periods (quarters), creating coarser spatial aggregations without using geospatial functions, spatial indexing, partitioning, and an alternative for storing and deriving the tile geometry.
"},{"location":"projects/speedtest/#citation","title":"Citation","text":"Speedtest\u00ae by Ookla\u00ae Global Fixed and Mobile Network Performance Maps.\nBased on analysis by Ookla of Speedtest Intelligence\u00ae data for [DATA TIME PERIOD].\nProvided by Ookla and accessed [DAY MONTH YEAR]. Ookla trademarks used under license\nand reprinted with permission.\nTwo layers are distributed as separate sets of files:
performance_mobile_tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a cellular connection type (e.g. 4G LTE, 5G NR).performance_fixed_tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a non-cellular connection type (e.g. WiFi, ethernet).Layers are generated based on a quarter year of data (three months) and files will be updated and added on a quarterly basis. A /year=2020/quarter=1/ period, the first quarter of the year 2020, would include all data generated on or after 2020-01-01 and before 2020-04-01.
Data is subject to be reaggregated regularly in order to honor Data Subject Access Requests (DSAR) as is applicable in certain jurisdictions under laws including but not limited to General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Lei Geral de Prote\u00e7\u00e3o de Dados (LGPD). Therefore, data accessed at different times may result in variation in the total number of tests, tiles, and resulting performance metrics.
"},{"location":"projects/speedtest/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var mobile_20210101 = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/mobile_tiles/2022-01-01_performance_mobile_tiles\");\nvar fixed_20210101 = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/fixed_tiles/2022-01-01_performance_fixed_tiles\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-FIXED-MOBILE-NETWORK-PERFORMANCE
Earth Engine files for mobile and fixed tiles across different quarters are arranged in the format, since quarters are 3 month intervals replace month variable by 01,04,07,10 which represents 3 month intervals
* ee.FeatureCollection(\"projects/sat-io/open-datasets/network/mobile_tiles/Year-month-01_performance_mobile_tiles\")\n* ee.FeatureCollection(\"projects/sat-io/open-datasets/network/fixed_tiles/Year-month-01_performance_mobile_tiles\")\nAs part of processing this datasets I further converted these datasets into 32 bit float rasters , these are produced at 610m resolution and feature property such as avg_d_kbps,avg_u_kbps,avg_lat_ms,devices,tests are converted in Bands for these images. The start and end date for each quarter are further added to the images however the quad information is not retained from vector to raster conversion. The result are two image collections for fixed and mobile datasets.
"},{"location":"projects/speedtest/#earth-engine-snippet_1","title":"Earth Engine Snippet","text":"var fixed = ee.ImageCollection(\"projects/sat-io/open-datasets/network/raster_tiles/performance_fixed_tiles\");\nvar mobile = ee.ImageCollection(\"projects/sat-io/open-datasets/network/raster_tiles/performance_mobile_tiles\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-FIXED-MOBILE-NETWORK-PERF-RASTER
"},{"location":"projects/speedtest/#license","title":"License","text":"These datasets are made available under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Provided by: Ookla
Curated in GEE by: Samapriya Roy
Keywords: : analytics,broadband,cities,civic,infrastructure,internet,network traffic, telecommunications,tiles
Last updated: 2024-08-01
"},{"location":"projects/spring_indices/","title":"High Res Extended Spring Indices database","text":"The Extended Spring Indices (SI-x) provide a comprehensive dataset for studying the timing of spring onset and its relationship to climate change. These models, derived from daily minimum and maximum temperatures, track the first leaf and first bloom events for key plant species. By transforming temperature data into consistent indices, the SI-x enable the calculation of the frost damage index. This dataset offers a multi-decadal, high-resolution (1 km) analysis of spring phenology across North America (1980-2022) and Europe (1950-2020).
At the present, the 1 km SI-x products are available over two study areas
North and Central America (located between 14\u00b002'31.3\"N and 55\u00b037'04.1\"N latitude and 56\u00b005'50.7\"W 126\u00b022'06.1\"W longitude). Daymet version 4 from 1980 to 2022, was used to generate this dataset. The daily maximunim and minimum temperature and daylength are available in GEE. These SI-x products are an updated version of those presented in Izquierdo-Verdiguier et al.
Europe (located between 35\u00b055'48.7\"N and 73\u00b032'47.1\"N latitude and 10\u00b036'29.5\"W and 44\u00b050'29.5\"E longitude). The daily maximum and minimum temperature come from the Downscaled version of European Observations (E-OBS) version 3 from 1950 to 2020, which is available for download here. The daylength is calculated (modelled) once the data are ingested in GEE (ingested because this data is not directly available in GEE).
Izquierdo-Verdiguier, Emma, Ra\u00fal Zurita-Milla, Toby R. Ault, and Mark D. Schwartz. \"Development and analysis of spring plant phenology products: 36\nyears of 1-km grids over the conterminous US.\" Agricultural and forest meteorology 262 (2018): 34-41.\n\nIzquierdo-Verdiguier, Emma, and Ra\u00fal Zurita-Milla. \"A multi-decadal 1 km gridded database of continental-scale spring onset products.\"\nScientific Data 11.1 (2024): 905. (https://www.nature.com/articles/s41597-024-03710-5).\n// Define Image Collections\nvar bloomDaymet = 'projects/sat-io/open-datasets/SIx_products/BloomDaymetv4';\nvar bloomEurope = 'projects/sat-io/open-datasets/SIx_products/BloomEuropev3';\nvar diDaymet = 'projects/sat-io/open-datasets/SIx_products/DI_Daymetv4';\nvar diEurope = 'projects/sat-io/open-datasets/SIx_products/DI_Europev3';\nvar leafDaymet = 'projects/sat-io/open-datasets/SIx_products/LeafDaymetv4';\nvar leafEurope = 'projects/sat-io/open-datasets/SIx_products/LeafEuropev3';\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/EXTENDED-SPRING-INDICES
Earth Engine App: https://emma.users.earthengine.app/view/spring-onset
"},{"location":"projects/spring_indices/#license","title":"License","text":"This work is licensed under a CC BY-NC 4.0 license.
Created by: Izquierdo-Verdiguier. 2024
Curated in GEE by : Samapriya Roy
Keyworks: spring onset, phenology, climate change
Last updated in GEE: 2024-08-29
"},{"location":"projects/srer_drone/","title":"Santa Rita Experimental Range Drone Imagery","text":"The technology around drones and image analysis is rapidly advancing which is making high volume workflows easier to implement. Larger quantities of monitoring data will significantly improve our understanding of the impact management actions have on land processes and ecosystem traits. This drone imagery data is used to support a research project investigating the ability to map ecological states across the Santa Rita Experimental Range (SRER) in southern Arizona. The imagery was collected in two campaigns: the first occurred in May 2019 and the second in Aug/Sept 2019. Imagery was collected using a DJI Phantom 4 RTK drone, flying 38 m above the ground and yielding ~1 cm ground sampling distance. The imagery is located at long-term transects and exclosures at SRER. The imagery was to be compared with field mapping of ecological states conducted by Dan Robbinett. The imagery and other data in this repository are connected with the 2021 Ecosphere publication Innovations to expand drone data collection and analysis for rangeland monitoring, which you can read here. You can find additional information about the researh site and experiments here Santa Rita Exprimental Range Website.
"},{"location":"projects/srer_drone/#citation","title":"Citation","text":"Gillan, Jeffrey K., Guillermo E. Ponce\u2010Campos, Tyson L. Swetnam, Alessandra Gorlier, Philip Heilman, and Mitchel P. McClaran.\n\"Innovations to expand drone data collection and analysis for rangeland monitoring.\" Ecosphere 12, no. 7 (2021): e03649.\nvar full_ortho_srer_may_2019_1cm = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_srer_may_2019_1cm\");\nvar full_ortho_srer_sept_2019_1cm = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_srer_sept_2019_1cm\");\nvar chm_sept_2019 = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/chm_sept_2019\");\nvar chm_may_2019 = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/chm_may_2019\");\n\n// Class mapping ['1', '2', '3', '4'] = ['Herb', 'Woody','Bareground','Shadow']\nvar class_sep_2019 = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_classified_sep_2019_5cm\");\nvar class_may_2019 = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_classified_may_2019_5cm\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/SRER-HIGHRES-DRONE
Data visualization tool in GEE App
App code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/SRER-APP-CODE
"},{"location":"projects/srer_drone/#license","title":"License","text":"GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Curated by: Jeffrey K. Gillan, Guillermo E. Ponce Campos
Keywords: cloud computing, high-performance computing, monitor,real-time kinematic (RTK), unmanned aerial systems
Last updated: 11/21/2019
"},{"location":"projects/sstg/","title":"Global gridded sea surface temperature (SSTG)","text":"Sea surface temperature (SST) is an important geophysical parameter that is essential for studying global climate change. Although sea surface temperature can currently be obtained through a variety of sensors (MODIS, AVHRR, AMSR-E, AMSR2, WindSat, in situ sensors), the temperature values obtained by different sensors come from different ocean depths and different observation times, so different temperature products lack consistency.
The SSTG dataset is a global sea surface temperature data during the period of 2002-2019, in Celsius, in monthly temporal and 0.041\u00b0 spatial resolution. It is produced by combing daily in situ SST data and daily satellite SST retrieval data from two infrared (MODIS and AVHRR) and three passive microwave (AMSR-E, AMSR2, Windsat) radiometers after calibration by using a temperature depth and observation time correction model. The accuracy assessments indicate that the reconstructed dataset exhibits significant improvements and can be used for mesoscale ocean phenomenon analyses.
"},{"location":"projects/sstg/#paper-citation","title":"Paper Citation","text":"Cao, M., Mao, K., Yan, Y., Shi, J., Wang, H., Xu, T., Fang, S., and Yuan, Z.: A new global gridded sea surface temperature data product based on\nmultisource data, Earth Syst. Sci. Data, 13, 2111\u20132134, https://doi.org/10.5194/essd-13-2111-2021, 2021.\nMengmeng cao, Kebiao Mao, Yibo Yan, Jiancheng Shi, Han Wang, Tongren Xu, Shu Fang, & Zijin Yuan. (2021). A New Global Gridded Sea Surface\nTemperature Data Product Based on Multisource Data (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4762067\nvar sstg = ee.ImageCollection(\"projects/sat-io/open-datasets/sstg\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL-GRIDDED-SST
"},{"location":"projects/sstg/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created by: Mengmeng cao, Kebiao Mao, Yibo Yan, Jiancheng Shi, Han Wang, Tongren Xu, Shu Fang, & Zijin Yuan
Curated by:Samapriya Roy
Keywords: Sea Surface Temperature, SST, Gridded
Last updated: 2021-01-05
"},{"location":"projects/streamflow_india/","title":"Streamflow reconstruction for Indian sub-continental river basins 1951\u20132021","text":"The Hydrological Model-Simulated Monthly Streamflow Dataset for Indian-Subcontinental (ISC) River Basins, spanning from 1951 to 2021, addresses a critical need for long-term streamflow observations in the ISC region. Given the essential role of streamflow data in water resources management, hydroclimatic analysis, and ecological assessments, this dataset provides a valuable resource for a wide range of applications. The dataset is constructed through a multi-faceted approach that combines meteorological data, sophisticated hydrological modeling, and a high-resolution vector-based routing model known as mizuRoute. By synthesizing these elements, the dataset yields monthly streamflow simulations for 9579 stream reaches within the ISC river basins.
Validation of the dataset against observed flows at gauge stations demonstrates its reliability, with a significant proportion of the gauge stations showing strong agreement, as evidenced by coefficient of determination (R\u00b2) and Nash-Sutcliffe efficiency (NSE) values exceeding 0.70. Such validation empowers the analysis of variability in low, average, and high flow within the stream networks. The dataset's significance extends to identifying long-term changes in streamflow patterns. Notably, it highlights a substantial decline in flow within the Ganga basin and an increase in semi-arid western India and the Indus basin, findings of great relevance for water management planning and climate change adaptation in the Indian subcontinent. Moreover, this resource alleviates the challenge posed by limited streamflow observations, especially in the case of the three major transboundary basins (Ganga, Indus, and Brahmaputra), where conventional monitoring falls short. You can read the paper here for more details
"},{"location":"projects/streamflow_india/#dataset-processing","title":"Dataset processing","text":"The datasets are available for download on Zenodo here the stream shapefile consists of the streamlines and segment information whereas additional datasets were provided without any file extensions. These were converted to csv files and renamed as needed along with making sure that all NaN values were replaced with 9999 since Earth Engine does not support mixed variable types in a column. These were then joined to the stream shapefile and segment id/seg_id was used to then combine variables to the stream network asset. The following contains the dataset and description
Variable Name Description Data Files and Arrangement Mean_flow This folder contains the river segment-wise mean annual and mean monsoon streamflow. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - annual_flow: seg_id, mean_annual_flow (m\u00b3/s)- monsoon_flow: seg_id, mean_monsoon_flow (m\u00b3/s) high&low_flow This folder contains the river segment-wise mean high and mean low streamflow. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - high_flow: seg_id, mean_high_flow (m\u00b3/s)- low_flow: seg_id, mean_low_flow (m\u00b3/s) Coefficient_of_variability This folder contains the river segment-wise coefficient of variability in mean annual and mean monsoon streamflow. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - CV_annual_flow: seg_id, coefficient_of_variability- CV_monsoon_flow: seg_id, coefficient_of_variability Trend_analysis This folder contains the list of stream reaches that exhibit a statistically significant trend in streamflow between 1951 and 2021. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - streamflow_trend: seg_id, hypothesis_test (h=1: significant), sen\u2019s_slope SSI This folder contains the standardized streamflow index (SSI) for the top four dry and wet months during the 1951-2021 period. The top four driest and wettest months were calculated based on the Standardized Precipitation Index (SPI) from the monthly average rainfall data for the Indian Subcontinent. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - dry_years (Sub-folder): - SSI_**monthyear**: seg_id, SSI- wet_years (Sub-folder): - SSI_**monthyear**: seg_id, SSI"},{"location":"projects/streamflow_india/#citation","title":"Citation","text":"Chuphal, D.S., Mishra, V. Hydrological model-based streamflow reconstruction for Indian sub-continental river basins, 1951\u20132021. Sci Data 10, 717\n(2023). https://doi.org/10.1038/s41597-023-02618-w\nChuphal, D. S., & Mishra, V. (2023). Reconstructed streamflow for Indian sub-continental river basins, 1951-2021 [Data set].\nZenodo. https://doi.org/10.5281/zenodo.8004633\nvar cv_annual_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/CV/cv_annual_flow\");\nvar cv_monsoon_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/CV/cv_monsoon_flow\");\nvar high_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/HIGH-LOW-FLOW/high_flow\");\nvar low_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/HIGH-LOW-FLOW/low_flow\");\nvar mean_annual_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/MEAN-FLOW/mean_annual_flow\");\nvar mean_monsoon_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/MEAN-FLOW/mean_monsoon_flow\");\nvar ssi_dry_july_1972 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_dry_july_1972\");\nvar ssi_dry_july_2002 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_dry_july_2002\");\nvar ssi_dry_june_2009 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_dry_june_2009\");\nvar ssi_dry_june_2014 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_dry_june_2014\");\nvar ssi_wet_august_2020 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_wet_august_2020\");\nvar ssi_wet_july_1988 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_wet_july_1988\");\nvar ssi_wet_september_1983 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_wet_september_1983\");\nvar ssi_wet_september_2019 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_wet_september_2019\");\nvar streamflow_trend = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/streamflow_trend\");\nvar streams = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/streams\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/STREAMFLOW-RECONSTRUCTION-INDIAN-SUBCONTINENT
"},{"location":"projects/streamflow_india/#license","title":"License","text":"These datasets are available under the Creative Commons Attribution 4.0 International license.
Provided by: Chuphal, D. S., & Mishra, V., Indian Institute of Technology (IIT) Gandhinagar
Curated in GEE by: Samapriya Roy
Keywords: H08, mizuRoute, Streamflow,India, Hydrology, Water Management, Climate change adaptation, Hydroclimatic extremes analysis
Last updated in GEE: 2023-10-18
"},{"location":"projects/survey_checkpoints/","title":"USGS Consolidated Survey-Grade Checkpoints 3DEP 2010 to 2017","text":"The USGS Consolidated Standardized Survey-Grade Checkpoints 3DEP dataset contains 41,958 survey-grade checkpoints collected between 2010 and 2017, covering 205 lidar and Interferometric Synthetic Aperture Radar (ifSAR) projects. These checkpoints were collected in the United States as part of the 3D Elevation Program (3DEP). The geospatial data is available in both Esri File-Geodatabase (GDB) and Open Geospatial Consortium\u2019s (OGC) GeoPackage (GPKG) formats. The dataset includes horizontal and vertical coordinates based on the North American Datum of 1983 (2011) in decimal degrees (EPSG:6318) and North American Vertical Datum of 1988 (2011) in meters (EPSG:5703). Updates to vertical datums for the contiguous United States (CONUS) used GEOID18, while updates for Hawaii and Alaska used GEOID12B via NOAA\u2019s VDatum tool.
This dataset is a valuable resource for validating and assessing the accuracy of lidar data collected for the 3DEP program and can be used in geospatial applications that require high-accuracy elevation data. Each checkpoint is characterized by attributes such as its unique identifier, survey type (NVA, VVA, or Unknown), geoid used, collection date, and more.
"},{"location":"projects/survey_checkpoints/#citation","title":"Citation","text":"Miller, B.Y., and Cannici, C., 2024, Consolidated Standardized Survey-Grade Checkpoints 3DEP 2010 to 2017: U.S. Geological Survey data release,\nhttps://doi.org/10.5066/P13NOW9E.\nvar survey_checkpoints = ee.FeatureCollection(\"projects/sat-io/open-datasets/USGS/SURVEY_CHECKPOINTS_3DEP_2010_2017\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/USGS-3DEP-CONSOLIDATED-SURVEY-CHECKPOINTS
"},{"location":"projects/survey_checkpoints/#license","title":"License","text":"This dataset is provided under the Creative Commons Zero v1.0 Universal license (CC0 1.0).
Provided by: U.S. Geological Survey (USGS)
Curated in GEE by: Samapriya Roy
Keywords: 3DEP, Lidar, Survey-Grade Checkpoints, GEOID18, GEOID12B, Elevation, Accuracy, Survey Data, USGS, Vertical Datum, Horizontal Datum
Last updated in GEE: 2024-10-24
"},{"location":"projects/swiss3d/","title":"swissSURFACE3D Raster (DSM)","text":"NoteThis dataset is currently only available to those in the insiders program
swissSURFACE3D Raster is a digital surface model (DSM) which represents the earth\u2019s surface including visible and permanent landscape elements such as soil, natural cover, and all sorts of constructive work with the exception of power lines and masts. swissSURFACE3D Raster is derived from airborne LiDAR data of swissSURFACE3D. To model the surface, the following elements (classified LiDAR points) are used:
To improve the surface representation of large rivers and lakes, watercourse vectors of the topographic landscape model TLM are implemented.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/swiss3d/#data-preprocessing","title":"Data preprocessing","text":"Datasets were preprocessed and distributed in multiple formats. The Cloud Optimized Geotiff files were made available and a program was written to go fetch all and current 31,321 tiles.
"},{"location":"projects/swiss3d/#citation","title":"Citation","text":"swissSURFACE3D Raster digital surface model (DSM). Last accessed **date** original data from https://www.swisstopo.admin.ch/en/geodata/height/\nsurface3d-raster.html\nvar swiss3d = ee.ImageCollection(\"projects/sat-io/open-datasets/swissSURFACE3D\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/swiss3D-RASTER
"},{"location":"projects/swiss3d/#license-terms-of-use","title":"License & Terms of Use","text":"The free geodata and geoservices of swisstopo may be used, distributed and made accessible. Furthermore, they may be enriched and processed and also used commercially. A reference to the source is mandatory. In the case of digital or analogue representations and publications, as well as in the case of dissemination, one of the following source references must be attached in any case:
Provided by: Federal Office of Topography swisstopo
Curated in GEE by : Samapriya Roy
keywords: LIDAR, Digital Surface Model, DSM, Topography
Last updated on GEE: 2023-01-28
"},{"location":"projects/sword/","title":"SWOT River Database (SWORD)","text":"The Surface Water and Ocean Topography (SWOT) satellite mission, successfully launched in December 2022, has revolutionized our ability to observe rivers by providing vast datasets of river water surface elevation, width, and slope measurements. To maximize the utility and flexibility of this data, the SWOT mission delivers a variety of data products, including river vector data in shapefile format for each SWOT overpass. These vector products offer immense potential for multi-temporal analysis of river systems, allowing researchers to track changes in river characteristics over time.
To enable this type of analysis, it was crucial to define consistent river reaches and nodes before the launch of SWOT. This ensures that data from different overpasses can be accurately assigned and compared. The SWOT River Database (SWORD) fulfills this critical role by combining multiple global datasets related to rivers and satellite observations. SWORD provides a standardized framework of high-resolution river nodes (spaced every 200 meters along river centerlines) and reaches (river segments of approximately 10 kilometers) in both shapefile and netCDF formats. These nodes and reaches are accompanied by a range of relevant hydrologic variables such as water surface elevation, width, slope, and information on river obstructions, flow accumulation, and more. This comprehensive dataset, covering global rivers 30 meters wide and greater, empowers researchers to conduct in-depth analysis of river systems and utilize SWOT data to its full potential.
SWORD integrates data from several existing global hydrography datasets, including the Global River Widths from Landsat (GRWL), MERIT Hydro, HydroBASINS, and the Global River Obstruction Database (GROD). It provides a wealth of attributes for each node and reach, such as:
Attribute Description Units x Longitude of each node ranging from 180\u00b0E to 180\u00b0W decimal degrees y Latitude of each node, ranging from 90\u00b0S to 90\u00b0N decimal degrees node_id Unique identifier for each node, formatted as: CBBBBBRRRRNNNT (C: Continent, B: Pfafstetter basin codes, R: Reach ID, N: Node ID within reach, T: Type) none node_length Length of the node measured along the high-resolution centerline points meters reach_id ID of the reach associated with each node, formatted as: CBBBBBRRRRT (C: Continent, B: Pfafstetter basin codes, R: Reach ID, T: Type) none wse Average water surface elevation of the node meters wse_var Variance of water surface elevation along the high-resolution centerline points used to calculate the average water surface elevation for each node meters^2 width Average width of the node meters width_var Variance of width along the high-resolution centerline points used to calculate the average width for each node meters^2 n_chan_max Maximum number of channels observed within the node none n_chan_mod Mode (most frequent) number of channels observed within the node none obstr_type Type of obstruction at the node based on GROD and HydroFALLS databases: 0 - No Dam, 1 - Dam, 2 - Lock, 3 - Low Permeable Dam, 4 - Waterfall none grod_id Unique GROD ID for nodes with obstr_type values 1-3 none hfalls_id Unique HydroFALLS ID for nodes with obstr_type value 4 none dist_out Distance from the river outlet to the node meters type Node type identifier: 1 - River, 3 - Lake on river, 4 - Dam/waterfall, 5 - Unreliable topology, 6 - Ghost node none facc Maximum flow accumulation value for the node kilometers^2 lakeflag GRWL water body identifier: 0 - River, 1 - Lake/reservoir, 2 - Canal, 3 - Tidally influenced river none max_width Maximum width across the channel for the node, including islands and bars meters river_name All river names associated with the node (separated by semicolons if multiple) none sinuosity Ratio of total reach length to the straight-line distance between reach endpoints, indicating the degree of meandering none meand_len Average length of meanders the node belongs to meters manual_add Binary flag indicating if the node was manually added to GRWL centerlines (1) or not (0) none trib_flag Binary flag indicating if a large tributary not in SWORD enters the node (1) or not (0) none Expand to show Reach attribute descriptions
Attribute Description Units x Longitude of the reach center point (ranging from 180\u00b0E to 180\u00b0W) decimal degrees y Latitude of the reach center point (ranging from 90\u00b0S to 90\u00b0N) decimal degrees reach_id Unique identifier for each reach, formatted as: CBBBBBRRRRT (C: Continent, B: Pfafstetter basin codes, R: Reach ID, T: Type) none reach_length Length of the reach measured along the high-resolution centerline points meters wse Average water surface elevation of the reach meters wse_var Variance of water surface elevation along the high-resolution centerline points used to calculate the average water surface elevation for each reach meters^2 width Average width of the reach meters width_var Variance of width along the high-resolution centerline points used to calculate the average width for each reach meters^2 n_nodes Number of nodes associated with the reach none n_chan_max Maximum number of channels observed within the reach none n_chan_mod Mode (most frequent) number of channels observed within the reach none obstr_type Type of obstruction within the reach based on GROD and HydroFALLS databases: 0 - No Dam, 1 - Dam, 2 - Lock, 3 - Low Permeable Dam, 4 - Waterfall none grod_id Unique GROD ID for reaches with obstr_type values 1-3 none hfalls_id Unique HydroFALLS ID for reaches with obstr_type value 4 none slope Average slope of the reach calculated along the high-resolution centerline points m/km dist_out Distance from the river outlet to the reach meters n_rch_up Number of upstream reaches connected to this reach none n_rch_down Number of downstream reaches connected to this reach none rch_id_up IDs of the upstream reaches connected to this reach none rch_id_dn IDs of the downstream reaches connected to this reach none lakeflag GRWL water body identifier: 0 - River, 1 - Lake/reservoir, 2 - Canal, 3 - Tidally influenced river none max_width Maximum width across the channel for the reach, including islands and bars meters type Reach type identifier: 1 - River, 3 - Lake on river, 4 - Dam/waterfall, 5 - Unreliable topology, 6 - Ghost reach none facc Maximum flow accumulation value for the reach kilometers^2 swot_obs Maximum number of SWOT passes intersecting the reach during the 21-day orbit cycle none swot_orbits List of SWOT orbit track numbers that intersect the reach during the 21-day cycle none river_name All river names associated with the reach (separated by semicolons if multiple) none trib_flag Binary flag indicating if a large tributary not in SWORD enters the reach (1) or not (0) none"},{"location":"projects/sword/#dataset-preprocessing","title":"Dataset preprocessing","text":"
Reaches and nodes shapefile datasets were downloaded zipped and uploaded as individual component shapefiles. The folder assets were then merged to create a single nodes and reaches files. Attributes were preserved as is from the shapefiles.
"},{"location":"projects/sword/#citation","title":"Citation","text":"Altenau et al., (2021) The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data\nProducts. Water Resources Research. https://doi.org/10.1029/2021WR030054\nElizabeth H. Altenau, Tamlin M. Pavelsky, Michael T. Durand, Xiao Yang, Renato P. d. M. Frasson, & Liam Bendezu. (2023). SWOT River Database (SWORD)\n(Version v16) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10013982\nvar nodes_merged = ee.FeatureCollection(\"projects/sat-io/open-datasets/SWORD/nodes_merged\");\nvar reaches_merged = ee.FeatureCollection(\"projects/sat-io/open-datasets/SWORD/reaches_merged\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/SWORD-NODES-REACHES-MERGED
Individual nodes and reaches files were ingested for reference and can be access by the users by using
var ee_nodes = ee.data.listAssets(\"projects/sat-io/open-datasets/SWORD/nodes\");\nvar ee_reaches = ee.data.listAssets(\"projects/sat-io/open-datasets/SWORD/reaches\");\n\nprint('Total of '+ee.List(ee_nodes.assets).size().getInfo()+ ' assets in nodes',ee_nodes.assets);\nprint('Total of '+ee.List(ee_reaches.assets).size().getInfo()+ ' reaches in nodes',ee_reaches.assets);\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/SWORD-NODES-REACHES
"},{"location":"projects/sword/#license","title":"License","text":"The datasets are provided under a Creative Commons 4.0 International License.
Provided by: Altenau et al., (2021)
Curated in GEE by: Samapriya Roy
Keywords: SWORD,SWOT,Rivers,Hydrology,Hydrography,River Networks,Global
Last updated in GEE: 2024-04-12
"},{"location":"projects/syn_ntl/","title":"GAN based Synthetic VIIRS (NTL) India","text":"This study utilizes nighttime light (NTL) data from two primary sources: the Defense Meteorological Satellite Program (DMSP) and the Visible and Infrared Imaging Suite (VIIRS) onboard the Suomi National Polar Partnership (SNPP) satellite. DMSP-OLS data provide monthly NTL observations from April 1992 to December 2013 at a 30-arc second spatial resolution, while VIIRS-DNB data offer monthly observations from April 2012 onwards at a finer 15-arc second resolution. Both datasets have been extensively used in research to monitor human activities and natural phenomena, but their different resolutions and temporal coverage present challenges for long-term analysis.
To address these limitations, this study uses preprocessed DMSP data to generate synthetic VIIRS-like imagery for the period 1992-2013. The model was trained using overlapping monthly NTL data for the years 2012 and 2013, and the generated imagery was validated against other VIIRS-like datasets and socio-economic indicators such as GDP and population.
The original DMSP monthly product (from which the current product is derived) have been captured by a series of satellites (F10-F18 ), and have been made available by EOG between 1992 and 2014. The different colours in the image below show its availability over Indian landmass; image source). The green ticks show the 216 monthly images that were used in our paper, for the generation of the improved VIIRS-like product. The red crosses display the files that are available on EOG, however, due to clouds over the Indian region, the products had large spatial gaps, and could not be used for creation of this improved product. The blue cells indicate months for which data was absent on the EOG portal, at the time of creation of this improved product.
Hence there are can be multiple cases where:
The data is not temporally continuous. Users are advised to use the monthly data appropriately.
"},{"location":"projects/syn_ntl/#output-data-characteristics","title":"Output Data Characteristics","text":"The study generates a monthly time series of VIIRS-like nighttime light images for India, spanning the period from April 1992 to December 2013. The images have the following characteristics:
Jindal, M., Gupta, P. K., & Srivastav, S. K. (2024). Generation of monthly VIIRS nighttime lights time-series (1992\u20132013) images using\ndeep learning (cGAN) technique. Remote Sensing Applications: Society and Environment, 35, 101263. https://doi.org/10.1016/j.rsase.2024.101263\nvar syn_ntl_india = ee.ImageCollection(\"projects/sat-io/open-datasets/gan-synthetic-viirs\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/SYNTHETIC-NTL-VIIRS-INDIA
"},{"location":"projects/syn_ntl/#license","title":"License","text":"Creative Commons Attribution 4.0 International
Provided by: Jindal et al
Curated in GEE by: Samapriya Roy
Keywords: VIIRS-DNB, Noise removal, DMSP-OLS, Monthly, Inter-calibration, Conditional GAN
"},{"location":"projects/tallo/","title":"Global tree allometry and crown architecture (Tallo) database","text":"The Tallo database (v1.0.0) is a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. Data were compiled from 61,856 globally distributed sites and include measurements for 5,163 tree species. Tallo includes nearly 500,000 georeferenced and taxonomically standardized records from more than 5000 tree species acquired at over 60,000 sites worldwide, including data from all major terrestrial biomes and some of the world's largest ever recorded trees. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence
You can read the paper here and download the database from
"},{"location":"projects/tallo/#citation","title":"Citation","text":"Jucker, Tommaso, Fabian J\u00f6rg Fischer, J\u00e9r\u00f4me Chave, David A. Coomes, John Caspersen, Arshad Ali, Grace Jopaul Loubota Panzou et al. \"Tallo: A global\ntree allometry and crown architecture database.\" Global change biology 28, no. 17 (2022): 5254-5268.\nJucker, Tommaso, Fischer, Fabian, Chave, J\u00e9r\u00f4me, Coomes, David, Caspersen, John, Ali, Arshad, Loubota Panzou, Grace Jopaul, Feldpausch, Ted,\nFalster, Daniel, Usoltsev, Vladimir, Adu-Bredu, Stephen, Alves, Luciana, Aminpour, Mohammad, Angoboy, Ilondea, Anten, Niels, Antin, C\u00e9cile, Askari,\nYousef, Avil\u00e9s, Rodrigo Mu\u00f1oz, Ayyappan, Narayanan, \u2026 Zavala, Miguel. (2022). Tallo database (1.0.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.6637599\nvar tallo = ee.FeatureCollection(\"projects/sat-io/open-datasets/tallo_database\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/TREE-ALLOMETRY-CROWN-ARCH-DATABASE
"},{"location":"projects/tallo/#license","title":"License","text":"This work is licensed under Creative Commons Attribution 4.0 International.
Created by: Jucker, Tommaso, et al. 2022
Curated in GEE by : Samapriya Roy
Keywords: stem diameter, tree height, crown size, tree allometry, tree architecture, forest biomass, remote sensing
Last updated on GEE: 2022-10-21
"},{"location":"projects/tbdem/","title":"Coastal National Elevation Database (CoNED) Project -Topobathymetric digital elevation models (TBDEMs)","text":"The Coastal National Elevation Database (CoNED) Project - topobathymetric digital elevation models (TBDEMs) are merged renderings of both topography (land elevation) and bathymetry (water depth) to provide seamless elevation products for select coastal regions in the United States (2011-present). This coastal elevation database integrates disparate light detection and ranging (lidar) and bathymetric data sources (such as sonar) into common databases aligned both vertically and horizontally to common reference systems.
This coastal elevation database integrates disparate light detection and ranging (lidar) and bathymetric data sources into common databases aligned both vertically and horizontally to common reference systems. CoNED Project - topobathymetric digital elevation models (TBDEMs) provide a required seamless elevation product for science application studies such as shoreline delineation, coastal inundation mapping, sediment-transport, sea-level rise, storm surge models, tsunami impact assessment, and analysis of the impact of various climate change scenarios on coastal regions.
Dataset description can be found here and the full datasets were downloaded from
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/tbdem/#citation","title":"Citation","text":"Coastal National Elevation Database (CoNED) Project - Topobathymetric Digital Elevation Model (TBDEM)\nDigital Object Identifier (DOI) number: /10.5066/F7Z60MHJ\nThe datasets were collected and made available with 3 meter, 2 meter, or 1 meter and different no data values. While GEE collections will allow for variable values for all of those, the nominal resolution and native CRS was left intact was no data value was reprocessed to -9999 by simply using gdalwarp. Some of the datasets were single mosaics with filesizes upward of 4GB to 200+GB, for efficiency and better handling of these gdal_retile tool was used to retile these into subparts while maintaining the file name for data tracing. I have added a function onto the example script which allows you to add the nominal scale as a property to the collection in case the user would like to split and apply different methods on top.
"},{"location":"projects/tbdem/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var tb_dem = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/CoNED_TBDEM\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/NOAA-CoNED-TBDEM
"},{"location":"projects/tbdem/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. There are no restrictions on the use of data received from the U.S. Geological Survey's Earth Resources Observation and Science (EROS) Center or NASA's Land Processes Distributed Active Archive Center (LP DAAC), unless expressly identified prior to or at the time of receipt. Depending on the product source, we request that the following statements be used when citing, copying, or reprinting data: Data available from the U.S. Geological Survey.
Intended use: Development, calibration and validation of coastal open-access EO-derived elevation/topography, vegetation and water quality products.
Provider: USGS, CMGP, NGP, NOAA and NGDC
Curated by: Samapriya Roy
Keywords: Elevation, topography, topobathymetric, bathymetry
Last updated on GEE: 2022-02-27
"},{"location":"projects/terraclim/","title":"Terraclimate Individual years for +2C and +4C climate futures","text":"TerraClimate layers commensurate with global mean temperatures +2C and +4C above preindustrial levels. These data are available for pseudo years 1985-2015. Future climate projections were developed for two different climate futures: (1) when global mean temperatures are 2C warmer than pre-industrial, and (2) when global mean temperatures are 4C above preindustrial. We use a pattern scaling approach that makes use of monthly projections from 23 CMIP5 global climate models as described in Qin et al., 2020 and provide projections for monthly climate by imposing projected changes in means and variance from the modes scalable to the change in global temperature. You can find more information here
This data is at two links
Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch, 2018, Terraclimate, a high-resolution global dataset of monthly climate and climatic water\nbalance from 1958-2015, Scientific Data\nAn automated script was created to fetch all datasets, which contains 31 annual NetCDF files with 12 bands each representing a month for each variable. The netcdf files for each variable was converted into Geotifs with global bounds and with lzw compressions. The collections were made available for both 2C and 4C scenarios and scale and offset were added as metadata for each collection to allow for processing in the appropriate units as mentioned in the variable list below.
"},{"location":"projects/terraclim/#variable-lists","title":"Variable lists","text":"Terraclimate variables and units can be found in the table below. As noted from their webpage these datasets have associated scales and offset value which has to be used to generate a correct representation of the variable data in the intended units. As part of data processing, the variable scale and offset values were included as metadata for each variable which can then be applied in computations directly.
variable units aet (Actual Evapotranspiration, monthly total) mm def (Climate Water Deficit, monthly total) mm pet (Potential evapotranspiration, monthly total) mm ppt (Precipitation, monthly total) mm q (Runoff, monthly total) mm soil (Soil Moisture, total column - at end of month) mm srad (Downward surface shortwave radiation) W/m2 swe (Snow water equivalent - at end of month) mm tmax (Max Temperature, average for month) C tmin (Min Temperature, average for month) C vap (Vapor pressure, average for month)\u00a0 kPa ws (Wind speed, average for month) m/s vpd (Vapor Pressure Deficit, average for month) kpa PDSI (Palmer Drought Severity Index, at end of month) unitless PDSI (Palmer Drought Severity Index, at end of month) unitless
"},{"location":"projects/terraclim/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aet_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/aet\");\nvar def_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/def\");\nvar pet_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/pet\");\nvar ppt_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/ppt\");\nvar soil_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/soil\");\nvar srad_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/srad\");\nvar swe_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/swe\");\nvar tmax_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/tmax\");\nvar tmin_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/tmin\");\nvar vap_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/vap\");\nvar vpd_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/vpd\");\nvar aet_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/aet\");\nvar def_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/def\");\nvar pet_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/pet\");\nvar ppt_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/ppt\");\nvar soil_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/soil\");\nvar swe_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/swe\");\nvar tmax_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/tmax\");\nvar tmin_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/tmin\");\nvar vpd_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/vpd\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/TERRACLIMATE-CLIMATE-FUTURES
"},{"location":"projects/terraclim/#license","title":"License","text":"This work is licensed under a public domain license.
Created by: Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch
Preprocessed and Curated in GEE by : Samapriya Roy
Keywords: Climate futures, +2C, +4C, TerraClimate
Last updated: 2021-04-15
Last updated on GEE: 2022-04-27
"},{"location":"projects/tile/","title":"Tile Drained Croplands (30m)","text":"Tile drainage is one of the dominant agricultural management practices in the United States and has greatly expanded since the late 1990s. It has proven effects on land surface water balance and quantity and quality of streamflow at the local scale. The effect of tile drainage on crop production, hydrology, and the environment on a regional scale is elusive due to lack of high-resolution, spatially-explicit tile drainage area information for the Contiguous United States (CONUS). We developed a 30-m resolution tile drainage map of the most-likely tile-drained area of the CONUS (AgTile-US) from county-level tile drainage census using a geospatial model that uses soil drainage information and topographic slope as inputs. Validation of AgTile-US with 16000 ground truth points indicated 86.03% accuracy at the CONUS-scale. Over the heavily tile-drained midwestern regions of the U.S., the accuracy ranges from 82.7% to 93.6%. These data can be used to study and model the hydrologic and water quality responses of tile drainage and to enhance streamflow forecasting in tile drainage dominant regions. You can read the full paper here
"},{"location":"projects/tile/#paper-citation","title":"Paper Citation","text":"Valayamkunnath, P., Barlage, M., Chen, F. et al. Mapping of 30-meter resolution tile-drained croplands using a\ngeospatial modeling approach. Sci Data 7, 257 (2020). https://doi.org/10.1038/s41597-020-00596-x\nvar tile30m = ee.Image(\"projects/sat-io/open-datasets/agtile/AgTile-US\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/US-TILE-DRAINED-CROPLANDS
"},{"location":"projects/tile/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Valayamkunnath, P., Barlage, M., Chen, F. et al.
Curated by: Samapriya Roy
Keywords: Agriculture,Tile Drainage,Subsurface,USA,CONUS,GIS,30 meter,data,gridded,raster
Last updated: 2021-08-16
"},{"location":"projects/tillage/","title":"Global crop production tillage practices","text":"No tillage (NT) is often presented as a means to grow crops with positive environmental externalities, such as enhanced carbon sequestration, improved soil quality, reduced soil erosion, and increased biodiversity. However, whether NT systems are as productive as those relying on conventional tillage (CT) is a controversial issue, fraught by a high variability over time and space. Here, we expand existing datasets to include the results of the most recent field experiments, and we produce a global dataset comparing the crop yields obtained under CT and NT systems. In addition to crop yield, our dataset also reports information on crop growing season, management practices, soil characteristics and key climate parameters throughout the experimental year. The final dataset contains 4403 paired yield observations between 1980 and 2017 for eight major staple crops in 50 countries. This dataset can help to gain insight into the main drivers explaining the variability of the productivity of NT and the consequence of its adoption on crop yields.
"},{"location":"projects/tillage/#data-citation","title":"Data Citation","text":"Su, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage systems. figshare https://doi.org/10.6084/m9.figshare.12155553 (2020). v14\nSu, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage systems. Scientific Data 8, 33 (2021).\nvar tillage = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_tillage_production\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-CROP-PRODUCTION-TILLAGE-PRACTICES
"},{"location":"projects/tillage/#property-mapping","title":"Property Mapping","text":"Property GEE property Author author Journal journal Year year Site country site_country Location location Latitude latitude Longitude longitude Soil information recorded in the paper soil_from_paper pH (surface layer) pH_surface_layer Replications in experiment replications_exp Crop crop Initial year of NT practice ( or first year of experiment if missing) init_yr_nt Sowing year sowing_year Harvest year harvest_year Years since NT started (yrs) yrs_from_nt Crop growing season recorded in the paper cgsp Crop rotation with at least 3 crops involved in CT crit Crop rotation with at least 3 crops involved in NT crint Crop sequence (details) c_seq Cover crop before sowing cc_bf_sowing Soil cover in CT soil_cover_ct Soil cover in NT soil_cover_nt Residue management of previous crop in CT (details) rm_ct Residue management of previous crop in NT (details) rm_nt Weed and pest control CT wp_ct Weed and pest control NT wp_nt Weed and pest control CT (details) wpc_ct Weed and pest control NT (details) wpc_nt Fertilization CT ft_ct Fertilization NT ft_nt N input n_inp N input rates with the unit kg N ha-1 (details) n_inp_unit Field fertilization (details) fft Irrigation CT i_ct Irrigation NT i_nt Water applied in CT w_ct Water applied in NT w_nt Other information other Yield of CT yield_ct Yield of NT yield_nt Relative yield change rel_yl_chg Yield increase with NT yl_inc_nt Outlier of CT outlier_ct Outlier of NT outlier_nt Sowing month sw_month Harvesting month hv_month P P E E PB PB Tave Tave Tmax Tmax Tmin Tmin ST ST"},{"location":"projects/tillage/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Yang Su et al.
Curated by: Samapriya Roy
Keywords: Conservation agriculture, Conventional tillage, crop yield, No tillage, No-till
Last updated: 2021-08-30
"},{"location":"projects/tzero/","title":"TransitionZero Solar Asset Mapper","text":"TransitionZero's Solar Asset Mapper is a global, satellite-derived dataset of utility-scale solar farms, created through a combination of machine learning and human annotation. The Q1 2024 dataset includes the location and shape of 63,616 assets, along with their estimated capacities. Construction dates are estimated for over 80% of these assets. This dataset covers over 19,100 square kilometers of solar farms across 183 countries, with a total estimated capacity of 711 GW.
This dataset represents the most comprehensive view of global asset-level solar installations, combining TransitionZero's detections with known solar farm geometries from other datasets. By integrating data from various sources, it provides a detailed and reliable picture of the current state of utility-scale solar farms worldwide.The data can be downloaded here
"},{"location":"projects/tzero/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The dataset fields constructed_before and constructed_after was converted to system:time_start and system:time_end for easy filtering. Nulls are kept as nulls for both of those columns.
Please refer to the suggested citation formats
\"TransitionZero Solar Asset Mapper, TransitionZero, May 2024 release.\"\n\"TZ-SAM, TransitionZero, May 2024 release.\"\n\"TransitionZero (2024) Solar Asset Mapper.\"\nPhillpott, M., O'Connor, J., Ferreira, A., Max, S., Kruitwagen, L., & Guzzardi, M. (2024). Solar Asset Mapper: A continuously-updated global\ninventory of solar energy facilities built with satellite data and machine learning (1.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.11368204\nvar tzero_solar = ee.FeatureCollection(\"projects/sat-io/open-datasets/TZERO/TZ-SOLAR-2024Q1\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/TZERO-GLOBAL-SOLAR-MAPPER
"},{"location":"projects/tzero/#license","title":"License","text":"TZ-SAM is made available under a Creative Commons Attribution Non-Commercial 4.0 International License (CC-BY-NC-4.0). Attribution to TransitionZero is required. You must also clearly indicate if you have made any changes to the TZ-SAM dataset and what these are.
Keywords: solar energy, energy transition, open data
Provided by; Transition Zero
Curated in GEE by: Samapriya Roy
Last updated in GEE: 2024-06-01
"},{"location":"projects/uhii/","title":"Urban Heat Island Intensity (UHII)","text":"The Urban Heat Island (UHI) effect, characterized by localized warming over urban areas, is a critical consequence of urbanization on climate. Traditional methods of estimating UHI intensity (UHII) have been constrained by limitations, such as focusing solely on clear-sky surface UHII and neglecting all-sky surface and canopy (air temperature) UHII. These approaches often overlook anthropogenic disturbances, leading to uncertainties in the estimates. To overcome these challenges, this study introduces a new dynamic equal-area (DEA) method designed to reduce the impact of confounding factors through a dynamic cyclic process. By applying the DEA method and integrating gridded temperature data, a comprehensive global-scale UHII dataset has been developed, covering over 10,000 cities and spanning more than 20 years with monthly temporal resolution. This dataset offers multi-faceted UHII estimates, including clear-sky surface, all-sky surface, and canopy UHII, providing a robust foundation for analyzing UHI trends in urban environments.
The dataset reveals that UHII is greater than zero in more than 80% of the studied cities, with global annual average magnitudes around 1.0\u00b0C (day) and 0.8\u00b0C (night) for surface UHII, and approximately 0.5\u00b0C for canopy UHII. Moreover, an interannual upward trend in UHII is observed in over 60% of cities, with global average trends exceeding 0.1\u00b0C per decade (day) and 0.06\u00b0C per decade (night) for surface UHII, and slightly over 0.03\u00b0C per decade for canopy UHII. A positive correlation is also identified between the magnitude and trend of UHII, indicating that cities with stronger UHII tend to experience faster growth in UHII over time. The dataset further highlights discrepancies in UHII estimates based on differences in data types (surface or air temperature), data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies. This comprehensive dataset and the accompanying analysis offer valuable insights for future urban climate studies and are publicly accessible at https://doi.org/10.6084/m9.figshare.24821538. A global dataset of urban heat island using multiple methods and including estimates for both air temperature and land surface temperature. It is available monthly from 2003 to 2020 (from 2001 for the dataset from MODIS Terra satellite). You can read more information in the paper here
NoteThe \"Diurnal\" field can be either \"Day\" or \"Nig\", signifying daytime and nighttime UHII, respectively. \"Year\u201d denotes the UHII year, and \"Month\u201d indicates the specific month. It\u2019s important to note that, besides monthly UHII results, we also provide quarterly and annual averages. When \u201cMonth\u201d takes values from 1 to 12, it signifies the monthly average. If \u201cMonth\u201d is between 21 and 24, it indicates the quarterly average (21 for March-May, 22 for June-August, 23 for September-November, and 24 for December-February). When \u201cMonth\u201d is 30, it denotes the annual average. The UHII value can be converted to degrees Celsius by multiplying a scaling factor of 0.01.
"},{"location":"projects/uhii/#dataset-details","title":"Dataset Details","text":"Indicator Data Source Period Description Surface UHI intensity estimated by the clear-sky LST data IMod1 (MOD11A1) 2001-2021 Clear-sky surface UHI from the MODIS Terra daily LST (A1) and 8-day LST (A2) products; both corresponding to an equatorial overpass time of 10:30 am local time during daytime and 10:30 pm at night IMod2 (MOD11A2) IMyd1 (MYD11A1) 2003-2021 Clear-sky surface UHI from the MODIS Aqua daily LST (A1) and 8-day LST (A2) products; both corresponding to an equatorial overpass time of 1:30 pm local time during daytime and 1:30 am at night IMyd2 (MYD11A2) Surface UHI intensity estimated by the seamless clear-sky LST data ISMod2 (Seamless MOD11A2) 2001-2020 Clear-sky surface UHI based on the seamless LST product DOI ISMyd1 (Seamless MYD11A1) 2003-2020 Clear-sky surface UHI based on a second seamless LST product DOI Surface UHI intensity estimated by the seamless all-sky LST data IAMod2 (All-sky MOD11A2) 2001-2020 All-sky surface UHI based on the seamless all-sky LST product DOI Canopy UHI intensity estimated by the surface air temperature data ISAT (Surface air temperature) 2001-2020 Air temperature or canopy UHI based on the global surface air temperature product DOI"},{"location":"projects/uhii/#citation","title":"Citation","text":"Yang, Qiquan, Yi Xu, T. C. Chakraborty, Meng Du, Ting Hu, Ling Zhang, Yue Liu et al. \"A global urban heat island intensity dataset: Generation,\ncomparison, and analysis.\" Remote Sensing of Environment 312 (2024): 114343.\nQiquan Yang.Global Urban Heat Island Intensity Dataset. Figshare. https://doi.org/10.6084/m9.figshare.24821538, 2024.\nvar AMOD2 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/AMOD2');\nvar MOD1 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/MOD1');\nvar MOD2 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/MOD2');\nvar MYD1 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/MYD1');\nvar MYD2 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/MYD2');\nvar SAT = ee.ImageCollection('projects/sat-io/open-datasets/UHII/SAT');\nvar SMOD2 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/SMOD2');\nvar SMYD1 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/SMYD1');\nThe datasets are provided under a Attribution 4.0 International (CC BY 4.0) license.
Provided by: Yang et al 2024
Curated in GEE by : Samapriya Roy
Keywords: urban, heat, climate, city
Last updated: 2024-09-06
"},{"location":"projects/uhii/#changelog","title":"Changelog","text":"Umbra satellites generate the highest resolution SAR imagery ever offered commercially from space (better than 25 cm / 10 inches). SAR satellites can capture images at night, through cloud cover, smoke and rain. SAR is unique in its abilities to monitor changes. The Open Data Program (ODP) monitors ten diverse locations around the world. Updated frequently with new images. ODP enables users to analyze the time-series data to detect changes in each location.
"},{"location":"projects/umbra_opendata/#ciation","title":"Ciation","text":"Umbra Synthetic Aperture Radar (SAR) Open Data was accessed on DATE from https://registry.opendata.aws/umbra-open-data.\nOnly GEC data was selected from the available collections since they were already encoded as geotiffs. SAR GEC data is a geocoded product flattened to a single elevation to reduce visual distortions. It is analysis-ready, though it also contains layover and foreshortening.SAR GEC data is often taken as a post product processed from slant or ground range radar images. It contains amplitude information only.
"},{"location":"projects/umbra_opendata/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var umbra_open = ee.ImageCollection('projects/sat-io/open-datasets/UMBRA/open-data');\nvar notoPeninsula = ee.ImageCollection('projects/sat-io/open-datasets/disaster/japan-earthquake-2024_UMBRA')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/UMBRA-OPENDATA
"},{"location":"projects/umbra_opendata/#license","title":"License","text":"All Umbra data is provided to customers under a modified version of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Creative Commons, the nonprofit that created and maintains CC BY 4.0. While Umbra will always remain the underlying copyright holder for data provided to customers, our licensing strategy allows customers to i) Freely publish the data, with attribution, under the CC BY 4.0 license ii) Resell our data, at a profit, without paying a royalty to Umbra iii) Create derivative works, like analytics, and sell them at a profit (again, without paying a royalty to Umbra)
Provided by: Umbra
Curated in GEE by: Samapriya Roy
Keywords: UMBRA, SAR, GED, Open data
Last updated in GEE: 23/03/2023
"},{"location":"projects/urban-watch/","title":"UrbanWatch 1m Land Cover & Land Use","text":"Very-high-resolution (VHR) land cover and land use (LCLU) is an essential baseline data for understanding fine-scale interactions between humans and the heterogeneous landscapes of urban environments. In this study, we developed a Fine-resolution, Large-area Urban Thematic information Extraction (FLUTE) framework to address multiple challenges facing large-area, high-resolution urban mapping, including the view angle effect, high intraclass and low interclass variation, and multiscale land cover types. FLUTE builds upon a teacher-student deep learning architecture and includes two new feature extraction modules \u2013 Scale-aware Parsing Module (SPM) and View-aware Embedding Module (VEM).
Our model was trained with a new benchmark database containing 52.43 million labeled pixels (from 2014 to 2017 NAIP airborne Imagery) to capture diverse LCLU types and spatial patterns. We assessed the credibility of FLUTE by producing a 1-meter resolution database named UrbanWatch for 22 major cities across the conterminous United States. UrbanWatch contains nine LCLU classes \u2013 building, road, parking lot, tree canopy, grass/shrub, water, agriculture, barren, and others, with an overall accuracy of 91.52%. You can read the entire paper UrbanWatch: A 1-meter resolution land cover and land use database for 22 major cities in the United States here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/urban-watch/#citation","title":"Citation","text":"Zhang, Yindan, Gang Chen, Soe W. Myint, Yuyu Zhou, Geoffrey J. Hay, Jelena Vukomanovic, and Ross K. Meentemeyer. \"UrbanWatch: A 1-\nmeter resolution land cover and land use database for 22 major cities in the United States.\"\nRemote Sensing of Environment 278 (2022): 113106.\nI added additional metadata to the images, including city names and abbreviations. While the uncompressed size for these datasets is 211 GB as per the paper, the total GEE collection size is only 4.54 GB. The city list is included in the sample code for easy filtering between the 23 cities.
A nine-class urban classification scheme with diverse geographic patches
Building A human-made structure with a roof (various sizes, shapes, colors, and materials) and walls across commercial, industrial, institutional, and residential areas,such as office buildings, stores, single family houses, townhouses, and condos. Road A long, narrow stretch with a leveled or paved surface that has specific orientation, length, and width. It differs from building and parking lot with its unique feature of connectivity, such as highway, bridge, sidewalk, driveway, railway, rural pathway, and airport runway. Parking Lot A cleared area intended for parking vehicles such as an on-the-ground or a surface parking lot. It differs from building and road with its unique feature of vehicle presence and/or surface markings. Tree Canopy Individual trees or tree patches representing woody vegetation typically taller than 2 m, such as trees in yards, along streets and utility corridors, and in parks and nature reserves. Grass/Shrub Small-sized perennial woody plants or herbaceous plants with height lower than 2 m, such as bushes, lawns, roadway medians, and grasslands. Agriculture Land for cultivating crops, such as corn, wheat, and soy, as well as fallow plots. Water Areas where water is predominantly present throughout the year, such as rivers, ponds, lakes, oceans, flooded plains, canals, streams, bays, estuaries, and swimming pools. Barren Areas of rock, sand or soil with very sparse to no vegetation all year round, such as exposed rock or soil, desert, dunes, dry salt flats, dried lake beds, clay, mud, quarries, golf course sand traps, mine lands, and construction site, etc. Others All other land cover/use not assigned to the above eight classes, such as outdoor tennis/basketball courts with artificial turf or acrylic surface, transmission towers, and areas covered by disturbed soils/sands without uniformed structures.
"},{"location":"projects/urban-watch/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var urban_watch = ee.ImageCollection(\"projects/sat-io/open-datasets/HRLC/urban-watch-cities\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/URBAN-WATCH-CITIES
"},{"location":"projects/urban-watch/#license","title":"License","text":"As per the authors, the urban watch data is freely accessible to support urban-related research, urban planning and management, and community outreach efforts. Therefore, the 1-m maps can be freely used for noncommercial purposes and cited; the assumed license is CC-BY-NC-4.0.
Produced by: Laboratory for Remote Sensing and Environmental Change (LRSEC) at the University of North Carolina
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, FLUTE
Last updated on GEE: 2022-06-12
"},{"location":"projects/urban_projection/","title":"Global urban projections under SSPs (2020-2100)","text":"These datasets include two separate global projections of future urban land under shared socioeconomic pathways (SSPs), one from Chen et al. (2020) and the other from Gao & O'Neill (2020). The Chen et al. dataset provides a binary classification of urban and non-urban land (pixel value of 2 for urban; 1 otherwise) at 1 km resolution for every 10th year from 2020 to 2100 (inclusive). On the other hand, the Gao & O'Neill (2020) dataset provides continuous values representing the probability of full urbanization of each \u215b degree grid for the same years.
When using these future projections, it is important to recognize that they are based on different methodologies, different training data, and different assumptions about future scenarios. For instance, the Gao & O'Neill dataset considers broad urbanization patterns across 375 sub-regions, while the Chen et al. data uses 32 regions. While both of these datasets are trained using the Global Human Settlement Layer (GHSL), the Chen et al. data are further calibrated against the European Space Agency's Climate Change Initiative (ESA CCI) data for 2015. There are many other differences and users should ideally go through the assumptions and methodology described in the respective papers before using the data.
As an example of these differences, below is a plot of projected urban percentage over time for Asia for different SSP scenarios from these datasets and the Li et al. (2021) urban extent data, which is also in the community catalog. Note that the Li et al. data are not based on GHSL, but on historical urban extent dataset from nighttime lights
"},{"location":"projects/urban_projection/#dataset-notes","title":"Dataset notes","text":"(Chen et al 2020): This dataset provides future estimates of urban expansion for all Shared Socioeconomic Pathways (SSPs) every 10 years from 2020 to 2100 (inclusive). The data are at 1 km resolution. Pixels have a value of 2 (for urban) or 1 (for non-urban). Each image corresponds to a date and there are separate bands for each SSP scenario.
(Gao et al 2020): This dataset provides future estimates of urban expansion for all Shared Socioeconomic Pathways (SSPs) every 10 years from 2020 to 2100 (inclusive). The data are at \u215b degree resolution. Probabilities of conversion of entire grid to urban is provided instead of a binary classification. Each image corresponds to a date and there are separate bands for each SSP scenario.
Also noting that these projections are all over the place. See the figure below (also comparing with the Li et al. data you have already ingested). Always good to have a word of caution about these datasets and encourage users to go back to the paper and understand the various assumptions, methodological differences, and what they might mean for use cases.
"},{"location":"projects/urban_projection/#citation","title":"Citation","text":"Chen, G., Li, X., Liu, X. et al. Global projections of future urban land expansion under shared socioeconomic pathways. Nat Commun 11, 537 (2020).\nhttps://doi.org/10.1038/s41467-020-14386-x\nGao, J., O\u2019Neill, B.C. Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. Nat Commun 11,\n2302 (2020). https://doi.org/10.1038/s41467-020-15788-7\nvar chenSSP = ee.ImageCollection(\"projects/sat-io/open-datasets/FUTURE-URBAN-LAND/CHEN_2020_2100\");\nvar gaoSSP = ee.ImageCollection(\"projects/sat-io/open-datasets/FUTURE-URBAN-LAND/GAO_2020_2100\");\nhttps://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-URBAN-SCENARIO-PROJECTIONS
"},{"location":"projects/urban_projection/#license","title":"License","text":"This work is licensed under Creative Commons Attribution 4.0 International for Gao et al 2022 and under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International for Chen et al 2020.
Created by: Gao, et al. 2022 and Chen, et al. 2022
Curated in GEE by : TC Chakraborty and Samapriya Roy
Keywords: urban, SSPs, urban projection, temporal models
Last updated on GEE: 2022-10-23
"},{"location":"projects/us_ftype_fgroup/","title":"US National Forest Type and Groups","text":""},{"location":"projects/us_ftype_fgroup/#forest-type","title":"Forest Type","text":"This dataset portrays 141 forest types across CONUS and Alaska. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, and ecoregions. The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Geospatial Technology and Applications Center. The purpose of this dataset is to portray broad distribution patterns of forest cover in the United States and provide input to national scale modeling projects.
"},{"location":"projects/us_ftype_fgroup/#forest-groups","title":"Forest Groups","text":"This dataset portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data. The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Geospatial Technology and Applications Center. Forest Type Groups are aggregations of forest types (Eyre 1980) into logical ecological groupings. There are 28 national forest type groups. Class accuracy was assessed using a randomly selected independent hold out of 6552 plots. Overall CONUS-wide accuracy for the forest type groups was 65%.
You can get detailed sample forest group metadata here
"},{"location":"projects/us_ftype_fgroup/#earth-engine-snippet-forest-type","title":"Earth Engine snippet: Forest Type","text":"var forest_type = ee.ImageCollection(\"projects/sat-io/open-datasets/USFS/national-forest-type\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/US-NATIONAL-FOREST-TYPE
"},{"location":"projects/us_ftype_fgroup/#earth-engine-snippet-forest-group","title":"Earth Engine snippet: Forest Group","text":"var forest_group = ee.ImageCollection(\"projects/sat-io/open-datasets/USFS/national-forest-group\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/US-NATIONAL-FOREST-GROUP
"},{"location":"projects/us_ftype_fgroup/#license","title":"License","text":"Although these data have been used by the USDA Forest Service, the USDA Forest Service shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data are not legal documents and are not intended to be used as such.
Created by: USDA Forest Service-Forest Inventory and Analysis (FIA) Program & Geospatial Technology and Applications Center (GTAC)
Curated in GEE by : Samapriya Roy
Keywords: forest type, forest group, forest, remote sensing
Last updated on GEE: 2022-10-25
"},{"location":"projects/usa_structures/","title":"USA Structures","text":"DHS, FIMA, FEMA\u2019s Response Geospatial Office, Oak Ridge National Laboratory, and the U.S. Geological Survey collaborated to build and maintain the nation\u2019s first comprehensive inventory of all structures larger than 450 square feet for use in Flood Insurance Mitigation, Emergency Preparedness and Response. To create the building outline inventory, FEMA, in conjunction with DHS Science and Technology, partnered with the Oak Ridge National Laboratory (ORNL) to extract the outlines via commercially available satellite imagery. You can download the datasets here or explore them using this link
"},{"location":"projects/usa_structures/#dataset-attributes","title":"Dataset Attributes","text":""},{"location":"projects/usa_structures/#building-occupancy-types","title":"Building Occupancy Types","text":"As of December 2021, the USA Structures dataset includes occupancy type (e.g., Residential, Commercial, Industrial) and primary occupancy type (e.g., Single Family Residential, Restaurant, Hospital) classifications for all structures. The team developed the data using a variety of sources including Census Housing Unit data, HIFLD, LightBox parcel data, and a modeled approach.
"},{"location":"projects/usa_structures/#universal-unique-identifier-uuid","title":"Universal Unique Identifier (UUID)","text":"In addition to the occupancy type and geometry, each polygon includes an Universally Unique Identifier (UUID) which is a unique ID for each structure across the entire dataset. This allows for connections to individual structures to unique data sources. The data schema is flexible enough to add new data fields and attributes.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or providers of the dataset and their works.
"},{"location":"projects/usa_structures/#citation","title":"Citation","text":"Oak Ridge National Laboratory (ORNL); Federal Emergency Management Agency (FEMA) Geospatial Response Office USA Structures : Last accessed date\nAll datasets are in the format
var state = ee.FeatureCollection('projects/sat-io/open-datasets/ORNL/USA-STRUCTURES/US_ST_{Two letter abbreviation for US state or territory}');\nfor a list of all US states and territories use this
var ee_folder = ee.data.listAssets(\"projects/sat-io/open-datasets/ORNL/USA-STRUCTURES\");\nHere are some example setups for two states/territories
var dc = ee.FeatureCollection('projects/sat-io/open-datasets/ORNL/USA-STRUCTURES/USA_ST_DC')\nvar arizona = ee.FeatureCollection('projects/sat-io/open-datasets/ORNL/USA-STRUCTURES/USA_ST_AZ')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/ORNL-US-STRUCTURES
"},{"location":"projects/usa_structures/#license","title":"License","text":"This work is licensed under a Creative Commons by Attribution (CC BY 4.0) license.
Created by: DHS, FEMA, ORNL
Curated in GEE by: Samapriya Roy
keywords: homeland security, homeland defense, emergency response, structures, building outlines, USA structures, buildings, FEMA, Federal Emergency Management Agency, ORNL, Oak Ridge National Laboratory, federal, industrial, education, assembly, residential, commercial
Last updated in GEE: 2022-12-01
"},{"location":"projects/usbuild_raster/","title":"Rasterized building footprint dataset for the US","text":"The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with vector layers is highly challenging in practice. Although vector layers provide accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost.
High Performance Computing (HPC) were used by the authors to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state 1. total footprint coverage 2. number of unique buildings intersecting each cell 3. number of building centroids falling inside each cell, and 4. Minimum area of the buildings that intersect each cell 5. Maximum area of the buildings that intersect each cell 6. Average area of the buildings that intersect each cell
These values are represented as raster layers with 30m cell size covering the 48 conterminous states, to better support incorporation of building footprint data into large-extent modelling.
This Project is funded by NASA\u2019s Biological Diversity and Ecological Forecasting program
Award : 80NSSC18k0341
You can download the datasets here
"},{"location":"projects/usbuild_raster/#data-citation","title":"Data Citation","text":"Heris, M.P., Foks, N., Bagstad, K., and Troy, A., 2020, A national dataset of rasterized building\nfootprints for the U.S.: U.S. Geological Survey data release, https://doi.org/10.5066/P9J2Y1WG.\nHeris, M.P., Foks, N.L., Bagstad, K.J. et al. A rasterized building footprint dataset for the\nUnited States. Sci Data 7, 207 (2020). https://doi.org/10.1038/s41597-020-0542-3\nvar avg_area = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_avg_area\");\nvar max_area = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_max_area\");\nvar min_area = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_min_area\");\nvar total_area = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_total_area\");\nvar building_count = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_count\");\nvar centroid_count = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_centroid_count\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/RASTERIZED-BUILDING-FOOTPRINT-US
"},{"location":"projects/usbuild_raster/#license","title":"License","text":"Datasets are freely available to the public under the Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/.
Created by: Heris et al, NASA, USGS
Curated by: Samapriya Roy
Keywords: : Building Footprint, Built Environment Density, Land cover, Land use
Last updated: 2021-11-26
"},{"location":"projects/usdm/","title":"United States Drought Monitor","text":"The U.S. Drought Monitor is a map released every Thursday, showing parts of the U.S. that are in drought. The map uses five classifications: abnormally dry (D0), showing areas that may be going into or are coming out of drought, and four levels of drought: moderate (D1), severe (D2), extreme (D3) and exceptional (D4).
The Drought Monitor has been a team effort since its inception in 1999, produced jointly by the National Drought Mitigation Center (NDMC) at the University of Nebraska-Lincoln, the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Department of Agriculture (USDA). The NDMC hosts the web site of the drought monitor and the associated data, and provides the map and data to NOAA, USDA and other agencies. It is freely available at droughtmonitor.unl.edu.
Unlike most of the weather maps people see in the news, the U.S. Drought Monitor is not a forecast. In fact, it looks backward. It\u2019s a weekly assessment of drought conditions, based on how much precipitation did or didn\u2019t fall, up to the Tuesday morning before the map comes out. That gives authors about two working days to review the latest data. If a lot of rain falls in a drought area on a Wednesday, the soonest drought would be removed from the map is the following week. Drought is a slow-moving hazard, so you can be certain that an area will still be in drought if it doesn\u2019t get rain. But it also may take more than one good rainfall to end a drought, especially if an area has been in drought for a long time.
"},{"location":"projects/usdm/#preprocessing","title":"Preprocessing","text":"Drought Monitor GIS Data is available as shapefiles. To create a consistent data structure, the shapefiles are ingested for all years starting from 2000 and with a weekly cadence. These have 5 different drought classes/categories and are converted into a raster with the DM(Drought Monitor class/category values) as raster property. This makes using it as collection and analysis of the data much easier. Start and end dates are added with the release week date as the end date and a week ago as a start date. For now the goal is to keep this collection updated so that this dataset is consistently synced with the source dataset.
"},{"location":"projects/usdm/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var usdm = ee.ImageCollection(\"projects/sat-io/open-datasets/us-drought-monitor\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/UNITED-STATES-DROUGHT-MONITOR
Earth Engine App: https://sat-io.earthengine.app/view/usdm-explorer
"},{"location":"projects/usdm/#license","title":"License","text":"The work is licensed under an Open data license for use.
The U.S. Drought Monitor is jointly produced by the National Drought Mitigation Center\nat the University of Nebraska-Lincoln, the United States Department of Agriculture\nand the National Oceanic and Atmospheric Administration. Map courtesy of NDMC.\nProduced by : National Drought Mitigation Center at the University of Nebraska-Lincoln, the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. Map courtesy of NDMC
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: \"National Drought Mitigation Center, NDMC, Drought, University of Nebraska-Lincoln, United States Department of Agriculture, USDA, National Oceanic and Atmospheric Administration, NOAA, USDM\"
Last updated: 2021-04-24
"},{"location":"projects/usgs_modis_et/","title":"USGS MODIS Evapotranspiration","text":"The evapotranspiration (ET) dataset presented here is the result of remote sensing techniques, primarily harnessing MODIS-thermal imagery alongside global weather datasets. This dataset corresponds to version 5 of the global ET product employed by Climate Engine. It provides valuable insights into the spatiotemporal dynamics of ET, covering the period from 2003 to 2023'. The dataset's cornerstone is the operational Simplified Surface Energy Balance (SSEBop) model, meticulously detailed by Senay et al. (2013). Built upon the foundations of the Simplified Surface Energy Balance (SSEB) approach, initially proposed by Senay et al. in 2007 and further refined in subsequent publications (Senay et al., 2011), the SSEBop model features unique parameterization specifically tailored for operational applications, akin to principles associated with psychrometry. Its robustness is underscored by a comprehensive model evaluation conducted by Velpuri et al. in 2013.
The global ET estimates are meticulously derived by integrating MODIS-based land surface temperature data, acquired from the Aqua satellite, with maximum air temperature data sourced from WorldClim. Additionally, reference ET values are obtained through global data assimilation systems (GDAS) for calibration and validation purposes, further enhancing the accuracy of this dataset. This comprehensive approach not only enriches our understanding of the intricate processes of evapotranspiration on a global scale but also offers invaluable temporal and spatial insights into these dynamics. Additional information regarding this dataset can be found here. You can find a link to this dataset within climate engine org here
"},{"location":"projects/usgs_modis_et/#dataset-details","title":"Dataset details","text":"Spatial extent Global Spatial resolution 1-km grid (1/96-deg) Temporal resolution Dekadal, monthly, and yearly Time span 2003 to Present Update frequency Updated every 10-12 days
Variables
Variable Evapotranspiration (ETa) ('et') Units Millimeters Scale factor 1.0
"},{"location":"projects/usgs_modis_et/#citation","title":"Citation","text":"Senay, G.B., Kagone S., Velpuri N.M., 2020, Operational Global Actual Evapotranspiration using the SSEBop model: U.S. Geological Survey data release, [https://doi.org/10.5066/P9OUVUUI.](https://doi.org/10.5066/P9OUVUUI) Publication: https://www.mdpi.com/1424-8220/20/7/1915\n\nSenay, G. B., Budde, M. E., & Verdin, J. P. (2011). Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Agricultural Water Management, 98(4), 606-618.\n\nSenay, G. B., Budde, M., Verdin, J. P., & Melesse, A. M. (2007). A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors, 7(6), 979-1000.\n\nVelpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P. (2013). A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET, Remote Sensing of Environment, 139, 35-49, [(Abstract and Article)](http://dx.doi.org/10.1016/j.rse.2013.07.013)\n// Read in dekadal, monthly, and annual Image Collections and get single image from each\nvar modis_et_d_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/usgs-ssebop/modis_et_v5_dekadal')\nvar modis_et_d_i = modis_et_d_ic.first()\nvar modis_et_m_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/usgs-ssebop/modis_et_v5_monthly')\nvar modis_et_m_i = modis_et_m_ic.first()\nvar modis_et_a_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/usgs-ssebop/modis_et_v5_annual')\nvar modis_et_a_i = modis_et_a_ic.first()\n\n// Print first image to see bands\nprint(modis_et_d_i)\nprint(modis_et_m_i)\nprint(modis_et_a_i)\n\n// Visualize select bands from first image\nvar et_palette = ['#f5e4a9', '#fff4ad', '#c3e683', '#6bcc5c', '#3bb369', '#20998f', '#1c8691']\nMap.addLayer(modis_et_d_i.select('et'), {min: 0, max: 10, palette: et_palette}, 'et, dekadal')\nMap.addLayer(modis_et_m_i.select('et'), {min: 0, max: 30, palette: et_palette}, 'et, monthly')\nMap.addLayer(modis_et_a_i.select('et'), {min: 0, max: 1000, palette: et_palette}, 'et, annual')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/USGS-MODIS-ET
"},{"location":"projects/usgs_modis_et/#license","title":"License","text":"USGS-authored or produced data and information are considered to be in the U.S. Public Domain.
Keywords: evapotranspiration, MODIS, ETa, SSEBop, global, near real-time, monthly, annual, dekadal
Created & provided by: USGS
Curated by: USGS & Climate Engine Org
"},{"location":"projects/usgs_topo/","title":"USGS Historical Topo Maps","text":"The history of USGS Topo Maps traces back to the late 19th century when the USGS embarked on a mission to map the entire United States in intricate detail. The 1:24,000 scale, also known as 7.5-minute quadrangle maps, emerged as one of the most widely used scales. Each map sheet covers an area of 7.5 minutes of longitude and latitude, resulting in a detailed representation of approximately 64 square miles (166 square kilometers).
A subset of these are ingested into the overall collection about 81,000+ and improvements and additions will be made in the future. Metadata including state name, place name and scale. States like Texas, California and others were not added directly but might be added over time.
You can read about the preprocessing steps here
"},{"location":"projects/usgs_topo/#citation","title":"Citation","text":"United States Geological Survey. (2019). Yosemite National Park [Topographic map, Map No. 12345]. 1:24,000. U.S. Geological Survey.\nvar usgs_topo = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/historical_topo\");\nvar map_index = ee.FeatureCollection(\"projects/sat-io/open-datasets/USGS/TOPO_24K_MAPINDEX\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/USGS-TOPO-RENDER
"},{"location":"projects/usgs_topo/#license-terms-of-use","title":"License & Terms of Use","text":"USGS topographic maps are typically in the public domain, which means they are not protected by copyright and can be freely used, reproduced, and distributed. The USGS allows the public to access and use its maps for various purposes without the need for a formal license or permission.
Provided by: USGS
Curated in GEE by : Samapriya Roy
keywords: USGS, Historical Topographical Maps, Orthophoto mosaics, Topography,Cartography
Last updated on GEE: 2023-11-25
"},{"location":"projects/usgs_viirs/","title":"USGS VIIRS Evapotranspiration","text":"The VIIRS Evapotranspiration (ET) dataset, based on Version 6 of the global ET product, is derived from remote sensing utilizing VIIRS thermal imagery and global weather datasets. It employs the SSEBop (Simplified Surface Energy Balance operational version) methodology, initially proposed by Senay et al. in 2007, with specialized parameterization tailored for operational applications using satellite psychrometry principles, as introduced by Senay in 2018. In SSEBop Version 6, the novel Forcing And Normalizing Operation (FANO) algorithm, as outlined by Senay et al. in 2023, is employed to establish the wet-bulb boundary condition, enabling robust modeling of spatiotemporal dynamics of ETa (actual evapotranspiration) across various landscapes and seasons, irrespective of vegetation cover density.
Notably, recent assessments of the global ETa product indicate its promising performance for drought monitoring through ETa Anomaly analysis. However, for studies involving water budget analysis necessitating absolute magnitudes, a local or region-specific bias correction procedure, as detailed by Senay et al. in 2020, may be required. The dataset's creation involves the integration of VIIRS-based land surface temperature, maximum air temperature from WorldClim, and reference ET obtained from gridded weather datasets such as TerraClimate by Abatzoglou et al. (2018) for global coverage and Chiew et al. (2002) for Australia.
"},{"location":"projects/usgs_viirs/#dataset-details","title":"Dataset details","text":"Spatial extent Global Spatial resolution 1-km grid (1/96-deg) Temporal resolution Dekadal, monthly, and yearly Time span 2012 to Present Update frequency Updated every 10-12 days
Variables
Variable Evapotranspiration (ETa) ('et') Units Millimeters Scale factor 1.0
"},{"location":"projects/usgs_viirs/#additional-information","title":"Additional information","text":"You can find additional information on these datasets in the links below - https://earlywarning.usgs.gov/fews/search/Global - https://earlywarning.usgs.gov/fews/product/461 - https://earlywarning.usgs.gov/fews/product/460 - https://earlywarning.usgs.gov/fews/product/458
"},{"location":"projects/usgs_viirs/#citation","title":"Citation","text":"Senay, G.B., Parrish, G.E., Schauer, M., Friedrichs, M., Khand, K., Boiko, O., Kagone, S., Dittmeier, R., Arab, S. and Ji, L., 2023. Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation. Remote Sensing,15(1), p.260. https://doi.org/10.3390/rs15010260\n\nSenay, G.B., Kagone S., Velpuri N.M., 2020, Operational Global Actual Evapotranspiration using the SSEBop model: U.S. Geological Survey data release, [https://doi.org/10.5066/P9OUVUUI.](https://doi.org/10.5066/P9OUVUUI) Publication: https://www.mdpi.com/1424-8220/20/7/1915\n\nAbatzoglou, J., Dobrowski, S., Parks, S. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u20132015. Sci Data 5, 170191 (2018). https://doi.org/10.1038/sdata.2017.191\n\nSenay, G. B. (2018). Satellite psychrometric formulation of the Operational Simplified Surface Energy Balance (SSEBop) model for quantifying and mapping evapotranspiration. Applied Engineering in Agriculture, 34(3), 555-566. https://doi.org/10.13031/aea.12614\n\nVelpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P. (2013). A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET, Remote Sensing of Environment, 139, 35-49, [(Abstract and Article)](http://dx.doi.org/10.1016/j.rse.2013.07.013)\n\nSenay, G. B., Budde, M., Verdin, J. P., & Melesse, A. M. (2007). A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors, 7(6), 979-1000.\n\nChiew, F, Q.J. Wang, F. McConachy, R. James, W. Wright, and G. deHoedt, (2002). Evapotranspiration maps for Australia. Hydrology and Water Resources Symposium, Melbourne, 20-23, 2002, Institution of Engineers, Australia.\n// Read in dekadal, monthly, and annual Image Collections and get single image from each\nvar viirs_et_d_ic = ee.ImageCollection('projects/usgs-ssebop/viirs_et_v6_dekadal')\nvar viirs_et_d_i = viirs_et_d_ic.first()\nvar viirs_et_m_ic = ee.ImageCollection('projects/usgs-ssebop/viirs_et_v6_monthly')\nvar viirs_et_m_i = viirs_et_m_ic.first()\nvar viirs_et_a_ic = ee.ImageCollection('projects/usgs-ssebop/viirs_et_v6_annual')\nvar viirs_et_a_i = viirs_et_a_ic.first()\n\n// Print first image to see bands\nprint(viirs_et_d_i)\nprint(viirs_et_m_i)\nprint(viirs_et_a_i)\n\n// Visualize select bands from first image\nvar et_palette = ['#f5e4a9', '#fff4ad', '#c3e683', '#6bcc5c', '#3bb369', '#20998f', '#1c8691']\nMap.addLayer(viirs_et_d_i.select('et'), {min: 0, max: 10, palette: et_palette}, 'et, dekadal')\nMap.addLayer(viirs_et_m_i.select('et'), {min: 0, max: 30, palette: et_palette}, 'et, monthly')\nMap.addLayer(viirs_et_a_i.select('et'), {min: 0, max: 1000, palette: et_palette}, 'et, annual')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/USGS-VIIRS-ET
"},{"location":"projects/usgs_viirs/#license","title":"License","text":"USGS-authored or produced data and information are considered to be in the U.S. Public Domain.
"},{"location":"projects/usgs_viirs/#keywords","title":"Keywords","text":"VIIRS, remote sensing, satellite, evapotranspiration, monthly, yearly, dekadal, USGS, global
Created & provided by: USGS
Curated by: USGS & Climate Engine Org
"},{"location":"projects/usgwd/","title":"United States Groundwater Well Database (USGWD)","text":"Groundwater wells are critical infrastructure that enable the monitoring, extraction, and use of groundwater, which has important implications for the environment, water security, and economic development. Despite the importance of wells, a unified database collecting and standardizing information on the characteristics and locations of these wells across the United States has been lacking. To bridge this gap, we have created a comprehensive database of groundwater well records collected from state and federal agencies, which we call the United States Groundwater Well Database (USGWD). Presented in both tabular form and as vector points, USGWD comprises over 14.2 million well records with attributes, such as well purpose, location, depth, and capacity, for wells constructed as far back as 1763 to 2023. Rigorous cross-verification steps have been applied to ensure the accuracy of the data. The USGWD stands as a valuable tool for improving our understanding of how groundwater is accessed and managed across various regions and sectors within the United States. You can read the paper here and download the dataset here.
"},{"location":"projects/usgwd/#dataset-preprocessing","title":"Dataset preprocessing","text":"The datasets were provided as state wide extracts and while the 50 state wide extracts were uploaded they were finally merged into a single feature collection for ease of use. While datasets were provided in both geospatial and tabular formats shapefiles are notorious for the property length truncation and size limit of 2GB, so tabular CSV datasets were selected which contained spatial information. However the tabular datasets themselves were has a lot of rows without location information which meant those rows had to be dropped and as such the files were reprocessed to allow us to select only those rows with location information for wells.
"},{"location":"projects/usgwd/#citation","title":"Citation","text":"Lin, CY., Miller, A., Waqar, M. et al. A database of groundwater wells in the United States. Sci Data 11, 335 (2024).\nhttps://doi.org/10.1038/s41597-024-03186-3\nLin, C., A. Miller, M. Waqar, L. Marston (2024). A Database of Groundwater Wells in the United States, HydroShare,\nhttps://doi.org/10.4211/hs.8b02895f02c14dd1a749bcc5584a5c55\nvar usgwd = ee.FeatureCollection(\"projects/sat-io/open-datasets/USGWD-TABULAR-MERGED\")\nIndividual states were also ingested for reference and can be access by the users by using
var usgwd_states = ee.data.listAssets(\"projects/sat-io/open-datasets/USGWD-TABULAR\");\n\nprint('Total of '+ee.List(usgwd_states.assets).size().getInfo()+ ' assets in nodes',usgwd_states.assets);\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/US-GROUNDWATER-WELL-DATABASE
"},{"location":"projects/usgwd/#license","title":"License","text":"The datasets are provided under a Creative Commons 4.0 International License.
Provided by: Lin, CY., Miller, A., Waqar, M. et al, (2024)
Curated in GEE by: Samapriya Roy
Keywords: USGWD, Groundwater well, Point of diversion, United States, Water infrastructure
Last updated in GEE: 2024-04-17
"},{"location":"projects/ussdo/","title":"United States Seasonal Drought Outlook","text":"The United States Drought Outlook raster dataset is produced by the National Weather Service Climate Prediction Center. It is released on the last day of each month and provides information on the drought outlook for the following month. The \"US Seasonal Drought Outlook\" dataset is released on a monthly basis, specifically on the third Thursday of each month. This dataset provides a qualitative assessment of the likelihood of drought conditions across different regions of the United States. The assessment is conducted using a four-category scale to characterize the anticipated drought conditions:
You can find additional information here and on the climate engine org website. You can download the datesets here
Categorical Values
Value Interpretation -9999 NoData Value 0 No drought 1 Drought removal likely 2 Drought remains but improves 3 Drought development likely 4 Drought persistsSpatial Information
Parameter Value Spatial extent United States Spatial resolution 500 m (1/48-deg) Temporal resolution Monthly Time span 2013-08-01 to present Update frequency Updated last day of each monthVariables
Variable Details Drought category ('drought_outlook_class') - Units: Drought outlook classification - Scale factor: 1.0 "},{"location":"projects/ussdo/#earth-engine-snippet","title":"Earth Engine Snippet","text":"// Read in Image Collection and get single image\nvar usdo_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-cpc-usdo-monthly')\nvar usdo_i = usdo_ic.first()\n\n// Print image to see bands\nprint(usdo_i)\n\n// Visualize a single image\n\nvar usdo_palette = [\"#ffffff\", \"#ABA362\", \"#DACBB5\", \"#FFD861\", \"#935743\"]\nMap.addLayer(usdo_i, {min:0, max:4, palette: usdo_palette}, 'usdo_i')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/US-DROUGHT-OUTLOOK
"},{"location":"projects/ussdo/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: drought, United Stated, outlook, forecast, NOAA, NWS, CPC, monthly
Provided by: NOAA
Curated in GEE by: Climate Engine Org
"},{"location":"projects/veg_dri/","title":"Vegetation Drought Response Index (VegDRI)","text":"The Vegetation Drought Response Index (VegDRI) is a weekly geospatial model that depicts drought stress on vegetation within the conterminous United States. The development of the VegDRI drought-monitoring tool was a collaborative effort by scientists at the USGS EROS Center, the National Drought Mitigation Center (NDMC) at the University of Nebraska, and the High Plains Regional Climate Center (HPRCC).
VegDRI methodology integrates remote sensing data from NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra platform with climate and biophysical data to create a seamless product with a 1 km spatial resolution. The satellite components related to general vegetation conditions are Percent Annual Seasonal Greenness (PASG) and Start of Season Anomaly (SOSA) data. PASG is calculated weekly from eMODIS Normalized Difference Vegetation Index (NDVI) composites.
The climate-based drought data include the Palmer Drought Severity Index (PDSI) and weekly Standardized Precipitation Index (SPI) data from the HPRCC. Climate data identify areas that are experiencing dryness to help distinguish vegetation stress due to drought. The biophysical characteristics of the environment are derived from land use/land cover, soil available water capacity, ecological setting, irrigation status, and elevation data. Environmental stressors such as land use change, soil conditions, pest infestations, disease, hail, flooding, or fire can also influence vegetation conditions.
This integrated approach considers climate and biophysical conditions to determine the cause of vegetation stress. This information is incorporated into the calculation of VegDRI to create an easy to interpret, color-coded map of drought stress on vegetation. Drought-monitoring maps are produced every week using the latest information and are usually posted each Monday by 10:30 a.m. CT. You can get access to Climate Engine ORG's website here. Additional information about DRI can be found here and from USGS here
"},{"location":"projects/veg_dri/#dataset-description","title":"Dataset Description","text":"Spatial Information
Attribute Details Spatial extent Conterminous United States Spatial resolution 1000m Temporal resolution Weekly Time span 2009-04-22 to present Update frequency Weekly on Monday by 10:30 a.m. CTVariables
Variable Units Offset Scale factor Description VegDRI (\u2018vegdri\u2019) Unitless -128 0.0625 Values provided as 8-bit integers that can be scaled to range consistent with Palmer Drought Severity Index. Water (\u2018water\u2019) Unitless N/A 1.0 Binary mask of water. Out-of-Season (\u2018out_of_season\u2019) Unitless N/A 1.0 Binary mask of out-of-season (see documentation for more information)."},{"location":"projects/veg_dri/#citation","title":"Citation","text":"Brown, J. F., Wardlow, B. D., Tadesse, T., Hayes, M. J., & Reed, B. C. (2008). The Vegetation Drought Response Index (VegDRI): A New Integrated\nApproach for Monitoring Drought Stress in Vegetation. GIScience & Remote Sensing, 45(1), 16\u201346. https://doi.org/10.2747/1548-1603.45.1.16\n// Import VegDRI\nvar vegdri_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-veg-dri')\nvar vegdri_i = vegdri_ic.first()\nprint(vegdri_i)\n\n// Link to methods webpage: https://www.usgs.gov/special-topics/monitoring-vegetation-drought-stress/science/methods-vegdri\n// Link to EROS page: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-vegetation-monitoring-vegetation-drought-response-index\n\n// VegDRI data are stored as 8-bit integer data and can be scaled into the values below\n// Drought categories from EROS page\n// Category Bitmap PDSI vals\n// Extreme drought: 001-064 -7.9375 - -4.0000\n// Severe drought: 065-080 -3.9375 - -3.0000\n// Moderate drought: 081-096 -2.9375 - -2.0000\n// Abnormally dry: 097-112 -1.9375 - -1.0000\n// Near normal: 113-160 -0.9375 - 2.0000\n// Abnormally wet: 161-176 2.0625 - 3.0000\n// Moderately wet: 177-192 3.0625 - 4.0000\n// Extremely wet: 193-255 4.0625 - 7.7500\n// Water: 253\n// Out of season: 254\n// Other landcover: 255\n\n// Function to apply stretch to make consistent with values above\nfunction scale_vegdri(img){\n\n // Select vegdri band and scale to PDSI range.\n var vegdri_scale = img.select('vegdri')\n .subtract(128) // convert to signed 8-bit integer\n .divide(16) // scale to PDSI range\n .rename('vegdri_scale') // rename image\n return img.addBands(vegdri_scale)\n}\nvegdri_ic = vegdri_ic.map(scale_vegdri)\nprint(vegdri_ic)\n\n// VegDRI color palette\nvar vegdri_palette = [\"#720206\", \"#cb3121\", \"#e36b09\", \"#fee301\", \"#ffffff\", \"#ffffff\", \"#ffffff\", \"#88f9c7\", \"#53c285\", \"#2b8032\"]\n\n// Select individual image and apply to map\nvar vegdri_i = vegdri_ic.first()\nMap.addLayer(vegdri_i.select('vegdri_scale'), {min: -5, max: 5, palette: vegdri_palette}, 'VegDRI')\nMap.addLayer(vegdri_i.select('out_of_season'), {min:254, max:254, palette: ['878787']}, 'VegDRI Out-of-Season')\nMap.addLayer(vegdri_i.select('water'), {min:253, max:253, palette: ['0000FF']}, 'Water')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/VEGETATION-DROUGHT-RESPONSE-INDEX
"},{"location":"projects/veg_dri/#license","title":"License","text":"USGS-authored or produced data and information are considered to be in the U.S. Public Domain.
Dataset provider: USGS
Keywords : Drought, Climate, Remote sensing, MODIS, PDSI, CONUS, United States
Curated in GEE by: Climate Engine Org
"},{"location":"projects/veg_dry/","title":"Vegetation dryness for western USA","text":"This dataset shows how dry or wet the vegetation is in western US. The dataset is available at 15-day temporal resolution and 250 m spatial resolution. It spans April 2016 to present.
The variable contained in the maps is live fuel moisture content. It is defined as the mass of water per unit mass of live biomass (expressed as a percentage quantity). For e.g., if a pixel value = 150, it means that the vegetation in that pixel has 1.5 Kg of water for every 1 Kg of live biomass. Live fuel moisture content was estimated from a deep learning model trained using Sentinel-1 C-band backscatter, Landsat-8 optical reflectance, and various other land surface characteristics like canopy height, soil texture, etc.
"},{"location":"projects/veg_dry/#citation","title":"Citation","text":"Rao, K., Williams, A.P., Flefil, J.F. & Konings, A.G. (2020). SAR-enhanced mapping\nof live fuel moisture content. Remote Sensing of Environment, 245, 111797.\nDOI: 10.1016/j.rse.2020.111797\nRead the paper here
"},{"location":"projects/veg_dry/#earth-engine-snippet","title":"Earth Engine Snippet","text":"
Asset Link
var asset_name = ee.ImageCollection(\"users/kkraoj/lfm-mapper/lfmc_col_25_may_2021\")\nSample Code
This script imports and visualizes average vegetation dryness for 2019, go to script
var start_date = '2019-01-01';\nvar end_date = '2019-12-31';\n\n// Import LFMC collection\nvar collection = ee.ImageCollection('users/kkraoj/lfm-mapper/lfmc_col_25_may_2021')\n .filterDate(start_date,end_date)\n\nvar image = collection.mean(); //calculate mean for the selected date range\nvar palette_lfmc = ['#703103','#945629','#ce7e45', '#df923d', '#f1b555', '#fcd163', '#99b718',\n '#74a901', '#66a000', '#529400', '#3e8601', '#207401', '#056201',\n '#004c00', '#023b01', '#012e01'\n , '#011d01', '#011301'];\n\nMap.addLayer(image, {min: [50], max: [200], palette: palette_lfmc, opacity: 0.95}, 'LFMC mean');\nMap.setCenter(-113.03, 38, 5);\nEarth Engine App https://kkraoj.users.earthengine.app/view/live-fuel-moisture
Earth Engine App Code https://code.earthengine.google.com/e6b336fa58124f4f8cda2b3be76d156f
The scripts supporting the analysis can be found at https://github.com/kkraoj/lfmc_from_sar
"},{"location":"projects/veg_dry/#license-information","title":"License Information","text":"CC BY-NC-ND 4.0 Under which you can copy and redistribute the material in any medium or format.
Created and Curated by: KrishnaRao, A. ParkWilliams, Jacqueline Fortin Flefil, Alexandra G.Konings
Keywords: vegetation, dryness, drought, wildfire, USA, live fuel moisture content
Last updated: 2021-06-29
"},{"location":"projects/vodca/","title":"Global Long-term Microwave Vegetation Optical Depth Climate Archive (VODCA)","text":"Vegetation optical depth (VOD) describes the attenuation of radiation by plants. VOD a function of frequency as well as vegetation water content, and by extension biomass. VOD has many possible applications in studies of the biosphere, such as biomass monitoring, drought monitoring, phenology analyzes or fire risk management. We merged VOD observations from various spaceborne sensors (SSM/I, TMI, AMSR-E, AMSR2, WindSat) to create global long-term vod time series. Prior to aggregation the data has been rescaled to AMSR-E, removing systematic differences between them.
There is a product for C-band (~6.9 GHz, 2002 - 2018), X-band (10.7 GHz, 1997 - 2018) and Ku-band (~19 GHz, 1987 - 2017). The data is global sampled on a regular 0.25 degrees grid.
Variables of data in VODCA files:
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/vodca/#dataset-preprocessing","title":"Dataset preprocessing","text":"The dataset were provided as zipped NetCDF files with subdatasets for VOD, Sensor flags and processing flags. The datasets including the subdatasets were exported as individual tif files and then stacked with the band order VOD, Sensor Flag and Processing Flag.
Bands Band Name Unit b1 VOD Unitless b2 Sensor Flag Refer to variable description b3 Processing Flag Refer to variable description
"},{"location":"projects/vodca/#dataset-citation","title":"Dataset citation","text":"Moesinger, Leander, Dorigo, Wouter, De Jeu, Richard, Van der Schalie, Robin, Scanlon, Tracy, Teubner, Irene, & Forkel, Matthias. (2019).\nThe Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA (1.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.2575599\nMoesinger, Leander, Wouter Dorigo, Richard de Jeu, Robin van der Schalie, Tracy Scanlon, Irene Teubner, and Matthias Forkel.\n\"The global long-term microwave vegetation optical depth climate archive (VODCA).\" Earth System Science Data 12, no. 1 (2020): 177-196.\nvar cband = ee.ImageCollection(\"projects/sat-io/open-datasets/VODCA/C-BAND\");\nvar kband = ee.ImageCollection(\"projects/sat-io/open-datasets/VODCA/K-BAND\");\nvar xband = ee.ImageCollection(\"projects/sat-io/open-datasets/VODCA/X-BAND\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/VODCA
"},{"location":"projects/vodca/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Moesinger, Leander, Wouter Dorigo, Richard de Jeu, Robin van der Schalie, Tracy Scanlon, Irene Teubner, and Matthias Forkel
Curated in GEE by: Samapriya Roy
Keywords: VOD, Vegetation Optical Depth, Biomass, Vegetation water content, Microwave Remote Sensing, Biosphere, Time Series, global, vegetation
Last updated : 2023-01-19
"},{"location":"projects/vt_lc/","title":"Vermont High Resolution Land Cover 2016","text":"High resolution land cover dataset for Vermont. The primary sources used to derive this land cover layer were 2013-2017 LiDAR data and 2016 NAIP imagery. Ancillary data sources included GIS data provided by the State of Vermont or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
This dataset was developed as part of the Vermont High-Resolution Land Cover. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. Eight land cover classes were mapped:
This assessment and development of methods necessary for its conduct were completed by the University of Vermont's Spatial Analysis Laboratory with funding from the State of Vermont Clean Water Fund, Vermont Agency of Natural Resources, Vermont Agency of Transportation, Lake Champlain Basin Program, and the Vermont Center for Geographic Information (VCGI). Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establishing tree canopy goals.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
University of Vermont Spatial Analysis Laboratory and VT Center for Geographic Information. Vermont High Resolution Land Cover. Accessed [Month\nYear] at https://geodata.vermont.gov/pages/land-cover\nvar VT_baseLC2016 = ee.Image(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/VT_BaseLC_2016\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/VT-BASE-LC-2016
"},{"location":"projects/vt_lc/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. VCGI and the State of Vermont make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.
Produced by: University of Vermont Spatial Analysis Laboratory, VT Center for Geographic Information
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, OBIA
Last updated on GEE: 2022-06-12
"},{"location":"projects/wa_lulc/","title":"West Africa Land Use Land Cover","text":"Started in 1999, the West Africa Land Use Dynamics project represents an effort to map land use and land cover, characterize the trends in time and space, and understand their effects on the environment across West Africa. The outcome of the West Africa Land Use Dynamics project is the production of a three-time period (1975, 2000, and 2013) land use and land cover dataset for the Sub-Saharan region of West Africa, including the Cabo Verde archipelago. The West Africa Land Use Land Cover Time Series dataset offers a unique basis for characterizing and analyzing land changes across the region, systematically and at an unprecedented level of detail.
Tappan, G. G., Cushing, W.M., Cotillon, S.E., Mathis, M.L., Hutchinson, J.A., Herrmann, S.M., and\nDalsted, K.J., 2016, West Africa Land Use Land Cover Time Series:\nU.S. Geological Survey data release, http://dx.doi.org/10.5066/F73N21JF\nvar wa1975 = ee.Image(\"projects/sat-io/open-datasets/wa-datasets/wa_lc_usgs_1975\");\nvar wa2000 = ee.Image(\"projects/sat-io/open-datasets/wa-datasets/wa_lc_usgs_2000\");\nvar wa2013 = ee.Image(\"projects/sat-io/open-datasets/wa-datasets/wa_lc_usgs_2013\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/WEST-AFRICA-LULC
"},{"location":"projects/wa_lulc/#license","title":"License","text":"Creative Commons Attribution-Share Alike 4.0 International License
Curated by: Samapriya Roy
Keywords: United States Geological Survey, USGS, Land Use, Land Cover, West Africa
Last updated: 2021-04-17
"},{"location":"projects/wacvm/","title":"West Africa Coastal Vulnerability Mapping","text":"The West Africa Coastal Vulnerability Mapping: Social Vulnerability Indices data set includes three indices: Social Vulnerability, Population Exposure, and Poverty and Adaptive Capacity. The Social Vulnerability Index (SVI) was developed using six indicators: population density (2010), population growth (2000-2010), subnational poverty and extreme poverty (2005), maternal education levels circa 2008, market accessibility (travel time to markets) circa 2000, and conflict data for political violence (1997-2013). Because areas of high population density and growth (high vulnerability) are generally associated with urban areas that have lower levels of poverty and higher degrees of adaptive capacity (low vulnerability), to some degree, the population factors cancel out the poverty and adaptive capacity indicators. To account for this, the data set includes two sub-indices, a Population Exposure Index (PEI), which only includes population density and population growth; and a Poverty and Adaptive Capacity Index (PACI), composed of subnational poverty, maternal education levels, market accessibility, and conflict. These sub-indices are able to isolate the population indicators from the poverty and conflict metrics. The indices represent Social Vulnerability in the West Africa region within 200 kilometers of the coast.
Purpose: To provide a measure of social vulnerability and \"defenselessness\" in the face of climate stressors in the coastal zone of West Africa.
The documentation for this dataset is available here
Use the following citation
Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. West Africa Coastal Vulnerability Mapping: Social Vulnerability Indices. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4H41PCK. Accessed DAY MONTH YEAR.\nvar wacvm_paci = ee.FeatureCollection(\"projects/sat-io/open-datasets/sedac/wacvm-social-vulnerability-indices-paci\");\nvar wacvm_pei = ee.FeatureCollection(\"projects/sat-io/open-datasets/sedac/wacvm-social-vulnerability-indices-pei\");\nvar wacvm_svi = ee.FeatureCollection(\"projects/sat-io/open-datasets/sedac/wacvm-social-vulnerability-indices-svi\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/WEST_AFRICA-COASTAL-VULN
Shared License: This work is licensed under the Creative Commons Attribution 4.0 International License. Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Curated by: Samapriya Roy
Keywords: census geography, GPWv4, gridded population, uniform distribution
Last updated: 2021-04-11
"},{"location":"projects/wpschool/","title":"Gridded Sex-Disaggregated School-Age Population (2020)","text":"Following the IIEP-UNESCO methodology for reconstructing georeferenced school-age populations (ISCED 1 to 3) by year and sex, these datasets were produced by WorldPop (University of Southampton) by applying the Sprague Multipliers to 30-arcsecond (approximately 1km at the equator) gridded datasets depicting the estimated spatial distribution of sex-disaggregated 5-year age groups. These datasets include the sex disaggregated school age population for countries and Dependent territories in Africa only.
"},{"location":"projects/wpschool/#inputs","title":"Inputs","text":"Bondarenko, M., Sorichetta, A., Vargas Mesa, G., Gagnon, A.A., Tatem, A.J. (2022). Gridded Sex Disaggregated School-Age Population Datasets for Countries\nand Dependent Territories in Africa in 2020, doi:10.5258/SOTON/WP00732\nvar f_primary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_F_PRIMARY\");\nvar f_secondary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_F_secondary\");\nvar m_primary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_M_PRIMARY\");\nvar m_secondary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_M_secondary\");\nvar fm_primary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_F_M_PRIMARY\");\nvar fm_secondary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_F_M_SECONDARY\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/WORLDPOP-GRIDDED-SCHOOL-AGE
"},{"location":"projects/wpschool/#license","title":"License","text":"WorldPop datasets are available under the Creative Commons Attribution 4.0 International License. This means that you are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially, provided attribution is included (appropriate credit and a link to the licence).
Created by: WorldPop (University of Southampton)
Curated in GEE by: Samapriya Roy
Keywords: gridded model, population data, school age, disaggregated, worldpop
"},{"location":"projects/wrc/","title":"Wildfire Risk to Communities (WRC)","text":"The Wildfire Risk to Communities dataset was created by the USDA Forest Service to provide a nationwide view of wildfire risk potential. The dataset includes spatial data on the following components of wildfire risk: wildfire likelihood, wildfire intensity, susceptibility, and exposure. The data is available at a 270-meter resolution and covers the entire United States. It is based on a variety of sources, including vegetation data, fuel models, historical fire data, and population data.
The Wildfire Risk to Communities dataset can be used to assess wildfire risk at the community level and to develop wildfire mitigation plans. It can also be used to identify communities that are most in need of assistance. The Wildfire Risk to Communities dataset was created by USDA Forest Service to help assess risk to homes, businesses, and other valued resources. The dataset contains nationally-consistent information for the purpose of comparing relative wildfire risk among communities nationally or within a state or county. In situ risk (risk at the location where the adverse effects take place on the landscape) are modeled using the large fire simulation system (FSim) and LANDFIRE fuel loading datasets from 2014. The original data at 250m has been upsampled to 30m for this dataset on Climate Engine. You can find additional information about the dataset here and read more about this on climate engine org page here. Here is a link to the report documentation
Spatial Extent United States Spatial Resolution 30 m Temporal Resolution Single point in time Time Span 2014 Update Frequency Static
Variables Burn probability ('BP') - Units: Fractional probability - Scale Factor: 1.0 Conditional flame length ('CFL') - Units: Feet - Scale Factor: 1.0 Conditional risk to potential structures ('CRPS') - Units: Percentile - Scale Factor: 1.0 Exposure type ('Exposure') - Units: Exposure type - Scale Factor: 1.0 Flame length exceedance probability - 4 ft ('FLEP4') - Units: Fractional probability - Scale Factor: 1.0 Flame length exceedance probability - 8 ft ('FLEP8') - Units: Fractional probability - Scale Factor: 1.0 Risk to potential structures ('RPS') - Units: Percentiles - Scale Factor: 1.0 Wildfire hazard potential index ('WHP') - Units: Unitless - Scale Factor: 1.0
"},{"location":"projects/wrc/#citation","title":"Citation","text":"Scott, Joe H.; Gilbertson-Day, Julie W.; Moran, Christopher; Dillon, Gregory K.; Short, Karen C.; Vogler, Kevin C. 2020. Wildfire Risk to\nCommunities: Spatial datasets of landscape-wide wildfire risk components for the United States. Fort Collins, CO: Forest Service Research Data\nArchive. Updated 25 November 2020.\n// Read in Image Collection and mosaic to single image\nvar wrc_i = ee.ImageCollection('projects/climate-engine-pro/assets/ce-wrc-static').mosaic()\n// Print image to see bands\nprint(wrc_i)\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar bp_palette = [\"#d53e4f\", \"#fc8d59\", \"#fee08b\", \"#ffffbf\", \"#e6f598\", \"#99d594\", \"#3288bd\"].reverse()\nvar exposure_palette = [\"#f6eff7\", \"#d0d1e6\", \"#a6bddb\", \"#67a9cf\", \"#3690c0\", \"#02818a\", \"#016450\"].reverse()\nvar crps_palette = [\"#ffffd4\", \"#fee391\", \"#fec44f\", \"#fe9929\", \"#ec7014\", \"#cc4c02\", \"#8c2d04\"]\nvar flep_palette = [\"#8c510a\", \"#d8b365\", \"#f6e8c3\", \"#f5f5f5\", \"#c7eae5\", \"#5ab4ac\", \"#01665e\"].reverse()\nvar rps_palette = [\"#ffffb2\", \"#fed976\", \"#feb24c\", \"#fd8d3c\", \"#fc4e2a\", \"#e31a1c\", \"#b10026\"]\nvar whp_palette = [\"#d73027\", \"#fc8d59\", \"#fee08b\", \"#ffffbf\", \"#d9ef8b\", \"#91cf60\", \"#1a9850\"].reverse()\nMap.addLayer(wrc_i.select('BP'), {min: 0, max: 0.025, palette: bp_palette}, 'BP')\nMap.addLayer(wrc_i.select('CFL').selfMask(), {min: 0, max: 15, palette: flep_palette}, 'CFL')\nMap.addLayer(wrc_i.select('CRPS'), {min: 30, max: 80, palette: crps_palette}, 'CRPS')\nMap.addLayer(wrc_i.select('Exposure'), {min: 0, max: 1, palette: exposure_palette}, 'Exposure')\nMap.addLayer(wrc_i.select('FLEP4'), {min: 0.1, max: 0.9, palette: flep_palette}, 'FLEP4')\nMap.addLayer(wrc_i.select('FLEP8'), {min: 0.1, max: 0.9, palette: flep_palette}, 'FLEP8')\nMap.addLayer(wrc_i.select('RPS'), {min: 0, max: 1, palette: rps_palette}, 'RPS')\nMap.addLayer(wrc_i.select('WHP'), {min: 0, max: 2000, palette: whp_palette}, 'WHP')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/WILDFIRE-RISK-COMMUNITIES
"},{"location":"projects/wrc/#license","title":"License","text":"CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Keywords: Wildfire, CONUS, United States, USDA, Forest Service, LANDFIRE
Provided by: USDA Forest Service
Curated in GEE by: Climate Engine Org
"},{"location":"projects/wsf/","title":"World Settlement Footprint & Evolution","text":""},{"location":"projects/wsf/#world-settlement-footprint-2015","title":"World Settlement Footprint 2015","text":"The World Settlement Footprint (WSF) 2015 is a 10m (0.32 arc sec) resolution binary mask outlining the 2015 global settlement extent derived by jointly exploiting multitemporal Sentinel-1 radar and Landsat-8 optical satellite imagery.
The world settlement footprint 2015 is now available in the official GEE catalog and you can find it here
The entire catalog consists of 306 GeoTIFF files (EPSG4326 projection, deflate compression) each one referring to a portion of 10x10 degree size (~1110x1110km) whose upper-left and lower-right corner coordinates are specified in the file name [e.g., the tile WSF2015_v1_EPSG4326_e010_n60_e020_n50.tif covers the area between (10E;60N) and (20E;50N)].
Settlements are associated with value 255; all other pixels are associated with value 0.
"},{"location":"projects/wsf/#world-settlement-footprint-wsf-2019","title":"World Settlement Footprint (WSF) 2019","text":"The World Settlement Footprint (WSF 2019) is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery.
The dataset is organized in 5138 GeoTIFF files (EPSG4326 projection) each one referring to a portion of 2x2 degree size (~222x222km on the ground) with an extra buffer of 0.1 degree to avoid any discontinuity between neighbour tiles. Each tile is identified by the lower-left corner coordinates specified in the file name [e.g., the tile WSF2019_v1_12_18.tif covers the area between (12E;18N) and (14E;20N)]. You can download the files here
Settlements are associated with value 255; all other pixels are associated with value 0.
"},{"location":"projects/wsf/#world-settlement-footprint-evolution-1985-2015","title":"World Settlement Footprint Evolution (1985-2015)","text":"This repository contains the World Settlement Footprint (WSF) Evolution, a 30m resolution layer outlining the global settlement extent on a yearly basis from 1985 to 2015 derived by means of multitemporal Landsat-5 and Landsat-7 imagery.
The dataset is organized in 5138 GeoTIFF files (EPSG4326 projection) each one referring to a portion of 2x2 degree size (~222x222km on the ground) with an extra buffer of 0.1 degree to avoid any discontinuity between neighbour tiles. Each tile is identified by the lower-left corner coordinates specified in the file name [e.g., the tile WSFevolution_v1_12_18.tif covers the area between (12E;18N) and (14E;20N)]. You can download the files here
Values range between 1985 and 2015 corresponding to the estimated year of settlement detection, whereas 0 is no data.
"},{"location":"projects/wsf/#world-settlement-footprint-evolution-input-data-consistency-score","title":"World Settlement Footprint Evolution: Input Data Consistency Score","text":"This repository contains the Input Data Consistency (IDC) score, which provides a suitable and intuitive measure that accounts for the goodness of the Landsat imagery used for generating the WSF evolution and supports a proper interpretation of the product.
The dataset is organized in 5138 GeoTIFF files (EPSG4326 projection) each one referring to a portion of 2x2 degree size (~222x222km on the ground) with an extra buffer of 0.1 degree to avoid any discontinuity between neighbour tiles. Each tile is identified by the lower-left corner coordinates specified in the file name [e.g., the tile IDC_Score_12_18.tif covers the area between (12E;18N) and (14E;20N)]. Values range from 6 to 1 with: 6) very good; 5) good; 4) fair; 3) moderate; 2) low; 1) very low. You can download the files here
"},{"location":"projects/wsf/#data-citation","title":"Data Citation","text":"Marconcini, Mattia; Metz-Marconcini, Annekatrin; \u00dcreyen, Soner; Palacios-Lopez,\nDaniela; Hanke, Wiebke; Bachofer, Felix; et al. (2020): World Settlement Footprint (WSF) 2015.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.10048412.v1\nYou can read the Outlining where humans live, the World Settlement Footprint 2015 here and Understanding Current Trends in Global Urbanisation \u2013 The World Settlement Footprint suite here
"},{"location":"projects/wsf/#paper-citation","title":"Paper Citation","text":"Marconcini, Mattia, Annekatrin Metz-Marconcini, Soner \u00dcreyen, Daniela Palacios-Lopez,\nWiebke Hanke, Felix Bachofer, Julian Zeidler et al. \"Outlining where humans live,\nthe World Settlement Footprint 2015.\" Scientific Data 7, no. 1 (2020): 1-14.\n\nMarconcini, M., Metz-Marconcini, A., Esch, T., Gorelick, N. (2021). Understanding Current Trends\nin Global Urbanisation \u2013 The World Settlement Footprint suite. GI_Forum, 1, 33-38.\nhttps://doi.org/10.1553/giscience2021_01_s33.\nThe dataset is single value only with a value of 255 for WSF 2015 and 2019 and pixel values are 1985 to 2015 for the WSF evolution dataset.
var wsf2015 = ee.ImageCollection(\"projects/sat-io/open-datasets/WSF/WSF_2015\");\nvar wsf2019 = ee.ImageCollection(\"projects/sat-io/open-datasets/WSF/WSF_2019\");\nvar wsf_evo = ee.ImageCollection(\"projects/sat-io/open-datasets/WSF/WSF_EVO\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/WORLD-SETTLEMENT-FOOTPRINT
the IDC Score is a measure of goodness of imagery used for evolution layers
var wsf_evo_idc = ee.ImageCollection(\"projects/sat-io/open-datasets/WSF/WSF_EVO_IDC\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/WORLD-SETTLEMENT-FOOTPRINT-IDC
"},{"location":"projects/wsf/#license","title":"License","text":"The World Settlement Footprint 2015 is released under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
The World Settlement Footprint 2019 is licensed under CC-BY-4.0.
The World Settlement Footprint evolution 1985-2015 is licensed under CC-BY-4.0.
Created by : Marconcini, et al
Curated in GEE by: Samapriya Roy
Keywords: World Settlement Footprint, Settlement Extent, Urbanization, Earth Observation, Remote Sensing, Sentinel-1, Landsat-8
Last updated : 2021-12-12
"},{"location":"reference/","title":"Reference & Citation","text":"While most datasets in our community catalog are citable themselves, it's important to note that the catalog as a whole should also be cited by users. Our citation information and release details are sourced from Zenodo and you can always find the latest DOI & Citation here.
We encourage participation in our releases by contributing code examples, tutorials, edits through pull requests, documentation, or by being involved in planning and developing the community catalog further. Your contributions ensure that you are recognized as part of the citation for each release.
"},{"location":"reference/#citation","title":"Citation","text":"Samapriya Roy, Swetnam, T., & Saah, A. (2024). samapriya/awesome-gee-community-datasets: Community Catalog (3.1.0).\nZenodo. https://doi.org/10.5281/zenodo.14042069\nSubmit a tutorial or Example
Create and share examples demonstrating how you've leveraged the catalog's data in your projects.
Submit a tutorial
Collaborate with Pull requests
Creating a new pull request means you fixed something that I missed & I and the community apppreciate it.
Create a pull request
The Advanced Search feature of the Community Catalog leverages Vertex AI to enhance documentation search. This search is powered by both the catalog pages and Data JSON tabular structures. As more people use the feature, the indexed results improve over time. The feature benefits from grounded search, meaning that its summaries are always linked to the source material, ensuring transparency and trustworthiness.
This is made possible by RAG (Retrieval Augmented Generation), which not only improves search relevancy but also minimizes misinformation by grounding the output in reliable sources. Learn more about RAGs here in an earlier blog post to understand why this is so powerful and why grounded search can be effective. Currently, this search is in Beta, allowing us to test and refine it further with your participation. You can still use the embedded search within the Community Catalog for basic keyword or text-match searches.
Find the Advanced Search here.
NoteIf the search doesn't work as expected, try disabling any widget blocker extensions or using an incognito window to troubleshoot.
"},{"location":"search_features/#salient-features","title":"Salient Features","text":"It is feasible to sometimes use a machine readable list of catalog assets. While we are going to introduce a STAC catalog again at some point the assets are also available in two specific formats with the total running count above.
\u00a0 Download latest JSON version here
"},{"location":"startup/catalog-assets/#json-format","title":"JSON format","text":"This holds information about the datasets in this structure as a JSON list. If the license is custom for a dataset license text is included to clarify the details. The structure is the following
Field Description Title The name of the dataset. Sample Code A link to a sample script demonstrating how to use the dataset in Google Earth Engine. Type The type of data (e.g., table). ID The unique identifier for the dataset in the Earth Engine catalog. Provider The organization or entity that provides the dataset. Tags Keywords associated with the dataset to help with search and categorization. License The licensing terms under which the dataset is provided. License Text Additional text explaining the license (if applicable). Docs A link to documentation or more information about the dataset. Thematic Group The category or group under which the dataset falls (e.g., Oceans and Shorelines, Hydrology).[\n {\n \"title\": \"Global Shoreline Dataset\",\n \"sample_code\": \"https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL_SHORELINES\",\n \"type\": \"table\",\n \"id\": \"projects/sat-io/open-datasets/shoreline/mainlands\",\n \"provider\": \"United States Geological Survey, USGS\",\n \"tags\": \"Global Shoreline, Shoreline, mainlands, Oceans\",\n \"license\": \"Creative Commons Attribution Share Alike 4.0 International\",\n \"docs\": \"https://gee-community-catalog.org/projects/shoreline/\",\n \"thematic_group\": \"Oceans and Shorelines\"\n },\n {\n \"title\": \"NWI_CO_Riparian_Project_Metadata\",\n \"sample_code\": \"https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-WETLANDS-INVENTORY\",\n \"type\": \"table\",\n \"id\": \"projects/sat-io/open-datasets/NWI/rpm/CO_Riparian_Project_Metadata\",\n \"provider\": \"U.S. Fish and Wildlife Service\",\n \"tags\": \"wetlands, conservation areas, habitats, fish, wildlife, drinking water, recreation, U.S. Fish and Wildlife Service,CO_Riparian_Project_Metadata\",\n \"license\": \"custom\",\n \"license_text\": \"The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US\",\n \"docs\": \"https://gee-community-catalog.org/projects/nwi/\",\n \"thematic_group\": \"Hydrology\"\n }\n]\nThe CSV file is created using a Github action within the repository and contains all fields in the JSON representation. Fields like license_text if empty for a specific license are left empty.
\u00a0 Download latest CSV version here
id provider title type tags sample_code license license_text docs_page thematic_group projects/sat-io/open-datasets/shoreline/mainlands United States Geological Survey, USGS Global Shoreline Dataset table Global Shoreline, Shoreline, mainlands, Oceans https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL_SHORELINES Creative Commons Attribution Share Alike 4.0 International NA https://gee-community-catalog.org/projects/shoreline/ Oceans and Shorelines projects/sat-io/open-datasets/NWI/rpm/CO_Riparian_Project_Metadata U.S. Fish and Wildlife Service NWI_CO_Riparian_Project_Metadata table wetlands, conservation areas, habitats, fish, wildlife, drinking water, recreation, U.S. Fish and Wildlife Service,CO_Riparian_Project_Metadata https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-WETLANDS-INVENTORY custom The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US https://gee-community-catalog.org/projects/nwi/ Hydrology"},{"location":"startup/catalog-examples/","title":"Access awesome-gee-catalog-examples repo","text":"The awesome GEE catalog dataset examples are now part of a repo. Add this to your code editor space for easy access and updates to datasets and examples.
\u00a0 Add examples repo to your GEE reader repository list
OR
\u00a0 Download GEE Community Catalog Examples Folder
"},{"location":"startup/navigation/","title":"Navigating the Catalog","text":"The awesome-gee-community catalog is designed to be completely compatible with the existing GEE master catalog. It is also designed to be as open and exploratory as possible. The catalog is grouped by domains for example datasets that belong to themes about Population and Socioeconomics will be within that group and you should be able to expand and look at the datasets.
You can also search by dataset name or keywords or tags in the search bar.
Each page also includes the assets for each datasets as well some example code for you to explore and load the dataset quickly. You can also add the entire catalog examples as a repo now so you can search for and use the dataset. But don't stop there bring new datasets to the catalog, suggest things we can add and share back examples you make with the data. Explore how to do this and more in the Getting started section.
If you use geeadd: Google Earth Engine Batch Asset Manager with Addons you can also search both the main catalog and the community catalog using that tool.
"},{"location":"tutorials/","title":"Community Catalog Tutorials","text":"In this section of the community catalog, we'll explore user contributed Tutorials featuring catalog datasets or a mix of community catalog and the main catalog.
"},{"location":"tutorials/#the-importance-of-tutorials","title":"The Importance of Tutorials","text":"Tutorials are an essential component of any learning platform, as they provide a clear and concise guide on how to achieve specific goals or solve particular problems. In the context of Earth Engine, tutorials are particularly valuable because they help users:
The Community Catalog tutorials are designed to be a comprehensive resource for learning about Earth Engine. These tutorials cover a range of topics, including:
These tutorials are not only helpful for beginners but also serve as a valuable reference for experienced users looking to expand their skills or explore new applications.
"},{"location":"tutorials/#encouraging-participation-contributing-code-examples-tutorials-edits-and-more","title":"Encouraging Participation: Contributing Code Examples, Tutorials, Edits, and More!","text":"We encourage participation in our releases by contributing code examples, tutorials, edits through pull requests, documentation, or by being involved in planning and developing the community catalog further. Your contributions ensure that you are recognized as part of the citation for each release.
"},{"location":"tutorials/#benefits-of-contributing","title":"Benefits of Contributing","text":"By participating in the development of the Community Catalog, you can:
The Community Catalog is a vital resource for learning about Earth Engine, and its tutorials are an essential component. By contributing code examples, tutorials, edits, or other forms of participation, you can help shape the catalog and benefit from the collective knowledge and expertise of the community.
"},{"location":"tutorials/examples/glc_fcs30d_lulc/","title":"Exploring the Global 30m Land Cover Change Dataset (1985-2022) GLC_FCS30D","text":"by Ujaval Gandhi from Spatial Thoughts
A temporally consistent global multi-class time-series classification dataset is critical to understand and quantify long-term changes. Previously, options were limited to lower resolution datasets such as MODIS Landcover (2000-present) at 500m resolution or ESA CCI (1992-present) at 300m resolution. The new GLC_FCS30D dataset provides a high-resolution landcover time-series derived from the Landsat archive (1984-2022) at 30m resolution with 35 classes. This dataset is valuable for studying landscape dynamics at high resolution and is available in the public domain. It can be downloaded from Zenodo as GeoTIFF files or accessed directly in the Google Earth Engine (GEE) Community catalog. In this tutorial, we will:
The original dataset was produced in 5\u00b0 x 5\u00b0 tiles with each image having bands for each year of classification. This structure was uploaded to the Earth Engine Community Catalog, split into datasets for five-yearly classifications (1985-90, 1990-95, and 1995-2000) and yearly classifications (2000-2022). GEE workflows are structured around ImageCollections rather than multiband images, so we need to transform the original data into an ImageCollection.
"},{"location":"tutorials/examples/glc_fcs30d_lulc/#steps","title":"Steps:","text":"Here's the Earth Engine code for the preprocessing step and a link to the code
// Example script showing how to pre-process the GLC_FCS30D landcover dataset\n\n// Yearly data from 2000-2022\nvar annual = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/annual');\n// Five-Yearly data for 1985-90, 1990-95 and 1995-2000\nvar fiveyear = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/five-years-map');\n\n// Classification scheme has 36 classes (35 landcover classes and 1 fill value)\nvar classValues = [10, 11, 12, 20, 51, 52, 61, 62, 71, 72, 81, 82, 91, 92, 120, 121, 122, 130, 140, 150, 152, 153, 181, 182, 183, 184, 185, 186, 187, 190, 200, 201, 202, 210, 220, 0];\nvar classNames = ['Rainfed_cropland', 'Herbaceous_cover_cropland', 'Tree_or_shrub_cover_cropland', 'Irrigated_cropland', 'Open_evergreen_broadleaved_forest', 'Closed_evergreen_broadleaved_forest', 'Open_deciduous_broadleaved_forest', 'Closed_deciduous_broadleaved_forest', 'Open_evergreen_needle_leaved_forest', 'Closed_evergreen_needle_leaved_forest', 'Open_deciduous_needle_leaved_forest', 'Closed_deciduous_needle_leaved_forest', 'Open_mixed_leaf_forest', 'Closed_mixed_leaf_forest', 'Shrubland', 'Evergreen_shrubland', 'Deciduous_shrubland', 'Grassland', 'Lichens_and_mosses', 'Sparse_vegetation', 'Sparse_shrubland', 'Sparse_herbaceous', 'Swamp', 'Marsh', 'Flooded_flat', 'Saline', 'Mangrove', 'Salt_marsh', 'Tidal_flat', 'Impervious_surfaces', 'Bare_areas', 'Consolidated_bare_areas', 'Unconsolidated_bare_areas', 'Water_body', 'Permanent_ice_and_snow', 'Filled_value'];\nvar classColors = ['#ffff64', '#ffff64', '#ffff00', '#aaf0f0', '#4c7300', '#006400', '#a8c800', '#00a000', '#005000', '#003c00', '#286400', '#285000', '#a0b432', '#788200', '#966400', '#964b00', '#966400', '#ffb432', '#ffdcd2', '#ffebaf', '#ffd278', '#ffebaf', '#00a884', '#73ffdf', '#9ebb3b', '#828282', '#f57ab6', '#66cdab', '#444f89', '#c31400', '#fff5d7', '#dcdcdc', '#fff5d7', '#0046c8', '#ffffff', '#ffffff'];\n\n// Mosaic the data into a single image\nvar annualMosaic = annual.mosaic();\nvar fiveYearMosaic = fiveyear.mosaic();\n\n// Rename bands from b1, b2, etc. to 2000, 2001, etc.\nvar fiveYearsList = ee.List.sequence(1985, 1995, 5).map(function(year) { return ee.Number(year).format('%04d'); });\nvar fiveyearMosaicRenamed = fiveYearMosaic.rename(fiveYearsList);\nvar yearsList = ee.List.sequence(2000, 2022).map(function(year) { return ee.Number(year).format('%04d'); });\nvar annualMosaicRenamed = annualMosaic.rename(yearsList);\nvar years = fiveYearsList.cat(yearsList);\n\n// Convert the multiband image to an ImageCollection\nvar fiveYearlyMosaics = fiveYearsList.map(function(year) {\n var date = ee.Date.fromYMD(ee.Number.parse(year), 1, 1);\n return fiveyearMosaicRenamed.select([year]).set({'system:time_start': date.millis(), 'system:index': year, 'year': ee.Number.parse(year)});\n});\nvar yearlyMosaics = yearsList.map(function(year) {\n var date = ee.Date.fromYMD(ee.Number.parse(year), 1, 1);\n return annualMosaicRenamed.select([year]).set({'system:time_start': date.millis(), 'system:index': year, 'year': ee.Number.parse(year)});\n});\nvar allMosaics = fiveYearlyMosaics.cat(yearlyMosaics);\nvar mosaicsCol = ee.ImageCollection.fromImages(allMosaics);\n\n// Recode the class values into sequential values\nvar newClassValues = ee.List.sequence(1, ee.List(classValues).length());\nvar renameClasses = function(image) {\n var reclassified = image.remap(classValues, newClassValues).rename('classification');\n return reclassified;\n};\nvar landcoverCol = mosaicsCol.map(renameClasses);\n\nprint('Pre-processed Collection', landcoverCol);\n\n// Visualize the data\nvar year = 2022;\nvar selectedLandcover = landcoverCol.filter(ee.Filter.eq('year', year)).first();\nvar palette = ['#ffff64', '#ffff64', '#ffff00', '#aaf0f0', '#4c7300', '#006400', '#a8c800', '#00a000', '#005000', '#003c00', '#286400', '#285000', '#a0b432', '#788200', '#966400', '#964b00', '#966400', '#ffb432', '#ffdcd2', '#ffebaf', '#ffd278', '#ffebaf', '#00a884', '#73ffdf', '#9ebb3b', '#828282', '#f57ab6', '#66cdab', '#444f89', '#c31400', '#fff5d7', '#dcdcdc', '#fff5d7', '#0046c8', '#ffffff', '#ffffff'];\nvar classVisParams = {min:1, max:36, palette: palette};\nMap.addLayer(selectedLandcover, classVisParams, 'Landcover ' + year);\nA useful way to visualize a landcover time-series is through a user interface that allows us to compare and contrast data for multiple years. Using a split-panel, we can load classifications for two different years and swipe to see changes between them. We will create a split panel interface with a dropdown selector allowing you to change the year and visualize the changes. To make the map interpretation easier, we will also construct a legend.
You can explore the app at Global Landcover Change Explorer.
Here's the source code for the app:
// Example script for an App to explore GLC_FCS30D landcover dataset using a split-panel\n\n// Pre-process the Collection\nvar annual = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/annual');\nvar fiveyear = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/five-years-map');\nvar classValues = [10, 11, 12, 20, 51, 52, 61, 62, 71, 72, 81,\n\n 82, 91, 92, 120, 121, 122, 130, 140, 150, 152, 153, 181, 182, 183, 184, 185, 186, 187, 190, 200, 201, 202, 210, 220, 0];\nvar newClassValues = ee.List.sequence(1, ee.List(classValues).length());\nvar allImages = annual.merge(fiveyear);\nvar renameClasses = function(image) {\n var reclassified = image.remap(classValues, newClassValues).rename('classification');\n return reclassified;\n};\nvar landcoverCol = allImages.map(renameClasses);\nvar years = ee.List.sequence(1985, 2022).map(function(year) { return ee.Number(year).format('%04d'); });\n\nvar visParams = {min:1, max:36, palette: ['#ffff64', '#ffff64', '#ffff00', '#aaf0f0', '#4c7300', '#006400', '#a8c800', '#00a000', '#005000', '#003c00', '#286400', '#285000', '#a0b432', '#788200', '#966400', '#964b00', '#966400', '#ffb432', '#ffdcd2', '#ffebaf', '#ffd278', '#ffebaf', '#00a884', '#73ffdf', '#9ebb3b', '#828282', '#f57ab6', '#66cdab', '#444f89', '#c31400', '#fff5d7', '#dcdcdc', '#fff5d7', '#0046c8', '#ffffff', '#ffffff']};\n\n// Create a Split-panel Map\nvar leftMap = ui.Map();\nvar rightMap = ui.Map();\nvar splitPanel = ui.SplitPanel({\n firstPanel: leftMap,\n secondPanel: rightMap,\n wipe: true,\n style: {stretch: 'both'}\n});\nui.root.clear();\nui.root.add(splitPanel);\n\nvar createLegend = function() {\n var legend = ui.Panel({\n style: {\n position: 'bottom-left',\n padding: '8px 15px'\n }\n });\n var legendTitle = ui.Label({\n value: 'Landcover Legend',\n style: {fontWeight: 'bold', fontSize: '14px', margin: '0 0 4px 0', padding: '0'}\n });\n legend.add(legendTitle);\n\n var makeRow = function(color, name) {\n var colorBox = ui.Label({\n style: {\n backgroundColor: color,\n padding: '8px',\n margin: '0 0 4px 0'\n }\n });\n var description = ui.Label({\n value: name,\n style: {margin: '0 0 4px 6px'}\n });\n return ui.Panel({\n widgets: [colorBox, description],\n layout: ui.Panel.Layout.Flow('horizontal')\n });\n };\n\n for (var i = 0; i < classNames.length; i++) {\n legend.add(makeRow(classColors[i], classNames[i]));\n }\n return legend;\n};\n\nvar legend = createLegend();\nleftMap.add(legend);\nvar intro = ui.Panel([\n ui.Label('Global Landcover Change Explorer', {fontWeight: 'bold', fontSize: '20px'}),\n ui.Label('Explore landcover change over time by selecting different years and comparing side-by-side. Zoom in and out to explore regions of interest.')\n]);\nleftMap.add(intro);\n\nvar createDropdown = function(map, labelText, defaultValue) {\n var yearLabel = ui.Label(labelText);\n var yearSelect = ui.Select({\n items: years.getInfo(),\n value: defaultValue,\n onChange: function(year) {\n var selectedImage = landcoverCol.filter(ee.Filter.eq('year', parseInt(year))).first();\n map.layers().set(0, ui.Map.Layer(selectedImage, visParams, 'Landcover ' + year));\n }\n });\n var panel = ui.Panel([yearLabel, yearSelect]);\n map.add(panel);\n return yearSelect;\n};\n\nvar leftYearSelect = createDropdown(leftMap, 'Select Left Year:', '1985');\nvar rightYearSelect = createDropdown(rightMap, 'Select Right Year:', '2022');\n\nleftMap.centerObject(landcoverCol.first().geometry(), 3);\nA crucial step for any analysis is to calculate the area covered by each landcover class over time and export the data for further analysis. Here, we will use the zonal statistics approach to compute the area of each class for the entire time series. We will create a table with landcover class and year-wise area statistics and export it as a CSV file.
Here's the Earth Engine code to achieve this. You can find the full code here
// Function to calculate area of each class\nvar calculateArea = function(image) {\n var areaImage = ee.Image.pixelArea().divide(10000).addBands(image);\n var areas = areaImage.reduceRegion({\n reducer: ee.Reducer.sum().group({\n groupField: 1,\n groupName: 'class'\n }),\n geometry: geometry,\n scale: 30,\n maxPixels: 1e10\n });\n return ee.Feature(null, areas);\n};\n\n// Apply the function on the ImageCollection\nvar areasCol = landcoverCol.map(calculateArea);\n\n// Flatten the collection to create a single FeatureCollection\nvar features = areasCol.map(function(feature) {\n var dict = ee.Dictionary(feature.get('groups')).map(function(key, value) {\n return ee.Number(value).get(0);\n });\n return ee.Feature(null, dict);\n});\n\n// Export the data\nExport.table.toDrive({\n collection: features,\n description: 'LandcoverAreaStatistics',\n fileFormat: 'CSV'\n});\nThe GLC_FCS30D dataset opens up new avenues for detailed and high-resolution landcover analysis. This tutorial demonstrated how to preprocess, visualize, and analyze the dataset within Google Earth Engine, providing a robust starting point for further exploration and research.
Keywords: GLC_FCS30D, Preprocessing, Mosaicking, Reclassification, Visualization, Split-panel App, Interactive Map, Legend, Land Cover Statistics, Zonal Statistics, Area Calculation, CSV Export
"},{"location":"tutorials/examples/global_shorelines/","title":"Using the Global Shoreline Dataset to Create Land and Ocean Masks with Google Earth Engine (GEE)","text":"by Ujaval Gandhi from Spatial Thoughts
"},{"location":"tutorials/examples/global_shorelines/#introduction","title":"Introduction","text":"The Global Shoreline dataset, hosted on the Gee-Community Catalog, is a valuable resource for creating land and ocean masks in Google Earth Engine (GEE). This tutorial provides an overview of how to use this dataset to generate these masks, which can be useful for various geospatial analyses and applications. The complete code can be found here
"},{"location":"tutorials/examples/global_shorelines/#dataset-description","title":"Dataset Description","text":"The Global Shoreline dataset comprises three sets of polygon features:
To begin working with this dataset on Google Earth Engine, first import the necessary collections:
// Importing Global Shoreline Dataset Collections\nvar mainlands ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/mainlands');\nvar big_islands ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/big_islands');\nvar small_islands ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/small_islands');\nThe next step is to merge the individual collections into a single collection, which can then be rasterized:
// Merge all collections\nvar merged mainlands.merge(big_islands).merge(small_islands);\n\n// Rasterize polygons using 'ee.Reducer.count()' to get land pixel counts\nvar mask merged.reduceToImage({\n reducer: ee.Reducer.count(),\n});\nThe rasterized image can now be visualized on the map using different color palettes - brown for land (with higher pixel counts) and blue for ocean:
// Add mask as a layer to display land areas in brown\nMap.addLayer(mask, {min: 0, max: 1}, 'Land Mask');\n\n// Create an inverted version of the mask to visualize ocean areas in blue\nvar invertMask mask.multiply(-1);\nMap.addLayer(invertMask, {min: -1, max: 0}, 'Ocean Mask');\nThe rasterized image can be used to create separate land and ocean masks for further processing within GEE:
// Create an ocean mask using '.selfMask()' on the inverted version of 'mask'\nvar oceanMask invertMask.updateMask(invertMask);\n\n// Use 'image.updateMask(oceanMask)' to remove ocean pixels from another image\n\n// Create a land mask by inverting 'oceanMask' and using '.selfMask()'\nvar landMask oceanMask.not().updateMask(oceanMask);\n\n// Use 'image.updateMask(landMask)' to remove land pixels from another image\nThis tutorial demonstrates how to use the Global Shoreline dataset in Google Earth Engine to create rasterized masks representing land and ocean areas, which can be visualized on a map or used for further processing. This approach provides a valuable resource for various geospatial analyses and applications.
Keywords: GEE, Global Shoreline Dataset, Land Mask, Ocean Mask, Rasterization, Shoreline, Landcover, Image Processing
"},{"location":"tutorials/examples/landscan_extracts/","title":"Comparing Global Population Trends with GeoBoundaries and Landscan","text":"by Ujaval Gandhi from Spatial Thoughts
"},{"location":"tutorials/examples/landscan_extracts/#introduction","title":"Introduction","text":"This tutorial demonstrates how to use GeoBoundaries and the Landscan Population Dataset to compare population data for different Admin1 regions using Earth Engine. You will learn how to load admin boundaries, filter a population dataset by date range, extract resolution information, and create a time-series chart comparing population data.
To view the complete code for this tutorial, click here.
"},{"location":"tutorials/examples/landscan_extracts/#section-1-load-admin-boundaries-geoboundaries-and-select-regions","title":"Section 1: Load Admin Boundaries (GeoBoundaries) and Select Regions","text":"Use the ee.FeatureCollection method to load the admin boundaries dataset from GeoBoundaries.
var admin0 = ee.FeatureCollection(\"projects/sat-io/open-datasets/geoboundaries/CGAZ_ADM0\");\nSelect two Admin1 regions to compare: Japan and Mexico.
var region1 = 'Japan';\nvar region2 = 'Mexico';\nUse the filter method to select the desired regions from the admin boundaries dataset.
var selectedRegions = admin0.filter(ee.Filter.inList('shapeName', [region1, region2]));\nprint('Filtered Admin1 collection', selectedRegions);\nUse the ee.ImageCollection method to load the Landscan population dataset.
var landscan = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL\");\nvar band = 'b1';\nSet the date range for the population data using ee.Date.fromYMD.
var startYear = 2000;\nvar endYear = 2020;\n\nvar startDate = ee.Date.fromYMD(startYear, 1, 1);\nvar endDate = ee.Date.fromYMD(endYear + 1, 1, 1);\nUse the filter method to filter the population dataset by date range.
var populationFiltered = landscan.filter(ee.Filter.date(startDate, endDate)).select(band);\nprint('Filtered Population Collection', populationFiltered);\nGet the resolution of the population dataset using projection.nominalScale.
var projection = populationFiltered.first().projection();\nvar resolution = projection.nominalScale();\nprint('Landscan Global Resolution', resolution);\nCreate a time-series chart comparing the population data for the selected regions.
var chartOptions = {\n title: 'Population Time Series',\n vAxis: {\n title: 'Population',\n },\n hAxis: {\n title: '',\n format: 'YYYY',\n gridlines: {color: 'transparent'}\n\n },\n }\n\nvar chart = ui.Chart.image.seriesByRegion({\n imageCollection: populationFiltered,\n regions: selectedRegions,\n reducer: ee.Reducer.sum(),\n scale: resolution,\n seriesProperty: 'shapeName'\n}).setChartType('ColumnChart')\n .setOptions(chartOptions);\nprint(chart);\nKeywords: GeoBoundaries, Landscan Population Dataset, Earth Engine, Admin1 regions, Population data
"}]} \ No newline at end of file +{"config":{"lang":["en"],"separator":"[\\s\\u200b\\-_,:!=\\[\\]()\"`/]+|\\.(?!\\d)|&[lg]t;|(?!\\b)(?=[A-Z][a-z])","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"awesome-gee-community-catalog","text":"The awesome-gee-community-catalog is an unfunded open source grassroots project with a mission to help collect community sourced and community generated geospatial datasets. Our goal is to make data accessible and tie it to an analysis platform fostering accessibility and reducing digital divide. This catalog lives and serves alongside the Google Earth Engine data catalog. This collaborative effort not only offers openly available, preprocessed research datasets but also caters to frequently requested ones under various open licenses. Stay updated by signing up for email updates, ensuring you receive the latest catalog news and in-depth explorations of valuable data.
You can read about the history and how this project started in the Medium Post article here
Community Datasets added by users and made available for use at large
Like, share and support the Github project. And you can now cite it too
"},{"location":"#citation","title":"Citation","text":"Samapriya Roy, Swetnam, T., & Saah, A. (2024). samapriya/awesome-gee-community-datasets: Community Catalog (3.1.0).\nZenodo. https://doi.org/10.5281/zenodo.14042069\nWelcome to the Awesome GEE Community Catalog, a comprehensive resource for discovering and contributing geospatial datasets designed for use with Google Earth Engine. The awesome-gee-community-catalog is an unfunded open source grassroots project with a mission to help collect community sourced and community generated geospatial datasets. Our goal is to make data accessible and tie it to an analysis platform fostering accessibility and reducing digital divide.
The catalog was created by and maintained by Dr. Samapriya Roy and this is currently a one person team. A Google Developer Expert for Google Earth Engine and Senior Product Manager at MAXAR,an open source developer and a Geospatial Consultant & Speaker. Dr. Roy leads Developer Relations and champions open data access. Leveraging geospatial expertise as an affiliate faculty at the University of Hawai\u02bbi at M\u0101noa and a Designated Campus Colleague at the University of Arizona, Dr. Roy further drives the mission of the catalog.
The catalog is further a result of data requests and tutorial contributions from the #awesome community who use the community catalog and input, advise and feedback from community members. Our mission is to make geospatial data accessible and analysis-ready, fostering collaboration and reducing the digital divide. The Awesome GEE Community Catalog thrives on community participation and open-source principles. We aim to build on creating accessibility to high-quality geospatial data, enabling researchers, developers, and enthusiasts to leverage these resources for their projects. This year the National Science Foundation (NSF) ACCESS program granted us 1.5 million Service Units or CPU Core hours to continue the work on the catalog through Jestream2 a NSF project which allow us to preprocess the datasets as requests are made.
"},{"location":"about_us/#community-contributions","title":"Community Contributions","text":"Our catalog is powered by the contributions of the GEE user base. Community members submit datasets that are then reviewed, usually downloaded and preprocess and made Earth Engine ready and finally added to the catalog for everyone to use. This collaborative approach ensures a diverse and rich collection of data, covering a wide range of topics from waterbodies and population distribution to drought monitoring and more. Each contribution helps expand our repository, making it a go-to resource for geospatial data. \ud83d\udd0d
"},{"location":"about_us/#update-schedule","title":"Update Schedule","text":"We understand the importance of keeping datasets current and reliable. While some datasets are regularly updated on a fixed cadence, others follow a more ad hoc schedule. Updates are made as requests come in or as additional information becomes available about a dataset. This flexible approach allows us to respond to the community's needs and maintain the relevance and accuracy of the data we provide. \ud83d\uddd3\ufe0f
We rely on users to spread the word and share the catalog with other users. Please cite and attribute the catalog using our citation making this project more visible and relevant.
To get involved check out our Get Involved Section
"},{"location":"changelog/","title":"Changelog","text":""},{"location":"changelog/#updated-2024-10-27","title":"Updated 2024-10-27","text":"Creating a code of conduct for any community, including the Awesome GEE community catalog on GitHub, is essential to promote a positive and inclusive environment for all participants. Below is a suggested code of conduct that you can use as a starting point:
"},{"location":"code_of_conduct/#awesome-gee-community-catalog-code-of-conduct","title":"Awesome GEE Community Catalog Code of Conduct","text":"The awesome-gee-community-catalog consists of community sourced geospatial datasets made available for use by the larger Google Earth Engine community and shared publicly as Earth Engine assets. The project was started with the idea that a lot of research datasets are often unavailable for direct use and require preprocessing before use. This catalog lives and serves alongside the Google Earth Engine data catalog and also houses datasets that are often requested by the community and under a variety of open license.
You can read about the history and how this project started in the Medium Post article here
Community Datasets added by users and made available for use at large.
"},{"location":"code_of_conduct/#please-cite-and-acknowledge-use","title":"Please Cite and Acknowledge Use","text":"Users of the Awesome GEE Community catalog must cite the work which allows the community and the project to grow. You can always find the latest citation here
"},{"location":"code_of_conduct/#be-respectful-and-inclusive","title":"Be Respectful and Inclusive","text":"Treat all community members, regardless of their background or experience level, with respect and empathy. Harassment, discrimination, or offensive behavior in any form will not be tolerated. Everyone deserves to feel welcome and valued in our community.
"},{"location":"code_of_conduct/#foster-a-positive-and-constructive-environment","title":"Foster a Positive and Constructive Environment","text":"Engage in discussions and debates in a constructive and positive manner. Disagreements are normal, but we expect community members to address conflicts professionally and respectfully. Focus on the ideas, not on personal attacks.
"},{"location":"code_of_conduct/#encourage-diversity","title":"Encourage Diversity","text":"We welcome contributions and ideas from people of all backgrounds, experiences, and identities. Embrace diversity and encourage the participation of individuals with different perspectives. A diverse community enriches everyone's learning and understanding.
"},{"location":"code_of_conduct/#provide-constructive-feedback","title":"Provide Constructive Feedback","text":"When providing feedback on projects or contributions, do so with the intention of helping others improve. Be constructive and offer suggestions for improvement, but avoid overly negative or unhelpful feedback.
"},{"location":"code_of_conduct/#respect-others-work","title":"Respect Others' Work","text":"Give proper credit to original authors and contributors. If you use or modify someone else's work, make sure to attribute them appropriately. Respect the licenses and terms associated with any resources you use or contribute.
"},{"location":"code_of_conduct/#use-welcoming-language","title":"Use Welcoming Language","text":"Use inclusive language in all interactions. Avoid using offensive or exclusionary language, jokes, or slurs. Be mindful of the impact your words may have on others.
"},{"location":"code_of_conduct/#report-inappropriate-behavior","title":"Report Inappropriate Behavior","text":"If you witness or experience any behavior that violates this code of conduct, please report it to the project maintainers at [email address or contact information]. All reports will be treated confidentially, and appropriate action will be taken as necessary.
"},{"location":"code_of_conduct/#compliance","title":"Compliance","text":"Participants who do not follow this code of conduct may face consequences, including but not limited to warnings, temporary bans, or permanent bans from the community.
"},{"location":"code_of_conduct/#be-responsible","title":"Be Responsible","text":"Community members should be responsible for their actions and their impact on others. If you make a mistake or hurt someone, apologize and try to make amends.
"},{"location":"code_of_conduct/#our-pledge","title":"Our Pledge","text":"In the interest of fostering an open and welcoming environment, we, as contributors and maintainers, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
"},{"location":"code_of_conduct/#acknowledgment","title":"Acknowledgment","text":"This code of conduct is adapted from the Contributor Covenant, version 2.0.
This code of conduct is based on the Contributor Covenant, which is a widely used standard for open-source projects. You can include this code of conduct in the README or CONTRIBUTING.md file of your Awesome GEE Community Catalog repository on GitHub. Remember to adapt the [email address or contact information] section to include the appropriate contact details for the project maintainers. It is essential to communicate the code of conduct to all community members and ensure its enforcement to maintain a healthy and respectful community environment.
"},{"location":"history/","title":"Building Data Commons","text":"I am a firm believer that Communities are what communities build together.The power of Google Earth Engine (GEE) lies not just in its processing capabilities, but also in its vibrant community. This community thrives on constant innovation and collaboration, evident in the ongoing iterations and shared code libraries. Inspired by this collaborative spirit, we embarked on a project to create a community-curated data repository \u2013 a space where users could contribute and access valuable geospatial datasets.
The impetus for this project arose from a specific user query. Someone inquired about Facebook's high-resolution population density maps, a dataset absent from the official GEE catalog. This presented a perfect opportunity to experiment with a community-driven data commons. The dataset, hosted by Columbia University, offered detailed population data at an impressive 30-meter resolution.You can read the foundational blog here
This Facebook dataset became the first and most frequently updated entry in the community catalog, now known as the #Awesome GEE Community Catalog.
"},{"location":"history/#guiding-principle","title":"Guiding Principle","text":"The guiding principle behind this catalog draws inspiration from Elinor Ostrom's groundbreaking work on commons governance, a philosophy that has underpinned successful open-source projects like Linux and collaborative platforms like Wikipedia. Just as shared norms within a physical commons benefit everyone, fostering a similar collaborative environment within the digital realm was our goal.The idea was to use the inspiration from Digital Commons and create a Community Data Commons in the form of the #Awesome GEE Community Catalog.
The #Awesome GEE Community Catalog aims to reduce barriers for users by providing easy access to a growing collection of public datasets. This democratizes access to valuable geospatial data, similar to how GEE itself has democratized access to processing capabilities. However, the challenge lies in effectively applying these principles to both large-scale datasets and smaller, user-contributed ones. The Earth Engine ecosystem itself thrives on a culture of community learning, adaptation, and iteration.This community data commons serves as a bridge, connecting users with the datasets they need and fostering further collaboration within the GEE community. The #Awesome GEE Community Catalog represents a collaborative effort, and its continued success relies on the active participation of its users.
"},{"location":"involved/","title":"Stay updated & contribute","text":"The Awesome GEE Community Catalog is created and maintained by Samapriya Roy with data, examples, tutorial contributions from our community. This is a community common meaning it needs involvement to survive as a grassroots open source project. Here are some ways in which you can get involved with this project and check out examples on how you can bring data, examples, bug reports and pull requests to the catalog here. Open up a Github discussion and create a pull request if you notice any issues so I can fix them. Sign Up for Updates: Never miss the latest catalog additions and in-depth explorations by subscribing to catalog updates through out datacommons blog.
"},{"location":"involved/#choose-your-adventure","title":"Choose your adventure","text":"Browse & Star the Catalog
Visit the website and star the Github Repo so it's easily discovered & you get updates.
Browse and Star
Integrate into Your Projects
Build with the datasets in your GEE projects, use example code and cite the project
Build and Cite
Enrich the Community Catalog
Bring datasets of value to the community catalog. Share it with the community by contributing new datasets
Enrich the Catalog
Submit a tutorial or Example
Create and share examples demonstrating how you've leveraged the catalog's data in your projects.
Submit a tutorial
Support the Project & Donate
We are an unfunded project so community donors and sponors make a world of difference to the project.
Support & Donate
Collaborate with Pull requests
Creating a new pull request means you fixed something that I missed & I and the community apppreciate it.
Create a pull request
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=======================================================================
Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the \u201cLicensor.\u201d The text of the Creative Commons public licenses is dedicated to the public domain under the CC0 Public Domain Dedication. Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at creativecommons.org/policies, Creative Commons does not authorize the use of the trademark \"Creative Commons\" or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses.
Creative Commons may be contacted at creativecommons.org.
"},{"location":"medium_blogs/","title":"Medium Blog posts","text":"Find a list of associated blog posts related to GEE and community catalog here.
Find a list of associated blog posts related to GEE and community catalog here. To get post subscribe to our
The Awesome GEE Community Catalog is an actively maintained and evolving project that serves a diverse user base with versatile backgrounds and needs. To efficiently address the requirements of all our users, evaluate change requests, and fix bugs, update datasets, I put in a lot of work and your contributions are helpful.The catalog is a collaborative effort, and I welcome your contributions! This catalog aims to provide a comprehensive and up-to-date list of community-driven datasets readily accessible within Google Earth Engine (GEE).
The Awesome GEE Community Catalog thrives on community contributions! Whether you've found a valuable dataset, spotted an error, or have a helpful tip to share, there are many ways to get involved. By contributing, you're not only helping us build a valuable resource for the GEE community, but you're also making it easier for others to find and utilize valuable Earth observation data.
"},{"location":"contributing/#how-you-can-contribute","title":"How you can contribute","text":"I know your time is valuable. That's why I've streamlined contributing to the Awesome GEE Community Catalog!
Ready to get started? Let's dive into the specific ways you can contribute!
"},{"location":"contributing/#creating-an-issue","title":"Creating an issue","text":"Bring or Add data to the Community Catalog
Submit or bring your data request to community catalog
Contribute data
Notice an outdated dataset? Submit an update request
Submit update request for dataset in community catalog
Submit an update
Notice a Bug? Submit a Bug report for review
Bug report for dataset in community catalog
Submit a bug report
Have a Tutorial you want to Contribute? Submit one
Submit tutorials for datasets in community catalog
Submit a tutorial
Have a question or need help?
Ask a question on our discussion board and get in touch with our community
Ask a question
Support the Project & Donate
We are an unfunded project so community donors and sponors make a world of difference to the project.
Support & Donate
Want to create a pull request?
Learn how to create a comprehensive and useful pull request (PR)
Create a pull request
Bug reports are useful information for the catalog. This can range for anything from a spelling mistake that breaks integration to change in asset path that may not have been updated in the documentation for example, incorrect doucentation or citation reference and many more. These are different from dataset updates as they do not pertain to availability of updated data or release information.
To submit a bug report for an existing awesome-gee-catalog dataset use this link
"},{"location":"contributing/example/","title":"Submit example for dataset in community catalog","text":"Examples are helpful in understanding different use cases for datasets as well as enabling rich visualization of an existing dataset from domain experts. The template allows you to point to an existing dataset and submit an example code link via code editor/colab link or otherwise for others to use. All example/code contributors get attribution in the code apart from dataset attributions which are already included.
To submit an example for an existing awesome-gee-catalog dataset use this link
"},{"location":"contributing/submit/","title":"Submit or bring your data request to community catalog","text":"The submit data request templates are further subdivded into two templates one for datasets that you might have created vs any dataset that might be valuable to the community catalog and you would like to submit for consideration. For both templates modify the markdown text as needed and fill in the pieces of information that is available to you as in the example below.
To submit a new dataset for the community catalog use this link
To bring your own dataset for the community catalog use this link
The submit updated data request templates is designed for requesting update to an existing data in the community catalog. This can range from new releases to continuous updates. Modify the markdown text in the template as needed and fill in the pieces of information that is available to you as in the example below.
To submit a suggested update to an existing awesome-gee-catalog dataset use this link
"},{"location":"insiders/","title":"Why donate to the Community Catalog","text":"The GEE Community Catalog is an Open Source and unfunded project that is developed and maintained by a one person team. You can read me and the work further in the about me section. While I started this as a personal side project in 2020, the realization was always present that this project has far reaching implications and applications in the larger geospatial community. I realized that this project could benefit not just the research community who are often producing valuable research products from their research but users who are interested in a share collection of community sources data sources. Behind the scenes most community catalog requests for adding a dataset to the community catalog is triaged by me, evaluated based on multiple factors such as license, data size and preprocessing complexity before I start the work on getting it ready.
The Insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. As such this program is for sponsors and data contributors to the project you can sponsor the project by clicking on the sponsor button above \u261d or submit a new dataset or tutorial request. If you fit under any of those categories fill this form to get insiders access. As an Insider, you'll be added to the \"catalog-contributors\" Google Group, giving you early access to datasets as they are processed, reviewed, and even those not yet released. You'll also receive occasional changelog and update emails, and have the ability to post questions, concerns, and thoughts to the group. And if we meet ask me for stickers to help spread the word \ud83d\ude0a.
Over the last 4 years the project backend now includes over 100,000+ lines of code to often preprocess the dataset or make it ingest ready for Google Earth Engine and making it available for the geospatial community of GEE users. Currently the site serves over 500,000 requests from over 160+ countries. This work is built around creating a Community Data Commons and if you can and wish to support and donate to the project which goes towards simple things like cost of hosting, preprocessing feel free to do so using Github Sponsorship Tier setup for this project.
\u00a0 Choose a sponsoring tier
"},{"location":"insiders/insiders_program/","title":"Insiders program","text":"The awesome GEE community catalog insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. As such this program is for sponsors and data contributors to the project you can sponsor the project by clicking on the sponsor button above or submit a new dataset request for example using this template. If you fit under any of those categories fill this form to get insiders access.
What do you get when you sign up for the Insiders program?
Any and all support is appreciated you can sponsor the project using the sponsorship links as well as contributing and helping data curation for the catalog.You can now find a list of insiders only datasets within the catalog for easily locating these.
"},{"location":"projects/","title":"Data Themes","text":"The Awesome GEE (Google Earth Engine) Community Catalog is a valuable resource for researchers, developers, and environmental scientists. It organizes a diverse range of geospatial datasets into thematic groups, making them more accessible and findable. This structured approach allows users to efficiently locate datasets pertinent to their specific fields of study or interest.
Insiders Program and Insiders only datasets
Some datasets are part of the Insiders only datasets and they can be found here. The insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. The Insiders Program grants access to a few special selection of datasets. You can be part of the program click on the link to find out more.
"},{"location":"projects/#thematic-groups","title":"Thematic Groups","text":"The datasets in the Awesome GEE Community Catalog are categorized into several thematic groups, for example:
Population and Socioeconomic Datasets: These datasets provide crucial information on demographics, economic activities, and social indicators, which are essential for urban planning, public health, and socio-economic research.
Hydrology Datasets: This category includes data on water bodies, hydrological cycles, and water quality, supporting research and decision-making in water resource management, flood risk assessment, and environmental conservation.
Global Land Use and Land Cover Datasets: These datasets offer insights into land use patterns and changes in land cover over time, aiding studies in agriculture, forestry, urbanization, and climate change.
Climate and Weather Datasets: Essential for climate science, these datasets include historical and real-time data on weather patterns, temperature, precipitation, and other climatic factors.
While every effort has been made to place datasets in the most suitable thematic groups, it is acknowledged that some datasets may rightfully belong to more than one category. Users are encouraged to explore multiple themes if their research spans across different areas.
"},{"location":"projects/#accessibility-and-findability","title":"Accessibility and Findability","text":"The thematic grouping of datasets in the Awesome GEE Community Catalog enhances their accessibility and findability. By organizing datasets into clearly defined categories, the catalog simplifies the process of searching and identifying relevant data. This organization not only saves time but also ensures that users can easily locate the most appropriate datasets for their specific needs with the changelog recording periodic updates.
"},{"location":"projects/GPWv4/","title":"Gridded Population of the World Version 4 Administrative Unit Center Points with Population Estimates","text":"The Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates, Revision 11 consists of UN WPP-adjusted population estimates and densities for the years 2000, 2005, 2010, 2015 and 2020, as well as the basic demographic characteristics (age and sex) for the year 2010. The data set also includes administrative name, land and water area, and data context by administrative unit center point (centroid) location. The center points are based on approximately 13.5 million input administrative units used in GPWv4, therefore, these files require hardware and software that can read large amounts of data into memory.
Purpose: To provide a vector (point) version of the input administrative units used in GPWv4 with population estimates, densities, 2010 basic demographic characteristics, and administrative name, area, and data context for use in data integration.
The documentation for this data set is available here
Use the following citation
Doxsey-Whitfield, Erin, Kytt MacManus, Susana B. Adamo, Linda Pistolesi, John Squires, Olena Borkovska, and Sandra R. Baptista. \"Taking advantage of the improved availability of census data: a first look at the gridded population of the world, version 4.\" Papers in Applied Geography 1, no. 3 (2015): 226-234.\nvar gpw = ee.FeatureCollection(\"projects/sat-io/open-datasets/sedac/gpw-v4-admin-unit-center-points-population-estimates-rev11\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GPW-v4
Shared License: This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: census geography, GPWv4, gridded population, uniform distribution
Last updated: 2021-04-07
"},{"location":"projects/S2TSLULC/","title":"ESRI 10m Annual Land Cover (2017-2023)","text":"Time series of annual global maps of land use and land cover (LULC) was updated to v3 with global 10m land cover from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These datasets produced by Impact Observatory and licensed by Esri were fetched from Impact Observatory
This map uses an updated model from the 10-class model and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory and you can find it in GEE here. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.
You can find more information starting with the first release here Kontgis, C. (2021, June 24). Mapping the world in unprecedented detail
"},{"location":"projects/S2TSLULC/#citation","title":"Citation","text":"Karra, Kontgis, et al. \u201cGlobal land use/land cover with Sentinel-2 and deep learning.\u201d\nIGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.\nWater Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.
Trees Any significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).
Flooded vegetation Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.
Crops Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.
Built Area Human made structures; major road and rail networks; large homogeneous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.
Bare ground Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.
Snow/Ice Large homogeneous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.
Clouds No land cover information due to persistent cloud cover.
Rangeland Open areas covered in homogeneous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.
For Accuracy Assessment information visit the ESRI release page
Class Value Remapped Value Land Cover Class Hex Code 1 1 Water #1A5BAB 2 2 Trees #358221 4 3 Flooded Vegetation #87D19E 5 4 Crops #FFDB5C 7 5 Built Area #ED022A 8 6 Bare Ground #EDE9E4 9 7 Snow/Ice #F2FAFF 10 8 Clouds #C8C8C8 11 9 Rangeland #C6AD8D
"},{"location":"projects/S2TSLULC/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var esri_lulc_ts= ee.ImageCollection(\"projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS\");\nThis dataset was produced by Impact Observatory for Esri. \u00a9 2021 Esri. This dataset is available under a Creative Commons BY-4.0 license and any copy of or work based on this dataset requires the following attribution:
"},{"location":"projects/S2TSLULC/#changelog","title":"Changelog","text":"Example path was changed
2023-04-10 Added LULC 2022 to collection
Curated in GEE by: Samapriya Roy
Keywords: : landcover, landuse, lulc, 10m, global, world, sentinel, sentinel 2, impact observatory
Last updated: 2024-06-07
"},{"location":"projects/aces_bhutan/","title":"ACES-Enhanced Rice Crop Maps for Bhutan (2016-2022)","text":"Annual crop type rice maps for 2016-2022 for enabling improved food security decision making has remained a challenge in Bhutan. These maps were developed in collaboration with the Bhutan Department of Agriculture and SERVIR. Through focusing on advancing Science, Technology, Engineering, and Mathematics (STEM) in Bhutan, an effort to co-develop a geospatial application known as the Agricultural Classification and Estimation Service (ACES) was created. This dataset and paper focuses on the co-development of an Earth observation informed climate smart crop type framework which incorporates both modeling and training sample collection. The ACES web application and subsequent ACES modeling software package enables stakeholders to more readily use Earth observation into their decision making process. Additionally, this data set and paper offers a transparent and replicable approach for addressing and combating remote sensing limitations due to topography and cloud cover, a common problem in Bhutan. Lastly, this approach resulted in a Random Forest \"LTE 555\" model, from a set of 3,600 possible models, with an overall test Accuracy of 85% and F-1 Score of .88 for 2020. The model was independently validated resulting in an independent accuracy of 83% and F-1 Score of .45 for 2020.
"},{"location":"projects/aces_bhutan/#citation","title":"Citation","text":"Mayer, Timothy, Biplov Bhandari, Filoteo G\u00f3mez Mart\u00ednez, Kaitlin Walker, Stephanie A. Jim\u00e9nez, Meryl Kruskopf, Micky Maganini et al. \"Employing the\nagricultural classification and estimation service (ACES) for mapping smallholder rice farms in Bhutan.\"\nFrontiers in Environmental Science 11 (2023): 1137835.\nvar Bhutan_ACES_Rice_Maps = ee.ImageCollection(\"projects/servir-sco-assets/assets/Bhutan/Rice_Extent_Mapper/Predicted_Rice_Post_Processed_IC\");\nMap.setCenter(90.37, 27.51,8)\nvar palettes = require('users/gena/packages:palettes');\n\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/132/light-gray\", \"Grayscale\");\n\nMap.addLayer(Bhutan_ACES_Rice_Maps,{min: 0,max: 1, palette: [\"fee6ce\",\"fdae6b\",\"e6550d\"]},\n\"ACES Rice Maps 2016-2022 \")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/ACES-BHUTAN
"},{"location":"projects/aces_bhutan/#license","title":"License","text":"This dataset is available under a Creative Commons BY-4.0 license
Keywords: agriculture, land use, land cover, Bhutan, rice
Created & provided by: Mayer et al 2023, NASA SERVIR
Curated by: Mayer et al 2023, NASA SERVIR
"},{"location":"projects/af_cmask/","title":"Ensemble Source Africa Cropland Mask 2016","text":"In order to produce the most accurate cropland layer at 30 m spatial resolution for Africa, the cropland layers extracted from four remote sensing land cover datasets were integrated. The four datasets covered the period 2015 to 2017. Hence, the constructed cropland layer was produced for the nominal year 2016. To build the final layer, the cropland mapping accuracies of the four cropland layers were firstly investigated at the units of Agro-ecological zones. Then, the best cropland layers for all zones were spatially joined. The resulted cropland layer is a binary mask with higher overall accuracy than individual layers and more consistent with FAO official statistics. You can download the datasets here. You can read additional details from the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/af_cmask/#citation","title":"Citation","text":"Nabil, Mohsen, Miao Zhang, Bingfang Wu, Jose Bofana, and Abdelrazek Elnashar. \"Constructing a 30m African Cropland Layer for 2016 by Integrating\nMultiple Remote sensing, crowdsourced, and Auxiliary Datasets.\" Big Earth Data 6, no. 1 (2022): 54-76.\nNabil, Mohsen; Zhang, Miao; Wu, Bingfang; Bofana, Jose; Elnashar, Abdelrazek (2021): A 30m African Cropland Layer for 2016 by Integrating Multiple\nRemote sensing, Crowdsource, and Auxiliary Datasets.. figshare. Dataset. https://doi.org/10.6084/m9.figshare.13520141.v1\nvar af_cropmask_2016 = ee.Image(\"projects/sat-io/open-datasets/landcover/AF_Cropland_mask_30m_2016_v3\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/AF-CROPLAND-MASK-30M-2016
"},{"location":"projects/af_cmask/#license","title":"License","text":"This dataset is made available under the CC BY Attribution 4.0 International License.
Created by: Nabil, Mohsen; Zhang, Miao; Wu, Bingfang; Bofana, Jose; Elnashar, Abdelrazek
Curated in GEE by: Samapriya Roy
Keywords: Agriculture, Africa, cropland, cropland maps, agriculture land use
"},{"location":"projects/af_trees/","title":"High resolution map of African tree cover","text":"This dataset leverages high-resolution satellite imagery obtained from a nano-satellite constellation, accessible in the tropics through Norway's International Climate and Forest Initiative (NICFI) programme. The primary goal of this dataset is to comprehensively map both forest and non-forest trees on a continental scale across Africa, surpassing the precision of previous endeavors in mapping woody vegetation at large scales.
Utilizing a machine learning approach, we employ 3\u2009m PlanetScope satellite imagery to segment tree canopy cover across Africa, reaching the level of individual scattered trees. The dataset provides a detailed quantification of the contribution of trees located outside traditional forested areas to the overall tree cover within each country. Notably, at the continental scale, our analysis reveals that 29% of the total tree cover exists outside regions classified as forests in a contemporary state-of-the-art map based on Sentinel-2 10\u2009m imagery. You can read the paper here
"},{"location":"projects/af_trees/#citation","title":"Citation","text":"Reiner, F., Brandt, M., Tong, X. et al. More than one quarter of Africa\u2019s tree cover is found outside areas previously\nclassified as forest. Nat Commun 14, 2258 (2023). https://doi.org/10.1038/s41467-023-37880-4\nReiner, F., Brandt, M., Tong, X., Skole, D., Kariryaa, A., Ciais, P., Davies, A., Hiernaux, P., Chave, J., Mugabowindekwe, M.,\nIgel, C., Oehmcke, S., Gieseke, F., Li, S., Liu, S., Saatchi, S., Boucher, P., Singh, J., Taugourdeau, S., \u2026 Fensholt, R.\n(2023). Africa tree cover map [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7764460\nvar tree_cover = ee.Image(\"projects/sat-io/open-datasets/PS_AFRICA_TREECOVER_2019_100m_V10\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/PS-AFRICA-TREECOVER
"},{"location":"projects/af_trees/#license","title":"License","text":"This tree cover map is made freely available for non-commercial purposes. All usage of the data must be attributed and should be cited with the paper citation. Please see the NICFI license for full terms of usage, available here
Provided by: Reiner et al
Curated in GEE by: Samapriya Roy
Keywords: Africa, NICFI, Planet, Tree cover, Land cover
Last updated in GEE: 2024-01-18
"},{"location":"projects/agera5_datasets/","title":"AgERA5 (ECMWF) dataset","text":"Daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modeling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models. Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1\u00b0 spatial resolution. The correction to the 0. 1\u00b0 grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1\u00b0 grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1\u00b0 resolution. This way the data are tuned to the finer topography, finer land use pattern, and finer land-sea delineation of the ECMWF HRES model. You can find additional information here and in the climate engine org dataset page here
"},{"location":"projects/agera5_datasets/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Global Spatial resolution 9600 m (1/10-deg) Temporal resolution Daily Time span 1979-01-01 to present Update frequency Updated daily with 7-day lag timeVariables
Variable Details Wind speed ('Wind_Speed_10m_Mean') - Units: Meters/second - Scale factor: 1.0 Minimum temperature, 2m ('Temperature_Air_2m_Min_24h') - Units: Degrees Kelvin - Scale factor: 1.0 Maximum temperature, 2m ('Temperature_Air_2m_Max_24h') - Units: Degrees Kelvin - Scale factor: 1.0 Mean temperature, 2m ('Temperature_Air_2m_Mean_24h') - Units: Degrees Kelvin - Scale factor: 1.0 Max temperature, 2m, daytime ('Temperature_Air_2m_Max_Day_Time') - Units: Degrees Kelvin - Scale factor: 1.0 Mean temperature, 2m, daytime ('Temperature_Air_2m_Mean_Day_Time') - Units: Degrees Kelvin - Scale factor: 1.0 Min temperature, 2m, nighttime ('Temperature_Air_2m_Min_Night_Time') - Units: Degrees Kelvin - Scale factor: 1.0 Mean temperature, 2m, nighttime ('Temperature_Air_2m_Mean_Night_Time') - Units: Degrees Kelvin - Scale factor: 1.0 Dewpoint temperature, 2m ('Dew_Point_Temperature_2m_Mean') - Units: Degrees Kelvin - Scale factor: 1.0 Precipitation ('Precipitation_Flux') - Units: Millimeters - Scale factor: 1.0 Precipitation rain duration fraction ('Precipitation_Rain_Duration_Fraction') - Units: Count - Scale factor: 1.0 Precipitation solid duration fraction ('Precipitation_Solid_Duration_Fraction') - Units: Count - Scale factor: 1.0 Snow depth ('Snow_Thickness_Mean') - Units: Centimeters - Scale factor: 1.0 Snow water equivalent ('Snow_Thickness_LWE_Mean') - Units: Centimeters - Scale factor: 1.0 Vapour pressure ('Vapour_Pressure_Mean') - Units: hPa - Scale factor: 1.0 Downward solar radiation ('Solar_Radiation_Flux') - Units: J m-2d-1 - Scale factor: 1.0 Cloud cover ('Cloud_Cover_Mean') - Units: Fraction - Scale factor: 1.0 Relative humidity, 2m 06h ('Relative_Humidity_2m_06h') - Units: Percent - Scale factor: 1.0 Relative humidity, 2m 15h ('Relative_Humidity_2m_15h') - Units: Percent - Scale factor: 1.0"},{"location":"projects/agera5_datasets/#citation","title":"Citation","text":"Copernicus Climate Change Service (C3S) (2017): ERA5 Ag: Agrometeorological indicators from 1979 to present derived from reanalysis. Copernicus\nClimate Change Service Climate Data Store (CDS), (date of access),\nhttps://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview\n// Read in Image Collection and get first image\nvar agera5_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-ag-era5/daily')\nvar agera5_i = agera5_ic.first()\n\n// Print first image to see bands\nprint(agera5_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(agera5_i.select('Precipitation_Flux'), {min: 0, max: 1, palette: prec_palette}, 'Precipitation_Flux')\nMap.addLayer(agera5_i.select('Temperature_Air_2m_Max_24h').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Temperature_Air_2m_Max_24h')\nMap.addLayer(agera5_i.select('Temperature_Air_2m_Min_24h').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Temperature_Air_2m_Min_24h')\nMap.addLayer(agera5_i.select('Temperature_Air_2m_Mean_24h').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Temperature_Air_2m_Mean_24h')\nMap.addLayer(agera5_i.select('Dew_Point_Temperature_2m_Mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Dew_Point_Temperature_2m_Mean')\nMap.addLayer(agera5_i.select('Snow_Thickness_Mean'), {min: 0, max: 100, palette: prec_palette}, 'Snow_Thickness_Mean')\nMap.addLayer(agera5_i.select('Snow_Thickness_LWE_Mean'), {min: 0, max: 20, palette: prec_palette}, 'Snow_Thickness_LWE_Mean')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/AGERA5-DATASETS
"},{"location":"projects/agera5_datasets/#license","title":"License","text":"Data are subject to the License to Use Copernicus Products: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf
Keywords: climate, reanalysis, near real-time, ECMWF, precipitation, temperature
Dataset provider: Copernicus
Dataset curated in GEE by: Climate Engine Org
"},{"location":"projects/ai0/","title":"Global Aridity Index","text":"The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 3 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon the implementation of a Penman Monteith Evapotranspiration equation for reference crop. The dataset follows the development and is based upon the WorldClim 2.1 at 30 arc seconds or ~ 1km at the equator. You can read the paper here
Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global Aridity Index_ET0 geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals.
"},{"location":"projects/ai0/#data-citation","title":"Data citation","text":"Zomer, Robert; Trabucco, Antonio (2019): Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.7504448.v6\nZomer, R.J.; Xu, J.; Trabuco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database.\nScientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1\nGlobal-AI grid layers are available as monthly averages (12 data layers, i.e. one layer for each month) or as an annual average (1 data layer) for the 1970-2000 period.
"},{"location":"projects/ai0/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aridity_index_yearly = ee.Image(\"projects/sat-io/open-datasets/global_ai/global_ai_yearly\");\nvar aridity_index_monthly = ee.Image(\"projects/sat-io/open-datasets/global_ai/global_ai_monthly\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-ARIDITY-INDEX
Aridity Index Value Climate Class <0.03 Hyper Arid 0.03-0.2 Arid 0.2-0.5 Semi-Arid 0.5-0.65 Dry sub-humid >0.65 Humid
"},{"location":"projects/ai0/#license","title":"License","text":"The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use under the CC BY 4.0 Attribution 4.0 International license.
Data Website: You can download the data and description here
Curated in GEE by: Samapriya Roy
Keywords: aridity index, evapotranspiration, geospatial modeling
Last updated: 2022-09-02
"},{"location":"projects/airtemp/","title":"Global Daily near-surface air temperature (2003-2020)","text":"Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable and critically needed in the scientific and policy communities, but is still not available. In this paper, we developed a global dataset of daily Tmax and Tmin dataset at 1-km resolution from 2003 to 2020 through the combined use of station-based ground Ta measurements and satellite observations (i.e., digital elevation model, and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP).
This gridded 1 km resolution global (50\u00b0?S ~79\u00b0?N) daily maximum and minimum near-surface air temperature dataset (2003 ?? 2020) was generated using a seamless 1 km resolution land surface temperature dataset (2003-2020), a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology.
You can read the preprint here and download the datasets here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/airtemp/#data-preprocessing","title":"Data preprocessing","text":"The datasets were generated regionally and for tmax and tmin. The tmax and tmin were combined into a single collection for the regions generated. Additional metadata called \"prop_type\" was added to allow for filtering along with other metadata like the day of year and the actual date information for date based filtering. The datasets were projected to EPSG 4326 before being ingested to Google Earth Engine.
"},{"location":"projects/airtemp/#citation","title":"Citation","text":"Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Chen, G., Hu, J., and Wang, L.: A global dataset of daily near-surface air temperature at 1-km resolution\n(2003\u20132020), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-233, in review, 2022.\nZhang, Tao; Zhou, Yuyu (2022): A global 1 km resolution daily near-surface air temperature dataset (2003 ?? 2020).\nIowa State University. Collection. https://doi.org/10.25380/iastate.c.6005185.v1\nvar africa = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/africa\");\nvar australia = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/australia\");\nvar eurasia = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/europe_asia\");\nvar latin_america = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/latin_america\");\nvar north_america = ee.ImageCollection(\"projects/sat-io/open-datasets/global-daily-air-temp/north_america\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-DAILY-NEAR-SURFACE-AIR-TEMP
"},{"location":"projects/airtemp/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Chen, G., Hu, J., and Wang, L.
Curated in GEE by : Samapriya Roy
keywords: Air Temperature, land surface temperature (LST), SVCM-SP, MODIS, Global
Last updated on GEE: 2022-08-05
"},{"location":"projects/amazon_peat/","title":"Amazonian Peatland Extent","text":"Tropical peatlands represent some of the most carbon-dense terrestrial ecosystems on the planet, playing a significant role in the global carbon cycle. However, substantial uncertainty exists in estimating their global extent and carbon storage potential. This dataset provides the first field-data-driven model of peatland distribution across the Amazon basin, developed using 2,413 ground reference points and a random forest model applied to a combination of remote sensing products.
The model predicts an Amazonian peatland extent of approximately 251,015 km\u00b2 (95th percentile confidence interval: 128,671\u2013373,359 km\u00b2), which is larger than that of the Congo Basin but 30% smaller than other recent model-based estimates. The dataset addresses key spatial gaps in ground reference data, particularly in regions like Brazil and Bolivia, where uncertainty remains high. The model highlights peatland areas with varying degrees of confidence, such as northern Peru, the Rio Negro basin, and Bolivia, providing a critical resource for future research and field validation efforts. You can read the paper here and download the datasets here
Data Highlights
Hastie, A., Householder, J. E., Coronado, E. N. H., Pizango, C. G. H., Herrera, R., L\u00e4hteenoja, O., de Jong, J., Winton, R. S., Corredor, G. A. A., Reyna, J., Montoya,\nE., Paukku, S., Mitchard, E. T. A., \u00c5kesson, C. M., Baker, T. R., Cole, L. E. S., Oroche, C. J. C., D\u00e1vila, N., \u00c1guila, J. D., \u2026 Lawson, I. T. (2024). A new data-driven map\npredicts substantial undocumented peatland areas in Amazonia. Environmental Research Letters, 19(9), 094019. https://doi.org/10.1088/1748-9326/ad677b\nvar Simple_AOI = ee.FeatureCollection(\"users/adamhastie50/Study_area_simplify\");\nvar Amazon_peat_map = ee.Image(\"projects/sat-io/open-datasets/INT_Amazon_peat_map\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/AMAZONIA-PEATMAP
"},{"location":"projects/amazon_peat/#license","title":"License","text":"The datasets are available under a Creative Commons Attribution 4.0 International license.
Created by: Hastie et al 2024
Curated in GEE by: Hastie et al 2024 and Samapriya Roy
Keywords: Peat, Tropical Peat, Amazon basin
Last updated in GEE: 2024-10-21
"},{"location":"projects/annual_nlcd/","title":"Annual NLCD Land Cover Dataset","text":"The USGS Land Cover program integrates methodologies from the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment, and Projection (LCMAP), along with advanced deep learning, to create Annual NLCD a dataset suite that includes six products, each representing various U.S. land cover and change characteristics. The U.S. Geological Survey\u2019s (USGS) Annual NLCD Collection 1.0 leverages innovations from the National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP) projects, incorporating modern deep learning techniques to deliver accurate, annual land cover and surface change data across the U.S.
Since 1985, Annual NLCD provides six products covering land cover, change, confidence, impervious surfaces, and spectral changes based on Landsat data, facilitating resource management and decision-making.These products leverage Landsat satellite data and are intended for applications in science, resource management, and decision-making, spanning from 1985 to 2023. This dataset supports various environmental analyses, such as urban growth studies, wetland monitoring, agricultural management, and climate impact assessments. Its annual updates and classification confidence features provide essential insights for long-term land use planning and change detection. You can acces User Guide here
"},{"location":"projects/annual_nlcd/#dataset-products-and-descriptions","title":"Dataset Products and Descriptions","text":"Land Cover: A sixteen-class system based on the modified Anderson Level II classification, categorizing dominant surface types like water, forests, and urban areas per pixel. RGB values visually differentiate these categories, ensuring compatibility across federal systems.
Land Cover Change: Tracks annual land cover shifts by comparing consecutive years, using concatenated codes (e.g., 9590 for wetland transitions) to identify changes. Areas without change retain their classification.
Land Cover Confidence: Provides confidence scores based on deep learning probabilities, indicating the model\u2019s certainty in class assignments. Scores are uncalibrated but gauge classification reliability.
Fractional Impervious Surface: Measures the percentage of impermeable surfaces (0-100%) within a 30-meter pixel, informing developed area classifications like urban or suburban based on defined thresholds.
Impervious Descriptor: Differentiates urban, non-urban, and road surfaces within developed areas, offering a clear map of roads distinct from other urban features for detailed analysis.
Spectral Change Day of Year: Identifies the day significant spectral changes occur (values 1-366), pinpointing disturbances (e.g., fires) beyond seasonal variations, enabling temporal change tracking.
Class Value Class Name RGB Color 11 Open Water #466b9f 12 Perennial Ice/Snow #d1def8 21 Developed, Open Space #dec5c5 22 Developed, Low Intensity #d99282 23 Developed, Medium Intensity #eb0000 24 Developed, High Intensity #ab0000 31 Barren Land #b3ac9f 41 Deciduous Forest #68ab5f 42 Evergreen Forest #1c5f2c 43 Mixed Forest #b5c58f 52 Shrub/Scrub #ccb879 71 Grassland/Herbaceous #dfdfc2 81 Pasture/Hay #dcd939 82 Cultivated Crops #ab6c28 90 Woody Wetlands #b8d9eb 95 Emergent Herbaceous Wetlands #6c9fb8
Layer Name Class Values (Range) Min Max NoData Value Land Cover Various land cover types (11, 12, ..., 95) N/A N/A 250 Land Cover Change Change class categories AABB AABB 9999 Land Cover Confidence Confidence levels 1 100 250 Fractional Impervious Surface Imperviousness percentage 0 100 250 Impervious Descriptor Impervious surface types (0: Non-Urban, 1: Roads, 2: Urban) N/A N/A 250 Spectral Change Day of Year Julian days of change 1 366 9999
"},{"location":"projects/annual_nlcd/#citation","title":"Citation","text":"U.S. Geological Survey (USGS), 2024, Annual NLCD Collection 1 Science Products: U.S. Geological Survey data release,\nhttps://doi.org/10.5066/P94UXNTS.\nvar nlcd_landcover = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/NLCD-ANNUAL-LANDCOVER
var nlcd_landcover_confidence = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER_CONFIDENCE\");\nvar nlcd_landcover_change = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER_CHANGE\");\nvar nlcd_fractional_impervious_surface = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/FRACTIONAL_IMPERVIOUS_SURFACE\");\nvar nlcd_impervious_descriptor = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/IMPERVIOUS_DESCRIPTOR\");\nvar nlcd_spectral_change_doy = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/SPECTRAL_CHANGE_DOY\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/NLCD-ANNUAL-LANDCOVER-LAYERS
"},{"location":"projects/annual_nlcd/#license","title":"License","text":"NLCD datasets are provided under a Creative Commons Zero v1.0 Universal license.
Provided by: USGS
Curated in GEE by: Samapriya Roy
Keywords: Land Cover, Land Change, Landsat, Deep Learning, Annual NLCD, USGS, Environmental Monitoring
Last updated in GEE: 2024-10-25
"},{"location":"projects/anusplin/","title":"ANUSPLIN Gridded Climate Dataset","text":"The ANUSPLIN Gridded Climate Dataset for Canada is a station based interpolated dataset produced using the Australian National University Spline (ANUSPLIN) model. It is produced by Agriculture and Agri-Food Canada and covers all of Canada. The dataset is available from 1950-2015 at daily and monthly timesteps for maximum temperature, minimum temperature, and total precipitation at 10km (0.1 degree) resolution. The ANUSPLIN Gridded Climate Dataset for Canada is a comprehensive and station-based interpolated dataset that has been meticulously produced using the Australian National University Spline (ANUSPLIN) model. Created by Agriculture and Agri-Food Canada, this dataset encompasses the entire geographical expanse of Canada and offers a valuable resource for researchers and climate enthusiasts alike.
Researchers and users interested in accessing the dataset can find it through the following external links: - Daily Data: ANUSPLIN Gridded Climate Dataset for Canada (Daily) - Monthly Data: ANUSPLIN Gridded Climate Dataset for Canada (Monthly)
It provides a detailed view of climate conditions with data available from 1950 to 2015, offering insights into daily and monthly variations in maximum temperature, minimum temperature, and total precipitation. This dataset offers a valuable resource for climate research, environmental studies, and various applications that require historical climate data for Canada and parts of the United States. Researchers can explore climate trends, assess climate change impacts, and derive valuable insights into the region's climate patterns using this comprehensive dataset.
"},{"location":"projects/anusplin/#dataset-description","title":"Dataset description","text":"Spatial Information
Parameter Value Spatial extent United States and Canada Spatial resolution 10-km (~0.1-deg) Temporal resolution Daily and monthly Time span 1950-01-01 to 2015-12-31 Update frequency StaticVariables
Variable Details Minimum temperature, 2m (\u2018maxt\u2019) - Units: Degrees Celsius - Scale factor: 1.0 Maximum temperature, 2m (\u2018mint\u2019) - Units: Degrees Celsius - Scale factor: 1.0 Precipitation ('pcp') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/anusplin/#citation","title":"Citation","text":"- Hutchinson, M. F., McKenney, D.W., Lawrence, K., Pedlar, J.H., Hopkinson, R.F., Milewska, E., Papadopol, P. (2009).\n\"Development and testing of Canada-Wide Interpolated Spatial Models of Daily Minimum-Maximum Temperature and Precipitation for 1961-2003.\"\nAmerican Meteorological Society(April): 725-741.\n\n- McKenney, D. W., Hutchinson, M.F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R., Price, D., Owen, T. (2011).\n\"Customized spatial climate models for North America.\" Bulletin of American Meteorological Society-BAMS December: 1612-1622.\n// Read in daily and monthly Image Collections and get single image from each collection\nvar anuspline_m_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-anusplin-monthly')\nvar anuspline_m_i = anuspline_m_ic.first()\nvar anuspline_d_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-anusplin-daily')\nvar anuspline_d_i = anuspline_d_ic.first()\n\n// Print each single image to see bands\nprint(anuspline_m_i)\nprint(anuspline_d_i)\n\n// Visualize each band from each single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(anuspline_m_i.select('pcp'), {min: 0, max: 200, palette: prec_palette}, 'pcp, monthly')\nMap.addLayer(anuspline_m_i.select('mint'), {min: -30, max: 30, palette: temp_palette}, 'mint, monthly')\nMap.addLayer(anuspline_m_i.select('maxt'), {min: -30, max: 30, palette: temp_palette}, 'maxt, monthly')\n\nMap.addLayer(anuspline_d_i.select('pcp'), {min: 0, max: 10, palette: prec_palette}, 'pcp, daily')\nMap.addLayer(anuspline_d_i.select('mint'), {min: -30, max: 30, palette: temp_palette}, 'mint, daily')\nMap.addLayer(anuspline_d_i.select('maxt'), {min: -30, max: 30, palette: temp_palette}, 'maxt, daily')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/ANUSPLIN-GRID
"},{"location":"projects/anusplin/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords : climate, precipitation, temperature, AAFC, daily, monthly, reanalysis
Provider: Agriculture and Agri-Food Canada
Curator: ClimateEngine.org
"},{"location":"projects/aogcm_cmip6/","title":"Current and projected climate data for North America (CMIP6 scenarios)","text":"Atmosphere-Ocean General Circulation Model (AOGCM) were developed to simulate climate variability on a wide range of time scales and are often tested in coupled simulations and data assimilation mode. You can read more about AOGCMs and CMIP6 here. The datasets on this page have been developed by AdaptWest, a project funded by the Wilburforce Foundation to develop information resources for climate adaptation planning. The data were generated using the ClimateNA software. ClimateNA uses data from PRISM and WorldClim for current climate, and downscales data from the Coupled Model Intercomparison Project phase 6 (CMIP6) database corresponding to the 6th IPCC Assessment Report for future projections.
Ensemble projections are average projections from 8 CMIP5 models (table below) that were chosen to represent all major clusters of similar AOGCMs. In addition to the ensemble projections, data are also provided from 9 individual AOGCMs (table below) that are representative of the larger ensemble. Nine individual models were selected to represent all major clusters of similar AOGCMs. A broader set of 8 AOGCMs were used to create the ensemble data. Ensemble projections are also provided here for a greater range of time periods and scenarios than are the projections from individual AOGCMs.
AOGCM Ensemble Models AOGCM Individual Models ACCESS-ESM1-5 ACCESS-ESM1-5 BCC-CSM2-MR CNRM-ESM2-1 CNRM-ESM2-1 CanESM5 EC-Earth3 EC-Earth3 GFDL-ESM4 GFDL-ESM4 GISS-E2-1-G GISS-E2-1-G INM-CM5-0 IPSL-CM6A-LR MIROC6 MIROC6 MPI-ESM1-2-HR MPI-ESM1-2-HR MRI-ESM2-0 MRI-ESM2-0 UKESM1-0-LL UKESM1-0-LL
"},{"location":"projects/aogcm_cmip6/#data-citation","title":"Data citation","text":"AdaptWest Project. 2022. Gridded current and projected climate data for North America at 1km resolution,\ngenerated using the ClimateNA v7.30 software (T. Wang et al., 2022). Available at adaptwest.databasin.org.\nYou can read the paper here and cite as as below
AdaptWest Project. 2022. Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.30 software (T. Wang et al., 2022). Available at adaptwest.databasin.org.\nFor further information and citation refer to:\n\nWang, T., A. Hamann, D. Spittlehouse, C. Carroll. 2016. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS One 11(6): e0156720 https://doi.org/10.1371/journal.pone.0156720\n\nMahony, C.R., T. Wang, A. Hamann, and A.J. Cannon. 2022. A global climate model ensemble for downscaled monthly climate normals over North America. International Journal of Climatology. 1-21. https://doi.org/10.1002/joc.7566\nThe current climatic variables included in the datasets for climate normals, AOGCM models and AOGCM ensemble model are listed below
Monthly Variables Description tmin minimum temperature for a given month (\u00b0C) tmax maximum temperature for a given month (\u00b0C) tave mean temperature for a given month (\u00b0C) ppt total precipitation for a given month (mm)
"},{"location":"projects/aogcm_cmip6/#earth-engine-snippet-climate-variables","title":"Earth Engine Snippet Climate variables","text":"var climate_models_ppt = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_ppt\");\nvar climate_models_tave = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tave\");\nvar climate_models_tmax = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tmax\");\nvar climate_models_tmin = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tmin\");\nvar climate_normals_ppt = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_ppt\");\nvar climate_normals_tave = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tave\");\nvar climate_normals_tmax = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tmax\");\nvar climate_normals_tmin = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tmin\");\nvar aogcm_ensemble_ppt = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_ppt\");\nvar aogcm_ensemble_tave = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tave\");\nvar aogcm_ensemble_tmax = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tmax\");\nvar aogcm_ensemble_tmin = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tmin\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/CMIP6-CURRENT-FUTURE-SCENARIOS
"},{"location":"projects/aogcm_cmip6/#post-processing-for-google-earth-engine-v73","title":"Post processing for Google Earth Engine v7.3","text":"var climate_models_bioclim = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_bioclim\");\nvar aogcm_ensemble_bioclim = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_bioclim\");\nvar climate_normals_bioclim = ee.ImageCollection(\"projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_bioclim\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/CMIP6-CURRENT-FUTURE-BIOCLIMATIC
There are a total of 33 bioclimatic variables included for the collections and models , the reference table is included below and you can filter using the metadata property bioclim_variable and the property names from the table.
Bioclimatic Variables Description MAT mean annual temperature (\u00b0C) MWMT mean temperature of the warmest month (\u00b0C) MCMT mean temperature of the coldest month (\u00b0C) TD difference between MCMT and MWMT, as a measure of continentality (\u00b0C) MAP mean annual precipitation (mm) MSP mean summer (May to Sep) precipitation (mm) AHM annual heat moisture index, calculated as (MAT+10)/(MAP/1000) SHM summer heat moisture index, calculated as MWMT/(MSP/1000) DD_0 degree-days below 0\u00b0C (chilling degree days) DD5 degree-days above 5\u00b0C (growing degree days) DD_18 degree-days below 18\u00b0C DD18 degree-days above 18\u00b0C NFFD the number of frost-free days FFP frost-free period bFFP the julian date on which the frost-free period begins eFFP the julian date on which the frost-free period ends PAS precipitation as snow (mm) EMT extreme minimum temperature over 30 years EXT extreme maximum temperature over 30 years Eref Hargreave's reference evaporation CMD Hargreave's climatic moisture index MAR mean annual solar radiation (MJ m-2 d-1) (excludes areas south of US and some high-latitude areas) RH mean annual relative humidity (%) CMI Hogg\u2019s climate moisture index (mm) DD1040 (10<DD<40) degree-days above 10\u00b0C and below 40\u00b0C Tave_wt winter (December to February) mean temperature (\u00b0C) Tave_sp spring (March to May) mean temperature (\u00b0C) Tave_sm summer (June to August) mean temperature (\u00b0C) Tave_at autumn (September to November) mean temperature (\u00b0C) PPT_wt winter (December to February) precipitation (mm) PPT_sp spring (March to May) precipitation (mm) PPT_sm summer (June to August) precipitation (mm) PPT_at autumn (September to November) precipitation (mm) PPT_at autumn (September to November) precipitation (mm)"},{"location":"projects/aogcm_cmip6/#known-issues","title":"Known issues:","text":"Some discontinuity in precipitation values occurs along the US/Canada border due to edge-matching issues between the PRISM data for the two nations.
Mean annual solar radiation (MAR) data are provisional and are slated to be revised in an upcoming release of the ClimateNA software.
These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
"},{"location":"projects/aogcm_cmip6/#changelog","title":"Changelog","text":"Data Website: You can download the data and description here
Explore the data in R-Shiny apps here
Created by: AdaptWest Project, Wang, T., A. Hamann, D. Spittlehouse, C. Carroll
Curated in GEE by: Samapriya Roy
Keywords: climate change, global circulation models, gridded climate data, north america,emission scenarios,climate variables
Last updated: 2023-03-24
"},{"location":"projects/aqualink/","title":"Aqualink ocean surface and subsurface temperature subset","text":"Aqualink is a philanthropically funded system to help people manage their local marine ecosystems in the face of increasing Ocean temperatures. The system consists of satellite-connected underwater temperature sensors and photographic surveys to allow for remote collaboration with scientists across the world. This export was created as a subset of datasets and sites available from aqualink.org as part of making ocean temperature readings in situ truly possible and globally accessible. The aqualink buoy is a collaboration of aqualink with sofarocean to deploy this buoy as sensors that capture ocean temperature both at surface and at varying depths. They are also capable of measuring things like wave height and wind conditions among other things. You can read about aqualink buoys here
The datasets were downloaded and processed using the pyaqua tool I wrote earlier and you can read about aqualink and the pyaqua tool here. These represent sea surface temperature as well as temperature at depth. These were generated only for deployed buoys and are exported CSVs are then imported into Google Earth Engine. The datasets have timestamp and value for said variable which can be used further to assess conditions over time.
This is a one year subset only for 56 deployed sites from 2020-01-04 to 2021-01-04 and is a subset for users to test and the format and duration of data might change in the future as this project evolves
"},{"location":"projects/aqualink/#data-citation","title":"Data Citation","text":"Citation rules will vary by journal or need but a good example would be
aqualink.org (2021). Clerke Reef West side, Australia SST. Retrieved from https://aqualink.org/sites/1218\nvar top_temp = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/aqualink_top_temp_2020\");\nvar bottom_temp = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/aqualink_bottom_temp_2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/AQUALINK-SUBSET-2020
"},{"location":"projects/aqualink/#license","title":"License","text":"All work and data under the aqualink project are under an MIT license and free and open to the community.
Created by: aqualink org
Curated by: Samapriya Roy
Keywords: : aqualink, buoy, temperature, sea surface temperature, sst, wave, oceans, bleaching, coral reefs, extreme-sea-level
Last updated: 2022-01-05
"},{"location":"projects/argo/","title":"Argo Float Data(Subset)","text":"Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level. Each instrument (float) spends almost all its life below the surface. The name Argo was chosen because the array of floats works in partnership with the Jason earth observing satellites that measure the shape of the ocean surface. (In Greek mythology Jason sailed on his ship the Argo in search of the golden fleece). To learn more about Argo, how it works, its data and technology, and its scientific and environmental impact, click here.
"},{"location":"projects/argo/#citation","title":"Citation","text":"These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System.
The general Argo DOI is below.
Argo (2000). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182
If you used data from a particular month, please add the month key to the end of the DOI url to make it reproducible. The key is comprised of the hashtag symbol (#) and then numbers. For example, the key for August 2020 is 76230. The citation would look like:
Argo (2020). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) \u2013 Snapshot of Argo GDAC of August 2020. SEANOE. https://doi.org/10.17882/42182#76230
"},{"location":"projects/argo/#argovis-citation","title":"ArgoVis citation","text":"Argovis API was used to parse through and get to the datasets, you can cite argovis using the following
Tucker, T., D. Giglio, M. Scanderbeg, and S.S. Shen, 2020: Argovis: A Web Application for Fast Delivery,\nVisualization, and Analysis of Argo Data. J. Atmos. Oceanic Technol., 37 (3), 401-416\nhttps://doi.org/10.1175/JTECH-D-19-0041.1\nArgo float dataset has been parsed into a small subset of about 20,000 feature collections flattened into a single collection with over 12.1 million features with total distinct argo float count at 866. The argo float property variables and GEE property names are listed below
Property GEE Property Property Type Platform ID pid integer Instrument Type inst_typ integer date date integer date added date_added integer profile number profile_number string maximum pressure max_pres float pres_max_for_TEMP pmax_temp float pres_min_for_TEMP pmin_temp float pres_max_for_PSAL pmax_psal float pres_min_for_PSAL pmin_psal float Temperature temp float Salinity psal float Pressure pres float
"},{"location":"projects/argo/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var argo = ee.FeatureCollection(\"projects/sat-io/open-datasets/argo-subset\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/ARGOFLOAT-SUBSET
"},{"location":"projects/argo/#license","title":"License","text":"Argo data are freely available without restriction and are released in a model similar to a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.
Created by : International Argo Program, Global Data Assembly Centre
Curated in GEE by: Samapriya Roy
Keywords: float, Argo, global ocean observing system, ocean circulation, in-situ, ocean pressure, sea water salinity, sea water temperature, multi-year, weather climate and seasonal observation, global-ocean
Last updated : 2021-07-30
"},{"location":"projects/aster/","title":"ASTER Global Digital Elevation Model (GDEM) v3","text":"The first version of the ASTER GDEM, released in June 2009, was generated using stereo-pair images collected by the ASTER instrument onboard Terra. ASTER GDEM coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99 percent of Earth's landmass.
The improved GDEM V3 adds additional stereo-pairs, improving coverage and reducing the occurrence of artifacts. The refined production algorithm provides improved spatial resolution, increased horizontal and vertical accuracy. The ASTER GDEM V3 maintains the GeoTIFF format and the same gridding and tile structure as V1 and V2, with 30-meter postings and 1 x 1 degree tiles. You can read more about the product in the user guide here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/aster/#source-data-structure","title":"Source Data structure","text":"The data are in Geotiff format, with each file divided into 1x1 degree tiles. To allow for adding a single image instead of a collection output, the zip files were unzipped and a single composite tif file was generated.
"},{"location":"projects/aster/#citation","title":"Citation","text":"NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team. ASTER Global\nDigital Elevation Model V003. 2018, distributed by NASA EOSDIS Land Processes DAAC,\nhttps://doi.org/10.5067/ASTER/ASTGTM.003\nvar gdem = ee.Image(\"projects/sat-io/open-datasets/ASTER/GDEM\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/ASTER-GDEM
"},{"location":"projects/aster/#license","title":"License","text":"All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. This license can thus be treated similar to a public domain CC0 license. ASTER GDEM Version 3 (ASTGTM V003) was released on August, 5, 2019 and contains no redistribution requirements. The LP DAAC kindly requests that you properly cite the data in your research.
Created by: NASA, METI, AIST, Japan Spacesystems and U.S./Japan ASTER Science Team
Curated in GEE by: Samapriya Roy
Keywords: ASTER, DEM, elevation, remote sensing
"},{"location":"projects/astwbd/","title":"ASTER Global Water Bodies Database (ASTWBD) Version 1","text":"The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Water Bodies Database (ASTWBD) Version 1 data product provides global coverage of water bodies larger than 0.2 square kilometers at a spatial resolution of 1 arc second (approximately 30 meters) at the equator, along with associated elevation information.
The ASTWBD data product was created in conjunction with the ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 data product by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was generated using ASTER Level 1A scenes acquired between March 1, 2000, and November 30, 2013. The ASTWBD data product was then generated to correct elevation values of water body surfaces.
To generate the ASTWBD data product, water bodies were separated from land areas and then classified into three categories: ocean, river, or lake. Oceans and lakes have a flattened, constant elevation value. The effects of sea ice were manually removed from areas classified as oceans to better delineate ocean shorelines in high latitude areas. For lake water bodies, the elevation for each lake was calculated from the perimeter elevation data using the mosaic image that covers the entire area of the lake. Rivers presented a unique challenge given that their elevations gradually step down from upstream to downstream; therefore, visual inspection and other manual detection methods were required. You can find above mentioned detail along with description here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/astwbd/#source-data-structure","title":"Source Data structure","text":"The data are in Geotiff format, with each file divided into 1x1 degree tiles. To allow for adding a single image instead of a collection output, the zip files were unzipped and a single composite tif file was generated.
"},{"location":"projects/astwbd/#citation","title":"Citation","text":"NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team. ASTER Global\nDigital Elevation Model V003. 2018, distributed by NASA EOSDIS Land Processes DAAC,\nhttps://doi.org/10.5067/ASTER/ASTGTM.003\nvar astwbd_att = ee.Image(\"projects/sat-io/open-datasets/ASTER/ASTWBD_ATT\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/ASTWBD
"},{"location":"projects/astwbd/#license","title":"License","text":"All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. This license can thus be treated similar to a public domain CC0 license. ASTER GDEM Version 3 (ASTGTM V003) was released on August, 5, 2019 and contains no redistribution requirements. The LP DAAC kindly requests that you properly cite the data in your research.
Created by: NASA, METI, AIST, Japan Spacesystems and U.S./Japan ASTER Science Team
Curated in GEE by: Samapriya Roy
Keywords: ASTER, DEM, elevation, remote sensing, Water Bodies Database
"},{"location":"projects/avhrr-ltdr/","title":"ESA Fire Disturbance Climate Change Initiative (CCI)","text":"The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Pixel v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.
The dataset provides monthly information on global burned area at 0.05-degree spatial resolution (the resolution of the AVHRR-LTDR input data) from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in monthly GeoTIFF files, packed in annual tar.gz files, and it includes 5 files: date of BA detection (labelled JD), confidence label (CL), burned area in each pixel (BA), number of observations in the month (OB) and a metadata file. For further information on the product and its format see the Product User Guide. You can download the datasets from this link
The Spatial resolution of this BA product is 0.05 degrees, which is the resolution of the AVHRR-LTDR input data.
The Coordinate Reference System (CRS) used is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid and using a Plate Carr\u00c3\u00a9e projection with geographical coordinates of equal pixel size.This product is distributed in global monthly files, grouped by year.
"},{"location":"projects/avhrr-ltdr/#details-of-the-pixel-product","title":"Details of the Pixel product","text":"The pixel product is composed of 5 files:
Chuvieco, E.; Pettinari, M.L.; Ot\u00f3n, G. (2020): ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Pixel product, version 1.1.Centre for Environmental Data Analysis, 21 December 2020. doi:10.5285/b1bd715112ca43ab948226d11d72b85e.\nhttps://dx.doi.org/10.5285/b1bd715112ca43ab948226d11d72b85e\nvar BA = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/AVHRR-LTDR/BA\");\nvar CL = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/AVHRR-LTDR/CL\");\nvar JD = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/AVHRR-LTDR/JD\");\nvar OB = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/AVHRR-LTDR/OB\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/ESA-FIRE-DISTURBANCE-CCI
"},{"location":"projects/avhrr-ltdr/#license","title":"License","text":"You can find license information here
Created by: Chuvieco, E.; Pettinari, M.L.; Ot\u00f3n, G, ESA
Curated in GEE by : Samapriya Roy
keywords: ESA, CCI, Pixel, Burned Area, Fire Disturbance, Climate Change, GCOS Essential Climate Variable
Last modified: 2020-12-21
Last updated on GEE: 2024-04-01
"},{"location":"projects/bii/","title":"Biodiversity Intactness Index (BII)","text":"The Biodiversity Intactness Index (BII) measures biodiversity change using abundance data on plants, fungi and animals worldwide. The Index shows how local terrestrial biodiversity responds to human pressures such as land use change and intensification. Generated by Impact Observatory, in collaboration with Vizzuality, these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020. Biodiversity Intactness data is based on the PREDICTS database of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies
Biodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods white paper entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d authored by Francis Gassert, Joe Mazzarello, Sam Hyde.
"},{"location":"projects/bii/#ancillary-dataset-citation","title":"Ancillary dataset Citation","text":"Hudson, Lawrence N., Tim Newbold, Sara Contu, Samantha LL Hill, Igor Lysenko, Adriana De Palma, Helen RP Phillips et al. \"The database of the\nPREDICTS (projecting responses of ecological diversity in changing terrestrial systems) project.\" Ecology and evolution 7, no. 1 (2017): 145-188.\nImpact Observatory and Vizzuality. Biodiversity Intactness Index (BII) [Data set]. Retrieved from [URL of the dataset]\nvar bdi_ic = ee.ImageCollection(\"projects/ebx-data/assets/earthblox/IO/BIOINTACT\")\nvar bdi_2017_20_comp = bdi_ic.mean()\nvar bdi_2017_comp = bdi_ic.filterDate('2017-01-01', '2017-12-31').mean()\n\n\nvar visualization = {\n bands: ['BioIntactness'],\n min: 0,\n max: 1,\n palette: ['e5f5e0', 'a1d99b', '31a354'], 4: ['edf8e9', 'bae4b3', '74c476', '238b45']\n};\n\nMap.addLayer(bdi_2017_20_comp, visualization, \"composite 2017-20\")\nMap.addLayer(bdi_2017_comp, visualization, \"composite 2017\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/biodiversity-ecosystems-habitat/BIODIVERSITY-INTACTNESS-INDEX
"},{"location":"projects/bii/#license","title":"License","text":"The datasets are made available under the CC BY 4.0 license
Keywords: Biodiversity, Ecology, Human Pressures, Gridded data
Dataset producer/licensor/processor: Impact Observatory and Vizzuality
Data hosted in Earth Engine by: Earth Blox
"},{"location":"projects/br_dwgd/","title":"Brazilian Daily Weather Gridded Data(BR-DWGD) 1961-2020","text":"The Comprehensive Brazilian Meteorological Gridded Dataset represents a significant advancement in meteorological research, addressing the growing demand for precise and extensive meteorological data. This dataset builds upon its predecessor by enhancing spatial resolution to 0.1\u00b0 x 0.1\u00b0 and expanding temporal coverage from January 1961 to July 2020. Incorporating elevation and temperature lapse rates, the dataset improves gridded interpolations for minimum and maximum temperatures, while also encompassing other crucial variables such as precipitation, solar radiation, wind speed, and relative humidity.
This dataset derives from a meticulous fusion of data from 11,473 rain gauges and 1,252 weather stations, enabling accurate interpolations. The selection of optimal interpolation methods, determined via ranked cross-validation statistics, underscores the dataset's commitment to precision. With two categories of gridded controls provided, researchers gain tools to assess interpolation accuracy against station data. As a comprehensive resource, the Comprehensive Brazilian Meteorological Gridded Dataset stands poised to catalyze advancements in climate, meteorology, and agricultural studies, offering nuanced insights for multifaceted scientific investigations.
These dataset presents the daily meteorological gridded data set from Brazil (BR-DWGD). The variables are Precipitation (pr, mm); maximum and minimum temperature (Tmax, tmin, \u00b0C); solar radiation (Rs, MJ/m2); relative humidity (RH, %); wind speed at 2 meters (u2, m/s) and evapotranspiration (ETo, mm). The temporal coverage is 1961/01/01-2020/07/31 (except precipitation: 1961/01/01-2022/12/31) and has the spatial resolution 0.1\u00b0 x 0.1\u00b0, just for Brazil territory. You can find links to the dataset here
"},{"location":"projects/br_dwgd/#dataset-post-processing","title":"Dataset post processing","text":"The datasets were provided as multiband netcdf files with each representing a single day since 1961 and then partitioned across 20 year intervals. There were then converted and split into single geotiff images and merged so they could be continious collections with about 21,762 images per collection except Precipitation which extends till 2022. The rain gauge and weather station location data was further added to the assets. The datasets must be scaled and offset should be applied to represent true values and they are included in the table below as well as the sample script.
Variable Variable name Units Offset Scale pr Precipitation mm 225 0.006866665 Eto evapotranspiration mm 0 0.051181102 Tmax maximum temperature C 15 0.001068148 Tmin minimum temperature C 15 0.001068148 RH Relative humidity Percentage 0 0.393700787 RS Solar radiation MJ/m2 0 0.157086614 U2 Wind speed m/s 0 0.059055118"},{"location":"projects/br_dwgd/#citation","title":"Citation","text":"Xavier, A. C., Scanlon, B. R., King, C. W., & Alves, A. I. (2022). New improved Brazilian daily weather gridded data (1961\u20132020).\nInternational Journal of Climatology, 42( 16), 8390\u2013 8404. https://doi.org/10.1002/joc.7731\nvar ET = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/ET\");\nvar PR = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/PR\");\nvar RH = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/RH\");\nvar RS = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/RS\");\nvar TMAX = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/TMAX\");\nvar TMIN = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/TMIN\");\nvar U2 = ee.ImageCollection(\"projects/sat-io/open-datasets/BR-DWGD/U2\");\nvar RAIN_GAUGES = ee.FeatureCollection(\"projects/sat-io/open-datasets/BR-DWGD/RAIN_GAUGES\");\nvar WEATHER_STATIONS = ee.FeatureCollection(\"projects/sat-io/open-datasets/BR-DWGD/WEATHER_STATIONS\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/BR-DWDG-EXAMPLE
"},{"location":"projects/br_dwgd/#license","title":"License","text":"The datasets are provided under a Attribution 4.0 International (CC BY 4.0) license.
Provided by: Xavier, A. C. et al
Curated in GEE by : Samapriya Roy
Keywords: Brazil, maximum temperature, minimum temperature, precipitation, solar radiation, wind speed, relative humidity, evapotranspiration
"},{"location":"projects/bss/","title":"Bare Earth\u2019s Surface Spectra 1980-2019","text":"This datasets provides global bare surface area and frequency for a 30 year time range using Landsat Imagery in Google Earth Engine.
From the paper we find
Earth\u2019s surface dynamics provide essential information for guiding environmental and agricultural policies. Uncovered and unprotected surfaces experience several undesirable effects, which can affect soil ecosystem functions. We developed a technique to identify global bare surface areas and their dynamics based on multitemporal remote sensing images to aid the spatiotemporal evaluation of anthropic and natural phenomena. Two additional products were obtained with a similar technique: a) Earth\u2019s bare surface frequency, which represents where and how many times a single pixel was detected as bare surface, based on Landsat series, and b) Earth\u2019s bare soil tendency, which represents the tendency of bare surface to increase or decrease. This technique enabled the retrieval of bare surfaces on 32% of Earth\u2019s total land area and on 95% of land when considering only agricultural areas.
Read the paper here
Use the following credit when these datasets or paper is cited:
Dematt\u00ea, Jos\u00e9 AM, et al. \"Bare earth\u2019s Surface Spectra as a proxy for Soil Resource Monitoring.\"\nScientific reports 10.1 (2020): 1-11.\nvar bare_surface = ee.Image('users/geocis/BareSurfaces/BS_1980_2019');\nvar bare_frequency = ee.Image('users/geocis/BareSurfaces/BF_1980_2019');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/BARE_EARTH_SPECTRA
App Website: App link here
Source Code to App: https://code.earthengine.google.com/6b2935468ce30e08ce693a9cc95f943c
Shared License: This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created & Curated by: Dematt\u00ea, Jos\u00e9 AM, et al
Keywords: Bare Earth Surface, Soil, Geomorphology, Landsat, Bare Surface Frequency
Last updated: 2021-06-12
"},{"location":"projects/ca_canopy_ht/","title":"Canopy height: forested ecosystems of Canada","text":"This dataset contains two canopy height maps from forested ecosystems of Canada at 250m spatial resolution \u2014 one using information from the spaceborne LiDAR GEDI, and the other from ICESat-2. GEDI and ICESat-2 are particular in acquiring canopy height information in Canada \u2014 the former providing more accurate information of vegetation, yet not reaching full coverage in Canada, whilst the latter is not specifically designed to provide vegetation information but has a global coverage. We created wall-to-wall maps using ATL08 LiDAR product from the ICESat-2 satellite, and GEDI L2A from GEDI.
The data were download for the mid growing season (June and August 2020). Points were filtered regarding solar background noise and atmospheric scattering, totaling 208,554 points from ICESat-2, and 1,249,354 points for GEDI after filtering and point thinning. These points were associated with 14 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, seasonal Sentinel-2 red and NIR bands, and annual PALSAR-2 HH and HV polarization. Afterwards, the random forest algorithm was used to extrapolate LiDAR observations and develop regression models with the abovementioned spatially continuous variables. GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m in relation to ALS data used for validation, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI.
You can read the complete paper here and download the dataset at this link
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ca_canopy_ht/#citation","title":"Citation","text":"Sothe, Camile, Alemu Gonsamo, Ricardo B. Louren\u00e7o, Werner A. Kurz, and James Snider. \"Spatially Continuous Mapping of Forest Canopy Height in Canada\nby Combining GEDI and ICESat-2 with PALSAR and Sentinel.\" Remote Sensing 14, no. 20 (2022): 5158.\nSothe, Camile; Gonsamo, Alemu; Snider, James; Louren\u00e7o, Ricardo B.; Kurz, Werner A. (2022): Spatially continuous canopy height maps of forested\necosystems of Canada. 4TU.ResearchData. Dataset. https://doi.org/10.4121/21363081.v1\nvar gedi_fc_ht = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/GEDI_forest_canopy_height_250m_v1\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-TREE-CANOPY-HEIGHT-GEDI
"},{"location":"projects/ca_canopy_ht/#earth-engine-snippet-icesat2","title":"Earth Engine Snippet: ICESat2","text":"var icesat_fc_ht = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/ICESat2_forest_canopy_height_250m_v1\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-TREE-CANOPY-HEIGHT-ICESAT
"},{"location":"projects/ca_canopy_ht/#license","title":"License","text":"This dataset is available under a Creative Commons BY-4.0 license
Created by: Sothe,Camile, et al. 2022
Curated in GEE by : Samapriya Roy
Keywords: LiDAR analysis, ICESat-2, GEDI, canopy height distribution, Carbon storage and distribution
Last updated on GEE: 2022-10-20
"},{"location":"projects/ca_fa/","title":"Landsat-derived forest age for Canada's forested ecosystems (2019)","text":"Landsat-derived forest age for Canada\u2019s forested ecosystems 2019. Satellite-based forest age map for 2019 across Canada\u2019s forested ecozones at a 30-m spatial resolution. Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada\u2019s forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an \"old tree\" category.
Forest area codes:
Map for displaying the approach followed to compute forest age for the treed areas in Canada\u2019s forested ecosystems for a given year, in this case 2019.
Forest area codes: 0: Non treed 1: Disturbance approach 2: Recovery approach 3: Allometric approach
See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada\u2019s National Inventory (NFI). Maltman et al. (2023)
"},{"location":"projects/ca_fa/#citation","title":"Citation","text":"Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada\u2019s forested ecosystems.\nRemote Sensing of Environment 290, 113529.\nvar age = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_forest_age_2019\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FOREST-AGE-2019
"},{"location":"projects/ca_fa/#earth-engine-snippet-approach-used-to-compute-the-landsat-derived-forest-age-for-canada-2019","title":"Earth Engine Snippet: Approach used to compute the Landsat-derived forest age for Canada (2019)","text":"var age_appro = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_forest_age_2019_approach\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FOREST-AGE-2019-APPROACH
Download Tool/Code snippets, if any: Download link https://opendata.nfis.org/downloads/forest_change/CA_forest_age_2019.zip
"},{"location":"projects/ca_fa/#required","title":"Required","text":"License Information: Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada)
Created by: Maltman et al. (2023)
Curated in GEE by: Spencer Bronson and Samapriya Roy
Keywords: Forest age, Forest inventory, Land cover, Landsat, Time since disturbance
Last updated: March 15th 2023
"},{"location":"projects/ca_fao/","title":"Canada Landsat derived FAO forest identification (2019)","text":"Landsat-based forest area consistent with FAO definitions for Canada's forested ecosystems. To conform with international reporting guidelines and programs, using Landsat data we map the forest area for Canada following the Food and Agricultural Organization of the United Nations (FAO) definition. The FAO definition incorporates land use, whereby trees removed by fire and harvesting for instance, remain forest as the trees will return. Annually representative maps were produced using over three decades of annual land cover data generated from Landsat derived time series land cover and change information (to generate a spatially explicit estimate of the forest area of Canada in 2019). We mapped the area of 'forest', as defined by the FAO, for Canada's 650 Mha of forested ecozones. The map includes the current forest cover in a given year (i.e. 2019), plus the satellite-based temporally informed forest area where tree cover had been temporarily lost due to fire or harvest. See Wulder et al. (2020) for an overview of the methods, data, image processing, as well as information on accuracy assessment using Canada\u2019s National Inventory (NFI). You can download the dataset here
Forest area codes for the dataset are
0: Non forest 1: Current forest area 2019 2: Temporally informed forest area 2019
"},{"location":"projects/ca_fao/#citation","title":"Citation","text":"Wulder, M.A., T. Hermosilla, G. Stinson, F.A. Gougeon, J.C. White, D.A. Hill, B.P. Smiley. (2020). Satellite-based time series land cover and change\ninformation to map forest area consistent with national and international reporting requirements. Forestry 93(3), 331-343.\nvar ca_fao = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_FAO_forest_2019\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FAO-FOREST-IDENTIFICATION-2019
"},{"location":"projects/ca_fao/#license","title":"License","text":"Open Government Licence - Canada
Created by: Wulder et al. (2020)
Curated by: Spencer Bronson and Samapriya Roy
Keywords: Forest area, temporally informed forest area, disturbance informed forest area, Forest inventory, Land cover, Landsat
Last updated: 2023-03-29
"},{"location":"projects/ca_fires/","title":"Canada 2023 Wildfires","text":"Canada's 2023 wildfire season represented the largest area burned in a single fire season in Canada\u2019s modern history. Using the Tracking Intra- and Inter-year Change (TIIC) algorithm, wildfires occurring within Canada\u2019s forested ecosystems during the 2023 fire season were detected at a 30-m resolution. Time series data used to identify wildfires originated from Sentinel-2A and -2B, and Landsat-8 and -9. Fires have been grouped into two classes based on detection period: summer fires and fall fires. Summer fires were detected between May 30 and September 17, and fall fires were detected between September 17 and October 25. For summer fires, burned pixels were identified by TIIC as changed and typed as fire.
For the fall period, TIIC only detected changes within a 4-km buffer of NRCan fire perimeters (https://cwfis.cfs.nrcan.gc.ca/datamart). This approach was used to limit commission errors that can occur due to known limitations of mapping with optical data in the fall due to phenology, snow cover, or low sun angles. For the 2023 fire season, the TIIC algorithm detected 12.74 Mha of burned area in Canada\u2019s forested ecozones, representing 1.8% of the total forest-dominated ecozone area. Of the 12.74 Mha, 11.57 Mha (90.9%) was burned by summer fires and 1.16 Mha (9.1%) by fall fires (Pelletier et al., 2024). You can download the dataset here
"},{"location":"projects/ca_fires/#citation","title":"Citation","text":"Pelletier, F., Cardille, J.A., Wulder, M.A., White, J.C., Hermosilla, T., 2024. Revisiting the 2023 wildfire season in Canada. Science of Remote Sensing. 10, 100145. https://doi.org/10.1016/j.srs.2024.100145\nvar image = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Wildfire_2023_Summer_Fall\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-2023
"},{"location":"projects/ca_fires/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada.
Created by: Pelletier et al. 2024
Curated in GEE by : Spencer Bronson and Samapriya Roy
keywords: Wildfire, Tracking Intra- Inter-year Change (TIIC), Landsat, Sentinel, Burned Area, Fire Occurrence, Canada
Last updated on GEE: 2024-08-29
"},{"location":"projects/ca_forest_fire/","title":"Canada Landsat Derived Wildfire disturbance & Magnitude 1985-2020","text":"The annual forest change data included in this product is national in scope (entire forested ecosystem) and represents the wall-to-wall characterization of wildfire in Canada at a 30-m spatial resolution. The information outcomes represent 36 years of wildfire change over Canada\u2019s forests, derived from a single, consistent, spatially explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985\u20132020 for Canada's 650 Mha forested ecosystems.
Landsat data has a 30 m spatial resolution, so the change information is highly detailed and informative regarding both natural and human driven changes. These data represent annual stand replacing forest changes. The stand replacing disturbance types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see ( Hermosilla et al. 2016). The data available is Change year for Wildfire Events. You can download the dataset here
"},{"location":"projects/ca_forest_fire/#canada-landsat-derived-forest-wildfire-change-magnitude-dnbr-1985-2020","title":"Canada Landsat-Derived Forest Wildfire Change Magnitude dNBR (1985-2020)","text":"Wildfire change magnitude dNBR 1985-2020. Spectral change magnitude for wildfires that occurred from 1985 and 2020 expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after a given change event. This layer value has been transformed for data storage efficiency. The actual dNBR value can be calculated as follows dNBR = value / 100. Higher dNBR values are related to higher burn severity. You can download the dataset here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ca_forest_fire/#dataset-citation","title":"Dataset Citation","text":"Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Campbell, L.B., 2016. Mass data processing of time series Landsat imagery:\npixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054.\nvar ca_forest_fire = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Fire_1985-2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-1985-2020
var ca_forest_fire_mag = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Wildfire_dNBR_1985_2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-MAGNITUDE-1985-2020
"},{"location":"projects/ca_forest_fire/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada.
Created by: Hermosilla et al. 2016
Curated in GEE by : Spencer Bronson and Samapriya Roy
keywords: Forest Fire, Forest inventory, Land cover, Landsat, Machine learning
Last updated on GEE: 2023-07-02
"},{"location":"projects/ca_forest_harvest/","title":"Canada Landsat Derived Forest harvest disturbance 1985-2020","text":"The annual forest change data included in this product is national in scope (entire forested ecosystem) and represents the wall-to-wall characterization of harvest in Canada at a 30-m spatial resolution. The information outcomes represent 36 years of harvest change over Canada\u2019s forests, derived from a single, consistent, spatially explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985\u20132020 for Canada's 650 Mha forested ecosystems.
Landsat data has a 30 m spatial resolution, so the change information is highly detailed and informative regarding both natural and human driven changes. These data represent annual stand replacing forest changes. The stand replacing disturbance types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see (Hermosilla et al. 2016). The data available is Change year for Harvest Events and can be downloaded here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ca_forest_harvest/#dataset-citation","title":"Dataset citation","text":"Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Campbell, L.B., 2016. Mass data processing of time series Landsat imagery:\npixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054.\nvar ca_forest_harvest = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Harvest_1985-2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FOREST-HARVEST-1985-2020
"},{"location":"projects/ca_forest_harvest/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada.
Created by: Hermosilla et al. 2016
Curated in GEE by : Samapriya Roy
keywords: Forest Harvest, Forest inventory, Land cover, Landsat, Machine learning
Last updated on GEE: 2023-01-28
"},{"location":"projects/ca_lc/","title":"High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2022)","text":"The annual time series of forest land cover maps are national in scope (entire 650 million hectare forested ecosystem) and represent a wall-to-wall land cover characterization yearly from 1984 to 2022. These time-series land cover maps were produced from annual time-series of Landsat image composites, forest change information, and ancillary topographic and hydrologic data following the framework described in Hermosilla et al. (2022), which builds upon the approach introduced in Hermosilla et al. (2018). The methodological innovations included (i) a refined training pool derived from existing land cover products using airborne and spaceborne measures of forest structure; (ii) selection of training samples proportionally to the land cover distribution using a distance=weighted approach; and (iii) generation of regional classification models using a 150x150 km tiling system. Maps are post-processed using disturbance information to ensure logical class transitions over time using a Hidden Markov Model. Hidden Markov Models assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar). For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) No. 112780. DOI: https://doi.org/10.1016/j.rse.2021.112780 and Hermosilla et al. (2018) https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719
The data represents annual forest land cover of Canada's forested ecosystems for 1984-2022. An image compositing window of August 1 -30 days was used to generate the best-available-pixel (BAP) image composites used as the source data for land cover classification. The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada's forests, were led by Canadian Forest Service of Natural Resources Canada, partnered with the University of British Columbia, with support from the Canadian Space Agency, augmented by processing capacity from WestGrid of Compute Canada.
"},{"location":"projects/ca_lc/#citation","title":"Citation","text":"Paper citation
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., 2022. Land cover classification in an era of big and open data: Optimizing localized\nimplementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. No. 112780.\n[Hermosilla et al. 2022](https://www.sciencedirect.com/science/article/pii/S0034425721005009)\nWhen using this data, please cite as:
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., 2022. Land cover classification in an era of big and open data: Optimizing localized\nimplementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. No. 112780.\nDOI: https://doi.org/10.1016/j.rse.2022.112780 [Open Access]\n#686868 Class Code: 0 Unclassified #3333ff Class Code: 20 Water #ccffff Class Code: 31 Snow/Ice #cccccc Class Code: 32 Rock/Rubble #996633 Class Code: 33 Exposed/Barren Land #ffccff Class Code: 40 Bryoids #ffff00 Class Code: 50 Shrubs #993399 Class Code: 80 Wetland #9933cc Class Code: 81 Wetland Treed #ccff33 Class Code: 100 Herbs #006600 Class Code: 210 Coniferous #00cc00 Class Code: 220 Broad Leaf #cc9900 Class Code: 230 Mixedwood
"},{"location":"projects/ca_lc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var forest_lc = ee.ImageCollection(\"projects/sat-io/open-datasets/CA_FOREST_LC_VLCE2\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FORESTED-ECOSYSTEM-LC
"},{"location":"projects/ca_lc/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada).
Created by: Hermosilla et al. 2022
Curated in GEE by : Samapriya Roy
keywords: Land cover; Classification; Machine learning; Land cover change; Landsat; Lidar; ICESat-2
Last updated on GEE: 2024-08-29
"},{"location":"projects/ca_sbfi/","title":"Canadian Satellite-Based Forest Inventory (SBFI)","text":"The Satellite-Based Forest Inventory (SBFI) provides information on Canada\u2019s forested land cover, disturbance recovery, structure, species, and stand age in 2020, as well as stand-replacing disturbances from 1985-2020. The SBFI polygons represent homogeneous forest conditions similar to those of stands delineated in a strategic forest inventory. More than 25 million SBFI polygons were delineated using a multiresolution segmentation algorithm applied to the 2020 Landsat surface-reflectance BAP image composite (30-m spatial resolution), fire year, and harvest year layers derived from Landsat using the C2C approach. A minimum map unit of 0.45 ha (5 pixels) was used to define polygons. The entirety of Canada\u2019s forest ecosystems were mapped using the same data, attributes, and temporal representation, resulting in a common vegetation inventory system of Canada\u2019s ~650 Mha forested ecosystems. Given the large and diverse forest area of Canada, the strength of an SBFI lies in the use of a consistent data source and methodology across jurisdictional boundaries, and managed and unmanaged forest areas, enabling consistently generated synoptic, spatially explicit information outputs. The data included herein are based upon free and open satellite data and information products following established and communicated approaches.
Full descriptions of feature attributes are found within the attached data dictionary or within the downloadable dataset found here
CA SBFI feature attributesGroup Field Description Units ID ID Unique polygon identifier TILE Tile identifier Geometry AREA_HA Area of the polygon ha PERIMETER_M Length of polygon\u2019s boundary m Stratification JURSDICTION Most represented province/territory ECOZONE Most represented terrestrial ecozone as defined by Ecological Stratification Working Group (1996) ECOPROVINCE Most represented ecoprovince as defined by Ecological Stratification Working Group (1996) ECOREGION Most represented ecoregion as defined by Ecological Stratification Working Group (1996) MANAGEMENT Most represented land status from the forest management classification from Stinson et al_ (2019) Land cover LC_WATER Area covered by water % of polygon area LC_SNOW_ICE Area covered by snow/ice % of polygon area LC_ROCK_RUBBLE Area covered by rock/rubble % of polygon area LC_EXPOSED_BARREN Area covered by exposed/barren land % of polygon area LC_BRYOIDS Area covered by bryoids % of polygon area LC_SHRUBS Area covered by shrubs % of polygon area LC_WETLAND Area covered by wetland % of polygon area LC_WETLAND-TREED Area covered by wetland-treed % of polygon area LC_HERBS Area covered by herbs % of polygon area LC_CONIFEROUS Area covered by coniferous % of polygon area LC_BROADLEAF Area covered by broadleaf % of polygon area LC_MIXEDWOOD Area covered by mixedwood % of polygon area LC_TREED Area covered by treed vegetation derived from combining the land cover classes % of polygon area LC_FAO_FOREST Area covered by forest consistent with FAO definitions (Wulder et al_ 2020) % of polygon area LC_WETLAND_VEGETATION Area covered by wetlands derived from combining the land cover classes % of polygon area Disturbances DISTURB_FIRE_PERC Area impacted by fire disturbances % of polygon area DISTURB_FIRE_YEAR Modal year of fire disturbances years DISTURB_FIRE_MAGNITUDE_MIN Minimum value of fire magnitude dNBR DISTURB_FIRE_MAGNITUDE_MAX Maximum value of fire magnitude dNBR DISTURB_FIRE_MAGNITUDE_AVG Average value of fire magnitude dNBR DISTURB_FIRE_MAGNITUDE_SD Standard deviation of fire magnitude dNBR DISTURB_FIRE_MAGNITUDE_MED Median value of fire magnitude dNBR DISTURB_HARVEST_PERC Area impacted by harvesting disturbances % of polygon area DISTURB_HARVEST_YEAR Modal year of harvesting disturbances years Recovery RECOVERY_FIRE_MIN Minimum value of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_FIRE_MAX Maximum value of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_FIRE_AVG Average value of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_FIRE_SD Standard deviation of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_FIRE_MED Median value of spectral recovery for fire disturbances % of pre-disturbance RECOVERY_HARVEST_MIN Minimum value of spectral recovery for harvesting disturbances % of pre-disturbance RECOVERY_HARVEST_MAX Maximum value of spectral recovery for harvesting disturbances % of pre-disturbance RECOVERY_HARVEST_AVG Average value of spectral recovery for harvesting disturbances % of pre-disturbance RECOVERY_HARVEST_SD Standard deviation of spectral recovery for harvesting disturbances % of pre-disturbance RECOVERY_HARVEST_MED Median value of spectral recovery for harvesting disturbances % of pre-disturbance Age AGE_MIN Minimum forest age years AGE_MAX Maximum forest age years AGE_AVG Average forest age years AGE_SD Standard deviation of forest age years AGE_MED Median forest age years AGE_0_10, AGE_10_20, AGE_20_30, AGE_30_40, AGE_40_50, AGE_50_60, AGE_60_70, AGE_70_80, AGE_80_90, AGE_90_100, AGE_100_110, AGE_110_120, AGE_120_130, AGE_130_140, AGE_140_150, AGE_GT_150 Ten-year age class frequency distribution % of treed area in polygon Forest structure STRUCTURE_CANOPY_HEIGHT_MIN Minimum canopy height m STRUCTURE_CANOPY_HEIGHT_MAX Maximum canopy height m STRUCTURE_CANOPY_HEIGHT_AVG Average canopy height m STRUCTURE_CANOPY_HEIGHT_SD Standard deviation of canopy height m STRUCTURE_CANOPY_HEIGHT_MED Median canopy height m STRUCTURE_CANOPY_COVER_MIN Minimum canopy cover % STRUCTURE_CANOPY_COVER_MAX Maximum canopy cover % STRUCTURE_CANOPY_COVER_AVG Average canopy cover % STRUCTURE_CANOPY_COVER_SD Standard deviation of canopy cover % STRUCTURE_CANOPY_COVER_MED Median canopy cover % STRUCTURE_LOREYS_HEIGHT_MIN Minimum Lorey\u2019s height m STRUCTURE_LOREYS_HEIGHT_MAX Maximum Lorey\u2019s height m STRUCTURE_LOREYS_HEIGHT_AVG Average Lorey\u2019s height m STRUCTURE_LOREYS_HEIGHT_SD Standard deviation of Lorey\u2019s height m STRUCTURE_LOREYS_HEIGHT_MED Median Lorey\u2019s height m STRUCTURE_BASAL_AREA_MIN Minimum basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_MAX Maximum basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_AVG Average basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_SD Standard deviation of basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_MED Median basal area m2 ha\u22121 STRUCTURE_BASAL_AREA_TOTAL Total basal area in polygon m2 STRUCTURE_AGB_MIN Minimum aboveground biomass t ha\u22121 STRUCTURE_AGB_MAX Maximum aboveground biomass t ha\u22121 STRUCTURE_AGB_AVG Average aboveground biomass t ha\u22121 STRUCTURE_AGB_SD Standard deviation of aboveground biomass t ha\u22121 STRUCTURE_AGB_MED Median aboveground biomass t ha\u22121 STRUCTURE_AGB_TOTAL Total aboveground biomass in polygon t STRUCTURE_VOLUME_MIN Minimum gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_MAX Maximum gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_AVG Average gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_SD Standard deviation of gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_MED Median gross stem volume m3 ha\u22121 STRUCTURE_VOLUME_TOTAL Total gross stem volume in polygon m3 Tree species SPECIES_NUMBER SPECIES_1 Name of the 1st most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_2 Name of the 2nd most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_3 Name of the 3rd most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_4 Name of the 4th most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_5 Name of the 5th most common leading tree species representing a percentage of treed area in polygon >2_5% SPECIES_1_PERC Area covered by the 1st most common leading tree species % of treed area in polygon SPECIES_2_PERC Area covered by the 2nd most common leading tree species % of treed area in polygon SPECIES_3_PERC Area covered by the 3rd most common leading tree species % of treed area in polygon SPECIES_5_PERC Area covered by the 5th most common leading tree species % of treed area in polygon SPECIES_CONIFEROUS_PERC Area covered by coniferous tree species % of treed area in polygon SPECIES_CML1 Name of the 1st most common tree species based on the class membership likelihood values SPECIES_CML2 Name of the 2nd most common tree species based on the class membership likelihood values SPECIES_CML3 Name of the 3rd most common tree species based on the class membership likelihood values SPECIES_CML4 Name of the 4th most common tree species based on the class membership likelihood values SPECIES_CML5 Name of the 5th most common tree species based on the class membership likelihood values SPECIES_CML1_PERC Distribution of the class membership likelihood values of the 1st most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML2_PERC Distribution of the class membership likelihood values of the 2nd most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML3_PERC Distribution of the class membership likelihood values of the 3rd most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML4_PERC Distribution of the class membership likelihood values of the 4th most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML5_PERC Distribution of the class membership likelihood values of the 5th most common tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML_CONIFEROUS_PERC Proportion of class membership likelihood values of coniferous tree species % of class membership likelihood from treed pixels in polygon SPECIES_CML_ASSEMBLAGES Name of the tree species conforming an assemblage SPECIES_CML_ASSEMBLAGES_PERC Proportion of class membership likelihood values conforming the assemblage % of class membership likelihood from treed pixels in polygon Symbology SYMB_LAND_BASE_LEVEL Land base level classification based on the NFI land cover hierarchy (Wulder et al_ 2008) SYMB_LAND_COVER_LEVEL Land cover level classification based on the NFI land cover hierarchy (Wulder et al_ 2008) SYMB_VEGETATION_LEVEL Vegetation level classification based on the NFI land cover hierarchy (Wulder et al_ 2008) SYMB_DISTURBANCE Simplified coding for disturbance type and year SYMB_RECOVERY Simplified coding for spectral recovery SYMB_AGE Simplified coding for forest age
"},{"location":"projects/ca_sbfi/#dataset-postprocessing","title":"Dataset postprocessing","text":"
The tile datasets are merged into a single feature collection for ease of use. The grid file is kept as is for users to understand how the grids are created.
"},{"location":"projects/ca_sbfi/#citation","title":"Citation","text":"Wulder, Michael A., Txomin Hermosilla, Joanne C. White, Christopher W. Bater, Geordie Hobart, and Spencer C. Bronson. \"Development and\nimplementation of a stand-level satellite-based forest inventory for Canada.\" Forestry: An International Journal of Forest Research (2024): cpad065.\nWulder, M.A., Hermosilla, T., White, J.C., Bater, C.W., Hobart, G., Bronson, S.C., 2024. Development and implementation of a stand-level\nsatellite-based forest inventory for Canada. Forestry: An International Journal of Forest Research. https://doi.org/10.1093/forestry/cpad065\nvar sbfi_merged = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/CA_SBFI_MERGED\");\nvar grid_fe = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/GRID_forested_ecosystems\");\nvar grid_labels = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/Grid_Labels\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SBFI
"},{"location":"projects/ca_sbfi/#license","title":"License","text":"This work is licensed under and freely available to the public under the Open Government Licence - Canada.
Created by: Wulder et al. 2024
Curated in GEE by : Samapriya Roy
Keyworks: Landsat, land cover, change detection, forest structure, biomass; NFI
Last updated in GEE: 2024-08-29
"},{"location":"projects/ca_species/","title":"High Resolution Tree Species Information for Canada","text":""},{"location":"projects/ca_species/#distance-to-second-class-for-the-leading-tree-species-map","title":"Distance-to-second class for the leading tree species map","text":"Distance-to-second class (D2SC) value used as indicator of attribution confidence for the leading tree species map produced from surface reflectance values in a spatially exhaustive, 30-m spatial resolution, Landsat image composite representing year 2019 conditions. Also included in the modeling of species are geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Regional classification models were generated based on Canada??s National Forest Inventory using a 150x150 km tiling system. D2SC is computed using the class membership probabilities derived from the first and second most voted classes from the Random Forests models.
"},{"location":"projects/ca_species/#high-resolution-maps-of-tree-species-membership-likelihood","title":"High Resolution Maps of tree species membership likelihood","text":"Tree species maps indicate the class membership probability of all possible classes on a pixel level. The maps are generated using a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Values represent the class membership probabilities derived from the Random Forests votes. Regional classification models were generated based on Canada??s National Forest Inventory (NFI) using a 150x150 km tiling system. The regional classification models utilize and aim to map only the trees species known to be present in a given tiling unit based on the information provided by the NFI.
"},{"location":"projects/ca_species/#high-resolution-map-of-leading-tree-species-distribution","title":"High Resolution Map of leading tree species distribution","text":"Leading tree species map produced from a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (xxxx). Regional classification models were generated based on Canada??s National Forest Inventory using a 150x150 km tiling system. The leading tree species are defined by representing the most voted tree species from the Random Forests classification models (i.e. the class with the highest class membership probability).
For an overview on the data, image processing, and methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) https://doi.org/10.1016/j.rse.2022.113276
"},{"location":"projects/ca_species/#citation","title":"Citation","text":"Hermosilla, T., Bastyr, A., Coops, N.C., White, J.C., Wulder, M.A., 2022. Mapping the presence and distribution of tree species in Canada's forested ecosystems. Remote Sensing of Environment 282, 113276.\nvar D2SC = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/DISTANCE2SECOND\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-DISTANCE-2-SECOND-CLASS
"},{"location":"projects/ca_species/#earth-engine-snippet-tree-species-membership-likelihood","title":"Earth Engine Snippet: tree species membership likelihood","text":"var membership_likelihood_prob = ee.ImageCollection(\"projects/sat-io/open-datasets/CA_FOREST/SPECIES_CLASS_MEMBERSHIP_PROBABILITIES\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SPECIES-CLASS-MEM-PROBABILITIES
"},{"location":"projects/ca_species/#earth-engine-snippet-leading-tree-species","title":"Earth Engine Snippet: leading tree species","text":"var lead_tree_species = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/LEAD_TREE_SPECIES\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-LEAD-TREE-SPECIES
"},{"location":"projects/ca_species/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government Licence - Canada.
Created by: Hermosilla et al. 2022
Curated in GEE by : Samapriya Roy
keywords: Tree species, Forest inventory, Land cover, Landsat, Machine learning, Classification
Last updated on GEE: 2022-10-11
"},{"location":"projects/ca_species_ts/","title":"Canada Long Term Tree Species (1984-2022)","text":"In this dataset, we share maps of annual dominant tree species (also known as leading tree species) from 1984-2022 covering the entirety of Canada\u2019s 650 Mha forested ecosystems using Landsat time-series imagery at a 30-m spatial resolution. Classifications are based on regionally representative Random Forests model using local training samples from Canada\u2019s National Forest Inventory (Hermosilla et al., 2022). Descriptive metrics provide information on spectral, geographic, climatic, and topographic characteristics. Initial annual tree species classifications were subjected to a time series post-classification process using the forward-backward Hidden Markov Model to improve the temporal consistency of tree species transitions within the time series. Assessment of the annual species maps using independent validation data resulted in an overall accuracy of 86.1% \u00b1 0.14% (95%-confidence interval). These data allow consistent comparison of trends and rates of change in tree species composition nationally and across regions using a common time frame, spatial resolution, and analytical approach.
"},{"location":"projects/ca_species_ts/#citation","title":"Citation","text":"Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Bater, C.W., Hobart, G.W., 2024. Characterizing long-term tree species dynamics in Canada's forested ecosystems using annual time series remote sensing data. Forest Ecology and Management 572, 122313. https://doi.org/10.1016/j.foreco.2024.122313 (Hermosilla et al. 2024)\nYou can download the files here, found under the title: Annual Tree Species 1984-2022 and Species_Names here
"},{"location":"projects/ca_species_ts/#dataset-post-processing","title":"Dataset Post processing","text":"The datasets were provided as an earth engine folder with images and have been converted to an imagecollection and start and end date have been added to each image in the image collection for filtering.
"},{"location":"projects/ca_species_ts/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var ca_species_ts = ee.ImageCollection(\"projects/sat-io/open-datasets/CA_FOREST/SPECIES-1984-2022\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SPECIES-TIME-SERIES
"},{"location":"projects/ca_species_ts/#license","title":"License","text":"This work is licensed under and freely available to the public Open Government License - Canada.
Created by: Hermosilla et al. 2024
Curated in GEE by : Spencer Bronson & Samapriya Roy
Keywords: Landsat, Time series analysis, Land cover, Land cover change, Forest succession, Dominant species
Last updated :2024-10-15
"},{"location":"projects/caml/","title":"Cyanobacteria Aggregated Manual Labels (CAML)","text":"Continuous monitoring for cyanobacteria blooms in small, inland water bodies via in-situ sampling and analysis can be challenging not only due to the number and locations of water bodies to cover, but also due to the dynamic nature of algal growth and toxin production. Detection targets vary with cyanobacteria strains as well as physical, chemical, and biological factors. Ground monitoring also lacks consistency as sampling methods, frequency, and analytical techniques vary from region to region. However, remote sensing allows systematic data collection over a large area to identify regions with potential harmful algal growth. We introduce the Cyanobacteria Aggregated Manual Labels (CAML), a large dataset of in-situ cyanobacteria measurements for investigations of cyanobacteria detection and severity classification in inland water bodies across the United States. Relevant satellite imagery from publicly available endpoints are applicable to use when applying the CAML dataset to models.
The dataset labels ground measurements of cyanobacteria cell counts at 23,570 points in U.S. inland water bodies over 2013 - 2021. Algorithms trained on this data could be used to estimate cyanobacteria cell counts in water bodies for timely water quality and public health interventions and to gain an understanding of environmental and anthropogenic factors associated with cyanobacteria incidence and proliferation. Data is provided in a comma-separated values (CSV) format. You can find the dataset here
Severity levels are based on World Health Organization (WHO) cyanobacteria density thresholds.
However users should feel free to to use their own thresholds as makes sense for their needs.
"},{"location":"projects/caml/#dataset-citation","title":"Dataset Citation","text":"S. Gupta, E. Gelbart, R. Gupta, K. Wetstone, and E. Dorne (2024). Cyanobacteria Aggregated Manual Labels Dataset (NASA and DrivenData). SeaBASS. http://dx.doi.org/10.5067/SeaBASS/CAML/DATA001\nvar caml = ee.FeatureCollection(\"projects/sat-io/open-datasets/HAB-DETECTION/CAML_cyanobacteria_abundance_20211229_R1\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/CYANOBACTERIA-AGG-MANUAL-LABELS
"},{"location":"projects/caml/#license","title":"License","text":"Following the NASA Earth Science Data and Information Policy, all SeaBASS data are publicly available.
Provided by: NASA, UC Berkeley,DrivenData, ASRC Federal
Curated in GEE by: Samapriya Roy
Keywords: water quality, HAB, Cyanobacteria, Manual Labels, Ground data
Last updated: 2024-03-20
"},{"location":"projects/can_drought_outlook/","title":"Canadian Drought Outlook","text":"The Canadian Drought Outlook raster dataset is produced by the Agriculture and Agri-Food Canada (AAFC). The Canadian Drought Outlook predicts whether drought across Canada will emerge, stay the same or get better over the target month. In calculating the outlook, consideration is given to Agroclimate indices, such as the Standard Precipitation Index (SPI), the Standard Precipitation Evaporation Index (SPEI), and the Palmer Drought Severity Index (PDSI). The drought outlook is issued on the first Thursday of each calendar month and is valid for 32 days from that date. You can get additional information about this dataset here and on the climate engine org dataset page here.
"},{"location":"projects/can_drought_outlook/#dataset-description","title":"Dataset Description","text":"Categorical Values
Value Interpretation -9999 NoData Value 0 No data 1 Drought removal 2 Drought improves 3 Drought develops 4 Drought persists 5 Drought worsensSpatial Information
Parameter Value Spatial extent Canada Spatial resolution ~0.8-km (1/100-deg) Temporal resolution Monthly Time span 2021-06-01 to present Update frequency Updated first week of each monthVariables
Variable Details Drought category ('drought_outlook_class') - Units: Drought outlook classification - Scale factor: 1.0"},{"location":"projects/can_drought_outlook/#citation","title":"Citation","text":"Agriculture and Agri-Food Canada, 2021, \"Canadian Drought Outlook\", Agroclimate, Geomatics and Earth Observation Division, Science and Technology\nBranch.\n// Read in Image Collection and get single image\nvar cdo_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-aafc-cdo-monthly')\nvar cdo_i = cdo_ic.first()\n\n// Print image to see bands\nprint(cdo_i)\n\n// Visualize a single image\nvar cdo_palette = [\"#ffffff\", \"#4a7733\", \"#dfb73d\", \"#b6a083\", \"#775412\", \"#c24d1b\"]\nMap.addLayer(cdo_i, {min:0, max:4, palette: cdo_palette}, 'cdo_i')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/CANADA-DROUGHT-OUTLOOK
"},{"location":"projects/can_drought_outlook/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: drought, Canada, forecast, AAFC
Provided by: Agriculture and Agri-Food Canada (AAFC)
Curated in GEE by: Climate Engine Org
"},{"location":"projects/canopy/","title":"ETH Global Sentinel-2 10m Canopy Height (2020)","text":"The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change, and prevent biodiversity loss. Here, we present the first global, wall-to-wall canopy height map at 10 m ground sampling distance for the year 2020. No single data source meets these requirements: dedicated space missions like GEDI deliver sparse height data, with unprecedented coverage, whereas optical satellite images like Sentinel-2 offer dense observations globally, but cannot directly measure vertical structures. By fusing GEDI with Sentinel-2, we have developed a probabilistic deep learning model to retrieve canopy height from Sentinel-2 images anywhere on Earth, and to quantify the uncertainty in these estimates.
The presented approach reduces the saturation effect commonly encountered when estimating canopy height from satellite images, allowing to resolve tall canopies with likely high carbon stocks. According to our map, only 5% of the global landmass is covered by trees taller than 30 m. Such data play an important role for conservation, e.g., we find that only 34% of these tall canopies are located within protected areas. Our model enables consistent, uncertainty-informed worldwide mapping and supports an ongoing monitoring to detect change and inform decision making. The approach can serve ongoing efforts in forest conservation, and has the potential to foster advances in climate, carbon, and biodiversity modelling. You can download the cloud optimized geotiffs here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/canopy/#citation","title":"Citation","text":"Lang, Nico, Walter Jetz, Konrad Schindler, and Jan Dirk Wegner. \"A high-resolution canopy height model of the Earth.\" arXiv preprint arXiv:2204.08322 (2022).\nvar canopy_height = ee.Image(\"users/nlang/ETH_GlobalCanopyHeight_2020_10m_v1\");\nvar standard_deviation = ee.Image(\"users/nlang/ETH_GlobalCanopyHeightSD_2020_10m_v1\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-10m-CANOPY-HEIGHT
GEE app link: https://nlang.users.earthengine.app/view/global-canopy-height-2020
GEE app source code link: https://code.earthengine.google.com/fefca6457efb90c0a3f8ae9806bee792
"},{"location":"projects/canopy/#license","title":"License","text":"The ETH Global Canopy Height 2020 product is provided free of charge, without restriction of use. For the full license information see the Creative Commons Attribution 4.0 International License publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.
Created by: Lang, Nico, Walter Jetz, Konrad Schindler, and Jan Dirk Wegner
Curated by: Samapriya Roy
Keywords: Sentinel-2, Forest, Canopy Height, Machine Learning, CNN
Last updated on GEE: 2022-03-29
"},{"location":"projects/carbon_projects/","title":"Carbon Offset Project Boundaries","text":"Nature-based climate solutions (NBS) have become an important component of strategies aiming to reduce atmospheric CO2 and mitigate climate change impacts. Carbon offsets have emerged as one of the most widely implemented NBS strategies, however, these projects have also been criticized for exaggerating offsets. Verifying the efficacy of NBS-derived carbon offset is complicated by a lack of readily available geospatial boundary data. Herein, we detail methods and present a database of nature-based offset project boundaries. This database provides the locations of 575 NBS projects distributed across 55 countries. Geospatial boundaries were aggregated using a combination of scraping data from carbon project registries (n=433, 75.3%) as well as manual georeferencing and digitization (n=127, 22.1%). Database entries include three varieties of carbon projects: avoided deforestation, afforestation, reforestation and re-vegetation, and improved forest management. An accuracy assessment of the georeferencing and digitizing process indicated a high degree of accuracy (intersection over union score of 0.98 \u00b1 0.015).
You can read the preprint here and find the dataset here.
"},{"location":"projects/carbon_projects/#database-notes","title":"Database notes","text":"The project accounting area is defined as the geographical area of the project that was used to calculate carbon credit issuance.
This database does not represent a census of nature-based carbon projects and does not contain all varieties of nature-based carbon projects.
Users should verify that any georeferencing inaccuracies will not significantly impact their analyses.
The boundaries included in the database reflect the data available in the registries at the time of access, with some projects regularly updating their information.
We note that we were unable to assess the accuracy of the boundaries constructed from linear features or from a developer provided protocol.
Karnik, Akshata, John Kilbride, Tristan Goodbody, Rachael Ross, and Elias Ayrey. \"An open-access database of nature-based carbon offset project\nboundarie.\" (2024).\nKarnik, A., Kilbride, J., Goodbody, T., Rachel, R., & Ayrey, E. (2024). A global database of nature-based carbon offset project boundaries [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11459391\nvar carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL');\n\nvar visParams = {\n palette: ['#9ab555'],\n min: 0.0,\n max: 1550000.0,\n opacity: 0.8,\n};\nvar carbonoffsets = ee.Image().float().paint(carbonoffsetscol, 'REP_AREA');\n\nMap.addLayer(carbonoffsets, visParams, 'Existing carbon projects area');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-CARBON-OFFSET-PROJECTS
"},{"location":"projects/carbon_projects/#license","title":"License","text":"This dataset is made available under Creative Commons Attribution 4.0 International license.
Keywords: carbon, carbon offsets, NBS, climate change, Nature-based climate solutions, Carbon offsets, Geospatial boundaries, Georeferencing,Forest carbon
Curated in GEE by: Filipe Silveiran and Samapriya Roy
Last updated : 2024-09-07
"},{"location":"projects/cc/","title":"Chesapeake Bay High Resolution Land Cover Dataset (2013-2014)","text":"This raster dataset was developed as part of the Land Cover Project, a cooperative agreement between the Chesapeake Conservancy and the National Park Service funded through an interagency agreement with the Environmental Protection Agency. Virginia Geographic Information Network (VGIN) coordinated with Worldview Solutions the creation of a separate VA statewide high-resolution land cover dataset, which has unique class names and descriptions. For the purposes of a matching bay-wide dataset, this VA dataset was reclassified and some classes were edited to better match the Chesapeake Bay Dataset class definitions below.
High Resolution mapping was used to develop consistent and extremely accurate land cover dataset for all the counties that comprise the Chesapeake Bay watershed. This land cover was created based on 2014 National Agriculture Imagery Program (NAIP) aerial imagery.
Dataset created and developed by the Conservation Innovation Center at the Chesapeake Conservancy. The creation of this dataset was made possible as a result of a cooperative agreement between the Chesapeake Conservancy and the National Park Service being funded through an interagency agreement with the Environmental Protection Agency. This dataset will enhance the ability to guide the most efficient use of resources in the Bay as well as aid the identification of priorities for conservation and restoration. Created based on 2013/2014 National Agriculture Imagery Program (NAIP) aerial imagery. You can find additional information here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/cc/#citation","title":"Citation","text":"Conservation Innovation Center at the Chesapeake Conservancy. Chesapeake Bay High Resolution Land Cover Dataset (2013-2014). Accessed [Month\nYear] at https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/\nvar chesapeake = ee.Image(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/Baywide_13Class_20132014\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CHESEPEAKE_BAY_2013-2014
"},{"location":"projects/cc/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. The organizations responsible for generating and funding this dataset make no representations of any kind including, but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data. Although every effort has been made to ensure the accuracy of information, errors may be reflected in data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors.
Produced by: Conservation Innovation Center at the Chesapeake Conservancy
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, Chesapeake Bay
Last updated on GEE: 2022-06-12
"},{"location":"projects/ccap_lc/","title":"C-CAP High-Resolution Land Cover","text":"The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats. The timeframe for this metadata is summer 2016. These maps are developed utilizing high resolution National Agriculture Imagery Program (NAIP) imagery, and can be used to track changes in the landscape through time. This trend information gives important feedback to managers on the success or failure of management policies and programs and aid in developing a scientific understanding of the Earth system and its response to natural and human-induced changes. This understanding allows for the prediction of impacts due to these changes and the assessment of their cumulative effects, helping coastal resource managers make more informed regional decisions. NOAA C-CAP is a contributing member to the Multi-Resolution Land Characteristics consortium and C-CAP products are included as the coastal expression of land cover within the National Land Cover Database.
These detailed products bring NOAA\u2019s national land cover mapping framework to the local level and are developed for specific project-based geographies (not the entire coastal land cover mapping boundary). Data are often developed in partnership with state and local groups. Attributes for this product are as follows: 0 Background, 1 Unclassified (Cloud, Shadow, etc), 2 Impervious, 3 4 5 Developed Open Space, 6 Cultivated Land, 7 Pasture/Hay, 8 Grassland, 9 Deciduous Forest, 10 Evergreen Forest, 11 Mixed Forest, 12 Scrub/Shrub, 13 Palustrine Forested Wetland, 14 Palustrine Scrub/Shrub Wetland, 15 Palustrine Emergent Wetland, 16 Estuarine Forested Wetland, 17 Estuarine Scrub/Shrub Wetland, 18 Estuarine Emergent Wetland, 19 Unconsolidated Shore, 20 Bare Land, 21 Open Water, 22 Palustrine Aquatic Bed, 23 Estuarine Aquatic Bed, 24 Tundra, 25 Snow/Ice, Recommended Citation. NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database.
This dataset was created by NOAA's Ocean Service, Office for Coastal Management (OCM). Random Forest Classification: The initial 1m spatial resolution 6 class high resolution land cover product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. You can read a sample metadata file here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ccap_lc/#citation","title":"Citation","text":"National Oceanic and Atmospheric Administration, Office for Coastal Management. \u201cName of Data Set.\u201d Coastal Change Analysis Program (C-\nCAP) High-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed [Month Year] at www.coast.noaa.gov/\nhtdata/raster1/landcover/bulkdownload/hires/.\nThe regional land cover dataset files were downloaded for each region. If the files were img then they were convert to GeoTIFF. Each region was converted into a collection and start and end dates were added based on available information and filenames.
"},{"location":"projects/ccap_lc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var CCAP_AS_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_AS_LC\");\nvar CCAP_CA_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_CA_LC\");\nvar CCAP_CT_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_CT_LC\");\nvar CCAP_GU_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_GU_LC\");\nvar CCAP_HI_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_HI_LC\");\nvar CCAP_LA_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_LA_LC\");\nvar CCAP_MA_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_MA_LC\");\nvar CCAP_ME_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_ME_LC\");\nvar CCAP_MP_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_MP_LC\");\nvar CCAP_OH_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_OH_LC\");\nvar CCAP_OR_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_OR_LC\");\nvar CCAP_PR_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_PR_LC\");\nvar CCAP_RI_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_RI_LC\");\nvar CCAP_VI_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_VI_LC\");\nvar CCAP_WA_LC = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/CCAP_WA_LC\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-HRLC-HI
"},{"location":"projects/ccap_lc/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.
Produced by: NOAA's Ocean Service, Office for Coastal Management (OCM)
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, OBIA, NOAA
Last updated on GEE: 2022-06-12
"},{"location":"projects/ccap_mlc/","title":"C-CAP Medium-Resolution Land Cover Beta","text":"The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats. These maps are developed utilizing high resolution National Agriculture Imagery Program (NAIP) imagery, and can be used to track changes in the landscape through time. This trend information gives important feedback to managers on the success or failure of management policies and programs and aid in developing a scientific understanding of the Earth system and its response to natural and human-induced changes. This understanding allows for the prediction of impacts due to these changes and the assessment of their cumulative effects, helping coastal resource managers make more informed regional decisions. NOAA C-CAP is a contributing member to the Multi-Resolution Land Characteristics consortium and C-CAP products are included as the coastal expression of land cover within the National Land Cover Database.
These data should be considered to be BETA-level or draft products. They are based on 1-meter land cover mapping that were entirely automated and the relationship of those data to existing wetlands data. As such, there may be issues that result from the different vintages of these products, as well as the errors included in each. While not perfect, the data should provide an example of what level of detail would be possible through such higher-resolution mapping. These data are not jurisdictional or intended for use in litigation. NOAA does not assume liability for any damages or misrepresentations caused by inaccuracies in the data, or as a result of the data used on a particular system. NOAA makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.
These detailed products bring NOAA\u2019s national land cover mapping framework to the local level and are developed for specific project-based geographies (not the entire coastal land cover mapping boundary). Data are often developed in partnership with state and local groups. Attributes for this product are as follows: 0 Background, 1 Unclassified (Cloud, Shadow, etc), 2 Impervious, 3 4 5 Developed Open Space, 6 Cultivated Land, 7 Pasture/Hay, 8 Grassland, 9 Deciduous Forest, 10 Evergreen Forest, 11 Mixed Forest, 12 Scrub/Shrub, 13 Palustrine Forested Wetland, 14 Palustrine Scrub/Shrub Wetland, 15 Palustrine Emergent Wetland, 16 Estuarine Forested Wetland, 17 Estuarine Scrub/Shrub Wetland, 18 Estuarine Emergent Wetland, 19 Unconsolidated Shore, 20 Bare Land, 21 Open Water, 22 Palustrine Aquatic Bed, 23 Estuarine Aquatic Bed, 24 Tundra, 25 Snow/Ice, Recommended Citation. NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database.
This dataset was created by NOAA's Ocean Service, Office for Coastal Management (OCM). Random Forest Classification: The initial 1m spatial resolution 6 class high resolution land cover product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. You can read a sample metadata file here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ccap_mlc/#citation","title":"Citation","text":"National Oceanic and Atmospheric Administration, Office for Coastal Management. \u201cName of Data Set.\u201d Coastal Change Analysis Program (C-\nCAP) 10m-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed [Month Year] at www.coast.noaa.gov/\nhtdata/raster1/landcover/bulkdownload/hires/.\nNational Oceanic and Atmospheric Administration, Office for Coastal Management. \u201cName of Data Set.\u201d Coastal Change Analysis Program (C-\nCAP) 30m-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed [Month Year] at www.coast.noaa.gov/\nhtdata/raster1/landcover/bulkdownload/hires/.\nThe regional land cover dataset files were downloaded for each region. If the files were img then they were convert to GeoTIFF. Each region was converted into a collection and start and end dates were added based on available information and filenames.
"},{"location":"projects/ccap_mlc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var CCAP_LC10 = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/ccap_10m\");\nvar CCAP_LC30 = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/ccap_30m\");\nSample Code LC: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-LC-BETA
var CCAP_IMP30 = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/ccap_30m_impervious\");\nSample Code Impervious: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-IMPERVIOUS
"},{"location":"projects/ccap_mlc/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.
Produced by: NOAA's Ocean Service, Office for Coastal Management (OCM)
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, OBIA, NOAA
Last updated on GEE: 2022-06-12
"},{"location":"projects/ccap_wpotential/","title":"C-CAP Wetland Potential 30m","text":"The probability rating which covers landcover mapping provides a continuum of wetness from dry to water. The layer is not a wetland classification but provides the wetland likelihood at a specific location. The rating was developed through a modelling process combining multiple GIS and remote sensing data sets including soil characteristics, elevation, existing wetland inventories, hydrographical extents and satellite imagery . Data can be downloaded here. This classification is based on GIS and remote sensing data sets with variable ranges from the 1977 to 2010.
This dataset was created by NOAA's Ocean Service, Office for Coastal Management Initial Classification: 1m spatial resolution land cover data developed by the Chesapeake Bay Conservancy, University of Vermont Spatial Analysis Laboratory, and The Virginia Geographic Information Network (VGIN) was the starting point for this dataset. This product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework applied to NAIP imagery and Lidar data. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. The resulting objects are the primary units for analysis. The original dataset can be downloaded here:https://chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/.
The resulting 1-meter land cover was then resampled to a 10-meter raster. This was done using a 10x10 focal pixel window to compute the percent value associated with either the impervious or tree classes, for each land cover type in the 100 pixel neighborhood around each target pixel, and subsequently performing a nearest neighbor resampling of the resulting values. The output values were then coded to the appropriate percentage (between 0 and 100). This 10-meter raster was resampled a second time, using the average of values within a 3x3 focal pixel window in order to obtain the appropriate values over each 30-meter pixel area. Output values between 0 and 100 represent the appropriate percentage mapped within each pixel. Class 127 identifies areas not included in this mapping, or no data areas.
"},{"location":"projects/ccap_wpotential/#class-values","title":"Class values","text":"0: This is the value for nodata. 1: The value indicates there is an extremely low likelihood of wetness. 2-9: The value indicates a likelihood of wetness, where 1 is very unlikely and 9 is highly likely. 10: The value indicates open water.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ccap_wpotential/#citation","title":"Citation","text":"National Oceanic and Atmospheric Administration, Office for Coastal Management. \u201cName of Data Set.\u201d Coastal Change Analysis Program (C-\nCAP) Wetland Potential Layer: NOAA Office for Coastal Management. Accessed [Month Year] at https://coast.noaa.gov/htdata/raster1/landcover/bulkdownload/wetlandpotential/.\nvar ccap_wetland_potential = ee.Image(\"projects/sat-io/open-datasets/NOAA/conus_ccap_wetland_potential\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-WETLAND-POTENTIAL
"},{"location":"projects/ccap_wpotential/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.
Produced by: NOAA's Ocean Service, Office for Coastal Management (OCM)
Curated in GEE by: Samapriya Roy
Keywords: Wetland, Coastal data, NOAA, Remote Sensing
Last updated on GEE: 2022-05-17
"},{"location":"projects/cci_agb/","title":"ESA CCI Global Forest Above Ground Biomass","text":"This dataset provides estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019, and 2020. These estimates are derived from a combination of Earth observation data, depending on the year, obtained from the Copernicus Sentinel-1 mission, Envisat's ASAR instrument, and JAXA's Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from other Earth observation sources. The dataset has been generated as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) program by the Biomass CCI team.
The dataset includes multi-temporal observations at L-band for all biomes and for each year. The above-ground biomass (AGB) maps utilize revised allometries, which are now based on a more extensive collection of spaceborne LiDAR data from the GEDI and ICESat-2 missions. The retrieval algorithm now incorporates temporal information to capture and preserve biomass dynamics as expressed in the remote sensing data.
The data products consist of two (2) global layers that include estimates of:
Above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots
Per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/cci_agb/#dataset-preprocessing-for-gee","title":"Dataset preprocessing for GEE","text":"Santoro, M.; Cartus, O. (2023): ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years\n2010, 2017, 2018, 2019 and 2020, v4. NERC EDS Centre for Environmental Data Analysis, 21 April 2023. doi:10.5285/af60720c1e404a9e9d2c145d2b2ead4e.\nhttps://dx.doi.org/10.5285/af60720c1e404a9e9d2c145d2b2ead4e\nvar agb = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA/ESA_CCI_AGB\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/ESA-CCI-ABOVEGROUND-BIOMASS
"},{"location":"projects/cci_agb/#license-and-access","title":"License and Access","text":"Use of these data is covered by the license information found here. The CCI BIOMASS datasets have been processed by the CCI BIOMASS consortium led by the University of Aberystwyth (U.K.). They are made available to the public by ESA and the consortium. When using these data you must cite them correctly using the citation given on the catalogue record. The dataset is under a public access with access to these data available to both registered and non-registered users
Created by: CCI BIOMASS consortium led by the University of Aberystwyth (U.K.)
Curated in GEE by: Samapriya Roy
Keywords: satellite observation, forest, biomass
Created: 2023-02-07
Last updated in GEE: 2023-07-02
"},{"location":"projects/cci_lc/","title":"CCI LAND COVER S2 PROTOTYPE LAND COVER 20M MAP OF AFRICA 2016","text":"The CCI Land Cover (LC) S2 prototype is a high resolution LC map at 20m over Africa based on 1 year of Sentinel-2A observations from December 2015 to December 2016. The main objective of the 'S2 prototype LC map at 20m of Africa 2016' release was to collect users feedback for further improvements. The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.
The legend of the S2 prototype LC 20m map of Africa 2016 was built after reviewing various existing typologies (e.g. LCCS, LCML\u2026), global (e.g. GLC-share, GlobeLand30) and national experiences (Africover, SERVIR-RMCD). The legend includes 10 generic classes that appropriately describe the land surface at 20m: \"trees cover areas\", \"shrubs cover areas\", \"grassland\", \"cropland\", \"vegetation aquatic or regularly flooded\", \"lichen and mosses / sparse vegetation\", \"bare areas\", \"built up areas\", \"snow and/or ice\" and \"open water\".
Among the Land Cover classes, two of them were largely identified thanks to external dataset: the \"open water\" class was based on the Global Surface Water product from JRC/EC and the \"urban areas\" relied both on the Global Human Settlement Layer from JRC/EC and on the Global Urban Footprint from DLR. Two classification algorithms, the Random Forest (RF) and Machine Learning (ML), were chosen to transform the cloud-free reflectance composites generated by the pre-processing module into a land cover map. The two maps resulting from both approaches are then combined either to select the best representation of a land cover class which will be part of the final S2 prototype LC 20m map of Africa 2016 or, in case of unreliable LC class delineation, the reference layer is used to consolidate the land cover classification.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/cci_lc/#earth-engine-snippet","title":"Earth Engine snippet","text":"var esa_cci = ee.Image(\"projects/sat-io/open-datasets/ESA/ESACCI-LC-L4-LC10-Map-20m-P1Y-2016-v10\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCI-LC-20M-AFRICA
"},{"location":"projects/cci_lc/#license-and-terms-of-use","title":"License and Terms of Use","text":"The present product is made available to the public by ESA and the consortium. You may use S2 prototype LC 20m map of Africa 2016 for educational and/or scientific purposes, without any fee on the condition that you credit the ESA Climate Change Initiative and in particular its Land Cover project as the source of the CCI-LC database:
Copyright notice:\n\u00a9 Contains modified Copernicus data (2015/2016)\n\u00a9 ESA Climate Change Initiative - Land Cover project 2017\nBy downloading the prototype product you recognize that this prototype is not a final product and you are aware of the consequences of using a prototype that has not been validated. By downloading the prototype product, you also commit to fill the User Feedback Form (see below). Should you write any scientific publication on the results of research activities that use one or several CCI-LC products as input, you shall acknowledge the ESA CCI Land Cover project in the text of the publication and provide the project with an electronic copy of the publication (due@esa.int).
If you wish to use S2 prototype LC 20m map of Africa 2016 in advertising or in any commercial promotion, you shall acknowledge the ESA CCI Land Cover project and you must submit the layout to the project for approval beforehand (due@esa.int).
Created by: ESA
Curated in GEE by : Samapriya Roy
keywords: Landcover, Sentinel-2 Landcover, ESA, Africa Landcover
Last updated on GEE: 2023-02-27
"},{"location":"projects/cems_fire/","title":"CEMS Fire Danger Indices","text":"Fire danger indices from the ECMWF, calculated using weather forecasts from historical simulations provided by ECMWF ERA5 reanalysis.The CEMS Fire Danger Indices dataset provides a comprehensive set of indices designed to assess and quantify fire danger and wildfire risk at regional and global scales. You can get additional information on the dataset here and on the climateengine.org dataset page here
Build-up Index: The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes.
Burning Index: The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same.
Drought Code: The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800.
Drought Factor: The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum
Duff Moisture Code: The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150.
Energy Release Component: The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential \"heat release\" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or \"build-up\" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative.
Fine Fuel Moisture Code: The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96.
Fire Daily Severity Rating: Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value.
Fire Danger Index: The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation
Fire Weather Index:The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions.
Ignition Component: The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions.
Initial Fire Spread Index: The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1.
Keetch-Byram Drought Index: The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation.
Spread Component: The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit.
Spatial Extent Global Spatial Resolution ~25km (0.25 deg) Temporal Resolution Daily Time Span 1940-01-01 to Present Update Frequency Updated daily with one week lag
Variables Build-up Index ('build_up_index') - Units: Dimensionless - Scale Factor: 1.0 Burning Index ('burning_index') - Units: Dimensionless - Scale Factor: 1.0 Drought Code ('drought_code') - Units: Dimensionless - Scale Factor: 1.0 Drought Factor ('drought_factor') - Units: Dimensionless - Scale Factor: 1.0 Duff Moisture Code ('duff_moisture_code') - Units: Dimensionless - Scale Factor: 1.0 Energy Release Component ('energy_release_component') - Units: J/m2 - Scale Factor: 1.0 Fine Fuel Moisture Code ('fine_fuel_moisture_code') - Units: Dimensionless - Scale Factor: 1.0 Fire Daily Severity Rating ('fire_daily_severity_rating') - Units: Dimensionless - Scale Factor: 1.0 Fire Danger Index ('fire_danger_index') - Units: Dimensionless - Scale Factor: 1.0 Fire Weather Index ('fire_weather_index') - Units: Dimensionless - Scale Factor: 1.0 Ignition Component ('ignition_component') - Units: % - Scale Factor: 1.0 Initial Fire Spread Index ('initial_fire_spread_index') - Units: Dimensionless - Scale Factor: 1.0 Keetch-Byram Drought Index ('keetch_byram_drought_index') - Units: Dimensionless - Scale Factor: 1.0 Spread Component ('spread_component') - Units: Dimensionless - Scale Factor: 1.0
"},{"location":"projects/cems_fire/#citation","title":"Citation","text":"Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libert\u00e1, G., & Krzeminski, B. (2020). ERA5-based global\nmeteorological wildfire danger maps. Scientific data, 7(1), 1-11. 'Contains modified Copernicus Climate Change Service information [Year]'\n\nCopernicus Climate Change Service, Climate Data Store, (2019): Fire danger indices historical data from the Copernicus Emergency Management\nService. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.0e89c522 (Accessed on DD-MMM-YYYY)\n// Read in Image Collection and get most recent image\nvar cems_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-cems-fire-daily-4-1')\nvar cems_i = cems_ic.filterDate('2023-01-01', ee.Date(Date.now())).sort('system:time_start', false).first()\n\n// Print first image to see bands\nprint(cems_i)\n\n// Visualize select bands from first image - additional variables are available in the Image Collection\nvar fire_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(cems_i.select('burning_index'), {min: 0, max: 50, palette: fire_palette}, 'burning_index')\nMap.addLayer(cems_i.select('fire_weather_index'), {min: 0, max: 50, palette: fire_palette}, 'fire_weather_index')\nMap.addLayer(cems_i.select('fire_danger_index'), {min: 0, max: 50, palette: fire_palette}, 'fire_danger_index')\nMap.addLayer(cems_i.select('ignition_component'), {min: 0, max: 50, palette: fire_palette}, 'ignition_component')\nMap.addLayer(cems_i.select('spread_component'), {min: 0, max: 10, palette: fire_palette}, 'spread_component')\nMap.addLayer(cems_i.select('energy_release_component'), {min: 0, max: 50, palette: fire_palette}, 'energy_release_component')\nMap.addLayer(cems_i.select('fire_daily_severity_rating'), {min: 0, max: 50, palette: fire_palette}, 'fire_daily_severity_rating')\nThe license for CEMS Fire Danger Indices data is the Copernicus Licence to Use Copernicus Products (the \"Licence\"). The Licence is a modified Creative Commons Attribution 4.0 International (CC BY 4.0) license, with the following additional terms: * You must acknowledge the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF) as the source of the data. * You must not use the data for commercial purposes without prior permission from the European Commission. * You must not modify the data in a way that could mislead the public about its source or accuracy.
Data are subject to the License to Use Copernicus Products found here
Keywords: ECMWF, Copernicus, wildfire, climate, reanalysis, ERA5, daily, near real-time
Provided by : Copernicus
Curated in GEE by: ClimateEngine Org
"},{"location":"projects/cflux/","title":"Global Forest Carbon Fluxes (2001-2023)","text":"Net forest carbon flux represents the net exchange of carbon between forests and the atmosphere between 2001 and 2023, calculated as the balance between carbon emitted by forests and removed by (or sequestered by) forests during the model period (megagrams CO2 emissions/ha). Net carbon flux is calculated by subtracting average annual gross removals from average annual gross emissions in each modeled pixel; negative values are where forests were net sinks of carbon and positive values are where forests were net sources of carbon between 2001 and 2023. Net fluxes are calculated following IPCC Guidelines for national greenhouse gas inventories in each pixel where forests existed in 2000 or were established between 2000 and 2012 according to the Global Forest Change tree cover change data of Hansen et al. (2013). This layer reflects the cumulative net flux during the model period (2001-2023) and must be divided by 23 to obtain average annual net flux; net flux values cannot be assigned to individual years of the model.
Forest carbon removals from the atmosphere (sequestration) by forest sinks represent the cumulative carbon captured (megagrams CO2/ha) by the growth of established and newly regrowing forests during the model period between 2001-2023. Removals include accumulation of carbon in both aboveground and belowground live tree biomass. Following IPCC Tier 1 assumptions for forests remaining forests, removals by dead wood, litter, and soil carbon pools are assumed to be zero. In each pixel, carbon removals are calculated following IPCC Guidelines for national greenhouse gas inventories where forests existed in 2000 or were established between 2000 and 2012 according to the Global Forest Change tree cover loss data of Hansen et al. (2013). Carbon removed by each pixel is based on maps of forest type (e.g., mangrove, plantation), ecozone (e.g., humid Neotropics), forest age (e.g., primary, old secondary), and number of years of carbon removal. This layer reflects the cumulative removals during the model period (2001-2023) and must be divided by 23 to obtain an annual average during the model duration; removal rates cannot be assigned to individual years of the model.
Forest carbon emissions represent the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modeled year (megagrams CO2 emissions/ha, between 2001 and 2022). Emissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil) and greenhouse gases (CO2, CH4, N2O). Emissions estimates for each pixel are calculated following IPCC Guidelines for national greenhouse gas inventories where stand-replacing disturbance occurred, as mapped in the Global Forest Change annual tree cover loss data of Hansen et al. (2013). The carbon emitted from each pixel is based on carbon densities in 2000, with adjustment for carbon accumulated between 2000 and the year of disturbance. Emissions reflect a gross estimate, i.e., carbon removals from subsequent regrowth are not included. Instead, gross carbon removals resulting from subsequent regrowth after clearing are accounted for in the companion forest carbon removals layer. The fraction of carbon emitted from each pixel upon disturbance (emission factor) is affected by several factors, including the direct driver of disturbance, whether fire was observed in the year of or preceding the observed disturbance event, whether the disturbance occurred on peat, and more. All emissions are assumed to occur in the year of disturbance. Emissions can be assigned to a specific year using the Hansen tree cover loss data.
All three layers are part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist governments and non-government actors with tracking greenhouse gas fluxes from forests and decreasing emissions or increasing removals by forests. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). Please also find the dataset on Global Forest Watch
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/cflux/#dataset-updates","title":"Dataset updates","text":"Each year, the tree cover loss, drivers of tree cover loss, and burned area are updated. In 2023 and 2024, a few model input data sets and constants were changed as well, as described below. Please refer to this blog post for more information.
"},{"location":"projects/cflux/#citation","title":"Citation","text":"Harris, N.L., Gibbs, D.A., Baccini, A. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234\u2013240 (2021).\nhttps://doi.org/10.1038/s41558-020-00976-6\nvar emissions = ee.ImageCollection(\"projects/sat-io/open-datasets/forest_carbon_fluxes/gross_emissions\");\nvar removals = ee.ImageCollection(\"projects/sat-io/open-datasets/forest_carbon_fluxes/gross_removals\");\nvar net_flux = ee.ImageCollection(\"projects/sat-io/open-datasets/forest_carbon_fluxes/net_flux\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FOREST-CARBON-FLUXES
"},{"location":"projects/cflux/#license","title":"License","text":"The Global Forest Carbon Fluxes (2001-2023) products are provided free of charge, without restriction of use. For the full license information see the Creative Commons Attribution 4.0 International License publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.
Created by: Harris, N.L., Gibbs, D.A., Baccini, A. et al
Curated in GEE by: Samapriya Roy
Keywords: Carbon emissions, forest change, climate, carbon
Last updated on GEE: 2024-06-06
"},{"location":"projects/cflux/#changelog","title":"Changelog","text":"The Climate Hazards Center InfraRed Precipitation With Station Data-Prelim (CHIRPS-Prelim) is a blend of CHIRPS data with in situ precipitation data to unbias the data and enhance its accuracy. The process of generating CHIRPS- Prelim is similar to the CHIRPS process, with the main difference being its reliance on Global Telecommunication System (GTS) stations only, which are available in near-real time. Blending of CHIRP with GTS-only stations allows for the latency of CHIRPS- Prelim to be <5 days. Note that, in general, the differences in CHIRPS-Prelim and CHIRPS are within acceptable limits, as both data sets share the same climatological mean. You can find additional information here and on climate org dataset page here.
This dataset is to be used in conjunction with CHIRPS Pentad/Daily collections, which are Earth Engine assets at: - UCSB-CHG/CHIRPS/PENTAD
Spatial Information
Parameter Value Spatial extent Global Spatial resolution 4.8-km grid (1/20 deg) Temporal resolution 5-day (pentad) Time span 2015 to present Update frequency Updated weeklyVariables
Variable Details Precipitation ('precipitation') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/chirps_prelim/#citation","title":"Citation","text":"Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.\nP., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p.,\nhttp://dx.doi.org/10.3133/ds832\n// Read in Image Collections and get single image\nvar chirps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-chirps-prelim-pentad')\nvar chirps_i = chirps_ic.first()\n\n// Print single image to see bands\nprint(chirps_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(chirps_i.select('precipitation'), {min: 0, max: 200, palette: prec_palette}, 'precipitation')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CHIRPS-PRELIM
"},{"location":"projects/chirps_prelim/#license","title":"License","text":"This datasets are in the public domain. To the extent possible under law, Pete Peterson has waived all copyright and related or neighboring rights to Climate Hazards Group Infrared Precipitation with Stations (CHIRPS).
Keywords: precipitation, near real-time, climate, CHIRPS
Provided by: Climate Hazards Group Infrared Precipitation with Stations (CHIRPS)
Curated in GEE by: Climate Engine org
"},{"location":"projects/cisi/","title":"Harmonized Global Critical infrastructure & Index (CISI)","text":"Critical infrastructure (CI) is fundamental for the functioning of a society and forms the backbone for socio-economic development. Natural and human-made threats, however, pose a major risk to CI. Therefore, geospatial data on the location of CI are fundamental for in-depth risk analyses, which are required to inform policy decisions aiming to reduce risk. We present a first-of-its-kind globally harmonized spatial dataset for the representation of CI.
In this study the users generated: (1) a harmonized detailed geospatial data of the world\u2019s main CI systems into a single geospatial database; and (2) a Critical Infrastructure Spatial Index (CISI) to express the global spatial intensity of CI. The datasets are generated from Open Streetmap extract from 8th January 2021 using https://planet.openstreetmap.org/. You can read the full paper here. You can download the spatial extracts for both the feature type and the Critical Infrastructure Spatial Index (CISI) here
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/cisi/#paper-citation","title":"Paper citation","text":"Nirandjan, S., Koks, E.E., Ward, P.J. et al. A spatially-explicit harmonized global dataset of critical infrastructure. Sci Data 9, 150 (2022).\nhttps://doi.org/10.1038/s41597-022-01218-4\nNirandjan, Sadhana, Koks, Elco E., Ward, Philip J., & Aerts, Jeroen C.J.H. (2021). A spatially-explicit harmonized global dataset of critical infrastructure (v1.0.0.)\n[Data set]. Zenodo. https://doi.org/10.5281/zenodo.4957647\nvar global_CISI = ee.Image(\"projects/sat-io/open-datasets/CISI/global_CISI\");\nvar infrastructure = ee.ImageCollection(\"projects/sat-io/open-datasets/CISI/amount_infrastructure\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/CRITICAL-INF-SPATIAL-INDEX(CISI)
"},{"location":"projects/cisi/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International license.
Produced by : Nirandjan, S., Koks, E.E., Ward, P.J. et al
Curated in GEE by : Samapriya Roy
Keywords: : Development indicator, global spatial data, gridded data, critical infrastructure, spatial index
Last updated on GEE: 2022-05-12
"},{"location":"projects/climate_trace/","title":"Climate Trace Global Emissions Data","text":"Climate TRACE is a non-profit coalition that has unveiled an open emissions database containing more than 352 million assets. The database provides a comprehensive accounting of greenhouse gas (GHG) emissions based primarily on direct, independent observation. It includes every country and territory in the world and covers various emitting activities such as energy production, industrial processes, and land use. The data are derived from satellites, remote sensing, and other public and commercial sources, making it the most comprehensive and granular dataset of recent GHG emissions ever created. The inventory allows for transparent assessment of each country's progress toward emission reduction goals.
For more detailed information, you can visit the Climate TRACE website at climatetrace.org.
"},{"location":"projects/climate_trace/#dataset-preprocessing","title":"Dataset preprocessing","text":"The downloaded datasets were processed to get system:time_start and system:time_end in epoch time and these were added to the data column in GEE. Each sector and their associated emissions dataset sources were processed providing 38,731,650 total features. Not all sectors had emissions locations and some were provided only at a country level.
"},{"location":"projects/climate_trace/#citation","title":"Citation","text":"Climate TRACE - Tracking Realtime Atmospheric Carbon Emissions (2022), Climate TRACE Emissions Inventory,\nhttps://climatetrace.org [Date Accessed].\nFor individual sectors refer to citation information from the downloads page.
"},{"location":"projects/climate_trace/#metadata-descriptors","title":"Metadata Descriptors","text":"Expand to show data attributes and definitions for the emissions databaseData-attribute Definition source_id The internal Climate TRACE identifier for each individual source of emissions. source_name Name of the entity or source that produced the emissions. source_type Description of the emission source classification. iso3_country Corresponds to the ISO 3166-1 alpha-3 specification of the country where the entity is physically located. original_inventory_sector Intergovernmental Panel on Climate Change (IPCC) emissions sector to which the emissions source belongs. start_time The time using Coordinated Universal Time (UTC) of emissions, either as an instance of start time of observation. end_time The time using Coordinated Universal Time (UTC) of emissions, either as an instance of end time of observation. lat Approximate latitude location of the source. lon Approximate longitude location of the source. geometry_ref Corresponds to the reference id to the geopackage file present in the downloads. gas Greenhouse gases for which emissions are reported in metric tonnes. emissions_quantity Quantity of gas emitted in metric tonnes. temporal_granularity Resolution of the data available. activity Activity of the entity producing the emissions, not including units. activity_units Units of reported \"activity\". emissions_factor Emissions factor of reported activity. emissions_factor_units Units of reported \"emissions factor\" field. capacity Capacity of the entity producing emissions, not including units. capacity_units Units of reported \"capacity\" field. capacity_factor Corresponds to the ratio of the actual source output (activity) to the source capacity. capacity_factor_units Units of repored \"capacity_factor\" field. other1 Additional data field available for the sub-sector. other1_def Definition of reported data of Other1 field. other2 Additional data field available for the sub-sector. other2_def Definition of reported data of Other2 field. other3 Additional data field available for the sub-sector. other3_def Definition of reported data of Other3 field. other4 Additional data field available for the sub-sector. other4_def Definition of reported data of Other4 field. other5 Additional data field available for the sub-sector. other5_def Definition of reported data of Other5 field. other6 Additional data field available for the sub-sector. other6_def Definition of reported data of Other6 field. other7 Additional data field available for the sub-sector. other7_def Definition of reported data of Other7 field. other8 Additional data field available for the sub-sector. other8_def Definition of reported data of Other8 field. other9 Additional data field available for the sub-sector. other9_def Definition of reported data of Other9 field. other10 Additional data field available for the sub-sector. other10_def Definition of reported data of Other10 field. created_date Date emissions source was added to the Climate TRACE database. modified_date Last date on which any updates were made to the dataset for the specific source.
"},{"location":"projects/climate_trace/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aluminum = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/aluminum_emissions-sources\");\nvar bauxiteMining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/bauxite-mining_emissions-sources\");\nvar cement = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/cement_emissions-sources\");\nvar chemicals = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/chemicals_emissions-sources\");\nvar coalMining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/coal-mining_emissions-sources\");\nvar copperMining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/copper-mining_emissions-sources\");\nvar croplandFires = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/cropland-fires_emissions-sources\");\nvar domesticAviation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/domestic-aviation_emissions-sources\");\nvar domesticShipping = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/domestic-shipping_emissions-sources\");\nvar electricityGeneration = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/electricity-generation_emissions-sources\");\nvar entericFermentationCattleFeedlot = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/enteric-fermentation-cattle-feedlot_emissions-sources\");\nvar entericFermentationCattlePasture = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/enteric-fermentation-cattle-pasture_emissions-sources\");\nvar forestLandClearing = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/forest-land-clearing_emissions-sources\");\nvar forestLandDegradation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/forest-land-degradation_emissions-sources\");\nvar forestLandFires = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/forest-land-fires_emissions-sources\");\nvar internationalAviation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/international-aviation_emissions-sources\");\nvar internationalShipping = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/international-shipping_emissions-sources\");\nvar ironMining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/iron-mining_emissions-sources\");\nvar manureLeftOnPastureCattle = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/manure-left-on-pasture-cattle_emissions-sources\");\nvar manureManagementCattleFeedlot = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/manure-management-cattle-feedlot_emissions-sources\");\nvar netForestLand = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/net-forest-land_emissions-sources\");\nvar netShrubgrass = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/net-shrubgrass_emissions-sources\");\nvar netWetland = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/net-wetland_emissions-sources\");\nvar oilAndGasRefining = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/oil-and-gas-refining_emissions-sources\");\nvar otherManufacturing = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/other-manufacturing_emissions-sources\");\nvar petrochemicals = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/petrochemicals_emissions-sources\");\nvar pulpAndPaper = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/pulp-and-paper_emissions-sources\");\nvar removals = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/removals_emissions-sources\");\nvar riceCultivation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/rice-cultivation_emissions-sources\");\nvar roadTransportation = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/road-transportation_emissions-sources\");\nvar shrubgrassFires = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/shrubgrass-fires_emissions-sources\");\nvar solidWasteDisposal = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/solid-waste-disposal_emissions-sources\");\nvar steel = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/steel_emissions-sources\");\nvar syntheticFertilizerApplication = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/synthetic-fertilizer-application_emissions-sources\");\nvar wastewaterTreatmentAndDischarge = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/wastewater-treatment-and-discharge_emissions-sources\");\nvar waterReservoirs = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/water-reservoirs_emissions-sources\");\nvar wetlandFires = ee.FeatureCollection(\"projects/sat-io/open-datasets/CLIMATE-TRACE/EMISSIONS/wetland-fires_emissions-sources\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/CLIMATE-TRACE-EMISSIONS
"},{"location":"projects/climate_trace/#license","title":"License","text":"All Climate TRACE data is freely available under the Creative Commons Attribution 4.0 International Public License, unless otherwise noted below
Created by: Climate Trace
Curated in GEE by: Samapriya Roy
Keywords: Climate Trace, Emissions, Sectors, Agriculture, Buildings, Fossil Fuel Operations, Forestry And Land Use, Manufacturing, Mineral Extraction, Power, Transportation, Waste
Last updated: 2024-01-17
"},{"location":"projects/cloudsen12/","title":"CloudSEN12 Global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2","text":"CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs) that consists of 49,400 image patches (IP) that are evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge cloud detection algorithms. Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates that match one of the following cloud cover groups:
clear (0%)
low-cloudy (1% - 25%)
almost clear (25% - 45%)
mid-cloudy (45% - 65%)
cloudy (65% >)
The dataset is available here. For more details check out the website and you can read the preprint of the paper here
"},{"location":"projects/cloudsen12/#data-citation","title":"Data Citation","text":"Aybar, C. et al. CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.\nScience Data Bank https://doi.org/10.57760/sciencedb.06669 (2022).\nAybar, C., Ysuhuaylas, L., Loja, J. et al. CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.\nSci Data 9, 782 (2022). https://doi.org/10.1038/s41597-022-01878-2\nCurrently included layers are:
"},{"location":"projects/cloudsen12/#earth-engine-snippet-hand-crafted-labels-high-quality","title":"Earth Engine Snippet: Hand-crafted labels - high-quality","text":"var cs12_high = ee.ImageCollection(\"projects/sat-io/open-datasets/cloudsen12/high\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-HIGH-QUALITY
"},{"location":"projects/cloudsen12/#earth-engine-snippet-hand-crafted-labels-scribble","title":"Earth Engine Snippet: Hand-crafted labels - scribble","text":"var cs12_scribble = ee.ImageCollection(\"projects/sat-io/open-datasets/cloudsen12/scribble\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-SCRIBBLE-QUALITY
"},{"location":"projects/cloudsen12/#earth-engine-snippet-hand-crafted-labels-nolabel","title":"Earth Engine Snippet: Hand-crafted labels - nolabel","text":"var cs12_nolabel = ee.ImageCollection(\"projects/sat-io/open-datasets/cloudsen12/nolabel\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-NO-LABEL
"},{"location":"projects/cloudsen12/#earth-engine-snippet-ips-footprint","title":"Earth Engine Snippet: IPs footprint","text":"var cs12_geom = ee.ImageCollection(\"projects/sat-io/open-datasets/cloudsen12/footprint\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-FOOTPRINT
"},{"location":"projects/cloudsen12/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated in GEE by: Samapriya Roy
Keywords: cloud, deep learning, Sentinel-2, Sentinel-1, U-Net
Last updated: 2022-09-18
"},{"location":"projects/cmapper/","title":"Carbon Mapper Data Portal Methane Emissions","text":"NoteThis dataset is currently only available to those in the insiders program
The Carbon Mapper data portal focused on collecting individual high emission methane point sources. The Carbon mapper portal provides methane plume imagery with emission rates and uncertainties from strong point sources as observed from NASA\u2019s next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and ASU\u2019s Global Airborne Observatory (GAO) airborne platforms.
As per the portal, these systems have near-identical capabilities and serve as prototypes for future sustained global monitoring. The plume concentration maps are available at spatial resolutions ranging from 3 to 8 meters (depending on aircraft altitude), allowing for precise attribution to individual emission sources.The API end point was used to query the overall area over North America yielding 8327 total sites.
You can use the download button too to download the curated zipped plume and geotiff data if you are inclined to use those. You can read their FAQ here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/cmapper/#citation","title":"Citation","text":"As per the portal the following papers were used to generate the portal and the user should use the appropriate ones as deemed fit
Multi-basin analysis: San Joaquin, Permian, Uinta, Denver-Julesburg, Marcellus (Data from 2020-2021)\n\nCusworth, D. H., Thorpe, A. K., Ayasse, A. K., Stepp, D., Heckler, J., Asner, G. P., Miller, C. E., Chapman, J. W., Eastwood, M. L., Green, R. O.,\nHmiel, B., Lyon, D., & Duren, R. M. (2022). Strong methane point sources contribute a disproportionate fraction of total emissions across multiple\nbasins in the U.S. PNAS. https://www.pnas.org/doi/10.1073/pnas.2202338119\n\nGulf of Mexico offshore platforms (Data from 2021)\nAyasse, A. K., Thorpe, A. K., Cusworth, D. H., Kort, E. A., Negron, A. G., Heckler, J., Asner, G., & Duren, R. M. (2022). Methane remote sensing and\nemission quantification of offshore shallow water oil and gas platforms in the Gulf of Mexico. Environmental Research Letters, 17(8), 084039.\nhttps://doi.org/10.1088/1748-9326/ac8566\n\nPermian point-source data (Data from 2019)\nCusworth, D. H., Duren, R. M., Thorpe, A. K., Olson-Duvall, W., Heckler, J., Chapman, J. W., Eastwood, M. L., Helmlinger, M. C., Green, R. O.,\nAsner, G. P., Dennison, P. E., & Miller, C. E. (2021). Intermittency of large methane emitters in the Permian Basin. Environmental Science &\nTechnology Letters, 8(7), 567\u2013573. https://doi.org/10.1021/acs.estlett.1c00173\n\nCalifornia methane point-source data (Data from 2016-2017)\nDuren, R. M., Thorpe, A. K., Foster, K. T., Rafiq, T., Hopkins, F. M., Yadav, V., Bue, B. D., Thompson, D. R., Conley, S., Colombi, N. K.,\nFrankenberg, C., McCubbin, I. B., Eastwood, M. L., Falk, M., Herner, J. D., Croes, B. E., Green, R. O., & Miller, C. E. (2019). California\u2019s methane\nsuper-emitters. Nature, 575(7781), 180\u2013184. https://doi.org/10.1038/s41586-019-1720-3\n\nDuren, R., Thorpe, A., & McCubbin, I. (2020). The California Methane Survey Final Report, CEC-500-2020-047. https://ww2.energy.ca.gov/\n2020publications/CEC-500-2020-047/CEC-500-2020-047.pdf\nBased on the Plume extracts that were also available from the data portal certain assumptions and metadata fields were renamed. The campaign-id field was used a plume-id field for all of these observation. While there is a separate plume-id field in the original metadata it seems to be missing for some observations and as such the use of campaign id. This was also to keep alignment with the way in which the plume extracts were created by the data portal itself. Flux rate and Flux uncertainity were also renamed to emission and emission uncertainity.
The plume geotiffs were provided as RGB for rendering to a fixed hex code and color palette, so the metadata was attached to each of these raster geotiff in the collection. The portal also provided the RGB underlying imagery and that too was ingested in a separate collection with the same metadata for easy join. Finally the results were exported into a table, all downloaded for S3 URLs were automated using a custom script I wrote and included in the metadata.
"},{"location":"projects/cmapper/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var plume_geotiffs = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon-mapper/plume_geo\");\nvar rgb_geotiffs = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon-mapper/rgb_geo\");\nvar plume_features = ee.FeatureCollection(\"projects/sat-io/open-datasets/carbon-mapper/plume_feature\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/CARBON-MAPPER-METHANE-EMISSIONS
"},{"location":"projects/cmapper/#license","title":"License","text":"Carbon Mapper data is provided for non-commercial purposes subject to the Modified Creative Commons Attribution ShareAlike 4.0 International Public License (\"CC License\"). All third-party use of the data is subject to the CC license at all times. The license details includes terms for Non commercial use and share alike clauses and you can read through the modified terms.
Provided by: Carbon Mapper, Inc.
Curated in GEE by : Samapriya Roy
keywords: Methane Emissions, AVIRIS-NG, Global Airborne Observatory, Plume Emissions , Carbon Mapper Data Portal
Last updated on GEE: 2023-04-16
"},{"location":"projects/cpc_morph/","title":"Climate Prediction Center (CPC) Morphing Technique (MORPH)","text":"The Satellite Precipitation - CMORPH Climate Data Record (CDR) consists of satellite precipitation estimates that have been bias corrected and reprocessed using the Climate Prediction Center (CPC) Morphing Technique (MORPH) to form a global, high resolution precipitation analysis at a 25-km (\u00bd-deg x \u00bd-deg) spatial resolution updated daily from 1980-present. Data is reprocessed on a global grid with daily temporal resolution. You can get additional information here or on climate engine org page here.
"},{"location":"projects/cpc_morph/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Global Spatial resolution 25-km (\u00bd-deg x \u00bd-deg) Temporal resolution Daily Time span 1998-01-01 to present Update frequency Updated daily with 2-day lagVariables
Variable Details Precipitation ('precip') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/cpc_morph/#citation","title":"Citation","text":"Xie, Pingping; Joyce, Robert; Wu, Shaorong; Yoo, S.-H.; Yarosh, Yelena; Sun, Fengying; Lin, Roger, NOAA CDR Program (2019): NOAA Climate Data Record\n(CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1 [indicate subset].\nNOAA National Centers for Environmental Information.\n// Read in Image Collections and get single image\nvar cmorph_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-cpc-cmorph/daily')\nvar cmorph_i = cmorph_ic.first()\n\n// Print single image to see bands\nprint(cmorph_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(cmorph_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CPC-MORPH
"},{"location":"projects/cpc_morph/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: NOAA, global, precipitation, climate, near real-time, NWS, CPC, MORPH, CMORPH
Dataset provided by: NOAA
Dataset curatd in GEE by: Climate Engine Org
"},{"location":"projects/csb/","title":"USDA Crop Sequence Boundaries 2016-2023","text":"The Crop Sequence Boundaries (CSB), developed in collaboration with the USDA's Economic Research Service, provides estimates of field boundaries, crop acreage, and crop rotations across the contiguous United States. This dataset utilizes satellite imagery along with other public data and is open source, enabling users to conduct area and statistical analysis of planted U.S. commodities. It offers valuable insights into farmer cropping decisions and practices.
NASS required a representative field dataset for predicting crop planting based on common rotations like corn-soy, while the Economic Research Service (ERS) employs the CSB to study changes in farm management practices such as tillage and cover cropping over time. The CSB dataset represents non-confidential single crop field boundaries within a specified timeframe. It does not include personal identifying information, ownership boundaries, or tax parcels. The data is sourced from satellite imagery and publicly available information, excluding contributions from producers or agencies like the Farm Service Agency. For access and further information, you can visit the CSB website.Explore the CSB GitHub repository for the codebase, and review the metadata associated with the dataset.
Crop Sequence Boundaries (CSB) represent geospatial algorithm-generated field polygons, originating from the NASS Cropland Data Layer (CDL). These polygonal entities cater to the demands of applications reliant on gridded datasets, necessitating analytical units for streamlined data aggregation. The primary objective of CSBs is to furnish comprehensive coverage spanning the contiguous 48 United States, ensuring precision and replicability across multiple years. These structures are forged by amalgamating historical CDLs within a specified time frame, while also integrating road and rail networks to accurately depict crop sequences within these simulated fields. The dataset is available for 2016 to 2023 growing seasons.
"},{"location":"projects/csb/#citations","title":"Citations","text":"Hunt, Kevin A., Jonathon Abernethy, Peter Beeson, Maria Bowman, Steven Wallander, and Ryan Williams. \"Crop Sequence Boundaries (CSB): Delineated\nFields Using Remotely Sensed Crop Rotations.\"\n\nAbernethy, Jonathon, Peter Beeson, Claire Boryan, Kevin Hunt, and Luca Sartore. \"Preseason crop type prediction using crop sequence boundaries.\" Computers and Electronics in Agriculture 208 (2023): 107768.\nThe datasets are made available as feature collections in Earth Engine for each state the 1623 reprents the year 2016-2023 growing season. The state names are part of the feature collection name. While it may not be necessary it is possible to merge them into a single collection and I created that for those would want to run some analysis on a combined feature collection.
"},{"location":"projects/csb/#earth-engine-snippet-source","title":"Earth Engine Snippet: Source","text":"var csbal23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBAL1623\");\nvar csbar23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBAR1623\");\nvar csbaz23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBAZ1623\");\nvar csbca23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBCA1623\");\nvar csbco23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBCO1623\");\nvar csbct23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBCT1623\");\nvar csbde23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBDE1623\");\nvar csbga23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBGA1623\");\nvar csbfl23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBFL1623\");\nvar csbia23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBIA1623\");\nvar csbid23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBID1623\");\nvar csbil23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBIL1623\");\nvar csbin23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBIN1623\");\nvar csbks23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBKS1623\");\nvar csbky23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBKY1623\");\nvar csbla23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBLA1623\");\nvar csbma23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMA1623\");\nvar csbmd23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMD1623\");\nvar csbme23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBME1623\");\nvar csbmi23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMI1623\");\nvar csbmn23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMN1623\");\nvar csbmo23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMO1623\");\nvar csbms23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMS1623\");\nvar csbmt23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBMT1623\");\nvar csbne23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNE1623\");\nvar csbnh23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNH1623\");\nvar csbnj23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNJ1623\");\nvar csbnm23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNM1623\");\nvar csbnv23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNV1623\");\nvar csbny23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNY1623\");\nvar csbnc23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBNC1623\");\nvar csbnd23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBND1623\");\nvar csboh23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBOH1623\");\nvar csbok23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBOK1623\");\nvar csbor23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBOR1623\");\nvar csbpa23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBPA1623\");\nvar csbri23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBRI1623\");\nvar csbsc23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBSC1623\");\nvar csbsd23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBSD1623\");\nvar csbtn23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBTN1623\");\nvar csbtx23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBTX1623\");\nvar csbut23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBUT1623\");\nvar csbvt23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBVT1623\");\nvar csbva23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBVA1623\");\nvar csbwa23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBWA1623\");\nvar csbwv23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBWV1623\");\nvar csbwi23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBWI1623\");\nvar csbwy23 = ee.FeatureCollection(\"projects/nass-csb/assets/csb1623/CSBWY1623\");\nvar combined_csb= ee.FeatureCollection('projects/sat-io/open-datasets/USDA/CSB_1623');\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/USDA-CSB-APP
App code: You can find the app here
"},{"location":"projects/csb/#license-and-liability","title":"License and Liability","text":"The USDA NASS Crop Sequence Boundaries and the data offered at https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries are provided to the public as is and are considered public domain and free to redistribute. Users of the Crop Sequence Boundaries (CSB) are solely responsible for interpretations made from these products. The CSB are provided 'as is' and the USDA NASS does not warrant results you may obtain using the data. Contact our staff at (SM.NASS.RDD.GIB@usda.gov) if technical questions arise.
Created by: USDA NASS, USDA ERS
Curated in GEE by : USDA NASS, USDA ERS, Samapriya Roy
keywords: agricultural lands, USDA, crop layer, CDL, crop sequence boundary
Last updated in GEE: 2024-05-25
"},{"location":"projects/csb/#changelog-notes-from-source","title":"Changelog notes from Source","text":"This dataset presents an index developed to assess carbon security across the sagebrush steppe in the Great Basin. The Carbon Security Index (CSI) incorporates data from three key sources: fractional plant cover from the Rangeland Analysis Platform, a fire probability model specific to the Great Basin (Smith et al., 2023), and a resistance and resilience dataset for the sagebrush steppe (Chambers et al., 2014, 2017).
The CSI is calculated as follows:
CSI = Preferred Rangeland Cover Index + Resistance & Resilience \u2013 P(Fire)
The resulting index ranges from -2 to +2 and allows for spatial comparisons of carbon security across the region.
NoteThe associated paper for this dataset has been accepted as part of a special issue but does not yet have a DOI assigned. A preprint version was not made available. Please note that the listed citations do not refer to this paper.
"},{"location":"projects/csi/#supplemental-citation","title":"Supplemental Citation","text":"[Smith, J.T., Allred, B.W., Boyd, C.S., Davies, K.W., Jones, M.O., Kleinhesselink, A.R., Maestas, J.D., Naugle, D.E. (2023). Where There's Smoke, There's Fuel: Dynamic Vegetation Data Improve Predictions of Wildfire Hazard in the Great Basin. Rangeland Ecol. Manage. 89:20-32. https://doi.org/10.1016/j.rama.2022.07.005](https://doi.org/10.1016/j.rama.2022.07.005)\n\n[Chambers, J.C., Miller, R.F., Board, D.I., Pyke, D.A., Roundy, B.A., Grace, J.B., Schupp, E.W., Tausch, R.J. (2014). Resilience and Resistance of Sagebrush Ecosystems: Implications for State and Transition Models and Management Treatments. Rangeland Ecol. Manage. 67:440-454. https://doi.org/10.2111/REM-D-13-00074.1](https://doi.org/10.2111/REM-D-13-00074.1)\n\n[Chambers, J.C., Maestas, J.D., Pyke, D.A., Boyd, C.S., Pellant, M., Wuenschel, A. (2017). Using Resilience and Resistance Concepts to Manage Persistent Threats to Sagebrush Ecosystems and Greater Sage-grouse. Rangeland Ecol. Manage. 70:149-164. https://doi.org/10.1016/j.rama.2016.08.005](https://doi.org/10.1016/j.rama.2016.08.005)\nvar carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL');\n\nvar visParams = {\n palette: ['#9ab555'],\n min: 0.0,\n max: 1550000.0,\n opacity: 0.8,\n};\nvar carbonoffsets = ee.Image().float().paint(carbonoffsetscol, 'REP_AREA');\n\nMap.setCenter(-52.692,-2.628,6)\nMap.addLayer(carbonoffsets, visParams, 'Existing carbon projects area');\n\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/15/subtle-grayscale\", \"Greyscale\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CARBON-SECURITY-INDEX
"},{"location":"projects/csi/#license","title":"License","text":"This dataset is made available under Creative Commons Attribution 4.0 International license.
Keywords: rangelands, sagebrush steppe, Great Basin, ecosystem function, environmental assessment and monitoring, environmental management
Curated in GEE by: Samapriya Roy
Last updated : 2024-07-24
"},{"location":"projects/daily_lst/","title":"MODIS Gap filled Long-term Land Surface Temperature Daily (2003-2020)","text":"High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. In this study the authors developed a novel spatiotemporal gap-filling framework by implementing data preprocessing (filtering pixels with low data quality and gap-filling missing values at one overpass using values at one of the other three overpasses each day) and spatiotemporal fitting (filtering the long-term trend (overall mean) of observations in each pixel, and then spatiotemporally interpolating residuals between observations and overall mean values for each day, and finally adding the overall mean and interpolated residuals), to generate a seamless high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. The paper on the gap-filling method will be published in near future.
The method was implemented to create a global gap filled LST observation.. The cross-validation indicates that the average root mean squared error (RMSE) for mid-daytime (1:30pm) and mid-nighttime (1:30am) LST is 1.88K and 1.33K, respectively. The gap-filled LST in the unit of 0.1 Celsius temperature (0.1 degree C) .You can read the abstract here
The datasets and entire collection is available at Figshare.
"},{"location":"projects/daily_lst/#citation","title":"Citation","text":"Paper Citation
Li, Xiaoma, Yuyu Zhou, Ghassem R. Asrar, and Zhengyuan Zhu. \"Creating a seamless 1 km resolution daily land surface\ntemperature dataset for urban and surrounding areas in the conterminous United States.\" Remote Sensing of\nEnvironment 206 (2018): 84-97.\nAbstract Citation
Zhang, Tao, Yuyu Zhou, and Zhengyuan Zhu. \"A spatiotemporal gap-filling method for building a seamless MODIS land\nsurface temperature dataset.\" In AGU Fall Meeting Abstracts, vol. 2020, pp. GC127-01. 2020.\nCollection Citation
Zhang, Tao; Zhou, Yuyu; Zhu, Zhengyuan; Li, Xiaoma; Asrar, Ghassem (2021): A global seamless 1 km resolution daily\nland surface temperature dataset (2003 \u2013 2020). Iowa State University. Collection. https://doi.org/10.25380/iastate.c.5078492.v1\nvar gf_day_1km = ee.ImageCollection(\"projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km\");\nvar gf_night_1km = ee.ImageCollection(\"projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/MODIS-GAPFILLED-LST-DAILY
"},{"location":"projects/daily_lst/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Zhang, Tao; Zhou, Yuyu; Zhu, Zhengyuan; Li, Xiaoma; Asrar, Ghassem
Curated in GEE by: Samapriya Roy
Keywords: Land Surface Temperature, LST, MODIS, gapfilled
Last updated: 2021-10-03
"},{"location":"projects/daylight_maps/","title":"Daylight Map Distribution map data","text":"Daylight is a complete distribution of global, open map data that\u2019s freely available with support from community and professional mapmakers. We combine the work of global contributors to projects like OpenStreetMap with quality and consistency checks from Daylight mapping partners to create a free, stable, and easy-to-use street-scale global map. Daylight Map Distribution will include a new dataset consisting of vectorized landcover features derived from the European Space Agency\u2019s 2020 World Cover (10m) rasters. This dataset provides global coverage and is suitable for use in maps up to 1:1 million (zoom level 8).
"},{"location":"projects/daylight_maps/#dataset-structure","title":"Dataset structure","text":"Dataset releases are created by the team periodically and will be ingested accordingly into the GEE collection.
"},{"location":"projects/daylight_maps/#attribution","title":"Attribution","text":"* \u00a9 OpenStreetMap contributors available under the Open Database License (www.openstreetmap.org/copyright)\n* Building data \u00a9 OpenStreetMap contributors, Microsoft, Esri Community Maps contributors\n* Australia Building Footprints (github.com/microsoft/AustraliaBuildingFootprints)\n* Canadian Building Footprints (github.com/microsoft/CanadianBuildingFootprints)\n* Uganda/Tanzania Building Footprints (github.com/microsoft/Uganda-Tanzania-Building-Footprints)\n* US Building Footprints (github.com/microsoft/USBuildingFootprints)\nvar water_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/DAYLIGHTMAP/water_polygons\");\nvar land_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/DAYLIGHTMAP/land_polygons\");\nLandcover layer: ESA 2020
var landcover = ee.FeatureCollection(\"projects/sat-io/open-datasets/DAYLIGHTMAP/LANDCOVER_ESA_2020\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/DAYLIGHT-LANDCOVER
"},{"location":"projects/daylight_maps/#license","title":"License","text":"Daylight Map Distribution is open data, licensed under the Open Data Commons Open Database License (ODbL). Daylight is built from upstream sources, primarily from OpenStreetMap contributors with optional additions from Esri Community Maps Contributors and Microsoft Corporation.
Provided by: Daylight Map Distribution
Curated in GEE by: Samapriya Roy
Keywords: Daylight Map Distribution, landcover, land polygons, water polygons, OSM, OpenStreetMap
Last updated in GEE: 2023-10-20
"},{"location":"projects/dea_croplands/","title":"Digital Earth Africa's cropland extent map Africa 2019","text":"These maps shows the estimated location of croplands in the following countries for the period January to December 2019 where cropland is defined as a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date. It was also noted that \"This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation.\" The provisional cropland extent maps have a resolution of 10 metres, and were built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent maps were built seperately using extensive training data from Eastern, Western, Northern, and Sahel Africa, coupled with a Random Forest machine learning model. A detailed exploration of the methods used to produce the cropland extent map can be found in the Jupyter Notebooks in DE Africa\u2019s crop-mask. Easiest place to download the datasets is from AWS Open data registry
The products contain three measurements:
mask: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification.
prob: This band displays the prediction probabilities for the \u2018crop\u2019 class. As this service uses a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted \u2018crop\u2019, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at > 50 % will produce a map identical to mask.
filtered: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has been filtered using an image segmentation algorithm see this paper for details on the algorithm used. During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as a complement to the mask band; small commission errors are removed by object-based filtering, and the \u2018salt and pepper\u2019 effect typical of classifying pixels is diminished.
You can details on the method and more here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/dea_croplands/#preprocessing-for-gee","title":"Preprocessing for GEE","text":"All images were download and merged into single collections. The metadata tags including regions, versions were maintained from the STAC metadata JSON files provided for the regional downloads. Validation datasets for each region were downloaded ingested and merged into a single feature collection.
"},{"location":"projects/dea_croplands/#earth-engine-snippet","title":"Earth Engine snippet","text":"var filtered = ee.ImageCollection(\"projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/filtered\")\nvar mask = ee.ImageCollection(\"projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/mask\");\nvar prob = ee.ImageCollection(\"projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/prob\");\nvar validation = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/validation\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/DEA-CROPLAND-EXTENT
"},{"location":"projects/dea_croplands/#license","title":"License","text":"This dataset is made available under the CC BY Attribution 4.0 International License.
Created by: Digital Earth Africa
Curated by: Samapriya Roy
Keywords: agriculture, cog, deafrica, earth observation, food security, geospatial, satellite imagery, stac,sustainability
Last updated in GEE: 2023-03-13
"},{"location":"projects/dea_lc/","title":"Digital Earth Australia(DEA) Landsat Land Cover 25m v1.0.0","text":"Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures.\u202fA consistent, Australia-wide land cover product helps\u202funderstanding of\u202fhow the different parts of the environment change and\u202finter-relate.\u202fEarth observation data recorded over\u202fa period of time\u202ffirstly allows the observation of\u202fthe state of land cover at a specific time and\u202fsecondly the way that land cover changes by comparison between times.
DEA Land Cover provides annual land cover classifications for Australia using the Food and Agriculture\u202fOrganisation\u202fLand Cover Classification System taxonomy Version 2 (Di Gregorio and Jansen, 1998; 2005). DEA Land Cover\u202fclassifications have been generated by combining quantitative (continuous) or qualitative (thematic) environmental information (referred to as Essential Descriptors; EDs) derived from Landsat\u202fsatellite\u202fsensor data. Several EDs have been generated previously by Geoscience Australia, including annual water summaries (Mueller et al., 2016), vegetation fractional cover (Scarth et al., 2010), mangrove extent (Lymburner et al., 2020) and the Inter Tidal Extent Model (ITEM; Sagar et al., 2017), whilst others have been developed more recently. These EDs have been combined to generate detailed, consistent and expandable annual classifications of Australia\u2019s land cover from 1988 through to 2020.\u202f
Additional information including descriptors above can be found here and you can also explore the map here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/dea_lc/#data-preprocessing","title":"Data preprocessing","text":"Datasets were downloaded from Amazon S3 buckets and each years tiles were composited to create single year derivatives. Start and end date for years were also added to metadata and the collection included the version 1.0.0 as v100. Since this is a classified imagery MODE pyramiding scheme was used and default nodata value from tif at 0 was utilized for no data value during upload.
"},{"location":"projects/dea_lc/#citation","title":"Citation","text":"Lucas R, Mueller N, Siggins A, Owers C, Clewley D, Bunting P, Kooymans C, Tissott B, Lewis B, Lymburner L, Metternicht G. Land Cover Mapping using Digital Earth Australia. Data.\n2019; 4(4):143. https://doi.org/10.3390/data4040143\n\nChristopher J. Owers, Richard M. Lucas, Daniel Clewley, Carole Planque, Suvarna Punalekar, Belle Tissott, Sean M. T. Chua, Pete Bunting, Norman Mueller & Graciela Metternicht\n(2021) Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development, Big Earth Data, 5:3, 368-\n390, DOI: 10.1080/20964471.2021.1948179\nvar dea_lcv100 = ee.ImageCollection(\"projects/sat-io/open-datasets/DEA/landcover_v100\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/DEA-LANDSAT-LC
"},{"location":"projects/dea_lc/#license","title":"License","text":"Creative Commons Attribution-Share Alike 4.0 International License
Created by: Geoscience Australia and Digital Earth Australia
Curated by: Samapriya Roy
Keywords: : Australia, land cover, remote sensing, landsat, satellite
Last updated in GEE: 2022-03-05
"},{"location":"projects/dea_shorlines/","title":"Digital Earth Australia Coastlines","text":"Digital Earth Australia Coastlines is a continental dataset that includes annual shorelines and rates of coastal change along the entire Australian coastline from 1988 to the present. The product combines satellite data from Geoscience Australia's Digital Earth Australia program with tidal modelling to map the most representative location of the shoreline at mean sea level for each year. The product enables trends of coastal retreat and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades.
The ability to map shoreline positions for each year provides valuable insights into whether changes to our coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers and policy makers to assess impacts from the range of drivers impacting our coastlines and potentially assist planning and forecasting for future scenarios. You can find additional details here and you can download the datasets here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
Annual shoreline vectors that represent the median or \u2018most representative\u2019 position of the shoreline at approximately 0 m Above Mean Sea Level for each year since 1988. Dashed shorelines have low certainty and Annual shorelines include the following attribute fields:
Attribute Description year The year of each annual shoreline. certainty A column providing important data quality flags for each annual shoreline. tide_datum The tide datum of each annual shoreline (e.g. \u201c0 m AMSL\u201d). id_primary The name of the annual shoreline\u2019s Primary sediment compartment from the Australian Coastal Sediment Compartments framework."},{"location":"projects/dea_shorlines/#citation","title":"Citation","text":"Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. 2021. Digital Earth Australia\nCoastlines. Geoscience Australia, Canberra. https://doi.org/10.26186/116268\nBishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia's dynamic\ncoastline at mean sea level using three decades of Landsat imagery. Remote Sensing of\nEnvironment, 267, 112734. Available: https://doi.org/10.1016/j.rse.2021.112734\n\nNanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into\nAustralia's coastal change using a national dataset derived from the multi-decadal Landsat\narchive. Estuarine, Coastal and Shelf Science, 265, p.107712. Available: https://doi.org/10.1016/\nj.ecss.2021.107712\n\nBishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., & Sixsmith, J. (2019). Sub-pixel\nwaterline extraction: Characterising accuracy and sensitivity to indices and spectra. Remote\nSensing, 11(24), 2984. Available: https://www.mdpi.com/2072-4292/11/24/2984\nvar shoreline_annual = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_shorelines_annual\");\nvar hotspot_zoom_1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_hotspots_zoom_1\");\nvar hotspot_zoom_2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_hotspots_zoom_2\");\nvar hotspot_zoom_3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_hotspots_zoom_3\");\nvar rate_of_change = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v220_shorelines_annual\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/DEA-Shorelines
"},{"location":"projects/dea_shorlines/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Digital Earth Australia
Curated in GEE by : Samapriya Roy
Keywords : Sea, ocean and coast, marine and coastal, coast, erosion, waterline extraction, subpixel waterlines, coastal change, DEA CoastLines, coastline data, coastal erosion
"},{"location":"projects/dea_shorlines/#changelog","title":"Changelog","text":"Last updated : 2024-09-13
"},{"location":"projects/deaf_shorlines/","title":"Digital Earth Africa Coastlines","text":"The Digital Earth Africa Coastlines is a continental dataset that includes annual shorelines and rates of coastal change along the entire African coastline. This is a provisional service that has been generated for 2000 to 2021 and we would like to improve and operationalize with users.
The product combines satellite data from the Digital Earth Africa program with tidal modelling to map the typical location of the coastline at mean sea level each year. The product enables trends of coastal erosion and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades.
The ability to map shoreline positions for each year provides valuable insights into whether changes to the coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers, and policy makers to assess impact from the range of drivers impacting the coastlines and potentially assist planning and forecasting for future scenarios. You can find additional details here and you can download the datasets here
"},{"location":"projects/deaf_shorlines/#acknowledgment","title":"Acknowledgment","text":"The Coastlines algorithms incorporated in this product are the work of Robbi-Bishop Taylor, Rachel Nanson, Stephen Sagar, and Leo Lymburner, Geoscience Australia. Digital Earth Africa acknowledges the work done by the Centre de Suivi Ecologique (CSE), Dakar, in assessing the accuracy of the service with the participation of West African WACA stakeholders. Acknowledgements also go to the Regional Center for Mapping Resources for Development (RCMRD) for stakeholder engagement and feedback. Digital Earth Africa thanks the Digital Earth Africa Product Development task team for the co-design, the co-development and early feedback on the Service.
"},{"location":"projects/deaf_shorlines/#citation","title":"Citation","text":"Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. 2021. Digital Earth Australia\nCoastlines. Geoscience Australia, Canberra. https://doi.org/10.26186/116268\nvar shoreline_annual = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_shorelines_annual\");\nvar hotspot_zoom_1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_hotspots_zoom_1\");\nvar hotspot_zoom_2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_hotspots_zoom_2\");\nvar hotspot_zoom_3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_hotspots_zoom_3\");\nvar rate_of_change = ee.FeatureCollection(\"projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_rates_of_change\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/DEAF-Shorlines-V040
"},{"location":"projects/deaf_shorlines/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Digital Earth Africa
Curated in GEE by : Samapriya Roy
Keywords : Sea, ocean and coast, marine and coastal, coast, erosion, waterline extraction, subpixel waterlines, coastal change, DEAF CoastLines, coastline data, coastal erosion
Last updated : 2023-03-26
"},{"location":"projects/delta_dtm/","title":"DeltaDTM Global coastal digital terrain model","text":"DeltaDTM is a global coastal Digital Terrain Model (DTM) with a horizontal spatial resolution of 1 arcsecond (\u223c30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. It improves upon the accuracy of existing global elevation datasets by correcting Copernicus DEM with spaceborne lidar data from the ICESat-2 and GEDI missions. This correction process involves bias correction, filtering of non-terrain cells (e.g., vegetation and buildings), and gap filling using interpolation. DeltaDTM specifically focuses on low-lying coastal areas (below 10 m above Mean Sea Level) that are particularly vulnerable to sea-level rise, subsidence, and extreme weather events.
DeltaDTM is a valuable resource for a wide range of applications, including coastal management, flood modeling, and adaptation planning. Its improved accuracy enables more precise assessments of coastal flood risks and supports the development of effective mitigation and adaptation strategies. The dataset is freely available in the public domain and can be easily accessed and utilized by researchers, policymakers, and coastal communities. You can read the paper here and download the dataset here.
"},{"location":"projects/delta_dtm/#citation","title":"Citation","text":"Pronk, M., Hooijer, A., Eilander, D. et al. DeltaDTM: A global coastal digital terrain model. Sci Data 11, 273 (2024).\nhttps://doi.org/10.1038/s41597-024-03091-9\nPronk, Maarten (2024): DeltaDTM: A global coastal digital terrain model. Version 2. 4TU.ResearchData. dataset.\nhttps://doi.org/10.4121/21997565.v2\nvar delta_dtm = ee.Image(\"projects/sat-io/open-datasets/DELTARES/deltadtm_v1\");\nvar elevation = delta_dtm.select('b1');\nelevation = elevation.updateMask(elevation.neq(10));\n\n//Setup basemaps\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/132/light-gray\", \"Grayscale\");\n\nvar elevationVis = {\n min: 0,\n max: 10.0,\n // cmocean deep\n palette: [\"281a2c\", \"3f396c\", \"3e6495\", \"488e9e\", \"5dbaa4\", \"a5dfa7\", \"fdfecc\"]\n};\n\nMap.setCenter(103, 0, 7); // South East Asia\nMap.addLayer(elevation, elevationVis, 'DeltaDTM');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/DELTA-DTM
"},{"location":"projects/delta_dtm/#license","title":"License","text":"DeltaDTM is licensed as CC-BY 4.0. DeltaDTM is produced using Copernicus WorldDEM-30 \u00a9 DLR e.V. 2010-2014 and \u00a9 Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.
Created by: Deltares, Pronk, M., Hooijer, A., Eilander, D. et al 2024
Curated in GEE by: Maarten Pronk and Samapriya Roy
Keywords: Altimetry, Digital Elevation Model (DEM), Digital terrain model (DTM), elevation, GEDI, ICESat-2, LiDAR
Last updated in GEE: 2023-10-30
"},{"location":"projects/dynqual/","title":"DynQual Global Surface Water Quality Dataset","text":"Maintaining optimal surface water quality is essential for preserving ecosystems and ensuring safe human water utilization. However, our understanding of surface water quality relies heavily on data from monitoring stations, which are spatially limited and temporally fragmented. Addressing these limitations, we introduce the dynamical surface water quality model (DynQual). This model offers simulations of water temperature (Tw), as well as concentrations of total dissolved solids (TDS), biological oxygen demand (BOD), and fecal coliform (FC). DynQual operates at a daily time step and boasts a spatial resolution of 5\u2009arcmin (\u223c\u200910\u2009km).
This comprehensive global model allows us to evaluate its performance against real-world water quality observations. In addition, we present insights into spatial patterns and temporal trends of TDS, BOD, and FC concentrations spanning the years 1980 to 2019. Our analysis identifies dominant sectors contributing to surface water pollution. Remarkably, DynQual reveals widespread multi-pollutant hotspots, particularly in northern India and eastern China, though water quality issues extend across all regions worldwide. The most significant declines in water quality have occurred in developing regions, especially sub-Saharan Africa and South Asia. Researchers can access the open-source model code (Jones et al., 2023) as well as the global datasets encompassing simulated hydrology, Tw, TDS, BOD, and FC at 5\u2009arcmin resolution on a monthly basis (Jones et al., 2022b). These datasets hold the potential to enhance diverse studies ranging from ecological research to human health and water scarcity assessments. Discover more at DynQual Model Code and Global Surface Water Quality Datasets. You can read the full paper here
The constituents, such as total dissolved solids (TDS), biological oxygen demand (BOD), and fecal coliform (FC), are concentrations. The routed versions of these constituents (routed_TDS, routed_BOD, and routed_FC) are masses of pollutants. These values represent the pollution without considering dilution, and therefore more accurately reflect the transport or \"export\" of pollution through the river network.
Please refer to the original paper on guidance on displaying the concentration maps, the authors recommend these are only plotted above a given discharge/channelStorage threshold
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/dynqual/#datasets-preprocessing","title":"Datasets preprocessing","text":"The datasets were downloaded and converted from NetCDF to Geotiff format for ingestion. Since this was a multi band monthly aggregated image and I wanted to allow the user to slice by time frame, the image bands were seperated as individual images and the overall results are image collections with date range information attached.
"},{"location":"projects/dynqual/#citation","title":"Citation","text":"Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.:\nDynQual v1.0: a high-resolution global surface water quality model, Geosci. Model Dev., 16, 4481\u20134500, https://doi.\norg/10.5194/gmd-16-4481-2023, 2023.\nJones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., & van Vliet, M. T. H.\n(2022). Global monthly hydrology and water quality datasets, derived from the dynamical surface water quality\nmodel (DynQual) at 10 km spatial resolution [Data set]. In Geoscientific Model Development (Vol. 16, pp.\n4481\u20134500). Zenodo. https://doi.org/10.5281/zenodo.7139222\nvar fc = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/fecal-coliform\");\nvar routed_fc = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/routed-fc\");\nvar discharge = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/discharge\");\nvar storage = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/channel-storage\");\nvar avg_annualDischarge = ee.Image(\"projects/sat-io/open-datasets/DYNQUAL/discharge-avg-annual\");\nvar tds = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/total-dissolved-solids\");\nvar routed_tds = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/routed-tds\");\nvar bod = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/biological-oxygen-demand\");\nvar routed_bod = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/routed-bod\");\nvar water_temp = ee.ImageCollection(\"projects/sat-io/open-datasets/DYNQUAL/water-temperature\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/DYNQUAL-EXAMPLE
"},{"location":"projects/dynqual/#license","title":"License","text":"Creative Commons Attribution 4.0 International Public License
Created by: Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.
Curated in GEE by: Samapriya Roy
Keywords: water quality, discharge, water temperature, total dissolved solids, TDS, salinity, biological oxygen demand, BOD, fecal coliform, FC
"},{"location":"projects/edge_matched/","title":"Edge-matched Global, Subnational and operational Boundaries","text":""},{"location":"projects/edge_matched/#global-edge-matched-subnational-boundaries-humanitarian-edge-matched","title":"Global Edge-matched Subnational Boundaries: Humanitarian Edge Matched","text":"Uses OCHA Common Operational Datasets (COD) when available, falling back to geoBoundaries for regions without coverage. Represents the latest available data for humanitarian operational use. Uses the OpenStreetMap International ADM0 worldview for edge-matching. You can find dataset links in different formats here
"},{"location":"projects/edge_matched/#license","title":"License","text":"The humanitaring edge matched datasets are under a Open Data Commons Open Database License (ODbL) license.
"},{"location":"projects/edge_matched/#attribution","title":"Attribution","text":"FieldMaps, OCHA, geoBoundaries, U.S. Department of State, OpenStreetMap
"},{"location":"projects/edge_matched/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var adm1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm1_polygons\");\nvar adm2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm2_polygons\");\nvar adm3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm3_polygons\");\nvar adm4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm4_polygons\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/HUMANITARIAN-EDGEMATCHED
"},{"location":"projects/edge_matched/#global-edge-matched-subnational-boundaries-open-edge-matched","title":"Global Edge-matched Subnational Boundaries: Open Edge Matched","text":"Uses geoBoundaries exclusively to ensure all data comes from sources with clearly defined licenses. Suitable for academic or commercial use. Uses the U.S. Geological Survey International ADM0 worldview for edge-matching. You can find dataset links in different formats here
"},{"location":"projects/edge_matched/#license_1","title":"License","text":"The open datasets are under a Creative Commons Attribution 4.0 International (CC BY 4.0) and any derived work must include attribution.
"},{"location":"projects/edge_matched/#attribution_1","title":"Attribution","text":"FieldMaps, geoBoundaries, U.S. Department of State, U.S. Geological Survey
"},{"location":"projects/edge_matched/#earth-engine-snippet_1","title":"Earth Engine Snippet","text":"var adm1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-open/adm1_polygons\");\nvar adm2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-open/adm2_polygons\");\nvar adm3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-open/adm3_polygons\");\nvar adm4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-open/adm4_polygons\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/OPEN-EDGEMATCHED
"},{"location":"projects/edge_matched/#international-adm0-boundaries","title":"International ADM0 boundaries","text":"International boundaries are built using either OpenStreetMap or U.S. Geological Survey coastlines. Within each set, ADM0 layers comes in several versions to represent different world views of disputed areas. Starting with the \"All\" version, areas are dissolved together based on varying international recognition. Only international boundaries were ingested, where the default version, uses OpenStreetMap (download) for coastlines so that it aligns with web maps using OSM for basemaps or other data. Specialty version, uses U.S. Geological Survey (download) for coastlines so that intellectual property and related rights in this dataset are absent.
"},{"location":"projects/edge_matched/#license_2","title":"License","text":"The OSM edge matched datasets are under a Open Data Commons Open Database License (ODbL) license while the USGS datasets are under a CC0 or public domain license.
"},{"location":"projects/edge_matched/#attribution_2","title":"Attribution","text":"The OSM datasets have attribution keywords defined such as FieldMaps, U.S. Department of State, OpenStreetMap
"},{"location":"projects/edge_matched/#earth-engine-snippet_2","title":"Earth Engine Snippet","text":"var adm0_osm = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/field-maps/OSM_adm0_polygons\");\nvar adm0_usgs = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/field-maps/USGS_adm0_polygons\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/AMD0-EDGEMATCHED
"},{"location":"projects/edge_matched/#common-operation-dataset-edge-matched","title":"Common Operation dataset Edge Matched","text":"The COD layers are obtained from the Humanitarian Data Exchange at the source URLs below before processed for edge matching. Extended layers can be downloaded and clipped to any ADM0. You can download the dataset in different formats here. Datasets were merged to create singular representation at each hierarchy so all ADM1 for example and ADM2 for example. Each component of the dataset retains the original license provided.
"},{"location":"projects/edge_matched/#earth-engine-snippet_3","title":"Earth Engine Snippet","text":"var adm1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm1_cod\");\nvar adm2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm2_cod\");\nvar adm3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm3_cod\");\nvar adm4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm4_cod\");\nThe Humanitarian Data Exchange at the source URLs below before processed for edge matching. Extended layers can be downloaded and clipped to any ADM0. You can download the dataset and find license information heredifferent formats here. Datasets were merged to create singular representation at each hierarchy so all ADM1 for example and ADM2 for example. Each component of the dataset retains the original license provided.
var adm = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm1_geoboundaries\");\nvar adm = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm2_geoboundaries\");\nvar adm = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm3_geoboundaries\");\nvar adm = ee.FeatureCollection(\"projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm4_geoboundaries\");\nsample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GEOBOUNDARIES-EDGEMATCHED
Curated in GEE by : Samapriya Roy
Keywords: FieldMaps, U.S. Department of State, OpenStreetMap,U.S. Geological Survey, geoboundaries
Last updated on GEE: 2022-10-30
"},{"location":"projects/elc/","title":"Continental-scale land cover mapping at 10 m resolution over Europe","text":"A land cover classification for Europe at 10 m resolution produced with a machine learning workflow driven by Sentinel optical and radar satellite imagery. The classification model was trained on land cover reference data form the LUCAS (Land Use/Cover Area frame Survey) dataset. The map represents conditions in 2018. You can read the preprint here
The pixel values, their definitions and suggested hex color codes include: 0 (not mapped #000000), 1 (Artificial land, #CC0303), 2 (Cropland, #CDB400), 3 (Woodland, #235123), 4 (Shrubland, #B76124), 5 (Grassland, #92AF1F), 6 (Bare land, #F7E174), 7 (Water/permanent snow/ice, #2019A4), 8 (Wetland, #AEC3D6).
"},{"location":"projects/elc/#citation","title":"Citation","text":"Venter, Zander S., and Markus AK Sydenham. \"Continental-scale land cover mapping at 10 m resolution\nover Europe (ELC10).\" arXiv preprint arXiv:2104.10922 (2021).\nvar elc10= ee.ImageCollection(\"projects/sat-io/open-datasets/NINA/ELC10\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/EUROPE-10m-LULC
Category Land Cover Class Hex Code 1 Artificial land #CC0303 2 Cropland #CDB400 3 Woodland #235123 4 Shrubland #B76124 5 Grassland #92AF1F 6 Bare land #F7E174 7 Water/permanent snow/ice #2019A4 8 Wetland #AEC3D6"},{"location":"projects/elc/#dataset-citation","title":"Dataset Citation","text":"Venter, Zander S., & Sydenham, Markus A.K. (2020). ELC10: European 10 m resolution land cover map 2018\n(Version 01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4407051\nCreative Commons Attribution-Share Alike 4.0 International License
Created by: Venter, Zander S., & Sydenham, Markus A.K.
Curated by: Samapriya Roy
Keywords: : land use, europe, land cover, remote sensing, copernicus, sentinel, satellite
Last updated: 2021-04-29
"},{"location":"projects/elc_gdp/","title":"Global Electric Consumption revised GDP","text":"In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1\u2009km\u2009\u00d7\u20091\u2009km gridded nighttime light data during 1992\u20132019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1\u2009km\u2009\u00d7\u20091\u2009km gridded revised real GDP and electricity consumption during 1992\u20132019 based on our calibrated nighttime light data.
Gridded population and nighttime light data are the most popular proxy tools, and have been adopted extensively because of their strong correlation with economic output and electricity use. Finally the authors note that although nighttime light data as a single indicator may ignore factors such as value added or reduced by forestry or desertification, it is still an effective proxy for calibrating economic growth.You can read the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/elc_gdp/#paper-citation","title":"Paper Citation","text":"Chen, J., Gao, M., Cheng, S., Hou, W., Song, M., Liu, X., & Liu, Y. (2022). Global 1 km\u00d7 1 km gridded revised real gross domestic product and\nelectricity consumption during 1992\u20132019 based on calibrated nighttime light data. Scientific Data, 9(1), 1-14. https://doi.org/10.1038/\ns41597-022-01322-5\nChen, Jiandong; Gao, Ming (2021): Global 1 km \u00d7 1 km gridded revised real gross domestic product and electricity consumption during 1992-2019 based\non calibrated nighttime light data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.17004523.v1\nvar global_ec = ee.ImageCollection(\"projects/sat-io/open-datasets/GRIDDED_EC\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GRIDDED-ELECTRICITY-CONSUMPTION
"},{"location":"projects/elc_gdp/#earth-engine-snippet-gridded-gdp-based-on-electricity-consumption","title":"Earth Engine Snippet: GRIDDED GDP based on Electricity Consumption","text":"var global_elc_gdp = ee.ImageCollection(\"projects/sat-io/open-datasets/GRIDDED_EC-GDP\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GRIDDED-ELECTRICITY-CONSUMPTION-GDP
"},{"location":"projects/elc_gdp/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated in GEE by: Samapriya Roy
Keywords: GDP, Electricity Consumption, Night Lights
Last updated: 2022-09-27
"},{"location":"projects/electric_grid/","title":"Facebook Electrical Distribution Grid Maps","text":"Facebook has produced a model to help map global medium voltage (MV) grid infrastructure, i.e. the distribution lines which connect high-voltage transmission infrastructure to consumer-serving low-voltage distribution. The data found here are model outputs for six select African countries: Malawi, Nigeria, Uganda, DRC, Cote D\u2019Ivoire, and Zambia. The grid maps are produced using a new methodology that employs various publicly-available datasets (night time satellite imagery, roads, political boundaries, etc) to predict the location of existing MV grid infrastructure. The model documentation and code are also available , so data scientists and planners globally can replicate the model to expand model coverage to other countries where this data is not already available.
Building Electrical Grid Maps begins by taking monthly images from the VIIRS satellite, and creating a composite. We then apply a custom image processing filter to remove background and reflected light, and identify locations that consistently demonstrate night-time lighting. These then serve as a proxy for the existence of grid electricity. Using known electrical grids as templates based on data available from energydata.info, we employ a custom algorithm to connect the communities and infer grid paths based on their likelihood to follow roads, avoid water, and follow the shortest paths possible. You can find the model code and documentation here: https://github.com/facebookresearch/many-to-many-dijkstra
Note: current model accuracy is approximately 70% when compared to existing ground-truthed data. Accuracy can be further improved by integrating other locally-relevant information into the model and running it again.
"},{"location":"projects/electric_grid/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var gmv_raster = ee.ImageCollection(\"projects/sat-io/open-datasets/facebook/global_medium_voltage_grid\")\nvar gmv_vector = ee.FeatureCollection(\"projects/sat-io/open-datasets/facebook/gmv_grid\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/FACEBOOK-ELECTRICAL-DIST-GRID-MAPS
"},{"location":"projects/electric_grid/#resolution","title":"Resolution:","text":"geotiff is provided at Bing Tile Level 20
"},{"location":"projects/electric_grid/#location","title":"Location","text":"C\u00f4te d'Ivoire, Democratic Republic of the Congo, Malawi, Nigeria, Uganda, Zambia
"},{"location":"projects/electric_grid/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 License.
Curated by: Samapriya Roy
Keywords: Electrical Distribution Grid, Facebook, Ivory Coast, Democratic Republic of the Congo, Malawi, Nigeria, Uganda, Zambia
Last updated: 2021-04-17
"},{"location":"projects/energy_farms/","title":"Harmonised global datasets of wind and solar farm locations and power","text":"Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet\u2019s limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code to enable users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate these data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.
Data is available for download from figshare here
"},{"location":"projects/energy_farms/#data-citation","title":"Data Citation","text":"Dunnett, S. Harmonised global datasets of wind and solar farm locations and power. figshare. Dataset. https://doi.org/10.6084/m9.figshare.11310269 (2020)\nDunnett, S., Sorichetta, A., Taylor, G. et al. Harmonised global datasets of wind and solar farm locations and power. Sci Data 7, 130 (2020). https://doi.org/10.1038/s41597-020-0469-8\nvar wind_farms = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_wind_farms_2020\");\nvar solar_farms = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_solar_farms_2020\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/HARMONIZED-WIND-SOLAR-FARMS
"},{"location":"projects/energy_farms/#property-mapping","title":"Property Mapping","text":"Name Detail X X coordinate (.csv only) Y Y coordinate (.csv only) x_id unique identifier for data record GID_0 ISO3 country code panels number of panels turbines number of turbines panel.area total panel area in km2\u00a0(p_area\u00a0for .gdb files) landscape.area landscape area in km2\u00a0(l_area\u00a0for .gdb files) water binary response indicating whether data record is classified as water urban binary response indicating whether data record is classified as urban centre power estimated power capacity in MW"},{"location":"projects/energy_farms/#license","title":"License","text":"Data adapted or built on OpenStreetMap data are required to be distributed under the same licence. These data are therefore made available under the Open Data Commons Open Database License (ODbL). Personal figshare accounts cannot currently present data under this licence so the data are currently (incorrectly) presented under a CC0 licence as a stopgap until this changes.
Created by: Dunnett et al.
Curated by: Samapriya Roy
Keywords: solar, wind, energy, renewable
Last updated: 2021-08-31
"},{"location":"projects/eog_viirs_ntl/","title":"EOG Annual VIIRS Night Time Light","text":"NoteThis dataset is currently only available to those in the insiders program
A new consistently processed time series of annual global VIIRS nighttime lights has been produced from monthly cloud-free average radiance grids spanning 2012 to 2021 is processed by the Earth Observation Group(EOG). The new methodology (version 2.1) is a modification of the original VNL V1 that is available in the Earth Engine catalogue. Compared to VNL V1, this improved VNL V2.1 version removes ephemeral lights and background noise.
In both methods there is an initial filtering to remove sunlit, moonlit and cloudy pixels, leading to rough composites that contains lights, fires, aurora and background. In the original method, the rough annual composites are made from a full year of nightly DNB data. In the new method, the rough composites are made on monthly increments and then combined to form rough annual composites. Both methods employ outlier removal to discard biomass burning pixels and isolate the background.
In the original method the outlier removal is performed on scattergrams generated for each 15 arc second grid cell, with outliers clipped off from both the high and low radiance sides of the scattergram. The discard of outlier pixels proceeds until the scattergram\u2019s standard deviation stabilizes. The new method uses the twelve-month median radiance to discard high and low radiance outliers, filtering out most fires and isolating the background. Background areas are zeroed out in both methods using the data range (DR) calculated from 3x3 grid cells. In both methods, the DR threshold for background is indexed to cloud-cover levels, with higher DR thresholds in areas having low numbers of cloud-free coverages. In the new method, particular attention is given to setting a single DR threshold for distinguishing lit grid cells from background for each 15 arc second grid cell. This is achieved by setting the DR threshold from a multiyear maximum median and a corresponding multiyear percent cloud-cover grids. The multiyear approach makes it possible to detect lighting present in each 15 arc second grid cell with a single DR threshold across all the years in the series. You can get additional information here and download v2.1 here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/eog_viirs_ntl/#data-preprocessing","title":"Data preprocessing","text":"Datasets were preprocessed and metadata was added to each image in collection. The year 2012 had two datasets for a varying period and as such was excluded from the collection for now and maybe added later.
"},{"location":"projects/eog_viirs_ntl/#citation","title":"Citation","text":"Elvidge, C.D, Zhizhin, M., Ghosh T., Hsu FC, Taneja J. Annual time series of global VIIRS nighttime lights derived from monthly averages:2012 to\n2019. Remote Sensing 2021, 13(5), p.922, https://doi.org/10.3390/rs13050922\nvar average = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/average\");\nvar maximum = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/maximum\");\nvar median = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/median\");\nvar minimum = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/minimum\");\nvar average_masked = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/average_masked\");\nvar median_masked = ee.ImageCollection(\"projects/sat-io/open-datasets/EOG_VNL_V21/median_masked\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/EOG-VNL-V21
"},{"location":"projects/eog_viirs_ntl/#license","title":"License","text":"Public domain license with properitary license language.
Colorado School of Mines data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no\nrestrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and\nis being provided without restriction on use and distribution.\nProvided by: Colorado School of Mines, Elvidge et al. 2019
Curated in GEE by : Samapriya Roy
keywords: Nighttime lights, VIIRS, Annual time series, Earth Observation Group , EOG, Colorado School of Mines
Last updated on GEE: 2023-01-28
"},{"location":"projects/era5_heat/","title":"ERA5-HEAT Dataset","text":"This dataset provides a complete historical reconstruction for a set of indices representing human thermal stress and discomfort in outdoor conditions. This dataset, also known as ERA5-HEAT (Human thErmAl comforT) represents the current state-of-the-art for bioclimatology data record production. The dataset is organized around two main variables: 1) the mean radiant temperature (MRT) and 2) the universal thermal climate index (UTCI) These variables describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, ventilation and radiation.
The dataset is computed using the ERA5 reanalysis from the European Centre for Medium-Range Forecasts (ECMWF). ERA5 combines model data with observations from across the world to provide a globally complete and consistent description of the Earth\u2019s climate and its evolution in recent decades. ERA5 is regarded as a good proxy for observed atmospheric conditions. Additional external information is available on this product here.
"},{"location":"projects/era5_heat/#dataset-description","title":"Dataset Description","text":"Spatial Information
Attribute Details Spatial extent Global Spatial resolution 27.75km (.25 deg) Temporal resolution Daily Time span 1940-01-01 to present Update frequency Updated daily with lag of 2 weeksVariables
Variable Units Scale factor Description Mean Radiant Temperature (\u2018mrt_mean\u2019, \u2018mrt_max\u2019, \u2018mrt_min\u2019, \u2018mrt_median\u2019) Degrees Kelvin 1.0 Provided in four bands for daily mean, maximum, minimum, and median. Universal Thermal Climate Index (\u2018utci_mean\u2019, \u2018utci_max\u2019, \u2018utci_min\u2019, \u2018utci_median\u2019) Degrees Kelvin 1.0 Provided in four bands for daily mean, maximum, minimum, and median."},{"location":"projects/era5_heat/#citation","title":"Citation","text":"Di Napoli C., Barnard C., Prudhomme C., Cloke HL and Pappenberger F. (2020): Thermal comfort indices derived from ERA5 reanalysis. Copernicus\nClimate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.553b7518 (Accessed on DD-MMM-YYYY)\n// Read in Image Collection and get first image\nvar era5_heat_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-era5-heat')\nvar era5_heat_i = era5_heat_ic.first()\n\n// Print first image to see bands\nprint(era5_heat_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(era5_heat_i.select('mrt_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Mean Radiant Temperature, Daily Mean')\nMap.addLayer(era5_heat_i.select('utci_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Universal Thermal Climate Index, Daily Mean')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/ERA5-HEAT
"},{"location":"projects/era5_heat/#license","title":"License","text":"ECMWF is available under an open license with terms of agreement available here
Keywords: heat exposure, climate, reanalysis, global, era5, thermal, public health, climate engine
Provided by: Copernicus
Curated in GEE by: Climate Engine Org
"},{"location":"projects/esa_iq/","title":"ESA WorldCover 10 m 2020 V100 InputQuality","text":"The ESA WorldCover 10 m 2020 V100 product is delivered in 3x3 degree tiles as Cloud Optimized GeoTIFFs (COGs) in EPSG:4326 projection (geographic latitude/longitude CRS). There are 2651 tiles and more information on accessing this dataset can be found here. The current collection focused on the Input Quality layers only, the Map layer is available in Google Earth Engine as an image collection.
The input quality layer is a per pixel quality indicator showing the quality of the Earth Observation (EO) input data. The layer is a 3 band GeoTIFF with
\u2022 Band 1: Number of Sentinel-1 GAMMA0 observations used in the classification workflow
\u2022 Band 2: Number of Sentinel-2 L2A observations used in the classification workflow
\u2022 Band 3 Percentage (0-100) of invalid S2 observations discarded in the classification workflow (after cloud and cloud shadow filtering).
Combining Band 2 and Band 3 yields the total absolute number of valid Sentinel-2 L2A observations used in the classification workflow.
"},{"location":"projects/esa_iq/#license","title":"License","text":"The ESA WorldCover product is provided free of charge, without restriction of use. For the full license information see the Creative Commons Attribution 4.0 International License.
Publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.
"},{"location":"projects/esa_iq/#citation","title":"Citation","text":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A.,\nPaccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li,\nLinlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. ESA WorldCover 10 m 2020 v100.\nhttps://doi.org/10.5281/zenodo.5571936\nvar esa_iq = ee.ImageCollection(\"projects/sat-io/open-datasets/ESA_WorldCover_Input_Quality\");\nData access page: ESA_WorldCover_v100
Provided by: Zanaga et al, ESA WorldCover consortium
Curated in GEE by: Samapriya Roy
Keywords: : land, cover, land use, land cover, lulc, 10m, global, world, sentinel-1, sentinel 2, ESA
Last updated: 2021-11-01
"},{"location":"projects/esrilc2020/","title":"ESRI 2020 Global Land Use Land Cover from Sentinel-2","text":"This layer displays a global map of land use/land cover (LULC). The map is derived from ESA Sentinel-2 imagery at 10m resolution. It is a composite of LULC predictions for 10 classes throughout the year in order to generate a representative snapshot of 2020. This map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.
The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map of 2020.
Processing platform Sentinel-2 L2A/B data was accessed via Microsoft\u2019s Planetary Computer and scaled using Microsoft Azure Batch.
You can find more information here Kontgis, C. (2021, June 24). Mapping the world in unprecedented detail
"},{"location":"projects/esrilc2020/#citation","title":"Citation","text":"Karra, Kontgis, et al. \u201cGlobal land use/land cover with Sentinel-2 and deep learning.\u201d\nIGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.\nWater Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.
Trees Any significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).
Grass Open areas covered in homogeneous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures.
Flooded vegetation Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.
Crops Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.
Scrub/shrub Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants
Built Area Human made structures; major road and rail networks; large homogeneous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.
Bare ground Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.
Snow/Ice Large homogeneous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.
Clouds No land cover information due to persistent cloud cover.
For Accuracy Assessment information visit the ESRI release page
Category Land Cover Class Hex Code 1 No Data #FFFFFF 2 Water #1A5BAB 3 Trees #358221 4 Grass #A7D282 5 Flooded Vegetation #87D19E 6 Crops #FFDB5C 7 Scrub/Shrub #EECFA8 8 Built Area #ED022A 9 Bare Ground #EDE9E4 10 Snow/Ice #F2FAFF 11 Clouds #C8C8C8
"},{"location":"projects/esrilc2020/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var esri_lulc2020= ee.ImageCollection(\"projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m\")\nTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.
"},{"location":"projects/esrilc2020/#credits-attributions-and-license","title":"Credits, Attributions and License","text":"This dataset was produced by Impact Observatory for Esri. \u00a9 2021 Esri. This dataset is available under a Creative Commons BY-4.0 license and any copy of or work based on this dataset requires the following attribution:
This dataset is based on the dataset produced for the Dynamic World Project\nby National Geographic Society in partnership with Google and the World Resources Institute.\nData download page: Esri 2020 Land Cover Downloader
Curated in GEE by: Samapriya Roy
Keywords: : land, cover, land use, land cover, lulc, 10m, global, world, sentinel, sentinel-2, sentinel 2, impact observatory, impact, 2020, deep learning
Last updated: 2021-06-25
"},{"location":"projects/et0/","title":"Global Reference Evapotranspiration Layers","text":"The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 3 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon the implementation of a Penman Monteith Evapotranspiration equation for reference crop. The dataset follows the development and is based upon the WorldClim 2.1 at 30 arc seconds or ~ 1km at the equator. You can read the paper here.
Potential Evapo-Transpiration (PET) is a measure of the ability of the atmosphere to remove water through Evapo-Transpiration (ET) processes. Among several equations to estimate PET, a FAO application of the Penman-Monteith equation (Allen et al. 1998), here referred as FAOPM, is currently widely considered as a standard method (Walter et al. 2000). The FAO introduced the definition of PET as the ET of a reference crop (ET0) under optimal conditions, having the characteristics of well-watered grass with an assumed height of 12 centimeters, a fixed surface resistance of 70 seconds per meter and an albedo of 0.23 (Allen et al. 1998). The FAO-PM is a predominately physically based approach, which can be used globally because it does not require estimations of additional site-specific parameters. However, a major drawback of the FAO-PM method is its relatively high need for specific data for a variety of parameters (i.e. windspeed, relative humidity, solar radiation, etc.).
"},{"location":"projects/et0/#data-citation","title":"Data citation","text":"Zomer, Robert; Trabucco, Antonio (2019): Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.7504448.v6\nZomer, R.J.; Xu, J.; Trabuco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database.\nScientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1\nGlobal-ET0 grid layers are available as monthly averages (12 data layers, i.e. one layer for each month) or as an annual average (1 data layer) as well as standard deviation for annual average for the 1970-2000 period.
"},{"location":"projects/et0/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var et_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/global_et0/global_et0_monthly\");\nvar et_yearly = ee.Image(\"projects/sat-io/open-datasets/global_et0/global_et0_yearly\");\nvar et_yearly_sd = ee.Image(\"projects/sat-io/open-datasets/global_et0/global_et0_yearly_sd\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-ET0
"},{"location":"projects/et0/#license","title":"License","text":"The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use under the CC BY 4.0 Attribution 4.0 International license.
Data Website: You can download the data and description here
Curated in GEE by: Samapriya Roy
Keywords: aridity index, evapotranspiration, geospatial modeling
Last updated: 2022-09-02
"},{"location":"projects/fabdem/","title":"FABDEM (Forest And Buildings removed Copernicus 30m DEM)","text":"FABDEM (Forest And Buildings removed Copernicus DEM) removes building and tree height biases from the Copernicus GLO 30 Digital Elevation Model (DEM) (Airbus, 2020). The data is available at 1 arc second grid spacing (approximately 30m at the equator) for the globe. The authors use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (\u223c30 m) grid spacing. You can read the paper here and the overall dataset can be downloaded here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/fabdem/#source-data-structure","title":"Source Data structure","text":"The data are in Geotiff format, with each file divided into 1x1 degree tiles. Files are divided into 10x10 degree zipped folders (detailed in Data structure section below). Files are labelled using the south-west corner of the tile. For example N51E005_FABDEM_V1-0.tif has an extent from 51-52 degrees N, 5-6 degrees E.
Zipped folders are labeled with the southwest corner to northeast corner. For example For example N10E010-N20E020_FABDEM_V1-0.zip has an extent from 10-20 degrees N, 10-20 degrees E.
"},{"location":"projects/fabdem/#citation","title":"Citation","text":"Hawker, Laurence, Peter Uhe, Luntadila Paulo, Jeison Sosa, James Savage, Christopher Sampson, and Jeffrey Neal. \"A 30m global map of elevation with\nforests and buildings removed.\" Environmental Research Letters (2022).\nvar fabdem = ee.ImageCollection(\"projects/sat-io/open-datasets/FABDEM\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/FABDEM
"},{"location":"projects/fabdem/#license","title":"License","text":"The FABDEM dataset is licensed under a Creative Commons \"CC BY-NC-SA 4.0\" license.
This is a non-commercial and ShareAlike license. In other words, FABDEM may not be used for commercial purposes, and if it is remixed, transformed or built upon you must redistribute your contributions under the same license.
When using the data, users must include the below statements, as per the requirement of the original license.
FABDEM is produced using Copernicus WorldDEM-30 \u00a9 DLR e.V. 2010-2014 and \u00a9 Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the Copernicus program by law or by delegation do not incur any liability for any use of the Copernicus WorldDEM-30
The original license can be found: https://docs.sentinel-hub.com/api/latest/static/files/data/dem/resources/license/License-COPDEM-30.pdf
Created by: Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., & Neal, J
Curated by: Samapriya Roy
Keywords: digital elevation model, bare-earth, terrain, remote sensing, machine learning
"},{"location":"projects/fabdem/#changelog","title":"Changelog","text":"This dataset is part of project but is not part of a peer reviewed publication. This will be updated if and when this is converted into a paper and as it progresses through review and publication cycles.Please keep this into consideration while using this dataset
fiboa is a collaborative initiative aimed at enhancing farm field boundary data interoperability and associated agricultural data. Introduced recently, fiboa is more than just a specification; it's a comprehensive system encompassing data adhering to the specification, ongoing discussions to refine the specs, and a vibrant community actively contributing to its development. This project focuses on fostering the creation of more data and open data concerning field boundaries and agriculture to facilitate informed decision-making processes. The emphasis lies on practical application and iterative refinement, rather than the pursuit of a perfect ontology in isolation. You can read about this project here.
"},{"location":"projects/fiboa_uk/#datasets","title":"Datasets","text":""},{"location":"projects/fiboa_uk/#uk-fields","title":"UK Fields","text":"The ukfields dataset is a publicly accessible Earth Engine asset comprising automatically delineated field boundaries across England, Wales, Scotland, and Northern Ireland. This dataset provides comprehensive field boundary information for the United Kingdom, derived from harmonic composites of Sentinel 2 imagery captured in 2021. The delineation process utilized the Segment Anything Model (SAM) developed by Meta, facilitating efficient field segmentation at scale. Furthermore, the segmented fields have been accurately masked to a 2021 Dynamic World composite of cropland, ensuring precise representation within the dataset.
"},{"location":"projects/fiboa_uk/#dataset-preprocessing","title":"Dataset preprocessing","text":"This dataset was further processed to drop empty geometries from the feature collection
"},{"location":"projects/fiboa_uk/#dataset-citation","title":"Dataset Citation","text":"Bancroft, S., & Wilkins, J. (2024). UKFields (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11110206\nvar uk_fields = ee.FeatureCollection(\"projects/sat-io/open-datasets/UK-FIELDS\");\n\nMap.centerObject(uk_fields.first(),12)\nvar empty = ee.Image().byte();\nvar outline = empty.paint({\n featureCollection: uk_fields,\n color: 'random',\n width: 3\n});\n\nMap.addLayer(outline.randomVisualizer(), {opacity:0.8}, 'UK Fields')\nMap.setOptions(\"SATELLITE\")\nThis product is licensed under a Creative Commons Attribution 4.0 International license.
Curated in GEE by: Samapriya Roy and Samuel Bancroft
Keywords: fields, agriculture, UK, england, scotland, wales, northern-ireland
Last updated: 2024-05-11
"},{"location":"projects/firms_vector/","title":"Archival NRT FIRMS Global VIIRS and MODIS vector data","text":"The Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m thermal anomalies / active fire product provides data from the VIIRS sensor aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 satellites. The 375 m data complements Moderate Resolution Imaging Spectroradiometer (MODIS) fire detection; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375 m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
VIIRS NRT 375 m active fire products are from: Suomi NPP (VNP14IMGTDL_NRT) and NOAA-20, formally known as JPSS-1, (VJ114IMGTDL_NRT).
"},{"location":"projects/firms_vector/#notes","title":"Notes","text":"Archival data was downloaded for each year and ingested as shapefiles. You can download the archives here
You can read more about the MODIS product here and the VIIRS product here
"},{"location":"projects/firms_vector/#citations","title":"Citations","text":"NRT VIIRS 375 m Active Fire product VJ114IMGTDL_NRT distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. doi: 10.5067/FIRMS/VIIRS/VJ114IMGT_NRT.002\nNRT VIIRS 375 m Active Fire product VNP14IMGT distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. doi:10.5067/FIRMS/VIIRS/VNP14IMGT_NRT.002\nMODIS Collection 61 NRT Hotspot / Active Fire Detections MCD14DL distributed from NASA FIRMS.Available on-line [https://earthdata.nasa.gov/firms]. 10.5067/FIRMS/MODIS/MCD14DL.NRT.0061\nMODIS Collection 6 Hotspot / Active Fire Detections MCD14ML distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. doi: 10.5067/FIRMS/MODIS/MCD14ML\nThe MODIS and VIIRS yearly exports were ingested and names based on their years (MODIS 2000-2020) and (VIIRS 2012-2021)
MODIS Path: projects/sat-io/open-datasets/MODIS_MCD14DL/MCD14DL_YYYY Example Path: projects/sat-io/open-datasets/MODIS_MCD14DL/MCD14DL_2000
VIIRS Path: projects/sat-io/open-datasets/VIIRS/VNP14IMGTDL_NRT_YYYY Example Path: projects/sat-io/open-datasets/VIIRS/VNP14IMGTDL_NRT_2012
"},{"location":"projects/firms_vector/#earth-engine-snippet","title":"Earth Engine Snippet","text":"Sample paths are provided for two years only change the year to get different years
var viirs_2012 = ee.FeatureCollection(\"projects/sat-io/open-datasets/VIIRS/VNP14IMGTDL_NRT_2012\");\nvar modis_2012 = ee.FeatureCollection(\"projects/sat-io/open-datasets/MODIS_MCD14DL/MCD14DL_2012\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/ARCHIVAL-NRT-FIRMS-VIIRS-DATA
"},{"location":"projects/firms_vector/#license","title":"License","text":"The FIRMS data is distributed under a license similar to Public domain license and distributed by Land, Atmosphere Near real-time Capability for EOS (LANCE) for Fire Information for Resource Management System (FIRMS)
"},{"location":"projects/firms_vector/#acknowledgements","title":"Acknowledgements","text":"We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) (https://earthdata.nasa.gov/firms), part of NASA's Earth Observing System Data and Information System (EOSDIS).
Created by: Land, Atmosphere Near real-time Capability for EOS (LANCE) for Fire Information for Resource Management System (FIRMS), NASA
Curated in GEE by : Samapriya Roy
Keywords: Archival fire, MODIS, VIIRS, Daytime, Nigh time, Thermal anomalies, FIRMS, LANCE, NASA, vector
Last updated: 2022-04-28
Last updated on GEE: 2022-04-28
"},{"location":"projects/flood/","title":"Global large flood events : Dartmouth Flood Observatory (1985-2016)","text":""},{"location":"projects/flood/#abstract","title":"Abstract","text":"The information presented highlights large flood events from 1985 to 2016 identified by the Dartmouth Flood Observatory. For more information visit . For mapping purposes, some types of flood events have been merged into one, under the \"MAINCAUSEF\" attribute. Please refer to the \"MAINCAUSE\" attribute for original data.
"},{"location":"projects/flood/#edition","title":"Edition","text":"G.R.Brakenridge (2017). Global Active Archive of Large Flood Events.\nDartmouth Flood Observatory, University of Colorado.\nRetrieved from https://floodobservatory.colorado.edu/Archives/index.html
"},{"location":"projects/flood/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var flood_events = ee.FeatureCollection(\"projects/sat-io/open-datasets/events/large_flood_events_1985-2016\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/GLOBAL-LARGE-FLOOD-EVENTS
"},{"location":"projects/flood/#data-quality","title":"Data Quality","text":"Each entry in the table and related \"area affected\" map outline represents a discrete flood event. However, repeated flooding in some regions is a complex phenomenon and may require a compromise between aggregating and dividing such events. The listing is comprehensive and global in scope. Deaths and damage estimates for tropical storms are totals from all causes, but tropical storms without significant river flooding are not included. Supplemental Information
The information presented in the Dartmouth Flood Observatory Archive is derived from news, governmental, instrumental, and remote sensing sources. The archive is \"\"active\"\" because current events are added immediately.
"},{"location":"projects/flood/#license-and-restrictions","title":"License and Restrictions","text":"Unless otherwise specified, no restriction applies.
Source http://ihp-wins.unesco.org/layers/geonode:types_flood_events1
For additional information, visit: floodobservatory.colorado.edu/Archives/index.html
Curated by: Samapriya Roy
Keywords: : flood events, flood type, flood cause, Dartmouth Flood Observatory, Intergovernmental Hydrologic Programme
Last updated: 2021-04-29
"},{"location":"projects/floodplain_lc/","title":"Mississippi River Basin Floodplain Land Use Change (1941-2000)","text":"A comprehensive dataset quantifying floodplain land use change along the 3.3 million km2 Mississippi River Basin (MRB) covering 60 years (1941\u20132000) at 250-m resolution.
"},{"location":"projects/floodplain_lc/#citation","title":"Citation","text":"Rajib, A., Zheng, Q., Golden, H.E, Wu, Q., Lane, C.R., Christensen, J.R., Morrison, R.R., Annis, A., & Nardi, F. (2021). The changing face of\nfloodplains in the Mississippi River Basin detected by a 60-year land use change dataset. _Scientific Data_, 8, 271.\nhttps://doi.org/10.1038/s41597-021-01048-w\nvar MRB_boundary = ee.FeatureCollection('users/giswqs/MRB/MRB_Boundary');\nvar floodplain = ee.Image('users/giswqs/MRB/USGS_Floodplain');\nvar img_1950 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1950');\nvar img_1960 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1960');\nvar img_1970 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1970');\nvar img_1980 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1980');\nvar img_1990 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1990');\nvar img_2000 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_2000');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/MISSISSIPPI-RIVER-BASIN-LUC
Earth Engine App: https://giswqs.users.earthengine.app/view/mrb-floodplain
"},{"location":"projects/floodplain_lc/#license","title":"License","text":"This dataset is shared under a Creative Commons Attribution-Share Alike 4.0 International License
Provided by: Rajib et al 2021
Curated by: Qiusheng Wu
Keywords: land use, floodplain, Mississippi River Basin, hydrology, river basin, ecysostems
Last updated: October 2021
"},{"location":"projects/forest_roads/","title":"Congo Basin Forest Roads","text":"This dataset provides high-detail mapping of road development in the tropical forests of the Congo Basin, created using Sentinel-1 and Sentinel-2 satellite imagery combined with a deep learning model. It delivers up-to-date road maps that are openly available and provide essential insights for forest conservation, sustainable management, and policy decisions.
Road construction in the Congo Basin forests, primarily driven by selective logging, poses significant ecological and climate risks. However, the full extent of these road networks, especially in remote areas, has been poorly understood. This dataset reveals all road networks established since 2019, providing a critical tool for studying the effects of logging, monitoring illegal forest activities, and assessing human impact on tropical forests at a large scale.
The road detection method integrates Sentinel-1 radar and Sentinel-2 optical imagery. Sentinel-2 provides high-resolution optical data in clear weather, while Sentinel-1's radar technology can penetrate clouds, offering consistent observation even during the rainy season. This combination ensures precise monthly updates on narrow and transient road segments. The map covers road development across the six Congo Basin countries: Cameroon, Central African Republic, Democratic Republic of the Congo, Equatorial Guinea, Gabon, and Republic of the Congo. This version presents 5 years of road development (46,311 km) from 2019-2023. You can read more in the paper here.
Attribute Description NetworkID A unique ID for each connected road network. SegLenM The length of the road segment (in meters). NetLenM The length of the connected road network (in meters). Month The road segment opening month. Year The road segment opening year. MonthNum The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations."},{"location":"projects/forest_roads/#citation","title":"Citation","text":"Slagter, Bart, Kurt Fesenmyer, Matthew Hethcoat, Ethan Belair, Peter Ellis, Fritz Kleinschroth, Marielos Pe\u00f1a-Claros, Martin Herold, and Johannes Reiche. \"Monitoring road\ndevelopment in Congo Basin forests with multi-sensor satellite imagery and deep learning.\" Remote Sensing of Environment (2024): 114380.\nSlagter, B., Fesenmyer, K., Hethcoat, M., Belair, E., Ellis, P., Kleinschroth, F., Pe\u00f1a-Claros, M., Herold, M., & Reiche, J. (2024). Forest roads (Congo Basin) [Data set]. In\nRemote Sensing of Environment: Vol. xxx (1.02, Number xxx). Zenodo. https://doi.org/10.5281/zenodo.13739812\nvar forest_roads = ee.FeatureCollection(\"projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/FOREST-ROADS
Earth Engine App: https://nrtwur.users.earthengine.app/view/forest-roads
"},{"location":"projects/forest_roads/#license","title":"License","text":"These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
Created by: Slagter et al. 2024
Curated in GEE by: Bart Slagter & Samapriya Roy
Keywords : Congo Basin, forest roads, road development, Sentinel-1, Sentinel-2, deep learning, selective logging, deforestation, illegal logging, forest conservation
Last updated in GEE: 2024-09-10
"},{"location":"projects/fpar/","title":"Sensor-Independent MODIS & VIIRS LAI/FPAR CDR 2000 to 2022","text":"This geospatial dataset encompasses crucial biophysical parameters, namely Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR), indispensable for characterizing terrestrial ecosystems. The dataset addresses the limitations observed in existing global LAI/FPAR products, including challenges related to spatial-temporal coherence and accuracy.
Drawing from a range of long-term global LAI/FPAR products, including MODIS&VIIRS, this dataset facilitates a comprehensive analysis of vegetation dynamics and their interplay with climate change. Developed as a Sensor-Independent (SI) LAI/FPAR Climate Data Record (CDR), this dataset is derived from Terra-MODIS, Aqua-MODIS, and VIIRS LAI/FPAR standard products.
Encompassing a substantial temporal scope spanning from 2000 to 2022, the SI LAI/FPAR CDR provides valuable insights at various spatial resolutions: 500 meters, 5 kilometers, and 0.05 degrees. Its temporal granularity includes 8-day intervals and bimonthly frequency. To facilitate diverse analyses and applications, this dataset is accessible in both sinusoidal and WGS1984 projections. It represents a comprehensive and refined resource for studying terrestrial ecosystems and their response to climate dynamics. You can read the paper here
"},{"location":"projects/fpar/#citation","title":"Citation","text":"Pu, J., Yan, K., Roy, S., Zhu, Z., Rautiainen, M., Knyazikhin, Y., and Myneni, R. B.: Sensor-independent LAI/FPAR CDR:\nreconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022, Earth Syst. Sci.\nData, 16, 15\u201334, https://doi.org/10.5194/essd-16-15-2024, 2024\nPu, J., Roy, S., Knyazikhin, Y., & Myneni, R. (2023). Sensor-Independent LAI/FPAR CDR [Data set]. In Sensor-independent LAI/\nFPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022 (Vol.\n16, Number 1, pp. 15\u201334). Zenodo. https://doi.org/10.5281/zenodo.8076540\nvar wgs_500m_8d = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_500m_8d\");\nvar wgs_5km_8d = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_5km_8d\");\nvar wgs_005degree_8d = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_005degree_8d\");\nvar wgs_500m_bimonthly = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_500m_bimonthly\");\nvar wgs_5km_bimonthly = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_5km_bimonthly\");\nvar wgs_005degree_bimonthly = ee.ImageCollection(\"projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_005degree_bimonthly\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LAI-FPAR-2000-2022
"},{"location":"projects/fpar/#license","title":"License","text":"The dataset is under a Creative Commons Attribution 4.0 International.
Provided by: Jiabin et al
Curated in GEE by : Samapriya Roy
keywords: Sensor-Independent, Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR), Climate Dataset Record (CDR)
Last updated on GEE: 2023-06-09
"},{"location":"projects/france5m/","title":"DEM France (Continental) 5m IGN RGE Alti","text":"The RGE ALTI\u00ae 5m dataset describes the ground elevation of France with a spatial resolution of 5x5m. It is produced by the National Institute of Geographic and Forest Information (IGN - https://www.ign.fr/).IGN or the National Institute for Geographic and Forest Information, is the State operator for geographic and forest information. The Institute intervenes in support of the evaluation and implementation of public risk prevention and regional planning policies The full dataset description is available here. The RGE ALTI\u00ae is updated from surveys obtained by airborne LIDAR or by correlation of aerial images. You can find the dataset description here. You can also find the Google Translated version of the document in English here.
"},{"location":"projects/france5m/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The dataset was preprocessed by Guillaume Attard from the ASCII files and converted into sub regional datasets. These images were then combined into a single Earth Engine image.
"},{"location":"projects/france5m/#suggested-citation-under-etalab-license","title":"Suggested Citation under ETALAB license","text":"Additional information on license and citation guide can be found here
Ministry of xxx\u2014Original data downloaded from http://www.data.gouv.fr/fr/ datasets/xxx/, updated on 14 February 2017.\nvar rge_alti5 = ee.Image(\"projects/sat-io/open-datasets/IGN_RGE_Alti_5m\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/IGN-RGE-France-DEM-5m
"},{"location":"projects/france5m/#license","title":"License","text":"The dataset is licensed under a Etalab Open License 2.0. The \u201cReuser\u201d is free to reuse the \u201cInformation\u201d - to reproduce it, copy it, - to adapt, modify, extract and transform it, to create \"Derived Information\", products or services, - to communicate, distribute, redistribute, publish and transmit it, - to exploit it for commercial purposes, for example by combining it with other information, or by including it in its own product or application.
Created by: National Institute of Geographic and Forest Information (IGN)
Curated in GEE by: Guillaume Attard and Samapriya Roy
Keywords: digital elevation model, terrain, remote sensing, France
"},{"location":"projects/fret/","title":"Forecast Reference Crop Evapotranspiration (FRET)","text":"The National Weather Service is now producing Forecast Reference Crop Evapotranspiration (FRET), a forecast estimate of the amount of evapotranspiration for a well-watered reference crop (grass or alfalfa) under prescribed conditions for a 24 hour period. Weekly FRET forecast calculations and NLDAS derived reference crop ET Climatology and departure from normal are available as well. The Forecast Reference Evapotranspiration (FRET) are for a short canopy (or 12cm grasses). The short canopy ET values are calculated using the Penman-Monteith Reference Evapotranspiration Equations, adopted by the Environmental Water Resources Institute - American Society of Civil engineers (ASCE-EWRI, 2004), and the National Weather Service forecast of temperatures, relative humidity, wind, and cloud cover. This product will be issued daily by 8 am local time, year round. You can get additional information about the dataset here and here. You can further find information about this on the climate engine org data page.
"},{"location":"projects/fret/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Conterminous United States Spatial resolution 4000 m (1/24-deg) Temporal resolution Daily Time span Next 1-7 days (updated every hour) Update frequency HourlyVariables
Variable Details ASCE Grass Reference Evapotranspiration (ETo) - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/fret/#citation","title":"Citation","text":"Palmer, C., Osborne, H., Krone-Davis, P., Melton, F., & Hobbins, M. National Weather Service\u2013Forecast Reference Evapotranspiration (FRET).\n// Read in Image Collection and get single image\nvar fret_ic = ee.ImageCollection('projects/climate-engine/fret/forecast/eto')\nvar fret_i = fret_ic.first()\n\n// Print image to see bands\nprint(fret_i)\n\n// Visualize a single image\nvar fret_palette = [\"#ffffb2\", \"#fed976\", \"#feb24c\", \"#fd8d3c\", \"#fc4e2a\", \"#e31a1c\", \"#b10026\"]\nMap.addLayer(fret_i, {min:0, max:10, palette: fret_palette}, 'fret_i')\nSample Code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/US-FRET
"},{"location":"projects/fret/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: drought, aridity, evaporative demand, ASCE, evapotranspiration, climate, forecast, CONUS, United States
Provided by: NOAA
Curated in GEE by: Climate Engine Org
"},{"location":"projects/gabam/","title":"30m Global Annual Burned Area Maps (GABAM)","text":"Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released.
30 m resolution global annual burned area maps (GABAM) of 1990-2021 are released for free download. The annual burned area map is defined as spatial extent of fires that occurs within a whole year and not of fires that occurred in previous years. GABAM was generated via an automated pipeline based on Google Earth Engine (GEE), using all the available Landsat images on GEE platform. The product was projected in a Geographic (Lat/Long) projection at 0.00025 degree\u200b\u200b (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10 degree \u00d7 10 degree tiles spanning the range 180W\u2013180E and 80N\u201360S.
You can get links to download the data here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/gabam/#paper-citation","title":"Paper Citation","text":"Long, Tengfei, Zhaoming Zhang, Guojin He, Weili Jiao, Chao Tang, Bingfang Wu, Xiaomei Zhang, Guizhou Wang, and Ranyu Yin. 2019. \"30 m Resolution Global Annual\nBurned Area Mapping Based on Landsat Images and Google Earth Engine\" Remote Sensing 11, no. 5: 489. https://doi.org/10.3390/rs11050489\nLong Tengfei; Zhang Zhaoming; He Guojin, 2021, \"30 m Resolution Global Annual Burned Area Product\", https://doi.org/10.7910/DVN/3CTMKP, Harvard Dataverse, V1\nTile names were modified to attach _year to allow for name based sorting as well as start and end year dates were added to each image in the collection.
Note: The user did notice that some 8 years of datasets were missing or not provided by the author such as 1986,1988,1990,1991,1993,1994,1997,1999
"},{"location":"projects/gabam/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var gabam = ee.ImageCollection(\"projects/sat-io/open-datasets/GABAM\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/GLOBAL-ANNUAL-BURNED-AREA-MAPS
"},{"location":"projects/gabam/#license","title":"License","text":"These datasets are made available under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information here.
"},{"location":"projects/gabam/#changelog","title":"Changelog","text":"Created by: Long Tengfei; Zhang Zhaoming; He Guojin
Curated in GEE by : Samapriya Roy
keywords: Global Fire, burned area, GABAM, remote sensing, Earth Engine
Last modified: 2022-07-14
Last updated on GEE: 2024-02-04
"},{"location":"projects/gcb/","title":"Global Channel Belt (GCB)","text":"The Global Channel Belt (GCB) datasets describes the global extent of river channel belts. A two-tier single-threaded (e.g., meandering) versus multi-threaded (e.g., braided) classification is provided showing the likely morphology of the associated active river channel. In addition, the GCB model provides a new global classification of riverine and lacustrine environments based on the mapped extent of the river channel belts.
To read more about the dataset check out the Nature Communications article here. The datasets are also publically available on the Zenodo data repository here.
"},{"location":"projects/gcb/#citation","title":"Citation","text":"Nyberg, B., Henstra, G., Gawthorpe, R.L. et al. Global scale analysis on the extent of river channel belts. Nat Commun 14, 2163 (2023).\nThe combined value of the 'Meandering' and 'Braided' bands yield the confidence of the channel belt extent. The remaining percentage out of a total 100, is the confidence that a pixel is classified as background (or non riverine).
"},{"location":"projects/gcb/#environments-dataset","title":"Environments Dataset","text":"var gcb = ee.Image('projects/sat-io/open-datasets/GCB/GRMM'); // Global Channel Belt Prediction 0 to 100% confidence\nvar env = ee.Image('projects/sat-io/open-datasets/GCB/Env'); // Global Depositional Environment Classifications\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-CHANNEL-BELT
For use as a basemap in QGIS or another GIS software use the following XYZ layer links.
https://storage.googleapis.com/ge_rivermaps/GRMM/tiles/{z}/{x}/{y} #GCB Map\nhttps://storage.googleapis.com/ge_rivermaps/riverClasses/tile/{z}/{x}/{y} #Environments Map\nThis dataset is available under the Creative Commons Attribution 4.0 International
Curated by: Bj\u00f6rn Nyberg
Curated in GEE by: Bj\u00f6rn Nyberg and Samapriya Roy
Keywords: Rivers, Hydrology, Morphology, Landforms, Ecosystems
Last updated: June 8, 2022
"},{"location":"projects/gcc/","title":"Global 1-km Cloud Cover","text":"The Cloud Cover Frequency dataset v1.0 measures over 15 years of twice daily MODIS images to analyze and quantify cloud dynamics and cloud predictions over areas. This allows us to understand global cloud heterogeneity over a spatial and temporal scale. The study establises a baseline for temporal variability of cloud forest, dynamics and allows for users to determine temporal windows of imaging and cloud free snapshots. The complete description of the project can be found here.
Please use Citation:
Wilson AM, Jetz W (2016) Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions. PLoS Biol 14(3):\ne1002415. doi:10.1371/journal. pbio.1002415\nShared Under: Creative Commons Attribution-Non Commercial 4.0 International License.
"},{"location":"projects/gcc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"//EarthEnv Cloud Frequency v1.0\nvar cloud_forest_prediction = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_CloudForestPrediction\");\nvar interannual_sd = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_interannualSD\");\nvar intrannual_sd = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_intraannualSD\");\nvar mean_annual = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_meanannual\");\nvar monthly_mean = ee.ImageCollection(\"projects/sat-io/open-datasets/gcc/MODCF_monthlymean\");\nvar seasonality_concentration = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_seasonality_concentration\");\nvar seasonality_rgb = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_seasonality_rgb\");\nvar seasonality_theta = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_seasonality_theta\");\nvar seasonality_visct = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_seasonality_visct\");\nvar spatial_sd_1deg = ee.Image(\"projects/sat-io/open-datasets/gcc/MODCF_spatialSD_1deg\");\nProject Website: http://www.earthenv.org/cloud
App Website: App link here
Metadata link: http://www.earthenv.org/metadata/Cloud_DataDescription.pdf
Curated by: Samapriya Roy
Created by: Wilson AM, Jetz W 2016
Keywords: Earthenv, cloud concentration, seasonality, MODIS, Global Cloud
Last updated: Refer to webpage
"},{"location":"projects/gcd/","title":"Global Database of Cement Production Assets","text":"The Global Database of Cement Production Assets provides information on global cement production plants that are operational today. The database contains 3,117 cement plants with exact geolocation and provides information about ownership, production type, plant type, capacity and production start year where available.
The process consists of three steps: the mixing of limestone with other materials; the heating of the limestone mixture to produce clinker and the grinding of clinker with different ingredients to produce cement. The grinding process can happen in integrated facilities where the clinker is also produced or in independent grinding facilities closer to its end market. While the bulk of greenhouse gas emissions associated with cement production stem from clinker production and integrated facilities, the database covers both integrated as well as independent grinding facilities.
"},{"location":"projects/gcd/#citation","title":"Citation","text":"McCarten, M., Bayaraa, M., Caldecott, B., Christiaen, C., Foster, P., Hickey, C., Kampmann, D.,\nLayman, C., Rossi, C., Scott, K., Tang, K., Tkachenko, N., and Yoken, D. 2021.\nGlobal Database of Cement Production Assets. Spatial Finance Initiative\nAdditional Information about the Spatial Finance Initiative can be found here
SNo Field Field_Description GEE_Field 1 accuracy The accuracy of the latitude and longitude accuracy 2 capacity Total cement production capacity (millions of tons) cap 3 capacity_source Source used to obtain or estimate the capacity, either a link to reported capacity information or \"Estimated.\" If \"Estimated\" then the capacity has been modelled based on annotated kiln and plant dimensions. cap_sr 4 city City in which the plant is located city 5 country Country in which the plant is located country 6 country_code Three-digit country code defined in ISO 3166-1 numeric country_code 7 iso3 Three-letter country code defined in ISO 3166-1 alpha 3 iso3 8 owner_name Name of the primary owner of the plant ow_name 9 owner_permid PermID of the primary owner of the plant* ow_pid 10 owner_source Source reporting the ownership link between the plant and owner ow_source 11 ownership_stake The percentage ownership attributed to the parent company if the plant is a joint venture. If the plant is majority owned by a single parent company then this column will be blank ow_stake 12 ownership_stake_2 The percentage ownership attributed to the 2nd parent company if the plant is a joint venture ow_stake2 13 parent_exchange The primary exchange for the ultimate parent, if the company is publicly traded pr_exc 14 parent_exchange_2 The primary exchange for the 2nd ultimate parent, if the company is publicly traded pr_exc2 15 parent_holding_status The holding status of the ultimate parent (Private or Public) pr_hstat 16 parent_holding_status_2 The holding status of the 2nd ultimate parent (Private or Public) pr_hstat_2 17 parent_lei Legal Entity Identifier (LEI) of the ultimate parent of the owner of the plant pr_lei 18 parent_lei_2 Legal Entity Identifier (LEI) of the 2nd ultimate parent pr_lei2 19 parent_name Name of the ultimate parent of the owner of the plant pr_name 20 parent_name_2 Name of the 2nd ultimate parent of the owner of the plant pr_name2 21 parent_permid PermID of the ultimate parent of the owner of the plant* pr_pid 22 parent_permid_2 PermID of the 2nd ultimate parent of the owner of the plant* pr_pid2 23 parent_ticker The primary ticker for the ultimate parent, if the company is publicly traded pr_tkr 24 parent_ticker_2 The primary ticker for the 2nd ultimate parent, if the company is publicly traded pr_tkr2 25 plant_type The type of cement plant (Integrated or Grinding) plant_type 26 production_type The production process used to produce the clinker at Integrated plants (Wet or Dry) prod_type 27 region Region in which the plant is located region 28 state State or province in which the plant is located state 29 status Current plant operating status status 30 sub_region Subregion in which the plant is located sub_region 31 uid Unique identifier for the cement plant uid 32 year Year the plant started production year"},{"location":"projects/gcd/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var global_cement = ee.FeatureCollection(\"projects/sat-io/open-datasets/SFI/global_cement_database_20210701\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-CEMENT-PRODUCTION-ASSETS
"},{"location":"projects/gcd/#acknowledgements","title":"Acknowledgements","text":"Both databases have been developed by the Oxford Sustainable Finance Programme, Satellite Applications Catapult, and The Alan Turing Institute as part of the Spatial Finance Initiative \u2018s GeoAsset Project. Project FAQ's can be found here
"},{"location":"projects/gcd/#license","title":"License","text":"The Global Database of Cement Production Assets can be used by others and is available under a CC BY 4.0 license
Data download page: Download Request Form
Curated in GEE by: Samapriya Roy
Keywords: : GeoAsset Project, Oxford Sustainable Finance Programme, Satellite Applications Catapult, Alan Turing Institute, McCarten et al , cement , Global database
Last updated: 2021-07-16
"},{"location":"projects/gcd_assets/","title":"Global database of cement production assets and upstream suppliers","text":"The Global Cement Production Dynamics dataset provides a comprehensive asset-level view of the cement industry, addressing the growing climate and sustainability concerns faced by cement producers and investors. It integrates greenhouse gas emissions disclosure, sourcing patterns of raw materials, and the age of production plants as key variables. Leveraging innovative techniques, including geospatial computer vision and Large Language Modelling, the dataset offers a holistic understanding of global cement production dynamics. It serves various applications, such as environmental impact assessment, investment decision-making, industry research, and policy development, contributing to more informed and responsible decision-making within the sector.
The dataset contains spatial information for 3,117 cement production assets, offering near-approximate location data within a 10-kilometer radius. This data, available for download in Excel format from the Dryad Repository, includes precise coordinates (WGS84), city, state, country, ISO codes, sub-region, and region, obtained through reverse geocoding. Plant-specific details encompass plant type (integrated or grinding), production process (wet or dry), capacity, and the year of production commencement, with corresponding sources for capacity data. Ownership information is also comprehensive, encompassing direct or subsidiary owner names, ultimate parent details, PermID where available, Legal Entity Identifier (LEI), holding status (public or private), ticker, and exchange for ultimate parents. In cases of joint ventures, information for both ultimate parents is provided, alongside source links for ownership data. You can read the paper here.
"},{"location":"projects/gcd_assets/#dataset-structure","title":"Dataset structure","text":"The datasets were renamed to end with the date of dataset upload to Dryad Repository and the primary layer consisting of the assets database includes the following fields. You can expand this section to get all field names or use the example code
Expand to show field names and description for primary asset databaseField Description uid Unique identifier for the cement plant city City in which the plant is located state State or province in which the plant is located country Country in which the plant is located iso3 Three-letter country code defined in ISO 3166-1 alpha 3 country_code Three-digit country code defined in ISO 3166-1 numeric region Region in which the plant is located sub_region Subregion in which the plant is located latitude Latitude for the geolocation of the plant (based on WGS84) longitude Longitude for the geolocation of the plant (based on WGS84) accuracy The accuracy of the latitude and longitude status Current plant operating status plant_type The type of cement plant (Integrated or Grinding) production_type The production process used to produce the clinker at Integrated plants (Wet or Dry) confdnc Accuracy of production capacity (in cases where numerous values are reported) capacity Total cement production capacity (millions of tons) capacity_source Source used to obtain the capacity estimate (news media, company website, or company disclosure reports) year Year the plant started production owner_permid PermID of the primary owner of the plant* owner_name Name of the primary owner of the plant owner_source Source reporting the ownership link between the plant and owner parent_permid PermID of the ultimate parent of the owner of the plant* parent_name Name of the ultimate parent of the owner of the plant ownership_stake The percentage ownership attributed to the parent company if the plant is a joint venture. If the plant is majority owned by a single parent company, then this column will be blank ('n/a') parent_lei Legal Entity Identifier (LEI) of the ultimate parent of the owner of the plant parent_holding_status The holding status of the ultimate parent (Private or Public) parent_ticker The primary ticker for the ultimate parent, if the company is publicly traded parent_exchange The primary exchange for the ultimate parent, if the company is publicly traded parent_permid_2 PermID of the 2nd ultimate parent of the owner of the plant* parent_name_2 Name of the 2nd ultimate parent of the owner of the plant ownership_stake_2 The percentage ownership attributed to the 2nd parent company if the plant is a joint venture parent_lei_2 Legal Entity Identifier (LEI) of the 2nd ultimate parent parent_holding_status_2 The holding status of the 2nd ultimate parent (Private or Public) parent_ticker_2 The primary ticker for the 2nd ultimate parent, if the company is publicly traded parent_exchange_2 The primary exchange for the 2nd ultimate parent, if the company is publicly traded sourcing Locally sourced, imported, or hybrid supply of input production materials raw_mtrl Typology of raw input materials (limestone, clay, gypsum, sand, coal) clinker Whether clinker was used as an input material
The second dataset provides facility and supplier information
Expand to show field names and description for facility and supplier informationField Description uid Unique identifier for the cement plant facility.country Country in which the plant is located supplier.country Country in which facility-supplier (mine) is located supplier.latitude Latitude for the geolocation of the facility-supplier (based on WGS84 (EPSG:4326)) supplier.longitude Longitude for the geolocation of the facility-supplier (based on WGS84 (EPSG:4326))
"},{"location":"projects/gcd_assets/#citation","title":"Citation","text":"Tkachenko, N., Tang, K., McCarten, M. et al. Global database of cement production assets and upstream suppliers. Sci Data 10, 696 (2023).\nhttps://doi.org/10.1038/s41597-023-02599-w\nTkachenko, Nataliya et al. (2023). Global database of cement production assets and upstream suppliers [Dataset]. Dryad.\nhttps://doi.org/10.5061/dryad.6t1g1jx4f\nvar assets_db = ee.FeatureCollection(\"projects/sat-io/open-datasets/SFI/global_cement_db_assets_20231004\");\nvar suppliers_producers_db = ee.FeatureCollection(\"projects/sat-io/open-datasets/SFI/global_cement_db_suppliers_20231004\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-CEMENT-SUPP-PROD-DB
"},{"location":"projects/gcd_assets/#license","title":"License","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license
Data download page: Dryad data download page
Provided by: Tkachenko, N., Tang, K., McCarten, M. et al
Curated in GEE by: Samapriya Roy
Keywords: : Computer vision, Remote sensing, Computer and information sciences, asset-level data, Decarbonisation, LLMs, spatial finance, supply chains, sustainable finance, cement
Last updated: 2023-10-18
"},{"location":"projects/gcep30/","title":"GFSAD Global Cropland Extent Product (GCEP)","text":"The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data of the globe for nominal year 2015 at 30 meter resolution. The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GCEP data product uses the pixel-based supervised classifiers, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products.
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
The datasets are coded as follows and you can find individual links to datasets here
Class Label Name Description 0 Ocean Ocean and Water bodies 1 Non-croplands Non-croplands 2 Croplands Croplands"},{"location":"projects/gcep30/#citation","title":"Citation","text":"Thenkabail, P.S., Teluguntla, P.G., Xiong, J., Oliphant, A., Congalton, R.G., Ozdogan, M., Gumma, M.K., Tilton, J.C., Giri, C., Milesi, C., Phalke,\nA., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., and Foley, D., 2021, Global Cropland-Extent Product at 30-m Resolution (GCEP30)\nDerived from Landsat Satellite Time-Series Data for the Year 2015 Using Multiple Machine-Learning Algorithms on Google Earth Engine Cloud: U.S.\nGeological Survey Professional Paper 1868, 63 p., https://doi.org/10.3133/pp1868.\nGFSAD30 Cropland Extent 2015 Africa 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001\n\nGFSAD30 Cropland Extent 2015 30 m Australia, New Zealand, China, Mongolia 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AUNZCNMOCE.001\n\nGFSAD30 Cropland Extent 2015 Europe, Central Asia, Russia, Middle East 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001\n\nGFSAD30 Cropland Extent 2015 North America 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30NACE.001\n\nGFSAD30 Cropland Extent 2015 South Asia, Afghanistan, Iran 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SAAFGIRCE.001\n\nGFSAD30 Cropland Extent 2015 South America 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SACE.001\n\nGFSAD30 Cropland Extent 2015 Southeast and Northeast Asia 30 m\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SEACE.001\n\nGFSAD30 Cropland Extent 2015 Global Validation\nDOI: https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30VAL.001\nvar gcep30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GFSAD/GCEP30\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GCEP-30-CROPLAND-EXTENT
"},{"location":"projects/gcep30/#license","title":"License","text":"GFSAD GCEP 30 data are freely available to the public (similar to a CC0 license) and are generated by leveraging other national programs including the Landsat satellite program.
Created by: U.S. Geological Survey Center for Earth Resources Observation and Science (EROS)
Curated by: Samapriya Roy
Keywords: Landsat, Global Food, Cropland Extent, GEE, USGS, EROS
Last updated in GEE: 2023-03-01
"},{"location":"projects/gci/","title":"Areas of global conservation value","text":"A series of global priority layers are provided from the NatureMap project. These maps were created by jointly optimizing biodiversity and NCPs such as carbon and/or water. They describe on a continuous scale the amount of land area with the greatest upper potential value for expanding conservation efforts.
NoteConservation in this context should be interpreted as not prescriptive (e.g. specifically the establishment of protected areas), but rather means that a certain area harbour great biophysical potential for contributing to the conservation of biodiversity, carbon and water assets.
"},{"location":"projects/gci/#usage-notes","title":"Usage notes","text":"The datasets cover roughly the period of 2015 to 2019 and with a spatial resolution of 10 km (50 km versions are available as well on the data repository). The datasets were copied over from the source paths to harmonize path and naming conventions within the community catalog and all dunder characters (double underscores __) were removed with a single underscore. Folder names were also split with a hyphen to separate the words like biodiversity-carbon rather than biodiversitycarbon.
The layers can be navigated openly through a dedicated Earth engine app (conservation importance).Coarser grained versions at 50km are also available see Zenodo data repository but not uploaded to Google Earth Engine.
"},{"location":"projects/gci/#citation","title":"Citation","text":"- Jung, M., Arnell, A., de Lamo, X. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat Ecol Evol 5, 1499\u20131509 (2021). https://doi.org/10.1038/s41559-021-01528-7\n\n- Jung, M., Arnell, A., De Lamo, X., Garc\u00eda-Rangelm, S., Lewis, M., Mark, J., Merow, C., Miles, L., Ondo, I., Pironon, S., Ravilious, C., Rivers, M., Schepashenko, D., Tallowin, O., van Soesbergen, A., Govaerts, R., Boyle, B. L., Enquist, B. J., Feng, X., \u2026 Visconti, P. (2021). Areas of global importance for conserving terrestrial biodiversity, carbon, and water (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5006332\n// ------------------------ //\n// Import the layers\n// -- Biodiv --\nvar biodiv_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity/minshort_speciestargets_biome_esh10km_repruns10_ranked\");\nvar biodiv_pa_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity/minshort_speciestargets_biome_withPA_esh10km_repruns10_ranked\");\nvar biodiv = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity/minshort_speciestargets_esh10km_repruns10_ranked\");\nvar biodiv_pa = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity/minshort_speciestargetswithPA_esh10km_repruns10_ranked\");\n// -- Biodiv Carbon--\nvar biodivcarbon_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon/minshort_speciestargets_biome_carbon_esh10km_repruns10_ranked\");\nvar biodivcarbon_pa_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon/minshort_speciestargets_biome_withPA_carbon_esh10km_repruns10_ranked\");\nvar biodivcarbon = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon/minshort_speciestargets_carbon_esh10km_repruns10_ranked\");\nvar biodivcarbon_pa = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon/minshort_speciestargetswithPA_carbon_esh10km_repruns10_ranked\");\n// -- Biodiv water--\nvar biodivwater_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-water/minshort_speciestargets_biome_water_esh10km_repruns10_ranked\");\nvar biodivwater_pa_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-water/minshort_speciestargets_biome_withPA_water_esh10km_repruns10_ranked\");\nvar biodivwater = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-water/minshort_speciestargets_water_esh10km_repruns10_ranked\");\nvar biodivwater_pa = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-water/minshort_speciestargetswithPA_water_esh10km_repruns10_ranked\");\n// -- Biodiv carbonwater--\nvar biodivcarbonwater_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon-water/minshort_speciestargets_biome_carbon_water_esh10km_repruns10_ranked\");\nvar biodivcarbonwater_pa_biome = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon-water/minshort_speciestargets_biome_withPA_carbon_water_esh10km_repruns10_ranked\");\nvar biodivcarbonwater = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon-water/minshort_speciestargets_carbon_water_esh10km_repruns10_ranked\");\nvar biodivcarbonwater_pa = ee.Image(\"projects/sat-io/open-datasets/naturemap/biodiversity-carbon-water/minshort_speciestargetswithPA_carbon_water_esh10km_repruns10_ranked\");\n// ------------------------ //\n\n// Define SLD style of discrete intervals to apply to the image.\nvar default_colours = {min: 1, max: 100, palette: ['7a0403','c92903','f56918','fbb938','c9ef34','74fe5d','1be5b5','35abf8','4662d8','30123b']};\n\n// Default entries\nvar what = \"Biodiversity, carbon and water\";\n\n// Visualize\nMap.addLayer(biodivcarbon, default_colours, \"Biodiversity and Carbon\", true);\n\n// The layers are area-consistent, thus through subsetting it becomes possible to identify for example\n// the 10% of land-areas with the greatest conservation value for biodiversity\n\nvar bio30x30 = biodiv.expression(\"b(0) <= 10\");\nMap.addLayer(bio30x30.mask(bio30x30.eq(1)), {'palette':['red']}, \"Top 10% value for biodiversity only\", false);\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/biodiversity-ecosystems-habitat/GLOBAL-CONSERVATION-IMP-BIODIV-CARBON-WATER
"},{"location":"projects/gci/#enter-license-information","title":"Enter license information","text":"The datasets are provided under a CC-BY-SA 4.0
"},{"location":"projects/gci/#additional-resources","title":"Additional resources","text":"You can explore the dataset layers using this app
Keywords: biodiversity, conservation importance, priorities, carbon, water
Provided by: IIASA
Curated in GEE by: IISA, Samapriya Roy
Last updated in GEE: 2023-10-31
"},{"location":"projects/gci30/","title":"Global Cropping Intensity Dataset 30m","text":"The Global Cropping Intensity Dataset is the first high-resolution (30m) cropping intensity product covering the entire globe. This dataset is constructed using reconstructed time series data of the Normalized Difference Vegetation Index (NDVI) from multiple satellite sources, including Landsat-8, Sentinel-2, and MODIS. The dataset quantifies cropping intensity by enumerating the total number of valid cropping cycles, determined through a binary crop phenophase profile that distinguishes between growing and non-growing periods. To calculate average cropping intensity, the total number of valid cropping cycles is divided by three (representing three years from 2016 to 2018). The implementation of this algorithm leverages the capabilities of the Google Earth Engine (GEE) cloud computing platform to produce global cropping intensity products. You can find additional information in the paper here
"},{"location":"projects/gci30/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The dataset comprises 504 separate GeoTIFF files, each projected in GCS_WGS_1984. The spatial resolution is 0.00026949459 degrees, with each file covering an area of 10\u00b0 \u00d7 10\u00b0. Files are named according to the format: Cropping_Intensity_30m_2016_2018_$regions$.tif, where \"regions\" designates the hemispherical and latitudinal/longitudinal coordinates of the top-left corner. Each file contains two layers:
The dataset includes two key layers for the average cropping intensity and the total number of crop cycles between 2016 and 2018. The Average Cropping Intensity layer classifies cropping patterns into three distinct values: '1' for single cropping, '2' for double cropping, and '3' for triple cropping. Areas with no data or masked regions are assigned a value of '-1'. The Total Number of Crop Cycles layer provides the original counts of crop cycles within the same period. Continuous cropping or instances of more than three crop cycles per year are indicated with a value of '127', while areas with no data or masked regions are also assigned '-1'. The images were ingested as a single image collection.
"},{"location":"projects/gci30/#citation","title":"Citation","text":"Zhang, Miao, Bingfang Wu, Hongwei Zeng, Guojin He, Chong Liu, Shiqi Tao, Qi Zhang et al. \"GCI30: A global dataset of 30-m cropping intensity using\nmultisource remote sensing imagery.\" Earth System Science Data Discussions 2021 (2021): 1-22.\nZhang, Miao; Wu, Bingfang; Zeng, Hongwei; He, Guojin; Liu, Chong; Nabil, Mohsen; Tian, Fuyou; Bofana, Jos\u00e9; Wang, Zhengdong; Yan, Nana, 2020,\n\"GCI30: Global Cropping Intensity at 30m resolution\", https://doi.org/10.7910/DVN/86M4PO, Harvard Dataverse, V2\nvar GCI30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GCI30\");\n\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/72543/assassins-creed-iv\", \"Greyscale\");\n\n// Average Cropping Intensity (Single/Double/Triple Cropping)\nvar average_cropping_intensity = GCI30.median().mask(GCI30.median().neq(-1));\nvar cropping_intensity_palette = ['#ffeda0', '#feb24c', '#f03b20', '#bd0026'];\n\nMap.addLayer(average_cropping_intensity.select('b1'), {\n min: 1,\n max: 3,\n palette: cropping_intensity_palette\n}, 'Average Crop Intensity Single/Double/Triple Cropping');\n\n// Total Number of Crop Cycles\nvar total_crop_cycles = GCI30.median().mask(GCI30.median().neq(-1));\n\n// Recode value 127 to 4 to make the palette continuous\nvar recoded_crop_cycles = total_crop_cycles.select('b2').remap([127], [4]);\n\nvar crop_cycles_palette = ['#762a83', '#af8dc3', '#e7d4e8', '#d9f0d3', '#7fbf7b', '#1b7837'];\n\nMap.addLayer(total_crop_cycles.select('b2'), {\n min: 1,\n max: 4,\n palette: crop_cycles_palette\n}, 'Total Number of Crop Cycles (Recode)');\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GCI30
"},{"location":"projects/gci30/#license","title":"License","text":"The Global Cropping Intensity Dataset is licensed under a CC0 1.0 Universal license.
Created by: Zhang et al 2021
Curated in GEE by: Samapriya Roy
Keywords : cropping intensity,crop cycle,average crop intensity
Last updated in GEE: 2024-10-02
"},{"location":"projects/gcl/","title":"Global Consensus Landcover","text":"The datasets integrate multiple global remote sensing-derived land-cover products and provide consensus information on the prevalence of 12 land-cover classes at 1-km resolution. For additional information about the integration approach and the evaluations of the datasets.
"},{"location":"projects/gcl/#paper-citation","title":"Paper citation","text":"Tuanmu, M.-N. and W. Jetz. 2014. A global 1-km consensus land-cover product for biodiversity and ecosystem modeling.\nGlobal Ecology and Biogeography 23(9): 1031-1045.\nvar barren = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/barren\");\nvar cultivated_and_managed_vegetation = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/cultivated_and_managed_vegetation\");\nvar deciduous_broadleaf_trees = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/deciduous_broadleaf_trees\");\nvar evergreen_deciduous_needleleaf_trees = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/evergreen-deciduous_needleleaf_trees\");\nvar evergreen_broadleaf_trees = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/evergreen_broadleaf_trees\");\nvar herbaceous_vegetation = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/herbaceous_vegetation\");\nvar mixed_other_trees = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/mixed-other_trees\");\nvar open_water = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/open_water\");\nvar regularly_flooded_vegetation = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/regularly_flooded_vegetation\");\nvar shrubs = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/shrubs\");\nvar snow_ice = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/snow-ice\");\nvar urban_built_up = ee.Image(\"projects/sat-io/open-datasets/global_consensus_landcover/urban-built-up\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:earthenv-bd-ecosystems-clim-layers/GLOBAL-CONSENSUS-LANDCOVER
"},{"location":"projects/gcl/#license","title":"License","text":"EarthEnv Global 1-km Consensus Land Cover Version 1 by Tuanmu & Jetz is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Permissions beyond the scope of this license may be available at http://www.earthenv.org/landcover.html.
"},{"location":"projects/gcl/#dataset-citation","title":"Dataset citation","text":"Tuanmu, M.-N. and W. Jetz. 2014. A global 1-km consensus land-cover product for biodiversity and ecosystem modeling.\nGlobal Ecology and Biogeography 23(9): 1031-1045. Data available on-line at http://www.earthenv.org/.\nProject Website: http://www.earthenv.org/landcover
App Website: App link here
Curated by: Samapriya Roy
Keywords: Earthenv, barren, cultivated and managed vegetation, deciduous broadleaf trees, evergreen broadleaf trees, mixed other trees, shrubs, urban built up, evergreen deciduous needleleaf trees, mixed other trees
Last updated: 2021-05-09
"},{"location":"projects/gcn250/","title":"Global Hydrologic Curve Number(GCN250)","text":"The GCN250 is a globally consistent, gridded dataset defining CNs at the 250\u2009m spatial resolution from new global land cover (300\u2009m) and soils data (250\u2009m). GCN250 represents runoff for a combination of the European space agency global land cover dataset for 2015 (ESA CCI-LC) resampled to 250\u2009m and geo-registered with the hydrologic soil group global data product (HYSOGs250m) released in 2018. The potential application of this data includes hydrologic design, land management applications, flood risk assessment, and groundwater recharge modeling. The CN values vary depending on antecedent runoff conditions (ARC), which is affected by the rainfall intensity and duration, total rainfall, soil moisture conditions, cover density, stage of growth, and temperature
"},{"location":"projects/gcn250/#paper-citation","title":"Paper Citation","text":"Jaafar, H.H., Ahmad, F.A. & El Beyrouthy, N. GCN250, new global gridded curve numbers for hydrologic modeling and design.\nSci Data 6, 145 (2019). https://doi.org/10.1038/s41597-019-0155-x\nvar GCN250_Average = ee.Image(\"users/jaafarhadi/GCN250/GCN250Average\")\nvar GCN250_Dry = ee.Image(\"users/jaafarhadi/GCN250/GCN250Dry\")\nvar GCN250_Wet = ee.Image(\"users/jaafarhadi/GCN250/GCN250Wet\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-HYDROLOGIC-CURVE-NUMBER
Extra Info: Dataset is also available as an Earth Engine App (Global Hydrologic Curve Number Explorer) It allows users to visualize the gridded hydrologic curve number dataset at ~250m resolution globally.
Link to the app: https://jaafarhadi.users.earthengine.app/view/hydrologic-curve-number#GEE
Link to app code: https://code.earthengine.google.com/086bf71b76fcaae637df3df2148fbce4
"},{"location":"projects/gcn250/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created and Curated by: Hadi H. Jaafar, Farah A. Ahmad, Naji El Beyrouthy
Keywords: Curve Number, Runoff, Hydrology
Last updated: 2019-06-20
"},{"location":"projects/gdat/","title":"Global Dam Tracker (GDAT) Database","text":"The Global Dam Tracker (GDAT) is one of the most comprehensive geo-referenced global dam databases, encompassing over 35,000 dams worldwide. It includes accurate geo-coordinates, satellite-derived catchment areas, and detailed attribute information such as completion year, dam height, length, purpose, and installed capacity. GDAT is built upon existing global datasets and enriched with regional data from governments, non-profits, and academic sources, especially in the Global South, which often lacks detailed coverage. The dataset is designed for inter-temporal analysis, allowing users to assess the environmental and socioeconomic impacts of dam construction over the past three decades, particularly focusing on changes in global surface water coverage. You can find the paper here and the dataset repository on Zenodo.
"},{"location":"projects/gdat/#citation","title":"Citation","text":"Zhang, Alice Tianbo, and Vincent Xinyi Gu. \"Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information.\"\nScientific data 10, no. 1 (2023): 111.\nZhang, A. T., & Gu, V. X. (2023). Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information [Data set].\nIn Scientific Data (Version v1, Vol. 10, Number 1, p. 111). Zenodo. https://doi.org/10.5281/zenodo.7616852\nvar gdat_catchments = ee.FeatureCollection(\"projects/sat-io/open-datasets/GDAT/GDAT_V1_CATCHMENTS\");\nvar gdat_dams = ee.FeatureCollection(\"projects/sat-io/open-datasets/GDAT/GDAT_V1_DAMS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/hydrology/GLOBAL-DAM-TRACKER
"},{"location":"projects/gdat/#license-information","title":"License Information","text":"The dataset is available under a Creative Commons 4.0 International License.
Provided by: Zhang et al. 2023
Curated in GEE by: Samapriya Roy
Keywords: River Barriers, Reservoirs, Hydropower Dams, Water Storage, Flood Control, Aquatic Ecosystems
Last updated: 2024-10-27
"},{"location":"projects/gde/","title":"Global Groundwater-Dependent Ecosystems (GDEs)","text":"Groundwater is the most widespread source of liquid freshwater, yet its critical role in supporting diverse ecosystems often goes unrecognized. The location and extent of groundwater-dependent ecosystems (GDEs) remain largely unknown in many regions, leading to inadequate protection measures. This dataset offers a high-resolution (approximately 30m) map of GDEs, revealing their presence on over one-third of the global drylands analyzed, including key biodiversity hotspots. GDEs are found to be more extensive and contiguous in areas dominated by pastoralism with lower groundwater depletion rates, indicating that many GDEs have likely already been lost due to unsustainable water and land use practices.
About 53% of the mapped GDEs are located in regions experiencing declining groundwater trends, underscoring the urgent need for protective measures. Despite their importance, only 21% of GDEs are within protected areas or regions with sustainable groundwater management policies, highlighting a significant gap in conservation efforts. Additionally, this dataset explores the connection between GDEs and cultural, socio-economic factors in the Greater Sahel region, emphasizing their role in supporting biodiversity and rural livelihoods. The GDE map is a crucial tool for policymakers, conservationists, and stakeholders to prioritize and develop strategies for safeguarding these vital ecosystems at local, regional, and international levels. You can read the paper here and individual tiles can be downloaded here.
"},{"location":"projects/gde/#citation","title":"Citation","text":"Rohde, M.M., Albano, C.M., Huggins, X. et al. Groundwater-dependent ecosystem map exposes global dryland protection needs.\nNature 632, 101\u2013107 (2024). https://doi.org/10.1038/s41586-024-07702-8\nRohde, M. M., Albano, C., Huggins, X., Klausmeyer, K., & Sharman, A. (2024). Data, code, and outputs for: groundwater-dependent ecosystem map\nexposes global dryland protection needs [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11062894\nvar imageCollection = ee.ImageCollection(\"projects/sat-io/open-datasets/GlobalGDEMap_v6_TNC\");\nprint(imageCollection)\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:biodiversity-ecosystems-habitat/GROUNDWATER-DEP-ECOSYSTEMS
Earth Engine App: https://codefornature.projects.earthengine.app/view/global-gde
"},{"location":"projects/gde/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: TNC, Rohde, M.M., Albano, C.M., Huggins, X. et al
Curated by: TNC & Samapriya Roy
Keywords: : Global Groundwater-Dependent Ecosystems, GDE Mapping, Conflict Hotspots, Climate Change, Food Security
Last updated: 2024-08-28
"},{"location":"projects/gdis/","title":"Geocoded Disasters (GDIS) Dataset (1960\u200a\u2013\u200a2018)","text":"The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region). You can access the dataset from NASA SEDAC and read the full paper here
"},{"location":"projects/gdis/#data-citation","title":"Data Citation","text":"Rosvold, E., and H. Buhaug. 2021. Geocoded Disasters (GDIS) Dataset. Palisades, NY: NASA Socioeconomic Data and\nApplications Center (SEDAC). https://doi.org/10.7927/zz3b-8y61. Accessed DAY MONTH YEAR.\nRosvold, E.L., Buhaug, H. GDIS, a global dataset of geocoded disaster locations. Sci Data 8, 61 (2021).\nhttps://doi.org/10.1038/s41597-021-00846-6\nvar gdis= ee.FeatureCollection(\"projects/sat-io/open-datasets/gdis_1960-2018\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/GEOCODED-DISASTERS-DATASET
"},{"location":"projects/gdis/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: NASA Socioeconomic Data and Applications Center (SEDAC)
Curated by: Samapriya Roy
Keywords: : droughts,earthquakes,floods,landslides,cyclones,volcanic eruptions
Last updated: 2021-08-16
"},{"location":"projects/gdw/","title":"Global Dam Watch (GDW) Database","text":"The Global Dam Watch (GDW) database provides a comprehensive, geo-referenced global repository of river barriers and reservoirs for large-scale analysis. Version 1.0 includes 41,145 river barriers and 35,295 associated reservoir polygons, detailing barrier attributes like height, purpose, year, volume, and discharge. The database is harmonized with global river networks (HydroSHEDS and RiverATLAS) to facilitate hydrological analysis and evaluate upstream/downstream effects. It integrates multiple sources, including satellite-derived data and machine learning techniques, to achieve consistent global coverage and supports various applications such as environmental impact assessments and freshwater system management. You can read more about the database in their paper
The GDW v1.0 database consists of two GIS layers: a point layer containing representative barrier locations with attributes, and a polygon layer of corresponding reservoir outlines with attributes. Each barrier point lies within its reservoir polygon, allowing spatial joining based on location. Both attribute tables share the same unique identification number for each barrier-reservoir pair. Version 1.0 includes 41,145 barrier points and 35,295 reservoir polygons, meaning 5,850 barrier locations have no polygon. These include navigation locks, diversion barrages, flood-event storage check dams, weirs, other instream control barriers, or dams under construction without filled reservoirs. The dataset and its accompanying resources are accessible through the Global Dam Watch platform https://www.globaldamwatch.org and the Figshare repository https://doi.org/10.6084/m9.figshare.25988293.
"},{"location":"projects/gdw/#citation","title":"Citation","text":"Lehner, Bernhard, Penny Beames, Mark Mulligan, Christiane Zarfl, Luca De Felice, Arnout van Soesbergen, Michele Thieme et al. \"The Global Dam Watch database of river barrier and reservoir information for large-scale applications.\" Scientific Data 11, no. 1 (2024): 1069.\nLehner, Bernhard; Beames, Penny; Mulligan, Mark; Zarfl, Christiane; De Felice, Luca; van Soesbergen, Arnout; et al. (2024). Global Dam Watch database version 1.0.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.25988293.v1\nvar gdw_barriers = ee.FeatureCollection(\"projects/sat-io/open-datasets/GDW/GDW_BARRIERS_V1_0\");\nvar gdw_reservoirs = ee.FeatureCollection(\"projects/sat-io/open-datasets/GDW/GDW_RESERVOIRS_V1_0\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/hydrology/GLOBAL-DAM-WATCH-DATABASE
"},{"location":"projects/gdw/#license-information","title":"License Information","text":"The GDW database is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Provided by: Lehner et al 2024, Global Dam Watch
Curated in GEE by: Samapriya Roy
Keywords: River Barriers, Reservoirs, Hydropower Dams, Water Storage, Flood Control, Aquatic Ecosystems
Last updated: 2024-10-27
"},{"location":"projects/gebco/","title":"General Bathymetric Chart of the Oceans (GEBCO)","text":"The GEBCO_2023 Grid is a global terrain model for ocean and land, providing elevation data in meters on a 15 arc-second interval grid. This means that the grid has a spatial resolution of about 1 kilometer at the equator. The data values are pixel-center registered, meaning that they refer to the elevation at the center of each grid cell.
The grid is accompanied by a Type Identifier (TID) Grid, which provides information on the types of source data that the GEBCO_2023 Grid is based on. The primary GEBCO_2023 grid contains land and ice surface elevation information. A version is also made available with under-ice topography/bathymetry information for Greenland and Antarctica.
The GEBCO_2023 Grid was published in April 2023 and is the fifth GEBCO grid developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and GEBCO, which aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor and make it available to all. The GEBCO_2023 Grid is a valuable resource for a variety of applications, including oceanography, geology, marine biology, climate change research, and disaster management.For information on the data sets included in the GEBCO_2021 Grid, please see the list of contributions included in this release of the grid.
"},{"location":"projects/gebco/#data-citation-attribution","title":"Data Citation & Attribution","text":"GEBCO Compilation Group (2023) GEBCO 2023 Grid (doi:10.5285/f98b053b-0cbc-6c23-e053-6c86abc0af7b)\nvar gebco_grid = ee.ImageCollection(\"projects/sat-io/open-datasets/gebco/gebco_grid\");\nvar gebco_sub_ice_topo = ee.ImageCollection(\"projects/sat-io/open-datasets/gebco/gebco_sub-ice-topo\");\nvar gebco_tid_grid = ee.ImageCollection(\"projects/sat-io/open-datasets/gebco/gebco_tid_grid\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/GEBCO
"},{"location":"projects/gebco/#gebco-type-identifier-tid-grid-coding","title":"GEBCO Type Identifier (TID) grid coding","text":"TID Definition 0 Land Direct measurements 10 Singlebeam - depth value collected by a single beam echo-sounder 11 Multibeam - depth value collected by a multibeam echo-sounder 12 Seismic - depth value collected by seismic methods 13 Isolated sounding - depth value that is not part of a regular survey or trackline 14 ENC sounding - depth value extracted from an Electronic Navigation Chart (ENC) 15 Lidar - depth derived from a bathymetric lidar sensor 16 Depth measured by optical light sensor 17 Combination of direct measurement methods Indirect measurements 40 Predicted based on satellite-derived gravity data - depth value is an interpolated value guided by satellite-derived gravity data 41 Interpolated based on a computer algorithm - depth value is an interpolated value based on a computer algorithm (e.g. Generic Mapping Tools) 42 Digital bathymetric contours from charts - depth value taken from a bathymetric contour data set 43 Digital bathymetric contours from ENCs - depth value taken from bathymetric contours from an Electronic Navigation Chart (ENC) 44 Bathymetric sounding - depth value at this location is constrained by bathymetric sounding(s) within a gridded data set where interpolation between sounding points is guided by satellite-derived gravity data 45 Predicted based on helicopter/flight-derived gravity data Unknown 70 Pre-generated grid - depth value is taken from a pre-generated grid that is based on mixed source data types, e.g. single beam, multibeam, interpolation etc. 71 Unknown source - depth value from an unknown source 72 Steering points - depth value used to constrain the grid in areas of poor data coverage"},{"location":"projects/gebco/#license","title":"License","text":"The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid indicates that the user accepts the conditions of use and disclaimer information given below. Users are free to: Copy, publish, distribute and transmit The GEBCO Grid. Adapt The GEBCO Grid. Commercially exploit The GEBCO Grid, by, for example, combining it with other information, or by including it in their own product or application.
Produced by : General Bathymetric Chart of the Oceans (GEBCO), Nippon Foundation-GEBCO Seabed 2030 Project
Curated by: Samapriya Roy
Keywords: :\"Nippon Foundation-GEBCO Seabed 2030 Project, GEBCO, General Bathymetric Chart of the Oceans, Bathymetry , Elevation\"
Last updated: 2023-08-28
"},{"location":"projects/gee_sebal/","title":"geeSEBAL-MODIS Continental scale ET for South America","text":"The geeSEBAL-MODIS Version 0-02 Evapotranspiration (ET) product is an 8-day product produced at 500 meter pixel resolution. The algorithm for ET calculation is based on the SEBAL model and the FAO Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, land cover and land surface temperature (LST).
The pixel values for the layers are the average of all eight days within the period, multiplied by 1000. Images must be multiplied by 0.001 for the actual values. Note that the last 8-day period of each year is a 5 or 6-day composite period, depending on the year. The dataset is available from 2002-07-01 to 2022-12-31. Band information is the following
Name Description Min Max Units Scale Offset ET_24h Daily actual evapotranspiration 0 6601 mm day-1 0.001 0 FE Evaporative fraction 0 1017 [-] 0.001 0 ETr Reference evapotranspiration -220 7543 mm day-1 0.001 0 Rn24h_G Daily average net radiation -84478 198081 w m-2 0.001 0 LE Instantaneous latent heat flux 0 2478572 w m-2 0.001 0 H Instantaneous sensible heat flux -829456 964618 w m-2 0.001 0 Rn Instantaneous net radiation -5435 753836 w m-2 0.001 0 G Instantaneous soil heat flux -2039206 102284 w m-2 0.001 0 End_Members Cold and hot endmember candidates. Pixels with -1.0 values are cold candidates and pixels equal to 1.0 are hot endmember candidates. -121 172 [-] 0.001 0 LST_lat Land surface temperature, corrected by the adiabatic lapse rate and normalized by the solar zenith angle. 272879 330413 K 0.001 0 LST_dem Land surface temperature, corrected by the adiabatic lapse rate 254456 331505 K 0.001 0"},{"location":"projects/gee_sebal/#citation-preprint","title":"Citation Preprint","text":"Comini, Bruno & Ruhoff, Anderson & Laipelt, Leonardo & Fleischmann, Ayan & Huntington, Justin & Morton, Charles & Melton, Forrest & Erickson,\nTyler & Roberti, D\u00e9bora & Souza, Vanessa & Biudes, Marcelo & Machado, Nadja & Santos, Carlos & Cosio, Eric. (2023). geeSEBAL-MODIS: Continental\nscale evapotranspiration based on the surface energy balance for South America. 10.13140/RG.2.2.17579.11041.\nvar geesebal = ee.ImageCollection(\"projects/et-brasil/assets/geesebal/myd11a2/sa/v0-02\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GEESEBAL-ET-SOUTH-AMERICA
"},{"location":"projects/gee_sebal/#license","title":"License","text":"The datset is made available under a CC-BY-4.0 license.
Curated by: Bruno Comini de Andrade, Anderson Ruhoff, Leonardo Laipelt
Keywords: evapotranspiration, South America, water resources
Last updated: 2023-03-26 https://code.earthengine.google.com/ba815cfffab1b2f60ef92446693b9170
"},{"location":"projects/geoboundary/","title":"geoBoundaries Global Database of Political Administrative Boundaries","text":"Produced and maintained by the William & Mary geoLab since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database is an online, open license resource of boundaries (i.e., state, county) for every country in the world. We currently track 199 total entities, including all 195 UN member states, Greenland, Taiwan, Niue, and Kosovo. All boundaries are available to view or download in common file formats, including shapefiles; the only requirement for use is acknowledgement. The most up-to-date information about geoBoundaries can be found at www.geoboundaries.org.
All boundary types have been ingested and are include the following with Admin level varying from 0-4 which have been ingested.
HPSCU - High Precision Single Country Unstandardized. The premier geoBoundaries release, representing the highest precision files available for every country in the world. No standardization is performed on these files, so (for example) two countries may overlap in the case of contested boundaries.
HPSCGS - High Precision Single Country Globally Standardized. A version of geoBoundaries high precision data that has been clipped to the U.S. Department of State boundary file, ensuring no contested boundaries or overlap in the dataset. This globally standardized product may have gaps between countries. If you need a product with no gaps, we recommend our simplified global product.
SSCU - Simplified Single Country Unstandardized. A simplified version of every file available for every country in the world. No standardization is performed on these files, so (for example) two countries may overlap in the case of contested boundaries.
SSCGS - Simplified Single Country Globally Standardized. A version of geoBoundaries simplified data that has been clipped to the U.S. Department of State boundary file, ensuring no contested boundaries or overlap in the dataset. This globally standardized product may have gaps between countries.
CGAZ - Comprehensive Global Administrative Zones. A global composite of the SSCGS ADM0, ADM1 and ADM2, with gaps filled between borders. Also available at higher levels of simplification.
Feature Collection Admin Levels HPSCU ADM0,ADM1,ADM2,ADM3,ADM4 HPSCGS ADM0,ADM1,ADM2,ADM3,ADM4 SSCU ADM0,ADM1,ADM2,ADM3,ADM4 SSCGS ADM0,ADM1,ADM2,ADM3,ADM4 CGAZ ADM0,ADM1,ADM2
"},{"location":"projects/geoboundary/#citation","title":"Citation","text":"You can read the paper here and cite using citation below
Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020)\ngeoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4):\ne0231866. https://doi.org/10.1371/journal.pone.0231866\nYou can also find more information on the webpage along with the github project page
"},{"location":"projects/geoboundary/#data-preprocessing-for-gee","title":"Data Preprocessing for GEE","text":"To make the datasets more amenable they were downloaded using the API and all features in a folder were then merged into single collections pertaining to varying boundary type and admin levels. There might be some missing pieces owing to issues with downloads and or upload to GEE but care has been taken to minimize those efforts.
"},{"location":"projects/geoboundary/#earth-engine-datasets","title":"Earth Engine Datasets","text":"var CGAZ_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/CGAZ_ADM0');\nvar CGAZ_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/CGAZ_ADM1');\nvar CGAZ_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/CGAZ_ADM2');\nvar HPSCGS_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM0');\nvar HPSCGS_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM1');\nvar HPSCGS_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM2');\nvar HPSCGS_ADM3 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM3');\nvar HPSCGS_ADM4 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM4');\nvar HPSCU_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM0');\nvar HPSCU_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM1');\nvar HPSCU_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM2');\nvar HPSCU_ADM3 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM3');\nvar HPSCU_ADM4 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/HPSCU-ADM4');\nvar SSCGS_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM0');\nvar SSCGS_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM1');\nvar SSCGS_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM2');\nvar SSCGS_ADM3 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM3');\nvar SSCGS_ADM4 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCGS-ADM4');\nvar SSCU_ADM0 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM0');\nvar SSCU_ADM1 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM1');\nvar SSCU_ADM2 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM2');\nvar SSCU_ADM3 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM3');\nvar SSCU_ADM4 = ee.FeatureCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/geoboundaries/SSCU-ADM4');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GEOBOUNDARIES
"},{"location":"projects/geoboundary/#code-attribution","title":"Code Attribution","text":"Code Provided by Ujaval Gandhi
"},{"location":"projects/geoboundary/#license","title":"License","text":"Individual data files in the geoBoundaries database are governed by the license or licenses identified within the metadata for each respective boundary and are all variants of partially or completely open licenses. All referenced licenses can be read in their entirety here. Computer code and derivative works generated by the geoBoundaries project are released under the Attribution 4.0 International (CC BY 4.0) license.
Produced and maintained by the William & Mary geoLab since 2017
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: : Metadata, Political Geography, Open Data, Built Structures, Physical Mapping
Last updated: 2021-07-10
"},{"location":"projects/geomorpho90/","title":"Geomorpho90m Geomorphometric Layers","text":"Topographical relief comprises the vertical and horizontal variations of the Earth\u2019s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90\u2009m) and 7.5 arc-second (~250\u2009m) resolutions under the WGS84 geodetic datum, and 100\u2009m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography.
Geomorpho90m is a set of geomorphometric variables derived from MERIT-DEM. The are available at 3 resolutions the ingested ones are the 3 arc-second (~90m) resolution.The layers can be downloaded from OpenTopography or from Google Drive.
Read about the methodology here
Use the following credit when these datasets are cited:
Amatulli, Giuseppe, Daniel McInerney, Tushar Sethi, Peter Strobl, and Sami Domisch. \"Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers.\" Scientific Data 7, no. 1 (2020): 1-18.\nvar geom = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/geom\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GEOMORPHO90-GEOMORPHOLOGICAL-FORMS
"},{"location":"projects/geomorpho90/#first-order-derivatives","title":"First order derivatives","text":"var slope = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/slope\");\nvar aspect = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/aspect\");\nvar aspect_cosine = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/aspect-cosine\");\nvar aspect_sine = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/aspect-sine\");\nvar eastness = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/eastness\");\nvar northness = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/northness\");\nvar convergence = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/convergence\");\nvar spi = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/spi\");\nvar cti = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/cti\");\nvar dx = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dx\");\nvar dy = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dy\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GEOMORPHO90-FIRST-ORDER-DERIVATIVE
"},{"location":"projects/geomorpho90/#second-order-derivatives","title":"Second order derivatives","text":"var dxx = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dxx\");\nvar dxy = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dxy\");\nvar dyy = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dyy\");\nvar pcurv = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/pcurv\");\nvar tcurv = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/tcurv\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GEOMORPHO90-SECOND-ORDER-DERIVATIVE
"},{"location":"projects/geomorpho90/#ruggedeness","title":"Ruggedeness","text":"var elev_stdev = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/elev-stdev\")\nvar vrm = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/vrm\");\nvar roughness = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/roughness\");\nvar tri = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/tri\");\nvar tpi = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/tpi\");\nvar dev_magnitude = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dev-magnitude\");\nvar dev_scale = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/dev-scale\");\nvar rough_magnitude = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/rough-magnitude\");\nvar rough_scale = ee.ImageCollection(\"projects/sat-io/open-datasets/Geomorpho90m/rough-scale\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GEOMORPHO90-RUGGEDENESS
"},{"location":"projects/geomorpho90/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: Geomorpho90m, geomorphometric layers, MERIT DEM, topographic index, terrain ruggedness index, slope
Last updated: 2023-04-04
"},{"location":"projects/gfa/","title":"Global Fire Atlas (2003-2016)","text":"The Global Fire Atlas is a new freely available global dataset that tracks the daily dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Data are available in easily accessible GIS-layers and can also be explored online here and a detailed description of the underlying methodology is provided by Andela et al. (2019).
The data provide unique insights in the environmental conditions that give rise to the world's most extreme wildfires. The world's largest wildfires were found in sparsely populated arid and semiarid grasslands and shrublands of interior Australia, Africa, and Central Asia. Strikingly, fires of these proportions were nearly absent in similar ecosystems of North and South America, possibly due to higher landscape fragmentation and different management practices, including active fire suppression.
While the world's largest fires occurred in more arid ecosystems, the longest fires burned for over 2 months in seasonal regions of the humid tropics and high-latitude forests. In these sparsely populated high fuel-load systems fires can continuously burn as long as weather conditions are favorable. Abnormal weather conditions often synchronized the occurrence of multiple extreme wildfires across larger regions. Global patterns of fire velocity were reversely related to fuel loads, and the highest fire velocities typically occurred in areas of low fuel loads.
"},{"location":"projects/gfa/#citation","title":"Citation","text":"Andela, Niels, Douglas C. Morton, Louis Giglio, Ronan Paugam, Yang Chen, Stijn Hantson, Guido R. Werf, and James T. Randerson.\n\"The Global Fire Atlas of individual fire size, duration, speed and direction.\" Earth System Science Data 11, no. 2 (2019): 529-552.\nvar day_of_burn = ee.ImageCollection(\"projects/sat-io/open-datasets/global-fire-atlas/day_of_burn\");\nvar fire_direction = ee.ImageCollection(\"projects/sat-io/open-datasets/global-fire-atlas/fire_direction\");\nvar fire_line = ee.ImageCollection(\"projects/sat-io/open-datasets/global-fire-atlas/fire_line\");\nvar fire_speed = ee.ImageCollection(\"projects/sat-io/open-datasets/global-fire-atlas/fire_speed\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/GLOBAL-FIRE-ATLAS
Data Location Online Webpage https://www.globalfiredata.org/fireatlas.html User Guide https://glihtdata.gsfc.nasa.gov/files/fire_atlas/Fire_Atlas_user_guide.pdf Overall data hosted by NASA https://glihtdata.gsfc.nasa.gov/files/fire_atlas/The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire
"},{"location":"projects/gfa/#global-fire-atlas-earth-engine-snippet","title":"Global Fire Atlas: Earth Engine Snippet","text":"var ignitions_2003 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2003\");\nvar ignitions_2004 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2004\");\nvar ignitions_2005 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2005\");\nvar ignitions_2006 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2006\");\nvar ignitions_2007 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2007\");\nvar ignitions_2008 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2008\");\nvar ignitions_2009 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2009\");\nvar ignitions_2010 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2010\");\nvar ignitions_2011 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2011\");\nvar ignitions_2012 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2012\");\nvar ignitions_2013 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2013\");\nvar ignitions_2014 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2014\");\nvar ignitions_2015 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2015\");\nvar ignitions_2016 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/ignitions/Global_fire_atlas_V1_ignitions_2016\");\nvar perimeter_2003 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2003\");\nvar perimeter_2004 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2004\");\nvar perimeter_2005 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2005\");\nvar perimeter_2006 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2006\");\nvar perimeter_2007 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2007\");\nvar perimeter_2008 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2008\");\nvar perimeter_2009 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2009\");\nvar perimeter_2010 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2010\");\nvar perimeter_2011 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2011\");\nvar perimeter_2012 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2012\");\nvar perimeter_2013 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2013\");\nvar perimeter_2014 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2014\");\nvar perimeter_2015 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2015\");\nvar perimeter_2016 = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-fire-atlas/perimeter/Global_fire_atlas_V1_perimeter_2016\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/GLOBAL-FIRE-ATLAS
GEE_Property_Name Property_Name Property_Example end_DOY: End Day of Year 33 end_date: End Date 2/2/2003 expansion: Daily fire expansion (km2\u00a0day-1) 0.21 fire_ID: Fire_ID 226089 fire_line: Daily Fire Line 0.46 lat: Latitude 39.8896 lon: Longitude -0.3178 perimeter: Perimeter (km) 1.85 size: Size(km2) 0.21 speed: Speed (km day-1) 0.46 start_DOY: Start Day of Year 33 start_date: Start Date 2/2/2003 tile_ID: Tile_ID h17v05"},{"location":"projects/gfa/#license-usage","title":"License & Usage","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: Human Dimensions, Natural Hazards, Wild Fires, Burned Area, Biosphere, Ecological Dynamics, Fire Ecology, Fire Dynamics, Fire Occurrence
Contact details & Created by: Niels Andela (niels.andela@nasa.gov)
Last updated: 2019-04-24
"},{"location":"projects/gfch/","title":"Global Forest Canopy Height from GEDI & Landsat","text":"A new, 30-m spatial resolution global forest canopy height map was developed through the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides footprint-based measurements of vegetation structure, including forest canopy height between 52\u00b0N and 52\u00b0S globally. The Global Land Analysis and Discover team at the University of Maryland (UMD GLAD) integrated the GEDI data available to date (April-October 2019) with the year 2019 Landsat analysis-ready time-series data (Landsat ARD). The GEDI RH95 (relative height at 95%) metric was used to calibrate the model. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling. The \u201cmoving window\u201d locally calibrated and applied bagged regression tree ensemble model was implemented to ensure high quality of forest height prediction and global map consistency. The model was extrapolated in the boreal regions (beyond the GLAD data range) to create the global forest height prototype map.
The global forest height map is a prototype product that has known issues related to GEDI data quality and Landsat optical time-series data availability and feasibility of characterizing forest structure. GEDI data overestimate forest height on slopes within temperate and subtropical mountain grasslands, e.g. in New Zealand and Lesotho. Tree height over cities and suburbs may be confounded with building height, as GEDI data do not discriminate between the height of vegetation and man-made objects. The GEDI calibration uncertainties (specifically, geolocation precision and land surface height estimation) may be responsible for some of the map errors. The forest height model saturated above 30m and does not adequately represent the height of the tallest trees. The global product will be updated in the future to address most of the issues. The newly processed GEDI data will include refinements to land surface detection algorithms, an urban mask, and improved geolocation. Planned integration of higher spatial resolution Sentinel-2 data will allow the implementation of texture metrics. Application of advanced machine learning tools (namely, convolution neural networks) will be tested to improve forest height modeling accuracy.
Map data within the GEDI data range provided in the geographic coordinates using the WGS84 reference system. 8-bit unsigned LZW-compressed GeoTiff. Pixel size is 0.00025 x 0.00025 degree. Data aggregated into continental mosaics which can be downloaded here. Pixel values: 0-60 Forest canopy height, meters, 101 Water, 102 Snow/ice ,103 No data
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/gfch/#citation","title":"Citation","text":"P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C. E. Silva, J. Armston, R.\nDubayah, J. B. Blair, M. Hofton (2020). https://doi.org/10.1016/j.rse.2020.112165\nvar gf = ee.ImageCollection(\"projects/sat-io/open-datasets/GLAD/GEDI_V27\");\nvar gbf = ee.ImageCollection(\"projects/sat-io/open-datasets/GLAD/GEDI_V25_Boreal\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FOREST-CANOPY-HT-GEDI-LANDSAT
Earth Engine app: http://glad.earthengine.app/view/global-forest-canopy-height-2019
"},{"location":"projects/gfch/#license","title":"License","text":"The global dataset is available online, with no charges for access and no restrictions on subsequent redistribution or use, as long as the proper citation is provided as specified by the Creative Commons Attribution License (CC BY)
Provided by : Potapov et al. 2020
Curated in GEE by : Potapov et al and Samapriya Roy
Keywords: GEDI, Canopy Height, Landsat, Tree
Last updated: 2020-07-25
"},{"location":"projects/gfm_100/","title":"Global Forest Management dataset 2015","text":"The global forest management dataset was published by Lesiv et al. (2022) in Nature Scientific Data. The resultant map at 100m resolution detials six global forest management categories, including 1) naturally regenerating forest without any signs of human activities, 2) naturally regenerating forest with signs of human activities, 3) planted forest, 4) short rotation plantations for timber, 5) oil palm plantations, and 6) agroforestry. The dataset included reference dataset of 226\u2009K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki.
Some of the published dataset included here are
Some of the fields were encoded to remove special characters and renamed for clarity.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/gfm_100/#citation","title":"Citation","text":"Lesiv, Myroslava, Dmitry Schepaschenko, Marcel Buchhorn, Linda See, Martina D\u00fcrauer, Ivelina Georgieva, Martin Jung et al. \"Global forest management\ndata for 2015 at a 100 m resolution.\" Scientific data 9, no. 1 (2022): 199.\nvar class_prob = ee.Image(\"projects/sat-io/open-datasets/GFM/ProbaV_LC100_epoch2015_global_v203\");\nvar crowdsourced_data = ee.FeatureCollection(\"projects/sat-io/open-datasets/GFM/original_crowdsourced_data\");\nvar validation_data = ee.FeatureCollection(\"projects/sat-io/open-datasets/GFM/validation_data_set\");\nvar fml = ee.Image(\"projects/sat-io/open-datasets/GFM/FML_v3-2\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FOREST-MANAGEMENT-DATASET-2015
"},{"location":"projects/gfm_100/#license","title":"License","text":"This dataset is available under a Creative Commons BY-4.0 license with attribution.
Provided by : Myroslava et al. 2022
Curated in GEE by : Samapriya Roy
Keywords: forest management, plantations, land use, map, remote sensing
Last updated in GEE: 2023-05-11
"},{"location":"projects/gfplain250/","title":"Global high-resolution floodplains (GFPLAIN250m)","text":"The GFPLAIN250m includes raster data of Earth's floodplains identified using a geomorphic approach presented in Nardi et al. (2006, 2018). The 250m floodplain dataset is derived by processing the NASA SRTM Digital Elevation model gathered from http://srtm.csi.cgiar.org/, and in particular the 250-m SRTM version 4.1 DTM. The coding used for each continent and additional information are detailed in the metadata included in the GFPLAIN250m data repository. You can find the dataset here
You can read the full paper here. The elevation data are processed by a fast geospatial tool for floodplain mapping available for download at https://github.com/fnardi/GFPLAIN. As per the paper, the GFPLAIN250m dataset can support many applications, including flood hazard mapping, habitat restoration, development studies, and the analysis of human-flood interactions.
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/gfplain250/#paper-citation","title":"Paper Citation","text":"Nardi, F. et al. GFPLAIN250m, a global high-resolution dataset of Earth\u2019s floodplains.\nSci. Data. 6:180309 doi: 10.1038/sdata.2018.309 (2019).\nNardi, Fernando; Annis, Antonio (2018): GFPLAIN250m. figshare. Dataset.\nhttps://doi.org/10.6084/m9.figshare.6665165.v1\nvar gfplain250 = ee.ImageCollection(\"projects/sat-io/open-datasets/GFPLAIN250\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-HIGHRES-FLOODPLAINS
"},{"location":"projects/gfplain250/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created by: Nardi, F. et al
Curated by: Samapriya Roy
Keywords: Floodplain, Digital Elevation Model (DEM), Terrain analysis, river networks, landscape features
Last updated: 2018-11-11
"},{"location":"projects/gfv/","title":"Global Freshwater Variables","text":"The dataset consists of near-global, spatially continuous, and freshwater-specific environmental variables in a standardized 1km grid. We delineated the sub-catchment for each grid cell along the HydroSHEDS river network and summarized the upstream environment (climate, topography, land cover, surface geology and soil) to each grid cell using various metrics (average, minimum, maximum, range, sum, inverse distance-weighted average and sum). All variables were subsequently averaged across single lakes and reservoirs of the Global lakes and Wetlands Database that are connected to the river network. Monthly climate variables were summarized into 19 long-term climatic variables following the \u201cbioclim\u201d framework.
"},{"location":"projects/gfv/#paper-citation","title":"Paper citation","text":"Domisch, S., Amatulli, G., and Jetz, W. (2015) Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution.\nScientific Data 2:150073 doi:10.1038/sdata.2015.73\nvar annual_air_temperature_range_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/annual_air_temperature_range_avg\");\nvar annual_sum_of_precipitation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/annual_sum_of_precipitation_avg\");\nvar barren_lands_sparse_vegetation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/barren_lands_sparse_vegetation_avg\");\nvar catchment_size_sum = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/catchment_size_sum\");\nvar cultivated_and_managed_vegetation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/cultivated_and_managed_vegetation_avg\");\nvar deciduous_broadleaf_trees_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/deciduous_broadleaf_trees_avg\");\nvar elevation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/elevation_avg\");\nvar elevation_range = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/elevation_range\");\nvar evergreen_broadleaf_trees_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/evergreen_broadleaf_trees_avg\");\nvar evergreen_deciduous_needleleaf_trees_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/evergreen_deciduous_needleleaf_trees_avg\");\nvar herbaceous_vegetation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/herbaceous_vegetation_avg\");\nvar mean_annual_air_temperature_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/mean_annual_air_temperature_avg\");\nvar mixed_other_trees_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/mixed_other_trees_avg\");\nvar open_water_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/open_water_avg\");\nvar precambrian_surface_lithology_wsum = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/precambrian_surface_lithology_wsum\");\nvar precipitation_seasonality_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/precipitation_seasonality_avg\");\nvar quaternary_surface_lithology_wsum = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/quaternary_surface_lithology_wsum\");\nvar regularly_flooded_shrub_herbaceous_vegetation_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/regularly_flooded_shrub_herbaceous_vegetation_avg\");\nvar shrubs_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/shrubs_avg\");\nvar slope_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/slope_avg\");\nvar slope_range = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/slope_range\");\nvar snow_ice_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/snow-ice_avg\");\nvar stream_length_sum = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/stream_length_sum\");\nvar urban_builtup_avg = ee.Image(\"projects/sat-io/open-datasets/global_freshwater_variables/urban_builtup_avg\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:earthenv-bd-ecosystems-clim-layers/GLOBAL-FRESHWATER-VARIABLES
"},{"location":"projects/gfv/#license","title":"License","text":"EarthEnv Near-global environmental information for freshwater ecosystems in 1km resolution Version 1 by Domisch et al. is licensed under a \u201cCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\u201d. Permissions beyond the scope of this license may be available at http://www.earthenv.org/.
"},{"location":"projects/gfv/#dataset-citation","title":"Dataset citation","text":"Domisch, S., Amatulli, G., and Jetz, W. (2015) Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution.\nScientific Data 2:150073 doi: 10.1038/sdata.2015.73. Data available online at http://www.earthenv.org/.\nProject Website: http://www.earthenv.org/streams
App Website: App link here
Curated by: Samapriya Roy
Keywords: Earthenv, stream length, urban builtup, slope, shrubs, precambrian surface lithology, barren_lands, precipitation seasonality, herbaceous vegetation
Last updated: 2021-05-09
"},{"location":"projects/gfwed/","title":"Global Fire WEather Database (GFWED)","text":"The Global Fire WEather Database (GFWED) integrates different weather factors influencing the likelihood of a vegetation fire starting and spreading at daily temporal resolution and a ~50-km (0.5-deg x 0.625-deg) spatial resolution from 1980-present. It is based on the Fire Weather Index (FWI) System, the most widely used fire weather system in the world. The FWI System was developed in Canada, and is composed of three moisture codes and three fire behavior indices. The moisture codes capture the moisture content of three generalized fuel classes and the behavior indices reflect the spread rate, fuel consumption and intensity of a fire if it were to start. Details on the development and testing of GFWED can be found in Field et al. (2015) and the evaluation of GFWED products in Field (2020a). Applications of the FWI System can be found in Taylor and Alexander (2006) and technical descriptions are provided by van Wagner (1987) and Dowdy et al. (2009). Additional information about this dataset can be found here and here. You can also find this dataset at the climate engine org page here.
Spatial Extent Global Spatial Resolution ~50-km (0.5-deg x 0.625-deg) Temporal Resolution Daily Time Span 1980-04-02 to present Update Frequency Updated daily with 5-month lag time Variables Fire Weather Index ('FWI') - Units: Unitless - Scale Factor: 1.0
"},{"location":"projects/gfwed/#citation","title":"Citation","text":"Field, R.D., A.C. Spessa, N.A. Aziz, A. Camia, A. Cantin, R. Carr, W.J. de Groot, A.J. Dowdy, M.D. Flannigan, K. Manomaiphiboon, F. Pappenberger, V.\nTanpipat, and X. Wang, 2015: Development of a global fire weather database. Nat. Hazards Earth Syst. Sci., 15, 1407-1423, doi:10.5194/\nnhess-15-1407-2015.\n\nMERRA-2 Overview: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Ronald Gelaro, et al., 2017, J. Clim.,\ndoi: 10.1175/JCLI-D-16-0758.1\n// Read in Image Collection and get first image\nvar merra2_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-merra2_fwi-daily')\nvar merra2_i = merra2_ic.filterDate('2020-08-01', '2020-08-05').first()\n\n// Print first image to see bands\nprint(merra2_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar fwi_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(merra2_i.select('FWI'), {min: 0, max: 100, palette: fwi_palette}, 'FWI')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/GLOBAL-FIRE-WEATHER-DB
Sample Script:
"},{"location":"projects/gfwed/#license","title":"License","text":"NASA Earth science data are made freely available to the public to the fullest extent possible, consistent with applicable laws and regulations. NASA Earth science data are not subject to copyright.
Keywords: climate, fire, wildfire, NASA, MERRA2, daily, global, GFWED
Provided by : GFWED development is supported by the NASA Precipitation Measurement Missions Science Team and the NASA Group on Earth Observations Work Program
Curated in GEE by: Climate Engine Org
"},{"location":"projects/ghap/","title":"Global High Air Pollutants(GHAP) PM2.5 Concentrations (2017-2022)","text":"This dataset is the first big data-derived gapless (100% spatial coverage) daily, monthly, and annual PM2.5 concentration product at 1km resolution (D1K, M1K, and Y1K) for global land areas from 2017 to 2022 and part of Global High Air Pollutants Dataset (GHAP). Leveraging machine learning and big data techniques, the dataset provides unprecedented insights into the spatiotemporal variability of PM2.5 pollution. In particular, it reveals extensive exposure to unhealthy air globally, identifies disparities in exposure between developed/developing countries, urban/rural areas, and within cities, captures the impact of events like COVID-19 lockdowns on air quality, and provides insights into nature-induced pollution episodes (e.g., biomass burning). The dataset boasts high quality, with a cross-validation coefficient of determination (CV-R2) of 0.91 and a root-mean-square error (RMSE) of 9.20 \u00b5g m-3 on the daily basis.
Data Coverage: Variables: Scaling factor Temporal: Daily data from 2017 to 2022 PM2.5 concentration (\u03bcg/m\u00b3) 0.1 Spatial: Global coverage (land areas)"},{"location":"projects/ghap/#citation","title":"Citation","text":"Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3\nDataset citations are available based on the year and how the releases were packaged. You can find them all here and find an example below
Wei, J., & Li, Z. (2024). GlobalHighPM2.5 (2022) (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10795662\nvar GHAP_DAILY = ee.ImageCollection(\"projects/sat-io/open-datasets/GHAP/GHAP_D1K_PM25\");\nvar GHAP_MONTHLY = ee.ImageCollection(\"projects/sat-io/open-datasets/GHAP/GHAP_M1K_PM25\");\nvar GHAP_YEARLY = ee.ImageCollection(\"projects/sat-io/open-datasets/GHAP/GHAP_Y1K_PM25\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GHAP-DATASETS
"},{"location":"projects/ghap/#license","title":"License","text":"These datasets are licensed under a Creative Commons Attribution 4.0 International license.
Provided by: Wei et al
Curated in GEE by: Samapriya Roy
Keywords: PM2.5, Pollutants, Air Quality, Air Pollutants, GHAP
Last updated in GEE: 2024-06-09
"},{"location":"projects/ghh/","title":"Global Habitat Heterogeneity","text":"The datasets contain 14 metrics quantifying spatial heterogeneity of global habitat at multiple resolutions based on the textural features of Enhanced Vegetation Index (EVI) imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). For additional information about the metrics and the evaluations of their utility for biodiversity modeling. The dataset is generated at 1km, 5km and 25km resolution and only the 1km assets are listed here, simply replace _1km by _5km and _25km as needed.
"},{"location":"projects/ghh/#paper-citation","title":"Paper Citation","text":"Tuanmu, M.-N. and W. Jetz. (2015) A global, remote sensing-based characterization of terrestrial habitat heterogeneity\nfor biodiversity and ecosystem modeling. Global Ecology and Biogeography. DOI: 10.1111/geb.12365.\nvar cov = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/coefficient_of_variation_1km\");\nvar contrast = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/contrast_1km\");\nvar corr = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/correlation_1km\");\nvar dissimilarity = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/dissimilarity_1km\");\nvar entropy = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/entropy_1km\");\nvar homogeneity = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/homogeneity_1km\");\nvar maximum = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/maximum_1km\");\nvar mean = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/mean_1km\");\nvar pielou = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/pielou_1km\");\nvar range = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/range_1km\");\nvar shannon = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/shannon_1km\");\nvar simpson = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/simpson_1km\");\nvar sd = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/standard_deviation_1km\");\nvar uniformity = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/uniformity_1km\");\nvar variance = ee.Image(\"projects/sat-io/open-datasets/global_habitat_heterogeneity/variance_1km\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:earthenv-bd-ecosystems-clim-layers/GLOBAL-HABITAT-HETEROGENEITY
"},{"location":"projects/ghh/#license","title":"License","text":"Global Habitat Heterogeneity Metrics Version 1 by Tuanmu & Jetz is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Permissions beyond the scope of this license may be available at http://www.earthenv.org/texture.html.
"},{"location":"projects/ghh/#dataset-citation","title":"Dataset Citation","text":"Tuanmu, M.-N. and W. Jetz. (2015) A global, remote sensing-based characterization of terrestrial habitat heterogeneity\nfor biodiversity and ecosystem modeling. Global Ecology and Biogeography. DOI: 10.1111/geb.12365.\nProject Website: http://www.earthenv.org/texture
App Website: App link here
Curated by: Samapriya Roy
Keywords: Earthenv, habitat heterogeneity, shannon, simpson, pielou, dissimilarity, homogeneity, variance, contrast
Last updated: 2021-05-09
"},{"location":"projects/ghm/","title":"Global human modification v1.5","text":"This updates v1 to v1.5, which provides additional datasets on 6 major stressors at 300 m resolution and two additional time steps (1995 and 2005), as well as reflecting minor data update and processing refinements. Users are advised to use these data instead of \"CSP gHM: Global Human Modification\" (https://developers.google.com/earth-engine/datasets/catalog/CSP_HM_GlobalHumanModification). These data were updated June 17, 2023.
Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 1995, 2000, 2005, 2010, 2015, and 2017 to evaluate temporal changes and spatial patterns of land use modification of terrestrial lands (excluding Antarctica). These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (0.09 km^2 resolution), temporally consistent (for 1990-2015, every 5 years), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset that represents ~2017 conditions and has 14 stressors for an even more comprehensive dataset, but the 2017 results should not be used to calculate change with the other datasets (1990-2015).You can read the paper here. The v1.5 datasets can also be accessed at.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/ghm/#updates-changelog-reasoning","title":"Updates Changelog & reasoning","text":"This updates v1 to v1.5, which provides additional datasets on 6 major stressors at 300 m resolution and two additional time steps (1995 and 2005), as well as reflecting minor data updates and processing refinements. Specifically:
Datasets are provided for each of the 6 stressor groups: built-up areas (BU), agricultural/timber harvest (AG), extractive energy and mining (EX), human intrusions (HI), natural system modifications (NS), and transportation & infrastructure (TI), available now at 300 m resolution for each of the time steps in the 1990-2015 time series.
It provides the addition\u00a0datasets for the years 1995 and 2005, calculated using linear interpolation when stressor data do not provide data at the specific year.
The ESA 150 m water-mask dataset (Lamarche et al. 2017) was used to provide better and more consistent alignment of datasets at the ocean-land-inland water interfaces.
The built-up stressor uses an updated version of the Global Human Settlement Layer (v2022A).
Values provided are 32-bit floating point values, with human modification values ranging from 0.0 to 1.0.
Theobald, David M., Christina Kennedy, Bin Chen, James Oakleaf, Sharon Baruch-Mordo, and Joe Kiesecker. \"Earth transformed: detailed mapping of\nglobal human modification from 1990 to 2017.\" Earth System Science Data 12, no. 3 (2020): 1953-1972.\nTheobald, David M., Kennedy, Christina, Chen, Bin, Oakleaf, James, Baruch-Mordo, Sharon, & Kiesecker, Joe. (2023). Data for detailed temporal\nmapping of global human modification from 1990 to 2017 (v1.5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7534895\nvar waterMask = ee.Image(\"projects/sat-io/open-datasets/GHM/ESACCI-LC-L4-WB-Ocean-Land-Map-150m-P13Y-2000-v40\");\nvar H2017static = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2017_300_60land\")\nvar H2015change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2015c_300_60land\");\nvar H2010change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2010c_300_60land\");\nvar H2005change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2005c_300_60land\");\nvar H2000change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_2000c_300_60land\");\nvar H1995change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_1995c_300_60land\");\nvar H1990change = ee.Image(\"projects/sat-io/open-datasets/GHM/ghm_v15_1990c_300_60land\");\nvar H2017_AG = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-AG\");\nvar H2017_BU = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-BU\");\nvar H2017_EX = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-EX\");\nvar H2017_HI = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-HI\");\nvar H2017_NS = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-NS\");\nvar H2017_TI = ee.ImageCollection(\"projects/sat-io/open-datasets/GHM/SG-TI\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GLOBAL-HUMAN-MODIFICATION
Earth Engine App: https://davidtheobald8.users.earthengine.app/view/global-human-modification-change
"},{"location":"projects/ghm/#license","title":"License","text":"This dataset is available under a CC-BY-SA-4.0.
Curated by: David M. Theobald & Samapriya Roy
Keywords: Global human modification, land use, human pressures, biodiversity
Last updated: June 17, 2023
"},{"location":"projects/ghsl/","title":"Global Human Settlement Layer 2023","text":"The Global Human Settlement Layer (GHSL) project is a comprehensive initiative that generates global spatial data and evidence-based analytics, offering insights into the distribution and characteristics of human presence on Earth. The project follows an open and unrestricted data and methods access policy. The knowledge derived from GHSL plays a crucial role in shaping European policies, fostering public discussions, and facilitating the implementation of international frameworks like the 2030 Development Agenda. This release offers enhanced built-up area information, including surface, volume, and height measurements, along with population data. Moreover, it introduces a new settlement model and a classification system for administrative and territorial units based on the \"Degree of Urbanisation\" framework. The GHSL Data Package 2023 consists of multitemporal products, that offers an insight into the human presence in the past (epochs from 1975 through 2020, 5 years interval) and the future (2025 and 2030). The datasets included can be found below along with descriptors and dataset citations. Methodological citations and additional details on the products themselves can be found here.
"},{"location":"projects/ghsl/#dataset-details","title":"Dataset details","text":"Pesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from\nSentinel2 composite and Landsat, multitemporal (1975-2030). European Commission, Joint Research Centre\n(JRC) [Dataset] doi: 10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA PID:\nhttp://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea\n\nPesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-H R2023A - GHS building height, derived from AW3D30,\nSRTM30, and Sentinel2 composite (2018). European Commission, Joint Research Centre (JRC) [Dataset] doi:\n10.2905/85005901-3A49-48DD-9D19-6261354F56FE PID: http://data.europa.eu/89h/85005901-3a49-\n48dd-9d19-6261354f56fe\n\nPesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-V R2023A - GHS built-up volume grids derived from\njoint assessment of Sentinel2, Landsat, and global DEM data, multitemporal (1975-2030). European\nCommission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/AB2F107A-03CD-47A3-85E5-139D8EC63283\nPID: http://data.europa.eu/89h/ab2f107a-03cd-47a3-85e5-139d8ec63283\n\nPesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-C R2023A - GHS Settlement Characteristics, derived\nfrom Sentinel2 composite (2018) and other GHS R2023A data. European Commission, Joint Research Centre\n(JRC) [Dataset] doi: 10.2905/3C60DDF6-0586-4190-854B-F6AA0EDC2A30 PID:\nhttp://data.europa.eu/89h/3c60ddf6-0586-4190-854b-f6aa0edc2a30\n\nSchiavina, Marcello; Freire, Sergio; Alessandra Carioli; MacManus, Kytt (2023): GHS-POP R2023A - GHS\npopulation grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:\n10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE PID: http://data.europa.eu/89h/2ff68a52-5b5b-4a22-\n8f40-c41da8332cfe\n\nSchiavina, Marcello; Melchiorri, Michele; Pesaresi, Martino (2023): GHS-SMOD R2023A - GHS settlement layers,\napplication of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A,\nmultitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:\n10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA PID: http://data.europa.eu/89h/a0df7a6f-49de-46ea9bde-563437a6e2ba\nvar GHS_BUILT_S_2018 = ee.ImageCollection(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_S_E2018_GLOBE_R2023A_54009_10_V1_0\");\nvar GHS_BUILT_S_2030 = ee.Image(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_S_E2030_GLOBE_R2023A_54009_100_V1_0\");\nvar GHS_BUILT_H = ee.Image(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_H_AGBH_E2018_GLOBE_R2023A_54009_100_V1_0\");\nvar GHS_BUILT_V = ee.Image(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_V_E2030_GLOBE_R2023A_54009_100_V1_0\");\nvar GHS_BUILT_C = ee.ImageCollection(\"projects/sat-io/open-datasets/GHS/GHS_BUILT_C_MSZ_E2018_GLOBE_R2023A_54009_10_V1_0\");\nvar GHS_POP_1975_2030 = ee.ImageCollection(\"projects/sat-io/open-datasets/GHS/GHS_POP\");\nvar GHS_SMOD_1975_2030 = ee.ImageCollection(\"projects/sat-io/open-datasets/GHS/GHS_SMOD\")\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/JRC-GHSL-2023
"},{"location":"projects/ghsl/#license","title":"License","text":"The GHSL has been produced by the EC JRC as open and free data. Reuse is authorised, provided the source is acknowledged. For more information, please read the use conditions European Commission Reuse and Copyright Notice.
Created by: ESA & JRC
Curated in GEE by : Samapriya Roy
keywords: Global Population, Population count, Urban structure, Built up area, Built up volume, Building height
Last modified: 2022-01-20
Last updated on GEE: 2023-09-06
"},{"location":"projects/gid/","title":"Global Database of Iron and Steel Production Assets","text":"The Global Database of Iron and Steel Production Assets provides information on global iron and steel production plants that are operational today. The database contains 1,598 production plants with exact geolocation and provides information about ownership, production type, plant type, capacity and production start year where available.
Primary steel production processes (blast furnace, basic oxygen furnace or open-hearth furnaces), typically use coal as an energy source and take place in large integrated facilities. Whereas secondary steel production processes (electric arc furnaces) typically use electricity as an energy source and take place in so called \u2018mini-mills\u2019. The database captures a wide range of assets across the steel production process, including the procurement and processing of raw materials (in particular coking and pelletisation plants), the production of crude steel (integrated plants and mini-mills) and the production of finished steel products (downstream plants).
"},{"location":"projects/gid/#citation","title":"Citation","text":"McCarten, M., Bayaraa, M., Caldecott, B., Christiaen, C., Foster, P., Hickey, C., Kampmann, D.,\nLayman, C., Rossi, C., Scott, K., Tang, K., Tkachenko, N., and Yoken, D., 2021.\nGlobal Database of Iron and Steel Production Assets. Spatial Finance Initiative\nAdditional Information about the Spatial Finance Initiative can be found here
SNo Field Field_Description GEE_Field 1 uid Unique identifier for the plant uid 2 city City in which the plant is located city 3 state State or province in which the plant is located state 4 country Country in which the plant is located country 5 iso3 Three-letter country code defined in ISO 3166-1 alpha 3 iso3 6 country_code Three-digit country code defined in ISO 3166-1 numeric country_code 7 region Region in which the plant is located region 8 sub_region Subregion in which the plant is located sub_region 9 latitude Latitude for the geolocation of the plant (based on WGS84 (EPSG:4326)) latitude 10 longitude Longitude for the geolocation of the plant (based on WGS84 (EPSG:4326)) longitude 11 accuracy The accuracy of the latitude and longitude accuracy 12 status Current plant operating status status 13 plant_type The type of iron and steel production facility. Plant types include: Integrated, Mini-Mill, DRI, Downstream, Coke, and Pelletisation plants. plant_type 14 primary_production_type The primary production type used at the plant prprod_typ 15 primary_product The primary product that is produced at the plant. Product types include: Crude Steel, Finished Steel, Iron, Coke and Pellets. primary_product 16 capacity Total steel production capacity (millions of tons) of the primary product cap 17 capacity_source Source used to obtain capacity information cap_sr 18 year Year the plant started production year 19 owner_permid PermID of the primary owner of the plant* ow_pid 20 owner_name Name of the primary owner of the plant ow_name 21 owner_source Source reporting the ownership link between the plant and owner ow_sr 22 parent_permid PermID of the ultimate parent of the owner of the plant* pr_pid 23 parent_name Name of the ultimate parent of the owner of the plant pr_name 24 ownership_stake The percentage ownership attributed to the parent company if the plant is a joint venture. If the plant is majority owned by a single parent company then this column will be blank ow_stake 25 parent_lei Legal Entity Identifier (LEI) of the ultimate parent of the owner of the plant pr_lei 26 parent_holding_status The holding status of the ultimate parent (Private or Public) pr_hstat 27 parent_ticker The primary ticker for the ultimate parent, if the company is publicly traded pr_tkr 28 parent_exchange The primary exchange for the ultimate parent, if the company is publicly traded pr_exc 29 parent_permid_2 PermID of the 2nd ultimate parent of the owner of the plant* pr_pid2 30 parent_name_2 Name of the 2nd ultimate parent of the owner of the plant pr_name2 31 ownership_stake_2 The percentage ownership attributed to the 2nd parent company if the plant is a joint venture ow_stake2 32 parent_lei_2 Legal Entity Identifier (LEI) of the 2nd ultimate parent pr_lei2 33 parent_holding_status_2 The holding status of the 2nd ultimate parent (Private or Public) pr_hstat2 34 parent_ticker_2 The primary ticker for the 2nd ultimate parent, if the company is publicly traded pr_tkr2 35 parent_exchange_2 The primary exchange for the 2nd ultimate parent, if the company is publicly traded pr_exc2"},{"location":"projects/gid/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var global_steel = ee.FeatureCollection(\"projects/sat-io/open-datasets/SFI/global_steel_database_20210701\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-IRON-STEEL-PRODUCTION-ASSETS
"},{"location":"projects/gid/#acknowledgements","title":"Acknowledgements","text":"Both databases have been developed by the Oxford Sustainable Finance Programme, Satellite Applications Catapult, and The Alan Turing Institute as part of the Spatial Finance Initiative \u2018s GeoAsset Project. Project FAQ's can be found here
"},{"location":"projects/gid/#license","title":"License","text":"The Global Database of Iron and Steel Production Assets can be used by others and is available under a CC BY 4.0 license
Data download page: Download Request Form
Curated in GEE by: Samapriya Roy
Keywords: : GeoAsset Project, Oxford Sustainable Finance Programme, Satellite Applications Catapult, Alan Turing Institute, McCarten et al , Iron, Steel , Global database
Last updated: 2021-07-16
"},{"location":"projects/gimms_ndvi/","title":"GIMMS Normalized Difference Vegetation Index 1982-2022","text":"The PKU GIMMS Normalized Difference Vegetation Index dataset (PKU GIMMS NDVI, version 1.2) offers consistent global NDVI data at half-month intervals and 1/12\u00b0 resolution, spanning from 1982 to 2022. Its primary objective is to address key uncertainties prevalent in existing global long-term NDVI datasets, specifically mitigating the impact of NOAA satellite orbital drift and AVHRR sensor degradation.
This dataset was generated through the utilization of biome-specific Back-Propagation Neural Network (BPNN) models, leveraging the GIMMS NDVI3g product, and drawing from a pool of 3.6 million high-quality global NDVI samples. To extend its temporal coverage up to 2022, a pixel-wise Random Forests fusion method was employed, integrating data from the MODIS NDVI (MOD13C1).Notably, the PKU GIMMS NDVI dataset demonstrates impressive accuracy when assessed against Landsat NDVI samples. It effectively eliminates the adverse effects of satellite orbital drift and sensor degradation, showcasing robust temporal consistency with MODIS NDVI data concerning pixel values and global vegetation trends. Consequently, this dataset holds significant potential as a foundational resource for research in the realm of global change studies.
The dataset is available in two versions for download: one exclusively reliant on AVHRR data covering the period from 1982 to 2015, and the other consolidated with MODIS NDVI, encompassing data from 1982 to 2022. Users are strongly encouraged to utilize the quality control (QC) layer provided within the dataset to enhance data reliability. Additionally, it is recommended to apply a threshold (e.g., 0.1) for the removal of sparse vegetation during trend analysis, following established methodologies (Zhou et al., 2001; Liu et al., 2016).
"},{"location":"projects/gimms_ndvi/#post-processing","title":"Post Processing","text":"The datasets were renames since periods are not allowed in earth engine filenames so v1.2 was renamed to v12 and dates were added as start dates to each image in the collection.
"},{"location":"projects/gimms_ndvi/#dataset-citation","title":"Dataset citation","text":"Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, & Shilong Piao. (2023). Spatiotemporally consistent global dataset of the GIMMS Normalized\nDifference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2) (V1.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8253971\nLi, Muyi, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao. \"Spatiotemporally consistent global dataset of the GIMMS Normalized\nDifference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022.\" Earth System Science Data 15, no. 9 (2023): 4181-4203.\nvar avhrr_modis_consolidated = ee.ImageCollection(\"projects/sat-io/open-datasets/PKU-GIMMS-NDVI/AVHRR_MODIS_CONSOLIDATED\");\nvar avhrr_solely = ee.ImageCollection(\"projects/sat-io/open-datasets/PKU-GIMMS-NDVI/AVHRR_SOLELY\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GIMMS-NDVI-1982-2022
"},{"location":"projects/gimms_ndvi/#license","title":"License","text":"This work is licensed under Creative Commons Attribution 4.0 International license.
Created by: Li, Muyi, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Curated in GEE by : Samapriya Roy
keywords: PKU GIMMS NDVI, Landsat, MODIS, Back Propagation Neural Network
Last updated on GEE: 2023-10-10
"},{"location":"projects/gisa/","title":"Global Impervious Surface Area (1972-2019)","text":"Using more than three million Landsat satellite images, this research developed the first global impervious surface area (GISA) dataset from 1972 to 2019. Based on 120,777 independent and random reference sites from 270 cities all over the world, the omission error, commission error, and F-score of GISA are 5.16%, 0.82%, and 0.954, respectively. Compared to the existing global datasets, the merits of GISA include: (1) it provided the global ISA maps before the year of1985, and showed the longest time span (1972\u20132019), and the highest accuracy (in terms of a large number of randomly selected and third-party validation sample sets); (2) it presented a new global ISA mapping method, including a semi-automatic global sample collection, a locally adaptive classification strategy, and a spatio-temporal post-processing procedure; and (3) it extracted ISA from the whole global land area (not from an urban mask) and hence reduced the underestimation. The GISA can contribute to further understanding on the human's utilization and reformation to nature during the past half century.
Pixel values in each map indicate the first year when ISA was detected. No-data was labeled as 0. A look-up table for the detected year and pixel value is provided as follow:
year of first ISA:[1972, 1978, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]
pixel value[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]
You can download the dataset here
You can read about the methodology in the paper here
"},{"location":"projects/gisa/#citation","title":"Citation","text":"Huang, X., Li, J., Yang, J. et al. 30 m global impervious surface area dynamics\nand urban expansion pattern observed by Landsat satellites: From 1972 to 2019.\nSci. China Earth Sci. (2021). https://doi.org/10.1007/s11430-020-9797-9\nXin Huang, Jiayi Li, Jie Yang, Zhen Zhang, Dongrui Li, & Xiaoping Liu. (2021).\n30 m global impervious surface area dynamics and urban expansion pattern observed\nby Landsat satellites: from 1972 to 2019 (Version 1.0.0)\n[Data set]. http://doi.org/10.1007/s11430-020-9797-9\nvar gisa = ee.ImageCollection(\"projects/sat-io/open-datasets/GISA_1972_2019\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-IMPERVIOUS-SURFACE-AREA
"},{"location":"projects/gisa/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Xin Huang, Jiayi Li, Jie Yang, Zhen Zhang, Dongrui Li, & Xiaoping Liu
Curated in GEE by: Samapriya Roy
Keywords: Landsat, Urban, Google Earth Engine, Impervious area, Urban expansion
Last updated : 2021-08-01
"},{"location":"projects/gisd30/","title":"Global 30m Impervious-Surface Dynamic Dataset (GISD30)","text":"The Global 30\u2009m Impervious-Surface Dynamic Dataset (GISD30) offers an invaluable resource for understanding the ever-changing landscape of impervious surfaces across the globe from 1985 to 2020. This dataset holds profound scientific significance and practical applications in the realms of urban sustainable development, anthropogenic carbon emissions assessment, and global ecological-environment modeling. The GISD30 was meticulously created through an innovative and automated methodology that capitalizes on the strengths of spectral-generalization and automatic-sample-extraction strategies. Leveraging time-series Landsat imagery on the Google Earth Engine cloud computing platform, the dataset provides comprehensive insights into impervious-surface dynamics.
In the dataset creation process, global training samples and corresponding reflectance spectra were automatically derived, enhancing accuracy and reliability. Spatiotemporal adaptive classification models were employed, taking into account the dynamic nature of impervious surfaces across different epochs and geographical tiles. Furthermore, a spatiotemporal-consistency correction method was introduced to enhance the reliability of impervious-surface dynamics. The GISD30 dynamic model exhibits remarkable accuracy, with an overall accuracy of 90.1% and a kappa coefficient of 0.865, validated using a substantial dataset of 23,322 global time-series samples. This dataset provides vital insights into the doubling of global impervious surface area over the past 35 years, from 1985 to 2020, with Asia experiencing the most substantial increase. The GISD30 dataset is freely accessible and serves as a crucial tool for monitoring urbanization at regional and global scales, offering invaluable support for diverse applications. Access the dataset here (Liu et al., 2021b).
The global dynamic dataset was used to label the expansion information in a single band; specifically, the pervious surface and the impervious surface before 1985 were, respectively, labeled 0 and 1, and the expanded impervious surfaces in the periods 1985\u20131990, 1990\u20131995, 1995\u20132000, 2000\u20132005, 2005\u20132010, 2010\u20132015 and 2015\u20132020 were labeled 2, 3, 4, 5, 6, 7 and 8.
Years Impervious Surface Labels Before 1985 1 1985\u20131990 2 1990\u20131995 3 1995\u20132000 4 2000\u20132005 5 2005\u20132010 6 2010\u20132015 7 2015\u20132020 8
"},{"location":"projects/gisd30/#citation","title":"Citation","text":"Zhang, X., Liu, L., Zhao, T., Gao, Y., Chen, X., and Mi, J.: GISD30: global 30\u2009m impervious-surface dynamic dataset from 1985 to 2020 using\ntime-series Landsat imagery on the Google Earth Engine platform, Earth Syst. Sci. Data, 14, 1831\u20131856,\nhttps://doi.org/10.5194/essd-14-1831-2022, 2022\nLiangyun,Liu; Xiao,Zhang; Tingting,Zhao; Yuan,Gao; Xidong,Chen; Jun,Mi. (2021). GISD30: global 30-m impervious surface dynamic dataset from 1985 to\n2020 using time-series Landsat imagery on the Google Earth Engine platform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5220816\nvar gisd30 = ee.Image(\"projects/sat-io/open-datasets/GISD30_1985_2020\");\n\n//zoom to an urban center\nMap.setCenter(31.16387, 30.97292,8)\n\nvar palette = [\"#808080\", \"#006400\", \"#228B22\", \"#32CD32\", \"#ADFF2F\", \"#FFFF00\", \"#FFA500\", \"#FF0000\"];\n\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/132/light-gray\", \"Grayscale\");\n\nMap.addLayer(gisd30,{min:1,max:8,palette:palette},'GISD 30')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/global-landuse-landcover/GLOBAL-IMPERVIOUS-30-GISD
"},{"location":"projects/gisd30/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Zhang, X., Liu, L., Zhao, T., Gao, Y., Chen, X., and Mi, J.
Curated in GEE by: Samapriya Roy
Keywords: Landsat, Urban, Google Earth Engine, Impervious area, Urban expansion, global dataset
Last updated in GEE: 2023-09-12
"},{"location":"projects/giulu/","title":"Global Intra-Urban Land Use","text":"This dataset provides land use maps for the year 2020 for all 4,000+ cities and metropolitan areas in the world with populations exceeding 100,000. The resulting product is the first freely available, global intra-urban land use maps at 5-meter resolution. The data includes a 4-tier land use taxonomy which at its root distinguishes open-space from built-up area. At the second tier, it subdivides the built-up category into nonresidential and residential areas. The third tier distinguishes formal from informal residential land use, and the fourth tier further subdivides formal and informal residential land uses into more detailed categories. Results of a separate road/street classification model based on the same methods are also provided. You can read more in the paper here
"},{"location":"projects/giulu/#citation","title":"Citation","text":"Guzder-Williams, Brookie, Eric Mackres, Shlomo Angel, Alejandro M. Blei, and Patrick Lamson-Hall. \"Intra-urban land use maps for a global sample of\ncities from Sentinel-2 satellite imagery and computer vision.\" Computers, Environment and Urban Systems 100 (2023): 101917.\nvar ULU = ee.ImageCollection('projects/wri-datalab/cities/urban_land_use/V1')\n\n// Define classes and set color parameters.\nvar CLASSES_7=[\n \"open_space\",\n \"nonresidential\",\n \"atomistic\",\n \"informal_subdivision\",\n \"formal_subdivision\",\n \"housing_project\",\n \"road\"]\nvar COLORS_7=[\n '33A02C',\n 'E31A1C',\n 'FB9A99',\n 'FFFF99',\n '1F78B4',\n 'A6CEE3',\n 'bdbdbd']\nvar CLASSES=CLASSES_7\nvar colors=COLORS_7\nvar ULU7Params = {bands: ['lulc'], min: 0, max: 6, opacity: 1, palette: colors};\n\n// Generate image of 6-class land use from the highest probability class at each pixel.\nvar ULUimage = ULU.select('lulc').reduce(ee.Reducer.firstNonNull()).rename('lulc')\nULUimage=ULUimage.mask(ULUimage.mask().gt(0))\n\n// Generate image of road areas based on a pixels with greater than 50% probability of being road.\nvar roadsImage = ULU.select('road').reduce(ee.Reducer.firstNonNull()).rename('lulc')\nvar roadProb = 50\nvar roadsMask = roadsImage.updateMask(roadsImage.gt(roadProb)).where(roadsImage, 1)\n\n// Composite 6-class land use and roads into as single image.\nvar ULUandRoads = ULUimage.where(roadsMask,6).select('lulc')\n\n// Map both the 6-class land use and composite images.\nMap.addLayer(ULUimage, ULU7Params, 'Intra-urban land use, 6-class (2020)');\nMap.addLayer(ULUandRoads, ULU7Params, 'Intra-urban land use, 7-class (2020)');\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-INTRA-URBAN-LANDUSE
"},{"location":"projects/giulu/#license","title":"License","text":"The dataset is provided under a Creative Commons BY-4.0 license
Keywords: Urban land use maps; Land use land cover; Sentinel-2; Neural networks; Computer vision; Supervised classification; Google Earth Engine; Informal settlements
Provided by: WRI
Curated in GEE by: WRI
Last updated in GEE: 2023-05-29
"},{"location":"projects/glacier/","title":"Global Glacier Elevation change products (2000-2019)","text":"This dataset provides a comprehensive and globally consistent record of glacier elevation and mass changes between 2000 and 2019. It utilizes extensive satellite imagery, primarily from NASA's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and advanced processing techniques to offer a high-resolution view of glacier fluctuations worldwide. The full dataset, including global, regional, tile, and per-glacier data, is publicly available here and you can read the full paper here.
While there are additional datasets provided by the publication only the elevation products are currently ingested.
"},{"location":"projects/glacier/#dataset-features","title":"Dataset Features:","text":"The dataset includes elevation change maps at a 100 m resolution, covering glaciers and their surrounding areas, for various time periods: 5-year intervals from 2000 to 2019, 10-year intervals (2000-2009 and 2010-2019), and the entire 20-year period. These maps, provided as GeoTIFF files, are organized by RGI region and split into 1\u00b0 x 1\u00b0 tiles for easier handling. Both the elevation change rates (meters per year) and their associated 1-sigma uncertainties are included, allowing for a comprehensive understanding of glacier elevation changes and their associated confidence levels. The file naming convention clearly identifies the location of each tile using its southwest corner coordinates.
To facilitate regional analysis, the dataset aggregates glacier change data for 19 major glacier regions around the world as defined by the Randolph Glacier Inventory 6.0. Recognizing the inherent uncertainties in such measurements, the dataset provides thorough uncertainty estimates for both elevation and mass changes. These estimates consider factors like observational coverage, spatial correlations due to instrument resolution and uncorrected noise, and the interpolation of the elevation time series. This ensures the reliability and transparency of the data. Further bolstering confidence in the data, the dataset has undergone extensive validation against independent, high-precision measurements from ICESat and Operation IceBridge campaigns, confirming its accuracy and suitability for a wide range of applications.
"},{"location":"projects/glacier/#citation","title":"Citation:","text":"Hugonnet, R., McNabb, R., Berthier, E. et al. Accelerated global glacier mass loss in the early twenty-first century. Nature 592, 726\u2013731 (2021). https://doi.org/10.1038/s41586-021-03436-z\nvar elevation_change = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-GLACIER-MASS-LOSS/elevation-change\");\nvar error = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-GLACIER-MASS-LOSS/elevation-change-error\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/GLACIER-ELEVATION
"},{"location":"projects/glacier/#license","title":"License","text":"This dataset is made available under the Creative Commons Attribution 4.0 International License.
Created by: Hugonnet, R., McNabb, R., Berthier, E. et al. 2021
Curated in GEE by: Samapriya Roy
Keywords: Altimetry, Digital Elevation Model (DEM), ICESat-2, Glaciers, Elevation change, ASTER, ICESat, Operation IceBridge, Randolph Glacier Inventory
Last updated in GEE: 2024-03-04
"},{"location":"projects/glance/","title":"Global Land Cover Estimation (GLanCE)","text":"The Global Land Cover Estimation (GLanCE) dataset provides high-quality, long-term records of annual land cover and land cover change from 2001 to 2019, using Landsat imagery at 30-meter spatial resolution. The dataset covers all global land areas except Antarctica, and includes 10 Science Data Sets (SDSs) that track land cover, land cover changes and greenness dynamics. The Continuous Change Detection and Classification (CCDC) algorithm is used to identify land cover and changes based on all available, clear Landsat observations.
The GLanCE SDSs are organized into three categories:
Version 1 data includes seven layers and their corresponding band names. Note that these band names differ slightly from those listed in the user guide on the LP DAAC website.
This table shows the layer names and their corresponding band names from Version 1 data.
Layer Name Band Name Land Cover Class LC Previous Class prevClass Change Date changeDate EVI2 Median EVI2median EVI2 Amplitude EVI2amplitude EVI2 Rate EVI2rate Change EVI2 Median changeMag
The user manual with more detailed information about each data layer can be found here.
"},{"location":"projects/glance/#notes","title":"Notes","text":"The citation paper will be updated once we finalize the release of V1 data.
"},{"location":"projects/glance/#citation","title":"Citation","text":"Friedl, M.A., Woodcock, C.E., Olofsson, P., Zhu, Z., Loveland, T., Stanimirova, R., Ar\u00e9valo, P., Bullock, E., Hu, K.-T., Zhang, Y., Turlej, K.,\nTarrio, K., McAvoy, K., Gorelick, N., Wang, J.A., Barber, C.P., Souza, C., 2022. Medium Spatial Resolution Mapping of Global Land Cover and Land\nCover Change Across Multiple Decades From Landsat. Frontiers in Remote Sensing 3. https://doi.org/10.3389/frsen.2022.894571\nAr\u00e9valo, P., R. Stanimirova, E. Bullock, Y. Zhang, K. Tarrio, K. Turlej, K. Hu, K. McAvoy, V. Pasquarella, C. Woodcock, P. Olofsson, Z. Zhu, N.\nGorelick, T. Loveland, C. Barber, M. Friedl. Global Land Cover Mapping and Estimation Yearly 30 m V001. 2022, distributed by NASA EOSDIS Land\nProcesses Distributed Active Archive Center, https://doi.org/10.5067/MEaSUREs/GLanCE/GLanCE30.001. Accessed YYYY-MM-DD.\nvar GLANCE = ee.ImageCollection(\"projects/GLANCE/DATASETS/V001\")\nSample Code 1: Load and visualize datasets
Sample Code 2: Load year of change and EVI2 change data
App: https://glance.earthengine.app/view/datasetviewer
"},{"location":"projects/glance/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created and Curated by: Friedl et al 2022
Keywords: land cover, land cover change, greenness, EVI2, CCDC, global, Landsat, NASA MEaSUREs
Last updated in GEE: 2024-09-25
"},{"location":"projects/glance_training/","title":"GLANCE Global Landcover Training dataset","text":"The GLanCE training dataset, available to the public, is designed for regional-to-global land cover and land cover change analyses. With a medium spatial resolution of 30 meters, it spans the years 1984 to 2020 and is geographically and spectrally representative of all global ecoregions. Offering up to 23 land cover characteristics per training unit, it provides a harmonized, standardized, and comprehensive database that includes information on abrupt and gradual land cover change processes, particularly spanning up to 36 years in select regions. The dataset's adaptability allows users to sub-sample and customize it based on their study region, classification algorithm, and desired classification legend, making it a versatile resource for in-depth land cover investigations. You can read about the dataset in the paper here
Column Name Description Lat Latitude Lon Longitude Start_Year Start year of segment, ranging from 1984 to 2020 (integer) End_Year End year of segment, ranging from 1984 to 2020 (integer) Glance_Class_ID_level1 Level 1 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Barren/sparsely vegetated), 5 (Trees), 6 (Shrub), and 7 (Herbaceous) Glance_Class_ID_level2 Level 2 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Soil), 5 (Rock), 6 (Beach/sand), 7 (Deciduous), 8 (Evergreen), 9 (Mixed), 10 (Shrub), 11 (Grassland), 12 (Agriculture), and 13 (Moss/lichen). NaN values present. Leaf_Type Tree leaf type: broadleaf (1), needleleaf (2), and mixed (3). NaN values present. Impervious_Percent Impervious percent for developed samples: low 0%-30% (1), medium 30%-60% (2), and high 60%-100% (3). NaN values present. Tree_Location Binary integer indicating whether trees are on the interior (0) or edge (1) of a forest. NaN values present. Veg_Density Vegetation density for trees and shrubs: sparse 0%-30% (1), open 30%-60% (2), and closed 60%-100% (3). NaN values present. Veg_Modifier Vegetation modifiers, which can include one or more of the following: Cropland, Plantation, Wetland, Riparian/Flood, Mangrove, Greenhouse, and Trees/Shrub Present. NaN values present. Segment_Type Indicates whether a segment is stable (0) or transitional (1). See Section 1 for a detailed description. Land cover for transitional segments is recorded at both the beginning and end of the time segment - typically the first and last three years. NaN values present. Change Indicates presence (1) or absence (0) of land cover change for Level 1 land cover labels. Includes both abrupt change and gradual change (transitional segments (1) from the Segment_Type attribute) if it happened at any time for that training unit. LC_Confidence Interpreter confidence in the Level 1 land cover label from 1 (lowest) to 3 (highest). NaN values present. Level1_Ecoregion Ecoregion Level 1 number based on World Wildlife Fund definitions. For North America we used ecoregions based on the Environmental Protection Agency\u2019s Ecoregions of North America product. Level2_Ecoregion Ecoregion Level 2 number based on the Environmental Protection Agency\u2019s Ecoregions of North America product. This field is available only for North America and is assigned a value of 0 for all other continents. Continent_Code Assigned continent number: North America (1), South America (2), Africa (3), Europe (4), Asia (5), and Oceania (6). Dataset_Code Assigned dataset number: 1, 2, 3, 4, 5, 902, 999, 700, 701, 702, 703, 704, 705, 706, and 707. Numbers correspond to each Dataset as follows: STEP, CLUSTERING, LCMAP, ABoVE, MapBiomas, Feedback, Training_augment, MODIS_algo, GeoWiki, RadEarth, Collaborator_data, BU_team_collected, GLC30, LUCAS, ASB_crop. For details see Scientific Data publication. Glance_ID Unique ID for each sample. ID ID for each unique combination of latitude and longitude. Change units have the same ID but different Glance_ID. Expand to show Glance Training Level descriptorsLevel 1 Level 2 Description Water (1) Water (1) Areas covered with water throughout the year: streams, canals, lakes, reservoirs, oceans. Ice/snow (2) Ice/snow (2) Land areas with snow and ice cover greater than 50% throughout the year. Developed (3) Developed (3) Areas of intensive use; land covered with structures, including any land functionally related to developed/built-up activity. Barren/sparsely vegetated (4) Land comprised of natural occurrences of soils, sand, or rocks where less than 10% of the area is vegetated. Soil (4) Land covered with less than 10% vegetation and dominated by soil. Rock (5) Land covered with less than 10% vegetation and dominated by rocks. Beach/sand (6) Land covered with less than 10% vegetation and dominated by beach/sand. Trees (5) Land where tree cover is greater than 30%. Note that cleared trees (i.e., clear-cuts) are mapped according to\u00a0current\u00a0cover (e.g., barren/sparsely vegetated, shrubs, or herbaceous). Deciduous (7) Land with tree cover greater than 30% and all trees present are deciduous. Evergreen (8) Land with tree cover greater than 30% and all trees present are evergreen. Mixed (9) Land with tree cover greater than 30% and neither deciduous nor evergreen trees dominate. Shrub (6) Shrub (10) Land with less than 30% tree cover, where total vegetation cover exceeds 10% and shrub cover is greater than 10%. Herbaceous (7) Land covered by herbaceous plants. Total vegetation cover exceeds 10%, tree cover is less than 30%, and shrubs comprise less than 10% of the area. Grassland (11) Herbaceous land covered with grass. Agriculture (12) Herbaceous land covered with cultivated cropland. Moss/lichen (13) Herbaceous land covered with lichen and/or moss.
"},{"location":"projects/glance_training/#citation","title":"Citation","text":"
Stanimirova, R., Tarrio, K., Turlej, K., McAvoy K., Stonebrook S., Hu K-T., Ar\u00e9valo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z.,\nBarber C.P., Souza C., Chen S., Wang J.A., Mensah F., Caldero\u0301n-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S.,\nSiampale A., and M.A. Friedl (2023) A global land cover training dataset from 1984 to 2020. Sci Data 10, 879\nhttps://doi.org/10.1038/s41597-023-02798-5\nStanimirova R., Tarrio K., Turlej K., McAvoy K., Stonebrook S., Hu K-T., Ar\u00e9valo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z.,\nBarber C.P., Souza C., Chen S., Wang J.A., Mensah F., Caldero\u0301n-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S.,\nSiampale A., and M.A. Friedl (2023) \"A Global Land Cover Training Dataset from 1984 to 2020\", Version 1.0, Radiant MLHub. [Date Accessed]\nhttps://doi.org/10.34911/rdnt.x4xfh3\nvar glance_training = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLANCE/GLANCE_TRAINING_DATA_V1\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLANCE-TRAINING
"},{"location":"projects/glance_training/#license","title":"License","text":"The dataset is provided under a Creative Commons Attribution 4.0 International Public License, unless otherwise noted.
Created by: Stanimirova et al, Boston University
Curated in GEE by: Samapriya Roy
Keywords: Glance, LULC, training dataset, Medium resolution, global dataset, land use, land cover
Last updated in GEE: 2024-01-02
"},{"location":"projects/glc10/","title":"Finer Resolution Observation and Monitoring of Global Land Cover 10m (FROM-GLC10)","text":"This work and the paper was designed with an aim to classify 10-m resolution images acquired in 2017 with a sensor on board a different satellite. We examined through the 10-m resolution map, FROM-GLC10, and compared it with our 2017 30-m global land cover map, FROM-GLC30. We found while the results are comparable the 10-m map did provide more spatial details. Although an overall accuracy comparable to the 30-m resolution data was achieved, the actual accuracy of the 10-m resolution map can only be properly assessed with test samples collected from the 10-m resolution data. You can read the paper here
"},{"location":"projects/glc10/#about-from-glc","title":"About FROM-GLC","text":"Global land cover data are key sources of information for understanding the complex interactions between human activities and global change. FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover) is the first 30 m resolution global land cover maps produced using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data.
You can download the dataset here the links are directly to a geotiff file and you can use a downloader like Uget to get to the files.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/glc10/#data-preprocessing","title":"Data preprocessing","text":"The datasets were downloaded and a MODE pyramiding scheme was applied owing to the fact that these are classified datasets. The RGB values were provided by the authors and these were converted to hex code for creating a palette. The sample script also takes into consideration remapping the values to provide a more continuous mix max distribution.
"},{"location":"projects/glc10/#data-citation","title":"Data Citation","text":"Chen, B., B. Xu, Z. Zhu, C. Yuan, H. Ping Suen, J. Guo, N. Xu, W. Li, Y. Zhao, and J. J. S. B. Yang. \"Stable classification with limited sample: Transferring a\n30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017.\" Sci. Bull 64 (2019): 370-373.\nClass Value Remapped Hex code Background 0 0 #000000 Cropland 10 1 #a3ff73 Forest 20 2 #267300 Grass 30 3 #4ce600 Shrub 40 4 #70a800 Water 60 5 #005cff Impervious 80 6 #c500ff Bareland 90 7 #ffaa00 Snow/Ice 100 8 #00ffc5 Cloud 120 9 #ffffff
"},{"location":"projects/glc10/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var GLC10 = ee.ImageCollection(\"projects/sat-io/open-datasets/FROM-GLC10\");\nThis dataset is available under a Creative Commons BY-4.0 license.
Curated in GEE by: Samapriya Roy
Keywords: : landcover, landuse, lulc, 10m, global, world, sentinel 2, FROM-GLC
Last updated on GEE: 2022-09-10
"},{"location":"projects/glc_fcs/","title":"GLC_FCS30D Global 30-meter Land Cover Change Dataset (1985-2022)","text":"The GLC_FCS30D dataset represents a pioneering advancement in global land-cover monitoring, offering comprehensive insights into land cover dynamics at a 30-meter resolution spanning the period from 1985 to 2022. Developed using continuous change detection methods and leveraging the extensive Landsat imagery archives within the Google Earth Engine platform, GLC_FCS30D comprises 35 land-cover subcategories with 26 time steps, updated every five years prior to 2000 and annually thereafter. Through a rigorous refinement process, including spatiotemporal classification and temporal-consistency optimization, the dataset achieves high-confidence accuracy, validated with over 84,000 global samples and achieving an overall accuracy of 80.88%. Notably, GLC_FCS30D elucidates significant trends, revealing forest and cropland variations as dominant drivers of global land cover change over the past 37 years, with a net loss of approximately 2.5 million km\u00b2 of forests and a net gain of around 1.3 million km\u00b2 in cropland area. With its diverse classification system, high spatial resolution, and extensive temporal coverage, GLC_FCS30D serves as a valuable resource for climate change research and sustainable development analysis. Access the dataset here.
Expand to show Land Cover classes, RGB values and hex codes
LC Id Classification System RGB value Color 10 Rainfed cropland (255,255,100) 11 Herbaceous cover cropland (255,255,100) 12 Tree or shrub cover (Orchard) cropland (255,255,0) 20 Irrigated cropland (170,240,240) 51 Open evergreen broadleaved forest (76,115,0) 52 Closed evergreen broadleaved forest (0,100,0) 61 Open deciduous broadleaved forest (0.15<fc<0.4) (170,200,0) 62 Closed deciduous broadleaved forest (fc>0.4) (0,160,0) 71 Open evergreen needle-leaved forest (0.15< fc <0.4) (0,80,0) 72 Closed evergreen needle-leaved forest (fc >0.4) (0,60,0) 81 Open deciduous needle-leaved forest (0.15< fc <0.4) (40,100,0) 82 Closed deciduous needle-leaved forest (fc >0.4) (40,80,0) 91 Open mixed leaf forest (broadleaved and needle-leaved) (160,180,50) 92 Closed mixed leaf forest (broadleaved and needle-leaved) (120,130,0) 120 Shrubland (150,100,0) 121 Evergreen shrubland (150,75,0) 122 Deciduous shrubland (150,100,0) 130 Grassland (255,180,50) 140 Lichens and mosses (255,220,210) 150 Sparse vegetation (fc<0.15) (255,235,175) 152 Sparse shrubland (fc<0.15) (255,210,120) 153 Sparse herbaceous (fc<0.15) (255,235,175) 181 Swamp (0,168,132) 182 Marsh (115,255,223) 183 Flooded flat (158,187,215) 184 Saline (130,130,130) 185 Mangrove (245,122,182) 186 Salt marsh (102,205,171) 187 Tidal flat (68,79,137) 190 Impervious surfaces (195,20,0) 200 Bare areas (255,245,215) 201 Consolidated bare areas (220,220,220) 202 Unconsolidated bare areas (255,245,215) 210 Water body (0,70,200) 220 Permanent ice and snow (255,255,255) 0, 250 Filled value (255,255,255)
"},{"location":"projects/glc_fcs/#dataset-postprocessing","title":"Dataset postprocessing","text":"
The datasets consist of about 961 tiles with the annual layers consisting of about 23 years worth of imagery with each band representing a year from 2000 and the 5 year ones start from 1985 with 3 band representing a gap of 5 year so 1985-1990 is b1, 1990-1995 is b2 and 1990-2000 is b3.
"},{"location":"projects/glc_fcs/#citation","title":"Citation","text":"Zhang, X., Zhao, T., Xu, H., Liu, W., Wang, J., Chen, X., and Liu, L.: GLC_FCS30D: the first global 30\u2009m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method, Earth Syst. Sci. Data, 16, 1353\u20131381, https://doi.org/10.5194/essd-16-1353-2024, 2024.\nLiangyun Liu, Xiao Zhang, & Tingting Zhao. (2023). GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification\nsystem from 1985 to 2022 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8239305\nvar annual = ee.ImageCollection(\"projects/sat-io/open-datasets/GLC-FCS30D/annual\");\nvar five_year = ee.ImageCollection(\"projects/sat-io/open-datasets/GLC-FCS30D/five-years-map\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLC-FCS30D
"},{"location":"projects/glc_fcs/#license","title":"License","text":"This work is licensed under and freely available to the public under Creative Commons Attribution 4.0 International license.
Created by: Zhang et al. 2023
Curated in GEE by : Samapriya Roy
Keywords: GLC_FCS30D, 1985-2022, Land-cover change, Landsat, change detection, Google Earth Engine
Last updated in GEE: 2024-02-20
"},{"location":"projects/glo30/","title":"Copernicus Digital Elevation Model (GLO-30 DEM)","text":"The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. We provide two instances of Copernicus DEM named GLO-30 Public and GLO-90. GLO-90 provides worldwide coverage at 90 meters. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Program. Note that in both cases ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs and was downloaded from the Amazon Open Registry. You can read the documentation here
"},{"location":"projects/glo30/#citation","title":"Citation","text":"Copernicus Digital Elevation Model (DEM) was accessed on DATE from\nvar glo30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GLO-30\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/COPERNICUS_GLO30
Earth Engine comparison app: https://samapriya.users.earthengine.app/view/glob-elevation
"},{"location":"projects/glo30/#license","title":"License","text":"GLO-30 Public is available on a free basis for the general public under the terms and conditions of the license found here.
\u00a9 DLR e.V. 2010-2014 and \u00a9 Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.
"},{"location":"projects/glo30/#disclaimer","title":"Disclaimer","text":"The organisations in charge of the Copernicus programme by law or by delegation do not incur any liability for any use of the Copernicus WorldDEM-30.See Article 6\u00a9 in https://docs.sentinel-hub.com/api/latest/static/files/data/dem/resources/license/License-COPDEM-30.pdf
Created by: European Space Agency, COPERNICUS
Curated in GEE by: Samapriya Roy
Keywords: digital elevation model, terrain, remote sensing, esa, copernicus
"},{"location":"projects/global-mining/","title":"Global Highres Mining Footprints","text":"Mining is of major economic, environmental and societal consequence, yet knowledge and understanding of its global footprint is still limited. Here, we produce a global mining land use dataset via remote sensing analysis of high-resolution, publicly available satellite imagery. The dataset comprises 74,548 polygons, covering ~66,000 km2 of features like waste rock dumps, pits, water ponds, tailings dams, heap leach pads and processing/milling infrastructure. Our polygons finely contour the edges of mine features and do not include the space between them. This distinguishes our dataset from others that employ broader definitions of mining lands. Hence, despite our database being the largest to date by number of polygons, comparisons show relatively lower global land use. Our database is made freely available to support future studies of global mining impacts. A series of spatial analyses are also presented that highlight global mine distribution patterns and broader environmental risks.
"},{"location":"projects/global-mining/#citation","title":"Citation","text":"Tang, Liang, and Tim T. Werner. \"Global mining footprint mapped from high-resolution satellite imagery.\"\nCommunications Earth & Environment 4, no. 1 (2023): 134.\nTang, Liang, & Werner, Tim T. (2023). Global mining footprint mapped from high-resolution satellite imagery.\nCommunications earth & environment, 4(134). https://doi.org/10.5281/zenodo.7894216\nvar mining_footprints = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-mining/global_mining_footprints\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-MINING-FOOTPRINTS
"},{"location":"projects/global-mining/#license","title":"License","text":"This dataset is available under a Creative Commons BY-4.0 license
Curated by: Tang, Liang, & Werner, Tim T.
Keywords: Mining, High Resolution, Global, coal, land-use, metal ores, minerals, raw material extraction
Last updated: April 26, 2023
"},{"location":"projects/global_buildings/","title":"Global Google-Microsoft Open Buildings Dataset","text":"This dataset consolidates Google's V3 Open Buildings and Microsoft's most recent Building Footprints, comprising a staggering 2,534,595,270 footprints. As of September 2023, it stands out as the most comprehensive openly accessible dataset. Encompassing 92% of Level 0 administrative boundaries, the dataset is organized into 182 partitions. Each footprint is explicitly labeled with its origin, denoting whether it is from Google or Microsoft. Accessible in cloud-native geospatial formats such as GeoParquet, FlatGeobuf, and PMTiles, this dataset provides a robust resource for various applications. Further details, including the dataset's comprehensive information and methodology, can be explored here and here, respectively.
"},{"location":"projects/global_buildings/#dataset-schema","title":"Dataset Schema","text":"Country level datasets were inegsted while each row in the dataset provides information on a specific building footprint with associated information on individual columns
Please cite the original citations from source dataset including date of access of the combined dataset for citation here is a sample citation
Google-Microsoft Open Buildings - combined by VIDA, https://beta.source.coop/repositories/vida/google-microsoft-open-buildings. Date Accessed: [Insert the date you accessed the webpage in the format YYYY-MM-DD]\nThe datasets were collected from the country level geoparquet files and only a subset of these are mentioned below while an earthengine ls should provide more information on all countries ingested. All feature collections are in the format
projects/sat-io/open-datasets/VIDA_COMBINED/\"3 letter country ISO code\"
for example India would be
var ind = ee.FeatureCollection(\"projects/sat-io/open-datasets/VIDA_COMBINED/IND\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-COMBINED-BUILDING-FOOTPRINTS-VIDA
Earth Engine App: https://sat-io.earthengine.app/view/global-buildings
"},{"location":"projects/global_buildings/#license","title":"License","text":"The data is shared under the Creative Commons Attribution (CC BY-4.0) license and the Open Data Commons Open Database License (ODbL) v1.0 license. As the user, you can pick which of the two licenses you prefer and use the data under the terms of that license.
Contact information: VIDA has provided contact information and if you'd like more information about the dataset or the processing steps, feel free to write an email to darell@vida.place.
Provided by: VIDA, Google, Microsoft
Curated in GEE by: Samapriya Roy
Last updated in GEE: 2023-11-28
"},{"location":"projects/global_earthquakes/","title":"USGS Global Earthquake dataset","text":"The USGS Earthquake Hazards Program (EHP) offers a comprehensive earthquake dataset, serving as a valuable resource for monitoring, research, and earthquake preparedness worldwide. This dataset encompasses information about earthquakes from various sources, including seismic stations, satellite imagery, and ground-based observations. Continuously updated, it contains a staggering collection of millions of records of earthquake daily.
The USGS earthquake dataset serves a multitude of purposes, including earthquake hazard assessment, which aids in identifying earthquake-prone regions and evaluating potential impacts on communities. Additionally, it supports the development of earthquake early warning systems, enabling timely alerts to mitigate disaster. Furthermore, the dataset is instrumental in the creation of earthquake preparedness and response plans, enhancing community resilience. Lastly, it fuels earthquake research endeavors, facilitating investigations into earthquake hazards and mitigation strategies.
"},{"location":"projects/global_earthquakes/#dataset-processing","title":"Dataset processing","text":"Since exports were allowed in specific chunks I wrote a program to fetch these over periods starting from 1923-2024. This can be updated since the records extend all the way to 1900 and only earthquakes greater than 2.5 and those reviewed were selected.
"},{"location":"projects/global_earthquakes/#dataset-citation","title":"Dataset Citation","text":"U.S. Geological Survey (USGS). (YEAR). Earthquake Hazards Program (EHP). Retrieved from https://earthquake.usgs.gov/earthquakes\nYear Value 1923 129 1924 132 1925 162 1926 276 1927 310 1928 310 1929 314 1930 296 1931 304 1932 513 1933 877 1934 609 1935 691 1936 580 1937 510 1938 565 1939 485 1940 523 1941 453 1942 455 1943 394 1944 348 1945 285 1946 526 1947 672 1948 580 1949 594 1950 604 1951 506 1952 861 1953 797 1954 842 1955 575 1956 688 1957 637 1958 595 1959 734 1960 1147 1961 884 1962 1028 1963 1308 1964 1076 1965 1225 1966 1070 1967 1202 1968 1543 1969 1685 1970 1492 1971 2129 1972 1596 1973 5386 1974 6721 1975 8823 1976 7621 1977 6808 1978 6929 1979 8207 1980 9663 1981 7831 1982 8514 1983 10402 1984 9374 1985 10312 1986 12341 1987 10896 1988 11111 1989 12307 1990 12213 1991 12713 1992 19893 1993 16333 1994 17041 1995 18667 1996 18669 1997 17459 1998 19307 1999 19594 2000 18373 2001 20627 2002 23647 2003 24515 2004 27466 2005 31323 2006 32478 2007 30997 2008 33039 2009 15618 2010 25037 2011 23285 2012 19939 2013 20559 2014 28577 2015 27015 2016 25233 2017 22860 2018 37558 2019 26600 2020 32212 2021 28650 2022 26916 2023 27291 2024 13528
"},{"location":"projects/global_earthquakes/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var usgs_earthquakes = ee.FeatureCollection(\"projects/sat-io/open-datasets/USGS/usgs_earthquakes\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/global-events-layers/USGS-EARTHQUAKES
"},{"location":"projects/global_earthquakes/#license","title":"License","text":"These datasets are public domain data with no use restrictions, though if modifications or derivatives of the product(s) are created, then please add some descriptive modifier to the data set to avoid confusion
"},{"location":"projects/global_earthquakes/#changelog","title":"Changelog","text":"Provided by: USGS
Curated in GEE by: Samapriya Roy
Last updated: 2024-07-28
"},{"location":"projects/global_esi/","title":"NOAA Evaporative Stress Index (ESI)","text":"The Evaporative Stress Index (ESI) is produced by the NOAA Center for Satellite Applications and Research (STAR) and USDA-ARS Hydrology and Remote Sensing Laboratory. The Evaporative Stress Index (ESI) is a thermal indicator of anomalous ET conditions that can be used for drought monitoring. The Evaporative Stress Index (ESI) describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. Here, ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST is a fast- response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. The ESI also demonstrates capability for capturing early signals of \u201cflash drought\u201d, brought on by extended periods of hot, dry and windy conditions leading to rapid soil moisture depletion. You can get additional information on this dataset here and climate engine org data page here.
Spatial Information
Parameter Value Spatial extent Global Spatial resolution 4-km (1/24-deg) Temporal resolution Weekly Time span 2001-01-01 to present Update frequency Updated weekly with 1 week lagVariables
Variable Details 4-week Evaporative Stress Index (\u2018ESI_4wk\u2019) - Units: Unitless - Scale factor: 1.0 12-week Evaporative Stress Index (\u2018ESI_12wk\u2019) - Units: Unitless - Scale factor: 1.0"},{"location":"projects/global_esi/#citation","title":"Citation","text":"- Anderson, M. C., J. M. Norman, G. R. Diak, W. P. Kustas, and J. R. Mecikalski, 1997: A two-source time-integrated model for estimating surface\nfluxes using thermal infrared remote sensing. Remote Sens. Environ., 60, 195-216.\n\n- Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas, 2007a: A climatological study of evapotranspiration and moisture\nstress across the continental U.S. based on thermal remote sensing: I. Model formulation. J. Geophys. Res., 112, D10117, doi:10110.11029/\n12006JD007506.\n\n- Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas, 2007b: A climatological study of evapotranspiration and moisture\nstress across the continental U.S. based on thermal remote sensing: II. Surface moisture climatology. J. Geophys. Res., 112, D11112, doi:11110.11029/\n12006JD007507.\n\n- Anderson, M. C., C. R. Hain, B. Wardlow, J. R. Mecikalski, and W. P. Kustas (2011), Evaluation of a drought index based on thermal remote sensing\nof evapotranspiration over the continental U.S., J. Climate, 24, 2025-2044.\n\n- McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. AMS Eighth conf. on Applied\nClimatology, Anaheim, CA, 179-184.\n\n- McKee, T. B., N. J. Doesken, and J. Kleist, 1995: Drought monitoring with multiple time scales. AMS Ninth conf. on Applied Climatology, Dallas,\nTX, 233-236.\n\n- Norman, J. M., W. P. Kustas, and K. S. Humes, 1995: A two-source approach for estimating soil and vegetation energy fluxes from observations of\ndirectional radiometric surface temperature. Agric. For. Met., 77, 263-293.\n\n- Svoboda, M., and Coauthors, 2002: The Drought Monitor. Bull. Amer. Meteorol. Soc., 83, 1181-1190.\n// Read in Image Collection and get first image\nvar esi_4wk_ic = ee.ImageCollection('projects/climate-engine/esi/4wk')\nvar esi_4wk_i = esi_4wk_ic.filterDate('2020-08-01', '2020-08-10').first()\nvar esi_12wk_ic = ee.ImageCollection('projects/climate-engine/esi/12wk')\nvar esi_12wk_i = esi_12wk_ic.filterDate('2020-08-01', '2020-08-10').first()\n\n// Print first image to see bands\nprint(esi_4wk_i)\nprint(esi_12wk_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar esi_palette = [\"#0000aa\", \"#0000ff\", \"#00aaff\", \"#00ffff\", \"#aaff55\", \"#ffffff\", \"#ffff00\", \"#fcd37f\", \"#ffaa00\", \"#e60000\", \"#730000\"]\nMap.addLayer(esi_4wk_i.select('ESI'), {min: -2.5, max: 2.5, palette: esi_palette}, 'ESI_4wk')\nMap.addLayer(esi_12wk_i.select('ESI'), {min: -2.5, max: 2.5, palette: esi_palette}, 'ESI_12wk')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-ESI-10KM
"},{"location":"projects/global_esi/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: Drought, vegetation, remote sensing, climate, USDA-ARS, NOAA, MODIS, LST, global, near real-time
Provided by NOAA,USDA-ARS
Curated in GEE by: Climate Engine Org
"},{"location":"projects/global_fertilizer/","title":"Global Fertilizer Use by crop & country","text":"Understanding how much inorganic fertilizer (referred to as fertilizer) is applied to different crops at national, regional and global levels is an essential component of fertilizer consumption analysis and demand projection. Good information on fertilizer use by crop (FUBC) is rarely available because it is difficult to collect and time-consuming to process and validate. To fill this gap, a first global FUBC report was published in 1992 for the 1990/1991 period, based on an expert survey conducted jointly by the Food and Agriculture Organization (FAO) of the UN, the International Fertilizer Development Center (IFDC) and the International Fertilizer Association (IFA). Since then, similar expert surveys have been carried out and published every two to four years in the main fertilizer-consuming countries. Since 2008 IFA has led these efforts and, to our knowledge, remains the only globally available data set on FUBC. This dataset includes data (in CSV format) from a survey carried out by IFA to represent the 2017\u201318 period as well as a collation of all historic FUBC data.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/global_fertilizer/#dataset-preprocessing","title":"Dataset Preprocessing","text":"LSIB country boundaries were used to join tow table, since the primary table is not geospatial the country names were first refactored to match those of LSIB before creating an internal join. Since these are large geometries the join was converted to centroids for each feature and exported as a feature collection.
"},{"location":"projects/global_fertilizer/#paper-citation","title":"Paper Citation","text":"Ludemann, C.I., Gruere, A., Heffer, P. et al. Global data on fertilizer use by crop and by country. Sci Data 9, 501 (2022).\nhttps://doi.org/10.1038/s41597-022-01592-z\nLudemann, Cameron; Gruere, Armelle; Heffer, Patrick; Dobermann, Achim (2022), Global data on fertilizer use by crop and by country, Dryad,\nDataset, https://doi.org/10.5061/dryad.2rbnzs7qh\nvar global_fertilizer_use = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_fertilizer_use_centroid\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FERTILIZER-USE-CROP-COUNTRY
"},{"location":"projects/global_fertilizer/#license","title":"License","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
Created by: Ludemann, Cameron; Gruere, Armelle; Heffer, Patrick; Dobermann, Achim
Curated in GEE by : Samapriya Roy
keywords: Global fertilizer use, agriculture, FAO, crop
Last modified: 2022-08-22
Last updated on GEE: 2022-09-05
"},{"location":"projects/global_ftype/","title":"Global Natural and Planted Forests","text":"The Global Natural and Planted Forests dataset offers a high-resolution (30-meter) map distinguishing natural from planted forests worldwide as of 2021. Created using over 70 million training samples generated from 30-meter Landsat images (1985\u20132021), this dataset supports improved environmental monitoring and conservation efforts, carbon sequestration assessment, and biodiversity management. The data includes rich spectral, structural, textural, and topographic attributes, enabling users to identify forest types and quantify forest coverage across various spatial scales.
"},{"location":"projects/global_ftype/#data-generation-and-classification","title":"Data Generation and Classification","text":"The dataset leverages a time-series change detection method applied to Landsat imagery, distinguishing planted forests from natural forests based on disturbance frequency. Using a locally adaptive random forest classifier (RF), this method achieved an overall accuracy of 85% when validated against independently interpreted reference data. This high precision makes the dataset an effective tool for global forest resource assessment.
"},{"location":"projects/global_ftype/#key-features","title":"Key Features","text":"The dataset is publicly available and can be accessed via: - Primary Data Source - Supplemented Tiles 300\u2013400
"},{"location":"projects/global_ftype/#citation","title":"Citation","text":"Xiao, Yuelong, Qunming Wang, and Hankui K. Zhang. \"Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m.\"\nJournal of Remote Sensing 4 (2024): 0204.\nXiao, Y. (2024). Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m [Data set].\nZenodo. https://doi.org/10.5281/zenodo.10701417\nvar global_forest_types = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-NATURAL-PLANTED-FORESTS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-NATURAL-PLANTED-FORESTS
"},{"location":"projects/global_ftype/#license","title":"License","text":"This dataset is licensed under a Creative Commons Attribution 4.0 International license.
Provided by: Xiao et al 2024
Curated in GEE by: Samapriya Roy
Keywords: Global Forest Mapping, Natural and Planted Forests, Carbon Sequestration, Forest Cover Classification, Biodiversity Monitoring, Forest Disturbance, Random Forest Classifier
Last updated in GEE: 2024-10-27
"},{"location":"projects/global_fungi/","title":"Global Fungi Database","text":"Fungi are essential components of ecosystems, contributing to vital functions such as carbon cycling, decomposition, plant associations, and pathogenicity. However, our knowledge of fungal biogeography and the factors driving these patterns is limited. To address this gap, we compiled and validated data on soil fungal communities from terrestrial environments, including soil and plant-associated habitats. This valuable dataset, available through the user interface at https://globalfungi.com, encompasses over 600 million observations of fungal sequences derived from more than 17,000 samples, with precise geographic information and metadata from 178 original studies. The dataset, which includes millions of unique nucleotide sequences of the fungal internal transcribed spacers (ITS) 1 and 2, represents an extensive atlas of global fungal distribution. It is designed to facilitate the integration of third-party data, promoting further exploration and enhancing our understanding of fungal biogeography and its environmental drivers. You can read the details in the paper here and you can get to the database here.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/global_fungi/#dataset-preprocessing","title":"Dataset preprocessing","text":"Feature field were transformed to make sure it meets the limits for characters. The overall global extract was created from globalfungi.com and converted to a shapefile. Changes are
'year_of_sampling': 'sample_yr'\n'ITS1_extracted': 'ITS1_extr',\n'ITS3_extracted': 'ITS2_extr'\nV\u011btrovsk\u00fd, Tom\u00e1\u0161, Daniel Morais, Petr Kohout, Cl\u00e9mentine Lepinay, Camelia Algora, Sandra Awokunle Holl\u00e1, Barbara Doreen Bahnmann et al.\n\"GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies.\" Scientific Data 7, no. 1 (2020): 228.\nGlobalFungi dataset release 4 (20.7.2021). Taxonomy based on UNITE version 8.2 (4.2.2020).\nActual number of samples in the database: 57184; actual number of studies included: 515.\nNumber of ITS sequence variants: 481 799 996; number of ITS1 sequences 791 513 743; number of ITS2 sequences 2 892 377 338.\nvar table = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLOBAL-FUNGI-DB/global-fungi-db-20230627\");\nSample code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FUNGI-DATABASE
"},{"location":"projects/global_fungi/#license","title":"License","text":"This database is available under a Creative Commons Attribution 4.0 International License
Curated by: V\u011btrovsk\u00fd, Tom\u00e1\u0161, et al. 2020
Keywords: Global Fungi database, Global dataset, carbon cycling
Last updated: June 27, 2023
"},{"location":"projects/global_irrigation/","title":"Global irrigation areas (2001 to 2015)","text":"About 40% of global crop production takes place on irrigated land, which accounts for approximately 20% of the global farmland. The great majority of freshwater consumption by human societies is associated with irrigation, which contributes to a major modification of the global water cycle by enhancing evapotranspiration and reducing surface and groundwater runoff. In many regions of the world irrigation contributes to streamflow and groundwater depletion, soil salinization, cooler microclimate conditions, and altered land-atmosphere interactions. Despite the important role played by irrigation in food security, water cycle, soil productivity, and near-surface atmospheric conditions, its global extent remains poorly quantified. To date global maps of irrigated land are often based on estimates from circa year 2000. Here we apply artificial intelligence methods based on machine learning algorithms to satellite remote sensing and monthly climate data to map the spatial extent of irrigated areas between 2001 and 2015. We provide global annual maps of irrigated land at \u22489km resolution for the 2001-2015 and we make this dataset available online.
"},{"location":"projects/global_irrigation/#citation","title":"Citation:","text":"Deepak Nagaraj, Eleanor Proust, Alberto Todeschini, Maria Cristina Rulli, Paolo D'Odorico,\nA new dataset of global irrigation areas from 2001 to 2015, Advances in Water Resources,\nVolume 152,2021,103910,ISSN 0309-1708,https://doi.org/10.1016/j.advwatres.2021.103910.\nYou can read the paper here : https://www.sciencedirect.com/science/article/pii/S0309170821000658
"},{"location":"projects/global_irrigation/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var irrigation_maps = ee.ImageCollection(\"users/deepakna/global_irrigation_maps\");\nYou can also get maps for individual years as TIF images:
var highly_irrigated_areas_2001 = ee.Image(\"users/deepakna/global_irrigation_maps/2001\")\n .expression(\"b(0) == 2 ? 1 : 0\");\nMap.addLayer(highly_irrigated_areas_2001.updateMask(highly_irrigated_areas_2001.neq(0))\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-IRRIGATION-AREAS
"},{"location":"projects/global_irrigation/#band-info-irrigation-classes-are-present-in-the-band-classification","title":"Band Info: Irrigation classes are present in the band \"classification\"","text":"Band Value Irrigation Class 0 no or very little irrigation 1 low-to-medium irrigation (<= 2000 hectares in 86 sq km square of land) 2 high irrigation (>2000 hectares in 86 sq km square of land)"},{"location":"projects/global_irrigation/#license","title":"License","text":"Creative Commons Attribution 4.0 International License
Curated by: Deepak Nagaraj
Keywords: Global irrigation, agriculture, water sustainability, machine learning
Last updated: 2020-06-10
"},{"location":"projects/global_mining/","title":"Global Mining Areas and Validation Datasets","text":"This data set provides spatially explicit estimates of the area directly used for surface mining on a global scale. It contains more than 21,000 polygons of activities related to mining, mainly of coal and metal ores. Several data sources were compiled to identify the approximate location of mines active at any time between the years 2000 to 2017. This data set does not cover all existing mining locations across the globe. The polygons were delineated by experts using Sentinel-2 cloudless (https://s2maps.eu by EOX IT Services GmbH (contains modified Copernicus Sentinel data 2017 & 2018)) and very high-resolution satellite images available from Google Satellite and Bing Imagery. The derived polygons cover the direct land used by mining activities, including open cuts, tailing dams, waste rock dumps, water ponds, and processing infrastructure.
The overall accuracy calculated from the control points was 88.4%
Read about the methodology here
Use the following credit when these data are cited:
Maus, Victor; Giljum, Stefan; Gutschlhofer, Jakob; da Silva, Dieison M; Probst, Michael; Gass, Sidnei L B; Luckeneder, Sebastian; Lieber, Mirko; McCallum, Ian (2020): Global-scale mining polygons (Version 1). PANGAEA https://doi.org/10.1594/PANGAEA.910894\nYou can cite the original paper using:
Maus, Victor, Stefan Giljum, Jakob Gutschlhofer, Dieison M. da Silva, Michael Probst, Sidnei LB Gass, Sebastian Luckeneder, Mirko Lieber, and Ian McCallum. \"A global-scale data set of mining areas.\" Scientific Data 7, no. 1 (2020): 1-13.\nvar mining = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-mining/global_mining_polygons\");\nvar validation = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-mining/global_mining_validation\");\n21,000 main polygons and 1000 validation polygons
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-MINING-AND-VALIDATION
Shared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: Mining, High Resolution, Global, coal, land-use, metal ores, minerals, raw material extraction
Last updated: 2021
"},{"location":"projects/global_palm_oil/","title":"Global Oil Palm Dataset 1990-2021","text":"NoteThis dataset is part of a paper in submission and citation and DOI information will be updated accordingly. This will be updated as the paper progresses through review and publication cycles.Please keep this into consideration while using this dataset
The dataset provides a comprehensive global map of oil palm plantations, including both industrial and smallholder plots, at a 10-meter resolution using Sentinel-1 data from 2016 to 2021. Additionally, it includes planting year estimates from 1990 to 2021 at a 30-meter spatial resolution derived from Landsat-5, -7, and -8 imagery. This dataset aims to support environmental monitoring and policy discussions by offering detailed and up-to-date information on the extent and age of oil palm plantations worldwide. You can read the preprint here.
This data repository can be found here offers comprehensive data on global oil palm plantations, including a 10-meter resolution global oil palm extent layer for the year 2021 and a 30-meter resolution oil palm planting year layer spanning from 1990 to 2021. The extent layer was generated using a convolutional neural network applied to Sentinel-1 data, identifying both industrial and smallholder plantations. The planting year layer was developed to detect early oil palm growth stages using the Landsat time series.
The key findings of the dataset reveal a total mapped area of 23.98 million hectares (Mha) of oil palm plantations, comprising 16.66 \u00b1 0.25 Mha of industrial and 7.59 \u00b1 0.29 Mha of smallholder oil palm. The accuracy of the data is high, with producers' and users' accuracy for industrial plantations at 91.9 \u00b1 3.4% and 91.8 \u00b1 1.0%, respectively, and for smallholders at 72.7 \u00b1 1.3% and 75.7 \u00b1 2.5%, respectively. The average age of the plantations is 14.1 years, and 6.28 Mha are over 20 years old, indicating a significant need for replanting within the coming decade.
"},{"location":"projects/global_palm_oil/#data-layers","title":"Data Layers","text":""},{"location":"projects/global_palm_oil/#1-grid_oilpalm2016-2021","title":"1. Grid_OilPalm2016-2021","text":"The oil palm extent and planting year data can be explored through a web map available at: Global Oil Palm Planting Year 1990-2021. This tool allows users to inspect the Landsat time series and view historical satellite images of oil palm plantations.
"},{"location":"projects/global_palm_oil/#citation","title":"Citation","text":"Descals, A., Gaveau, D. L. A., Wich, S., Szantoi, Z., and Meijaard, E.: Global mapping of oil palm planting year from 1990 to 2021\nEarth Syst. Sci Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-157, in review, 2024.\nDescals, A. (2024). Global oil palm extent and planting year from 1990 to 2021 (v1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11034131\n/*\nGlobalOilPalm_YoP_2021: year of oil palm plantation establishment\ngrid_oilpalm: Delineates the 609 grid cells of 100 x 100 km where oil palm was detected\nglobaloilpalm_extent2021: Deep learning classification at a 10-meter spatial resolution\n- **Classes:**\n - [0] Other land covers that are not oil palm.\n - [1] Industrial oil palm plantations.\n - [2] Smallholder oil palm plantations.\nvalidation: Contains 17,812 points used to validate the global oil palm extent and age layers. Each point includes:\n - \u2018Class\u2019: Assigned by visual interpretation (class values same as extent layer).\n - \u2018OP2016-2021\u2019 and \u2018OP2019\u2019: Mapped classes in this dataset and the 2019 global oil palm layer (Descals et al., 2021), respectively.\n\n - **Classes:**\n - [0] Other land covers that are not oil palm.\n - [1] Industrial oil palm plantations.\n - [2] Smallholder oil palm plantations.\n\n*/\nvar grid_oilpalm = ee.FeatureCollection('projects/sat-io/open-datasets/global-oil-palm/Grid_OilPalm_2021_v1-1');\nvar globaloilpalm_extent = ee.ImageCollection('projects/sat-io/open-datasets/global-oil-palm/GlobalOilPalm_extent_2021');\nvar globaloilpalm_yop_2021 = ee.ImageCollection(\"projects/sat-io/open-datasets/global-oil-palm/GlobalOilPalm_YoP_2021\");\nvar validation = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-oil-palm/Validation_points_GlobalOP2016-2021_v1-1\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/global-landuse-landcover/GLOBAL-OIL-PALM-1990-2021-APP
Earth Engine App: https://ee-globaloilpalm.projects.earthengine.app/view/global-oil-palm-planting-year-1990-2021
"},{"location":"projects/global_palm_oil/#license","title":"License","text":"This product is licensed under a Creative Commons Attribution 4.0 International license.
Curated in GEE by: Descals et al 2024 and Samapriya Roy
Keywords: oil palm, planting year, global crop mapping, remote sensing, deep learning, Sentinel-1
Last updated in GEE: 2024-04-28
"},{"location":"projects/global_pm25/","title":"Global Monthly Satellite-derived PM2.5","text":"This dataset provides annual and monthly estimates of ground-level fine particulate matter (PM2.5) from 2000 to 2019. The data are derived by integrating Aerosol Optical Depth (AOD) retrievals from multiple NASA instruments\u2014MODIS, MISR, SeaWIFS, and VIIRS\u2014with the GEOS-Chem chemical transport model. The initial PM2.5 estimates are then calibrated using a residual Convolutional Neural Network (CNN) against global ground-based observations.
"},{"location":"projects/global_pm25/#key-features","title":"Key Features","text":"Updates in V6.GL.02:
Annual and monthly datasets are provided in NetCDF (.nc) format, with gridded files using the WGS84 projection. These estimates are primarily intended to aid in large-scale studies. Annual and coarse-resolution averages correspond to a simple mean of within-grid values. Gridded datasets are provided to allow users to agglomerate data as best meets their particular needs. High-resolution (0.01\u00b0 \u00d7 0.01\u00b0) datasets are gridded at the finest resolution of the information sources that were incorporated but are unlikely to fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. You can read the paper here and download the dataset here
"},{"location":"projects/global_pm25/#citation","title":"Citation","text":"Shen, S. Li, C. van Donkelaar, A. Jacobs, N. Wang, C. Martin, R. V.: Enhancing Global Estimation of Fine Particulate Matter Concentrations by\nIncluding Geophysical a Priori Information in Deep Learning. (2024) ACS ES&T Air. DOI: 10.1021/acsestair.3c00054\nvar pm25_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-SATELLITE-PM25/MONTHLY\")\nvar pm25_yearly = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL-SATELLITE-PM25/ANNUAL\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-SATELLITE-PM25
"},{"location":"projects/global_pm25/#license","title":"License","text":"The datasets are made available under the Creative Commons Attribution 4.0 International license.
Keywords: PM2.5, Remote Sensing, MODIS, SeaWIFS, VIIRS, MISR, AOD
Provided by: Atmospheric Composition Analysis Group at Washington University in St Louis
Curated in GEE by: Samapriya Roy
Last updated : 2024-06-19
"},{"location":"projects/global_power/","title":"Predictive mapping of the global power system using open data","text":"Read about the methodology here
Download the dataset here
Use the following credit when these datasets are cited:
Arderne, Christopher, NIcolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data (Version 1.1.1) [Data set]. Nature Scientific Data. Zenodo. http://doi.org/10.5281/zenodo.3628142\nCite the paper using
Arderne, Christopher, Conrad Zorn, Claire Nicolas, and E. E. Koks. \"Predictive mapping of the global power system using open data.\" Scientific data 7, no. 1 (2020): 1-12.\nCurrent version: v1.1.1 released 2020-01-16 You can access the app here: https://gridfinder.org/
"},{"location":"projects/global_power/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var lv = ee.Image(\"projects/sat-io/open-datasets/predictive-global-power-system/lv\");\nvar targets = ee.Image(\"projects/sat-io/open-datasets/predictive-global-power-system/targets\");\nvar transmission = ee.FeatureCollection(\"projects/sat-io/open-datasets/predictive-global-power-system/distribution-transmission-lines\");\nlv is at 250m, targets at 463.83 m
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/PREDICTED-GLOBAL-POWER-SYSTEMS
Shared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: Global transmission lines, electricity, infrastructure, power
Last updated: 2021-04-03
"},{"location":"projects/global_pv/","title":"Global Photovoltaics Inventory (2016-2018)","text":"Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. The authors point out that energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. The authors further locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423\u2009gigawatts (\u221275/+77\u2009gigawatts) at the end of 2018.
For installations over 10,000 m2 (approximately 600 kW), achieved precision was 98.6% relative to our test set, with a modest trade-off in recall which drops to 90% (Supplementary Fig. 6). The IoU of the final dataset for installations over 10,000 m2 is 90%\u2014sufficient for the wide range of uses based on the user report. You can read the paper here
"},{"location":"projects/global_pv/#citation","title":"Citation:","text":"Kruitwagen, L., Story, K.T., Friedrich, J. et al. A global inventory of photovoltaic solar energy generating units.\nNature 598, 604\u2013610 (2021). https://doi.org/10.1038/s41586-021-03957-7\nKruitwagen, Lucas, Story, Kyle, Friedrich, Johannes, Byers, Logan, Skillman, Sam, & Hepburn, Cameron. (2021). A global\ninventory of solar photovoltaic generating units - dataset (1.0.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5005868\nvar predicted_set = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/predicted_set\");\nvar cv_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/cv_polygons\");\nvar cv_tiles = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/cv_tiles\");\nvar test_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/test_polygons\");\nvar test_tiles = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/test_tiles\");\nvar trn_tiles = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/trn_tiles\");\nvar trn_polygons = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_photovoltaic/trn_polygons\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-PHOTOVOLTAICS-INVENTORY
"},{"location":"projects/global_pv/#layer-name-and-description-table","title":"Layer name and description table","text":"File Name Description trn_tiles 18,570\u00a0rectangular areas-of-interest used for sampling training patch data. trn_polygons 36,882\u00a0polygons obtained from OSM in 2017\u00a0used to label training patches cv_tiles 560\u00a0rectangular areas-of-interest used for sampling cross-validation data seeded from\u00a0WRI GPPDB cv_polygons 6,281 polygons corresponding to all PV solar generating units present in cv_tiles at the end of 2018. test_tiles 122 rectangular regions-of-interest used for building the test set. test_polygons 7,263 polygons corresponding to all utility-scale (>10kW) solar generating units present in test_tiles at the end of 2018. predicted_set 68,661 polygons corresponding to predicted polygons in global deployment, capturing the status of deployed photovoltaic solar energy generating capacity at the end of 2018."},{"location":"projects/global_pv/#license","title":"License","text":"Creative Commons Attribution 4.0 International License
Created by: Kruitwagen et al
Curated by: Samapriya Roy
Keywords: photovoltaic solar remote sensing geospatial data computer vision
Last updated: 2021-10-28
"},{"location":"projects/global_salinity/","title":"Global Soil Salinity Maps (1986-2016)","text":"This dataset includes global soil salinity layers for the years 1986, 1992, 2000, 2002, 2005, 2009 and 2016. The maps were generated with a random forest classifier that was trained using seven soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. The validation accuracy of the resulting maps was in the range of 67\u201370%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Further details are provided in a peer-reviewed journal article (https://doi.org/10.1016/j.rse.2019.111260). The main data page for this dataset can be found here along with links to the VRT and tiff files.
"},{"location":"projects/global_salinity/#paper-citation","title":"Paper Citation","text":"Ivushkin, Konstantin, Harm Bartholomeus, Arnold K. Bregt, Alim Pulatov, Bas Kempen, and Luis De Sousa. \"Global mapping of soil salinity change.\"\nRemote sensing of environment 231 (2019): 111260.\nvar soil_salinity = ee.ImageCollection(\"projects/sat-io/open-datasets/global_soil_salinity\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/GLOBAL-SOIL-SALINITY
"},{"location":"projects/global_salinity/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Ivushkin et al
Curated by: Samapriya Roy
Keywords: : salinity, digital soil mapping, electrical conductivity, global map, soilgrids, landsat, thermal, salinisation
Last updated: 2021-11-25
"},{"location":"projects/global_tcc/","title":"Global 30m Landsat Tree Canopy Cover v4","text":"The Global 30m Landsat Tree Canopy Version 4 (TCC) product is a 30-meter resolution dataset that shows tree canopy coverage per pixel between 0% and 100%. The TCC product was announced in May 2019 and was processed from the Landsat image archive. It replaces the previous version of global tree canopy cover estimates for 2000, 2005, 2010, and 2015. It also includes annual tree cover estimates from 2010 to 2015 for North and South America. The TCC datasets are based on Landsat and Sentinel-2 imagery. The most recent TCC version 2021.4 product suite released in 2023 includes several components, including an annual Science product with maps and data for years 2008-2021. You can find additional information here and download the datasets here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/global_tcc/#citation","title":"Citation","text":"Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K.M., Channan, S., DiMiceli, C., Townshend, J.R.G. (2013). Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS Vegetation Continuous Fields with lidar-based estimates of error.\nInternational Journal of Digital Earth, 130321031236007. doi:10.1080/17538947.2013.786146.\nvar GFCC30TC= ee.ImageCollection(\"projects/sat-io/open-datasets/GFCC30TC\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GFCC30TC-TREE-CANOPY-COVER
"},{"location":"projects/global_tcc/#license","title":"License","text":"This data is open to the public and browse images are freely available without restriction.
Created by: Sexton, J. O et al
Curated in GEE by : Samapriya Roy
keywords: Global Tree Canopy, Forestry, Time series, Landsat
Last updated on GEE: 2023-07-05
"},{"location":"projects/globathy/","title":"GLOBathy (Global lakes bathymetry dataset)","text":"We developed a novel GLObal Bathymetric (GLOBathy) dataset of 1.4+\u2009million waterbodies to align with the well-established global dataset, HydroLAKES. GLOBathy uses a GIS-based framework to generate bathymetric maps based on the waterbody maximum depth estimates and HydroLAKES geometric/geophysical attributes of the waterbodies. The maximum depth estimates are validated at 1,503 waterbodies, making use of several observed data sources.
We also provide estimations for head-Area-Volume (h-A-V) relationships of the HydroLAKES waterbodies, driven from the bathymetric maps of the GLOBathy dataset. The h-A-V relationships provide essential information for water balance and hydrological studies of global waterbody systems. You can read the full paper here
You can find the datasets here
Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/globathy/#citation","title":"Citation","text":"Khazaei, B., Read, L.K., Casali, M. et al. GLOBathy, the global lakes bathymetry dataset. Sci Data 9, 36 (2022).\nhttps://doi.org/10.1038/s41597-022-01132-9\nKhazaei, Bahram; Read, Laura K; Casali, Matthew; Sampson, Kevin M; Yates, David N (2022): GLOBathy Bathymetry Rasters. figshare.\nDataset. https://doi.org/10.6084/m9.figshare.13404635.v1\nvar globathy = ee.Image(\"projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry\");\nvar globathy_param = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLOBathy/GLOBathy_basic_parameters\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBathy
"},{"location":"projects/globathy/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. The organizations responsible for generating and funding this dataset make no representations of any kind including, but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data. Although every effort has been made to ensure the accuracy of information, errors may be reflected in data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors.
Produced by: Khazaei, B., Read, L.K., Casali, M. et al.
Curated in GEE by: Samapriya Roy
Keywords: bathymetry and depth, lake systems, reservoir management, Hydrological Modelling, Limnology, Geographic information systems (GIS), Geomorphology, topographic analysis
Last updated on GEE: 2022-01-26
"},{"location":"projects/globcover_esa/","title":"GlobCover Global Land Cover","text":"GlobCover is an ESA initiative which began in 2005 in partnership with JRC, EEA, FAO, UNEP, GOFC-GOLD and IGBP. The aim of the project was to develop a service capable of delivering global composites and land cover maps using as input observations from the 300m MERIS sensor on board the ENVISAT satellite mission. ESA makes available the land cover maps, which cover 2 periods: December 2004 - June 2006 and January - December 2009. You can download the datasets here.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/globcover_esa/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var globcoverv23 = ee.Image(\"projects/sat-io/open-datasets/ESA/GLOBCOVER_L4_200901_200912_V23\");\nvar globcoverv22 = ee.Image(\"projects/sat-io/open-datasets/ESA/GLOBCOVER_200412_200606_V22_Global_CLA\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/ESA-GLOBCOVER
"},{"location":"projects/globcover_esa/#license","title":"License","text":"The GlobCover products have been processed by ESA and by the Universit\u00e9 Catholique de Louvain. They are made available to the public by ESA. You may use the GlobCover land cover map for educational and/or scientific purposes, without any fee on the condition that you credit ESA and the Universit\u00e9 Catholique de Louvain as the source of the GlobCover products:
\u00a9 ESA 2010 and UCLouvain\nAccompanied by a link to our ESA DUE GlobCover website: http://due.esrin.esa.int/page_globcover.php\nShould you write any scientific publication on the results of research activities that use GlobCover products as input, you shall acknowledge the ESA GlobCover 2009 Project in the text of the publication and provide ESA with an electronic copy of the publication (due@esa.int). If you wish to use the GlobCover 2009 products in advertising or in any commercial promotion, you shall acknowledge the ESA GlobCover 2009 Project and you must submit the layout to ESA for approval beforehand (due@esa.int).
Created by: ESA and by the Universit\u00e9 Catholique de Louvain
Curated in GEE by : Samapriya Roy
keywords: Landcover, Global Landcover, ESA, JRC, EEA, FAO, UNEP, GOFC-GOLD and IGBP, ENVISAT product
Last updated on GEE: 2023-02-28
"},{"location":"projects/globgm/","title":"GLOBGM v1.0 global-scale groundwater model","text":"The GLOBGM v1.0 dataset marks a significant milestone in global groundwater modeling, offering a parallel implementation of a 30\u2009arcsec PCR-GLOBWB-MODFLOW model. Developed by Jarno Verkaik et al., this dataset presents a comprehensive understanding of global groundwater dynamics at a spatial resolution of approximately 1\u2009km at the Equator. Leveraging two model layers and the MODFLOW 6 framework, the dataset utilizes available 30\u2032\u2032 PCR-GLOBWB data to drive simulations, enabling researchers to explore groundwater flow dynamics on a global scale. The computational implementation is parallelized using the message-passing interface, facilitating efficient processing on distributed memory parallel clusters.
Covering the globe, excluding Greenland and Antarctica, the GLOBGM v1.0 dataset offers insights into various aspects of groundwater behavior. Despite its uncalibrated nature, the dataset undergoes limited evaluation using USGS National Water Information System (NWIS) head observations for the contiguous United States (CONUS). You can read the paper here to understand the methodology better.
"},{"location":"projects/globgm/#data-strucutre","title":"Data strucutre","text":"This table provides a structured overview of the model raster outputs for the GLOBGM dataset, including file paths and descriptions of each file.
File Path Description /steady-state/globgm-heads-lower-layer-ss.tif Computed steady-state groundwater head [m] for the lower model layer /steady-state/globgm-heads-lower-layer-ss.tif Computed steady-state groundwater head [m] for the upper model layer /steady-state/globgm-wtd-ss.tif Computed water table depth [m] (sampled from upper to lower layer) /transient_1958-2015/globgm-wtd-.tif Computed water table depth [m] (sampled from upper to lower layer) /transient_1958-2015/globgm-wtd-bot-*.tif Computed water table depth [m] (lower layer only)"},{"location":"projects/globgm/#citation","title":"Citation","text":"Verkaik, Jarno, Edwin H. Sutanudjaja, Gualbert HP Oude Essink, Hai Xiang Lin, and Marc FP Bierkens. \"GLOBGM v1. 0: a parallel implementation of a 30\narcsec PCR-GLOBWB-MODFLOW global-scale groundwater model.\" Geoscientific Model Development 17, no. 1 (2024): 275-300.\nVerkaik, J., Hughes J.D., Langevin, C.D., (2021). Parallel MODFLOW 6.2.1 prototype release 0.1 (6.2.1_0.1). Zenodo.\nvar wtd = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBGM/TRANSIENT/WTD\");\nvar wtd_bt = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBGM/TRANSIENT/WTD-BOTTOM\");\nvar globgm_wtd_ss = ee.Image(\"projects/sat-io/open-datasets/GLOBGM/STEADY-STATE/globgm-wtd-ss\");\nvar globgm_heads_lower_layer_ss = ee.Image(\"projects/sat-io/open-datasets/GLOBGM/STEADY-STATE/globgm-heads-lower-layer-ss\");\nvar globgm_heads_upper_layer_ss = ee.Image(\"projects/sat-io/open-datasets/GLOBGM/STEADY-STATE/globgm-heads-upper-layer-ss\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBGM-GROUNDWATER-MODEL
"},{"location":"projects/globgm/#license","title":"License","text":"GLOBGM v1.0 is open source and distributed under the terms of GNU General Public License v3.0, or any later version, as published by the Free Software Foundation.
Created by: Verkaik et al. 2024
Curated in GEE by : Samapriya Roy
Keywords: GLOBGM,groundwater,global-scale modeling,PCR-GLOBWB,MODFLOW,high performance computing
Last updated in GEE: 2024-02-04
"},{"location":"projects/glodap/","title":"Global Ocean Data Analysis Project (GLODAP) v2.2023","text":"The Global Ocean Data Analysis Project (GLODAP) v2.2023 represents a significant advancement in the synthesis of ocean biogeochemical bottle data. With a primary focus on seawater inorganic carbon chemistry, this update builds upon GLODAPv2.2022, incorporating several key enhancements. Notably, 43 new cruises have been added to expand the dataset's coverage until 2020. The data quality control process involved the removal of entries with missing temperatures. Moreover, the inclusion of digital object identifiers (DOIs) for each cruise enhances data traceability. GLODAPv2.2022 also includes minor corrections for improved data accuracy.
This dataset encompasses more than 1.4 million water samples from 1108 cruises across the global oceans, covering 12 essential variables such as salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4. The data are available in two formats: the raw data format, updated to WOCE exchange format, and a merged data product with bias-minimizing adjustments. Rigorous quality control procedures were applied, and adjustments were made by comparing new cruise data with the quality-controlled data from GLODAPv2.2020. The dataset is believed to provide accurate measurements within specific limits for each variable.
To access this valuable resource and its documentation, including DOIs, visit the Ocean Carbon Data System of NOAA NCEI at this link. Additionally, the merged data product is available, offering a single global file and regional files for the Arctic, Atlantic, Indian, and Pacific oceans. These files contain ancillary and approximated data, derived from interpolation or calculation. For comprehensive information and data access, please visit GLODAP's official website. Researchers can benefit from this living dataset by following the provided resources and documentation.
"},{"location":"projects/glodap/#data-quality-and-accuracy","title":"Data Quality and Accuracy","text":"The dataset has undergone extensive quality control with a focus on systematic evaluation of bias. The adjustments made aim to remove potential biases stemming from errors related to measurement, calibration, and data handling practices, while preserving known or likely time trends or variations in the evaluated variables.
The compiled and adjusted data product is believed to have a high level of accuracy, with consistent measurements:
Salinity: Better than 0.005 Oxygen: 1% Nitrate: 2% Silicate: 2% Phosphate: 2% Dissolved Inorganic Carbon: 4 \u03bcmolkg\u207b\u00b9 Total Alkalinity: 4 \u03bcmolkg\u207b\u00b9 pH: 0.01\u20130.02 (varies by region) Halogenated Transient Tracers: 5% Other variables in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), have not undergone bias comparison or adjustments.
"},{"location":"projects/glodap/#variable-key","title":"Variable key","text":"Variable / Parameter Abbreviation Unit Observation Type Sampling Instrument Quality Flag Convention Researcher Name Temperature temperature \u00b0C measured, data synthesis CDT, Niskin Bottles WOCE quality control flags are used GLODAP Group Potential temperature theta \u00b0C calculated GLODAP Group Salinity salinity measured, data synthesis CDT, Niskin Bottles WOCE quality control flags are used GLODAP Group Potential density sigma0 kg m\u22123 calculated GLODAP Group Potential density, ref 1000 dbar sigma1 kg m\u22123 calculated GLODAP Group Potential density, ref 2000 dbar sigma2 kg m\u22123 calculated GLODAP Group Potential density, ref 3000 dbar sigma3 kg m\u22123 calculated GLODAP Group Potential density, ref 4000 dbar sigma4 kg m\u22123 calculated GLODAP Group Neutral density gamma kg m\u22123 calculated GLODAP Group Oxygen oxygen \u03bcmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Apparent oxygen utilization aou \u03bcmol kg\u22121 calculated GLODAP Group Nitrate nitrate \u03bcmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Nitrite nitrite \u03bcmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Silicate silicate \u03bcmol kg\u22121 measured, data synthesis CDT, Niskin Bottles WOCE quality control flags are used GLODAP Group Phosphate phosphate \u03bcmol kg\u22121 measured, data synthesis CDT, Niskin Bottles WOCE quality control flags are used GLODAP Group TCO2 tco2 \u03bcmol kg\u22121 measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group TAlk talk \u03bcmol kg\u22121 measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group fCO2 fco2 microatmospheres measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group pH at total scale, 25 \u00b0C and zero dbar of pressure phts25p0 measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group pH at total scale, in situ temperature and pressure phtsinsitutp measured, calculated, data synthesis CTD, Niskin Bottles Simplified WOCE quality control flags are used GLODAP Group CFC-11 cfc11 pmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group pCFC-11 pcfc11 ppt measured, data synthesis CTD, Niskin Bottles GLODAP Group CFC-12 cfc12 pmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group pCFC-12 pcfc12 ppt measured, data synthesis CTD, Niskin Bottles GLODAP Group CFC-113 cfc113 pmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group CCl4 ccl4 pmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group pCCl4 pccl4 ppt calculated GLODAP Group SF6 sf6 fmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group pSF6 psf6 ppt calculated GLODAP Group \u03b413C c13 % measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group \u220614C c14 % measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group \u220614C counting error c14err % calculated GLODAP Group 3H h3 TU measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group 3H counting error h3err TU calculated GLODAP Group \u03b43He \u03b43He % measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group 3He counting error he3err % calculated GLODAP Group He He nmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group He counting error heerr % calculated GLODAP Group Neon neon nmol kg\u22121 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Neon counting error neonerr nmol kg\u22121 calculated GLODAP Group \u03b418O o18 % measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Total organic carbon toc \u03bcmol L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Dissolved organic carbon doc \u03bcmol L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Dissolved organic nitrogen don \u03bcmol L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Total dissolved nitrogen tdn \u03bcmol L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group Chlorophyll a chla ug L-1 measured, data synthesis CTD, Niskin Bottles WOCE quality control flags are used GLODAP Group"},{"location":"projects/glodap/#citation","title":"Citation","text":"Whenever GLODAPv2 is used, the following citations must be included:
Olsen, A., R. M. Key, S. van Heuven, S. K. Lauvset, A. Velo, X. Lin, C. Schirnick, A. Kozyr, T. Tanhua, M. Hoppema, S. Jutterstr\u00f6m, R. Steinfeldt,\nE. Jeansson, M. Ishii, F. F. P\u00e9rez and T. Suzuki. The Global Ocean Data Analysis Project version 2 (GLODAPv2) \u2013 an internally consistent data\nproduct for the world ocean, Earth Syst. Sci. Data, 8, 297\u2013323, 2016, doi:10.5194/essd-8-297-2016\n\nLauvset, S. K, R. M. Key, A. Olsen, S. van Heuven, A. Velo, X. Lin, C. Schirnick, A. Kozyr, T. Tanhua, M. Hoppema, S. Jutterstr\u00f6m, R. Steinfeldt, E.\nJeansson, M. Ishii, F. F. P\u00e9rez, T. Suzuki and S. Watelet. A new global interior ocean mapped climatology: the 1\u00b0x1\u00b0 GLODAP version 2, Earth Syst.\nSci. Data, 8, 325\u2013340, 2016, doi:10.5194/essd-8-325-2016\n\nKey, R.M., A. Olsen, S. van Heuven, S. K. Lauvset, A. Velo, X. Lin, C. Schirnick, A. Kozyr, T. Tanhua, M. Hoppema, S. Jutterstr\u00f6m, R. Steinfeldt, E.\nJeansson, M. Ishii, F. F. Perez, and T. Suzuki. 2015. Global Ocean Data Analysis Project, Version 2 (GLODAPv2), ORNL/CDIAC-162, NDP-093. Carbon\nDioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee. doi:10.3334/CDIAC/OTG.\nNDP093_GLODAPv2\nThe matlab derived products add a G2 infront of all column names and they are left in place as is,with an additional column called system:time_start and datetime added to reflect epoch time and UTC datetime derived from the existing columns. Adding the system:time_start and datetime allow for easily filtering across the earth engine collection. While the merged collection is provided individual feature collections are still maintained to provide the user with the flexibility of loading a smaller subset of features for operations.
"},{"location":"projects/glodap/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var merged = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Merged_Master_File_formatted\");\nvar arctic_ocean = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Arctic_Ocean_formatted\");\nvar atlantic_ocean = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Atlantic_Ocean_formatted\");\nvar indian_ocean = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Indian_Ocean_formatted\");\nvar pacific_ocean = ee.FeatureCollection(\"projects/sat-io/open-datasets/GLODAP_V2/GLODAPv2_2023_Pacific_Ocean_formatted\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLODAP-V2_2023_MERGED
"},{"location":"projects/glodap/#license","title":"License","text":"The dataset is distributed under a public licese. Distribution liability: NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data. If appropriate, NCEI can only certify that the data it distributes are an authentic copy of the records that were accepted for inclusion in the NCEI archives.
Provided by: NCEI, NOAA, Olsen et al
Curated in GEE by: Samapriya Roy
Last updated in GEE: 2023-10-25
"},{"location":"projects/gloric/","title":"Global River Classification (GloRiC)","text":"The Global River Classification (GloRiC) provides river types and sub-classifications for all river reaches contained in the HydroRIVERS database. GloRiC has been developed by utilizing the river network delineation of HydroRIVERS combined with the hydro-enviromental characteristics from the HydroATLAS database and auxiliary information.
Version 1.0 of GloRiC provides a hydrologic, physio-climatic, and geomorphic sub-classification, as well as a combined river type for every river reach, resulting in a total of 127 river reach types. It also offers a k-means statistical clustering of the reaches into 30 groups. The dataset comprises 8.5 million river reaches with a total length of 35.9 million km.
You can find overall technical documentation here and technical information for GloRiC Canada here
"},{"location":"projects/gloric/#preprocessing","title":"Preprocessing","text":"Besides the global version of GloRiC, there is also a regional version for Canada available, GloRiC-Canada, which follows the same classification principles but with Canada-specific adaptations. GloRiC-Canada categorizes all river reaches of Canada into 23 types.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/gloric/#citation","title":"Citation","text":"Ouellet Dallaire, C., Lehner, B., Sayre, R., Thieme, M. (2019). A multidisciplinary framework to derive global river reach classifications at high\nspatial resolution. Environmental Research Letters, 14(2): 024003. https://doi.org/10.1088/1748-9326/aad8e9\n\nOuellet Dallaire, C., Lehner, B., Creed, I. (2020): Multidisciplinary classification of Canadian river reaches to support the sustainable management\nof freshwater systems. Canadian Journal of Fisheries and Aquatic Sciences, 77(2): 326\u2013341. https://doi.org/10.1139/cjfas-2018-0284\nvar gloric = ee.FeatureCollection(\"projects/sat-io/open-datasets/GloRiC/GloRiC_v10\");\nvar gloric_canada = ee.FeatureCollection(\"projects/sat-io/open-datasets/GloRiC/GloRiC_Canada_v10\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-CLASSIFICATION(GLORIC)
"},{"location":"projects/gloric/#license","title":"License","text":"The data is licensed under a Creative Commons Attribution 4.0 International License (see section 4). By downloading and using the data the user agrees to the terms and conditions of this license.
Created by: Ouellet Dallaire, C., Lehner, B., Sayre, R., Thieme, M. & Schmitt, O
Curated by: Samapriya Roy
Keywords: water,hydrology, rivers, discharge, depth, volume, area, gloric
Last updated: 2022-07-09
"},{"location":"projects/gmd/","title":"Global Mangrove Distribution, Aboveground Biomass, and Canopy Height","text":"This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models. Also provided are (4) in situ field measurement data for selected sites across a wide variety of forest structures (e.g., scrub, fringe, riverine and basin) in mangrove ecotypes of the global equatorial region. Within designated plots, selected trees were identified to species and diameter at breast height (DBH) and tree height was measured using a laser rangefinder or clinometer. Tree density (the number of stems) can be estimated for each plot and expressed per unit area. These data were used to derive plot-level allometry among AGB, basal area weighted height (Hba), and maximum canopy height (Hmax) and to validate the remotely sensed estimates.
Spatially explicit maps of mangrove canopy height and AGB derived from space-borne remote sensing data and in situ measurements can be used to assess local-scale geophysical and environmental conditions that may regulate forest structure and carbon cycle dynamics. Maps revealed a wide range of canopy heights, including maximum values (> 62 m) that surpass maximum heights of other forest types.
There are 348 data files in GeoTIFF format (.tif) with this dataset representing three data products for each of 116 countries. The in situ tree measurements are provided in a single .csv file. You can grab the dataset here
"},{"location":"projects/gmd/#preprocessing","title":"Preprocessing","text":"The tree measurements CSV has lat lon 2,3,4 removed and lat and lon1 were renamed to lat lon. The dataset table is as below. This and additional metadata can be found here. The datasets divided into subsets of 116 datasets each were ingested into Google Earth Engine collections.
Column name Units/format Type Description ID Character A unique identifier for locating a specific observation. A combination of plot_name and tree number fields.\u00a0 region Character Continent or subcontinent of observation subregion Character Country or state of observation biome Character Biome type of observation - all mangrove date yyyy-mm-dd Date Date of observation plot_name Character Plot name tree_number Character Unique identifier for a tree in a specific plot. Trees characterized as \u201cSevere inclined\u201d were sometimes measured without assigning a number. These trees have been assigned M1, M2, M3, etc. within a plot. genus Character Genus species Character Species dbh cm Numeric Diameter at breast height (1.3 m), check tree_notes as some were estimated dbh height meters Numeric height of tree, check tree_notes as some were modeled height live Numeric 1 indicates tree is alive. 0 indicates tree is dead tree_notes Character specific notes about a tree use_for_allometry Numeric 1 indicates tree was used for allometry, 0 indicates it was not.\u00a0 plot_type Character f = fixed plot size, v = variable plot size plot_shape Character shape of plot: s and square; r, circle, and circular. Missing when plot_type = v. baf Numeric basal area factor: with a value of 5 for plot_type = v, otherwise missing plot_area m^2 Numeric plot area lat Numeric latitude of plot location (center of circular and variable shape plots or a plot corner for square plots) lon Numeric longitude of plot location (center of circular and variable shape plots or a plot corner for square plots) collected_by Character Collector of field observations digitized_by Character Performer of GIS activities"},{"location":"projects/gmd/#paper-citation","title":"Paper Citation","text":"Simard, M., L. Fatpyinbo, C. Smetanka, V.H. Rivera-Monroy, E. Castaneda-Moya, N. Thomas, and T. Van der Stocken. 2019. Mangrove canopy height\nglobally related to precipitation, temperature and cyclone frequency. Nature Geoscience, 12: 40\u201345. https://doi.org/10.1038/s41561-018-0279-1\nSimard, M., T. Fatoyinbo, C. Smetanka, V.H. Rivera-monroy, E. Castaneda, N. Thomas, and T. Van der stocken. 2019. Global Mangrove Distribution,\nAboveground Biomass, and Canopy Height. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1665\nVersion 1.3: The in situ tree measurement data file and documentation were added in April 2021. No changes to previously archived data.
Version 1.2: Data files were updated in May 2019 because the height to biomass (AGB) conversion equations in the associated Nature Geoscience publication were correct but were implemented incorrectly when generating the publicly available data files. These have now been corrected. The Hba and Hmax data were updated so that they are now capped at the 95th percentile of the maximum value (55 m), as outlined in the publication. Countries without Hba and Hmax data have been omitted.
Version 1.1: Science-quality data were released in March 2019. All preliminary data files were replaced with new files that incorporated some changes to the aboveground biomass estimation algorithm. In addition, several files with missing data were replaced.
Version 1.0: Preliminary data were released in November 2018 to accompany the publication of the Simard et al, 2019 paper in Nature Geosciences.
var agb = ee.ImageCollection(\"projects/sat-io/open-datasets/global_mangrove_distribution/agb\");\nvar hba95 = ee.ImageCollection(\"projects/sat-io/open-datasets/global_mangrove_distribution/hba95\");\nvar hmax95 = ee.ImageCollection(\"projects/sat-io/open-datasets/global_mangrove_distribution/hmax95\");\nvar americas_tree = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_mangrove_distribution/americas_tree_measurements\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-MANGROVE-BIOMASS-HEIGHT
"},{"location":"projects/gmd/#license","title":"License","text":"Public Domain/No restrictions (CC0): Under the terms of this license you are free to use the material for any purpose without any restrictions.
Created by: Simard et al
Curated by: Samapriya Roy
Keywords: : global mangrove, above ground biomass, canopy height, basal-area weighted height, ecosystem, mangroves
Last updated: 2021-12-15
"},{"location":"projects/gnatsgo/","title":"gNATSGO (gridded National Soil Survey Geographic Database)","text":"The gNATSGO (gridded National Soil Survey Geographic Database) database is a composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. This collection is sourced for the raster data only. Since the original format of the data is proprietary the source COGs are sourced from Planetary Computer STAC catalog.
The gNATSGO database was created by combining data from three sources: the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS). SSURGO is a USDA-NRCS Soil & Plant Science Division (SPSD) flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains.
STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are next-generation soil survey databases developed using advanced digital soil mapping methods. The gNATSGO database is composed primarily of SSURGO data, with STATSGO2 data used to fill in the gaps. The RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is relatively limited at this time but is expected to increase in the coming years. To use the map unit values contained in the mukey raster asset, you will need to join to tables represented as Items in the gNATSGO Tables Collection. Many items have commonly used values encoded in additional raster assets.
Expand to show current asset collectionsTitles STAC Key Roles Description Aws0_5 aws0_5 Data Available water storage estimate (AWS) in a standard zone 1 (0-5 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_20 aws0_20 Data Available water storage estimate (AWS) in standard zone 2 (0-20 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_30 aws0_30 Data Available water storage estimate (AWS) in standard zone 3 (0-30 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_100 aws0_100 Data Available water storage estimate (AWS) in standard zone 4 (0-100 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_150 aws0_150 Data Available water storage estimate (AWS) in standard zone 5 (0-150 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws0_999 aws0_999 Data Available water storage estimate (AWS) in total soil profile (0 cm to the reported depth of the soil profile), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws5_20 aws5_20 Data Available water storage estimate (AWS) in standard layer 2 (5-20 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws20_50 aws20_50 Data Available water storage estimate (AWS) in standard layer 3 (20-50 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws50_100 aws50_100 Data Available water storage estimate (AWS) in standard layer 4 (50-100 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws100_150 aws100_150 Data Available water storage estimate (AWS) in standard layer 5 (100-150 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Aws150_999 aws150_999 Data Available water storage estimate (AWS) in standard layer 6 (150 cm to the reported depth of the soil profile), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available. Soc0_5 soc0_5 Data Soil organic carbon stock estimate (SOC) in standard zone 1 (0-5 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 5 cm. NULL values are presented where data are incomplete or not available. Soc0_20 soc0_20 Data Soil organic carbon stock estimate (SOC) in standard zone 2 (0-20 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 20 cm. NULL values are presented where data are incomplete or not available. Soc0_30 soc0_30 Data Soil organic carbon stock estimate (SOC) in standard zone 3 (0-30 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 30 cm. NULL values are presented where data are incomplete or not available. Soc0_100 soc0_100 Data Soil organic carbon stock estimate (SOC) in a standard zone (0-100 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 100 cm. NULL values are presented where data are incomplete or not available. Soc0_150 soc0_150 Data Soil organic carbon stock estimate (SOC) in a standard zone (0-150 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 150 cm. NULL values are presented where data are incomplete or not available. Soc0_999 soc0_999 Data Soil organic carbon stock estimate (SOC) in a standard zone (0-999 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 999 cm. NULL values are presented where data are incomplete or not available. Soc100_150 soc100_150 Data Soil organic carbon stock estimate (SOC) in a standard zone (100-150 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 100 and 150 cm depth. NULL values are presented where data are incomplete or not available. Soc150_999 soc150_999 Data Soil organic carbon stock estimate (SOC) in a standard zone (150-999 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 150 and 999 cm depth. NULL values are presented where data are incomplete or not available. Soc20_50 soc20_50 Data Soil organic carbon stock estimate (SOC) in a standard zone (20-50 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 20 and 50 cm depth. NULL values are presented where data are incomplete or not available. Soc50_100 soc50_100 Data Soil organic carbon stock estimate (SOC) in a standard zone (50-100 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 50 and 100 cm depth. NULL values are presented where data are incomplete or not available. Soc5_20 soc5_20 Data Soil organic carbon stock estimate (SOC) in a standard zone (5-20 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter between 5 and 20 cm depth. NULL values are presented where data are incomplete or not available. Mukey mukey Data Map unit key is the unique identifier of a record in the Mapunit table. Use this column to join the Component table to the Map Unit table and the Valu1 table to the MapUnitRaster_10m raster map layer to map valu1 themes. Droughty droughty Data Zone for commodity crops that is less than or equal to 6 inches (152 mm) expressed as \u201c1\u201d for a drought vulnerable soil landscape map unit or \u201c0\u201d for a non-droughty soil landscape map unit or NULL for miscellaneous areas (includes water bodies) or where data were not available. Nccpi3sg nccpi3sg Data National Commodity Crop Productivity Index for Small Grains (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). NULL values are presented where data are incomplete or not available. Tk0_100a tk0_100a Data Thickness of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk0_100s tk0_100s Data Thickness of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Tk0_150a tk0_150a Data Thickness of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk0_150s tk0_150s Data Thickness of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Tk0_999a tk0_999a Data Thickness of soil components used in total soil profile (0 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk0_999s tk0_999s Data Thickness of soil components used in total soil profile (0 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Tk20_50a tk20_50a Data Thickness of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk20_50s tk20_50s Data Thickness of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Musumcpct musumcpct Data The sum of the comppct_r (SSURGO component table) values for all listed components in the map unit. NULL values are presented where data are incomplete or not available. Nccpi3all nccpi3all Data National Commodity Crop Productivity Index that has the highest value among Corn and Soybeans, Small Grains, or Cotton (weighted average) for major earthy components. NULL values are presented where data are incomplete or not available. Nccpi3cot nccpi3cot Data National Commodity Crop Productivity Index for Cotton (weighted average) for major earthy components. NULL values are presented where data are incomplete or not available. Nccpi3soy nccpi3soy Data National Commodity Crop Productivity Index for Soybeans (weighted average) for major earthy components. NULL values are presented where data are incomplete or not available. Pwsl1pomu pwsl1pomu Data Potential Wetland Soil Landscapes (PWSL) is expressed as the percentage of the map unit that meets the PWSL criteria. NULL values are presented where data are incomplete or not available. Rootznaws rootznaws Data Root zone available water storage estimate (RZAWS), expressed in mm, is the volume of plant available water that the soil can store within the root zone. NULL values are presented where data are incomplete or not available. Rootznemc rootznemc Data Root zone depth is the depth within the soil profile that commodity crop roots can effectively extract water and nutrients for growth. NULL values are presented where data are incomplete or not available. Tk50_100a tk50_100a Data Thickness of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk50_100s tk50_100s Data Thickness of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Musumcpcta musumcpcta Data The sum of the comppct_r (SSURGO component table) values used in the available water storage calculation for the map unit. NULL values are presented where data are incomplete or not available. Musumcpcts musumcpcts Data The sum of the comppct_r (SSURGO component table) values used in the soil organic carbon calculation for the map unit. NULL values are presented where data are incomplete or not available. Nccpi3corn nccpi3corn Data National Commodity Crop Productivity Index for Corn (weighted average) for major earthy components. NULL values are presented where data are incomplete or not available. Pctearthmc pctearthmc Data The National Commodity Crop Productivity Index map unit percent earthy is the map unit summed comppct_r for major earthy components. NULL values are presented where data are incomplete or not available. Tk100_150a tk100_150a Data Thickness of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk100_150s tk100_150s Data Thickness of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available. Tk150_999a tk150_999a Data Thickness of soil components used in standard layer 6 (150 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available. Tk150_999s tk150_999s Data Thickness of soil components used in standard layer 6 (150 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.
"},{"location":"projects/gnatsgo/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aws0_100 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_100');\nvar aws0_150 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_150');\nvar aws0_20 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_20');\nvar aws0_30 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_30');\nvar aws0_5 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_5');\nvar aws0_999 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws0_999');\nvar aws100_150 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws100_150');\nvar aws150_999 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws150_999');\nvar aws20_50 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws20_50');\nvar aws50_100 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws50_100');\nvar aws5_20 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/aws5_20');\nvar mukey = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/mukey');\nvar soc0_100 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_100');\nvar soc0_150 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_150');\nvar soc0_20 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_20');\nvar soc0_30 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_30');\nvar soc0_5 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_5');\nvar soc0_999 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc0_999');\nvar soc100_150 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc100_150');\nvar soc150_999 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc150_999');\nvar soc20_50 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc20_50');\nvar soc50_100 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc50_100');\nvar soc5_20 = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/soc5_20');\nvar droughty = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/droughty');\nvar musumcpct = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/musumcpct');\nvar musumcpcta = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/musumcpcta');\nvar musumcpcts = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/musumcpcts');\nvar nccpi3all = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3all');\nvar nccpi3corn = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3corn');\nvar nccpi3cot = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3cot');\nvar nccpi3sg = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3sg');\nvar nccpi3soy = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/nccpi3soy');\nvar pctearthmc = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/pctearthmc');\nvar pwsl1pomu = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/pwsl1pomu');\nvar rootznaws = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/rootznaws');\nvar rootznemc = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/rootznemc');\nvar tk0_100a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_100a');\nvar tk0_100s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_100s');\nvar tk0_150a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_150a');\nvar tk0_150s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_150s');\nvar tk0_20a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_20a');\nvar tk0_20s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_20s');\nvar tk0_30a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_30a');\nvar tk0_30s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_30s');\nvar tk0_5a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_5a');\nvar tk0_5s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_5s');\nvar tk0_999a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_999a');\nvar tk0_999s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk0_999s');\nvar tk100_150a = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk100_150a');\nvar tk100_150s = ee.ImageCollection('projects/sat-io/open-datasets/gNATSGO/raster/tk100_150s');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/gNATSGO-DATABASE
"},{"location":"projects/gnatsgo/#license","title":"License","text":"The data from the Gridded National Soil Survey Geographic Database (gNATSGO) provided by the USDA Natural Resources Conservation Service (NRCS) is available under the Public Domain license (CC0 1.0 Universal Public Domain Dedication).
Provided by: United States Department of Agriculture, Natural Resources Conservation Service
Hosted by: Microsoft
Curated in GEE by: Samapriya Roy
Keywords: Soil Survey, USDA, NRCS, Raster Data, Gridded Data
Last updated: 2024-10-22
"},{"location":"projects/gnatsgo/#changelog","title":"Changelog","text":"GOODD, a global dataset of more than 38,000 georeferenced dams as well as their associated catchments. The source paper presents the development of the global database through systematic digitisation of satellite imagery globally by a small team and highlights the various approaches to bias estimation and to validation of the data. The following datasets are provided (a) raw digitised coordinates for the location of dam walls (that may be useful for example in machine learning approaches to dam identification from imagery), (b) a global vector file of the watershed for each dam.
Read the paper for methodology and further details here
You can download the dataset here
"},{"location":"projects/goodd/#data-citation","title":"Data Citation","text":"van Soesbergen, Arnout; Mulligan, Mark; S\u00e1enz, Leonardo (2020): GOODD global dam dataset\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.9747686.v1\nMulligan, Mark, Arnout van Soesbergen, and Leonardo S\u00e1enz.\n\"GOODD, a global dataset of more than 38,000 georeferenced dams.\"\nScientific Data 7, no. 1 (2020): 1-8.\nvar catchments = ee.FeatureCollection(\"projects/sat-io/open-datasets/GOODD/GOOD2_catchments\");\nvar dams = ee.FeatureCollection(\"projects/sat-io/open-datasets/GOODD/GOOD2_dams\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-GEOREF-DATABASE-DAMS
"},{"location":"projects/goodd/#license","title":"License","text":"The dataset is released under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
Created by : Mulligan, Mark, Arnout van Soesbergen, and Leonardo S\u00e1enz
Curated in GEE by: Samapriya Roy
Keywords: Global dams, global catchments, vector, Hydrology
Last updated : 2021-07-24
"},{"location":"projects/gowt/","title":"Global offshore wind turbine dataset","text":"This dataset including two contents including the validation datasets, only the location dataset has been ingested and the validation dataset can be downloaded. The location dataset consist of geocoded information on global offshore wind turbines (OWTs) derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. It identified 6,924 wind turbines comprising of more than 10 nations. Data is available at 10 m spatial resolution, providing an explicit dataset for planning, monitoring, and managing marine space. The global OWTs are stored in Shapefile (.shp) format. The attributes and metadata are organized with referenced to the WGS84 datum, and each record consists of seven attributes: centroid latitude (centr_lat), centroid longitude (centr_lon), continent, country, sea area (sea_area), appearance year (occ_year) and month (occ_month).
You can download both location and validation datasets here
You can read about the methodology in the paper here
"},{"location":"projects/gowt/#citation","title":"Citation","text":"Zhang, T., Tian, B., Sengupta, D. et al. Global offshore wind turbine dataset.\nSci Data 8, 191 (2021). https://doi.org/10.1038/s41597-021-00982-z\nZhang, Ting; Tian, Bo; Sengupta, Dhritiraj; Zhang, Lei; Si, Yali (2020):\nGlobal offshore wind turbine dataset. figshare. Dataset.\nhttps://doi.org/10.6084/m9.figshare.13280252.v5\nvar gowt = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_offshore_wind_turbine_v1-3\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-OFFSHORE-WIND-TURBINES
"},{"location":"projects/gowt/#license","title":"License","text":"The dataset is released under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
Created by : Zhang, T., Tian, B., Sengupta, D. et al
Curated in GEE by: Samapriya Roy
Keywords: Offshore energy systems, Coastal engineering, Google Earth Engine Platform, Ocean engineering, Marine planning, Coastal management, Sentinel-1
Last updated : 2021-07-27
"},{"location":"projects/gpm/","title":"Global Precipitation Measurement (GPM)","text":"Global Precipitation Measurement (GPM) is an international satellite mission to provide next-generation observations of rain and snow worldwide every three hours. NASA and the Japanese Aerospace Exploration Agency (JAXA) launched the GPM Core Observatory satellite on February 27th, 2014, carrying advanced instruments that set a new standard for precipitation measurements from space. The data they provide is used to unify precipitation measurements made by an international network of partner satellites to quantify when, where, and how much it rains or snows around the world. Note: Additional bands (\u2018HQprecipitation\u2019, \u2018IRprecipitation\u2019, \u2018precipitationUncal\u2019, \u2018randomError\u2019) are provided with documentation provided here. You can find additional information here and climate engine org dataset page here.
This dataset is derived from the Earth Engine asset, NASA/GPM_L3/IMERG_V06, using the processing steps: 1. The GPM Daily dataset hosted on Climate Engine is sourced from the 30-min data. These data are summed to daily based on 0 UTC time zone. 2. This collection contains provisional products that are regularly replaced with updated versions when the data become available. This transition typically occurs about 1-2 years out.
Spatial Information
Parameter Value Spatial extent Global Spatial resolution 11-km (1/10-deg) Temporal resolution Daily Time span 2000-06-01 to present Update frequency Updated daily with 2 day lag timeVariables
Variable Details Precipitation (Calibrated) - daily data is derived from 30-minute data ('precipitationCal') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/gpm/#citation","title":"Citation","text":"Jackson, Gail & Berg, Wesley & Kidd, Chris & Kirschbaum, Dalia & Petersen, Walter & Huffman, George & Takayabu, Yukari. (2018). Global Precipitation Measurement (GPM): Unified Precipitation Estimation from Space. 10.1007/978-3-319-72583-3_7.\n// Read in Image Collections and get single image\nvar gpm_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-gpm-imerg-daily')\nvar gpm_i = gpm_ic.first()\n\n// Print single image to see bands\nprint(gpm_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(gpm_i.select('precipitationCal'), {min: 0, max: 200, palette: prec_palette}, 'precipitationCal')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-PRECIP-MEASUREMENT
"},{"location":"projects/gpm/#license","title":"License","text":"NASA promotes the full and open sharing of all data with research and applications communities, private industry, academia, and the general public.
Keywords: precipitation, climate, NASA, JAXA, satellite, near real-time
Provided by: Climate Engine Org, NASA
Curated in GEE by: Climate Engine Org
"},{"location":"projects/gridded_gdp_hdi/","title":"Gridded Global GDP and HDI (1990-2015)","text":"Two global key indicators of development are Gross Domestic Product (GDP) and Human Development Index (HDI). While \u2018GDP measures the monetary value of final goods and services\u2014that is, those that are bought by the final user\u2014produced in a [given area] in a given period of time, HDI is a composite index of \u2018average achievement in key dimensions of human development:
Gap-filled multiannual datasets in gridded form for Gross Domestic Product (GDP) and Human Development Index (HDI). To provide a consistent product over time and space, the sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics. This resulted in annual gridded datasets for GDP per capita (PPP), total GDP (PPP), and HDI, for the whole world at 5\u2009arc-min resolution for the 25-year period of 1990\u20132015. Additionally, total GDP (PPP) is provided with 30\u2009arc-sec resolution for three time steps (1990, 2000, 2015).
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
Dataset Dimensions Note GDP per capita (PPP) Timesteps: 26 Gridded GDP per capita, derived from a combination of sub-national and national datasets GDP (PPP)-5\u2009arc-min Timesteps: 26 Total GDP (PPP) of each grid cell, derived from GDP per capita (PPP) which is multiplied by gridded population data HYDE 3.2 GDP (PPP)-30\u2009arc-sec Timesteps: 3 Total GDP (PPP) of each grid cell, derived from GDP per capita (PPP) which is multiplied by gridded population data GHS Pedigree of GDP data Timesteps: 26 Reports the scale (national, sub-national) and type (reported, interpolated, extrapolated) of each year of data HDI Timesteps: 26 Gridded HDI, derived from a combination of sub-national and national datasets Pedigree of HDI data Timesteps: 26 Reports the level (national, sub-national) and type (reported, interpolated, extrapolated) of each year of data Administrative units Products: 2 Represents the administrative units used for GDP per capita (PPP) and HDI. National admin units have id 1\u2013999, sub-national ones 1001-"},{"location":"projects/gridded_gdp_hdi/#dataset-notes","title":"Dataset notes","text":"Units for GDP is US dollar
Pedigree GDP: Pedigree index numbers, coded as follows: 1-regional reported; 2-regional interpolated; 3-regional extrapolated; 5-national reported; 6-national interpolated; 7-national extrapolated
Pedigree HDI: Pedigree index numbers, coded as follows: 1-regional reported; 2-regional scaled; 4-national reported; 5-national interpolated; 6-national extrapolated; 7-no data, regional average used
Kummu, M., Taka, M. & Guillaume, J. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990\u20132015. Sci Data 5, 180004 (2018).\nhttps://doi.org/10.1038/sdata.2018.4\nKummu, Matti; Taka, Maija; Guillaume, Joseph H. A. (2020), Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015,\nDryad, Dataset, https://doi.org/10.5061/dryad.dk1j0\nvar gdp_ppp = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/GDP_PPP_1990_2015_5arcmin_v2\");\nvar gdp_ppp_30arc = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/GDP_PPP_30arcsec_v3\");\nvar gdp_per_capita = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/GDP_per_capita_PPP_1990_2015_v2\");\nvar hdi = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/HDI_1990_2015_v2\");\nvar admin_areas = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/admin_areas_GDP_HDI\");\nvar pedigree_gdp = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/pedigree_GDP_per_capita_PPP_1990_2015_v2\");\nvar pedigree_hdi = ee.Image(\"projects/sat-io/open-datasets/GRIDDED_HDI_GDP/pedigree_HDI_1990_2015_v2\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GLOBAL-GDP-HDI
"},{"location":"projects/gridded_gdp_hdi/#license","title":"License","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
Produced by : Kummu, Matti; Taka, Maija; Guillaume, Joseph H. A.
Curated in GEE by : Samapriya Roy
Keywords: : Development indicator, global spatial data, gridded data, Gross Domestic Product (GDP), Human Development Index (HDI), Purchasing Power Parity (PPP)
Last updated on GEE: 2022-04-30
"},{"location":"projects/gridded_livestock/","title":"Gridded Livestock Density Kazakhstan (2000-2019)","text":"This dataset provides medium-resolution (1 km) gridded livestock density estimates for horses and small ruminants (sheep and goats) in Kazakhstan from 2000 to 2019. The database was developed using random forest regression modeling, incorporating vegetation proxies, climatic factors, socioeconomic variables, topographic data, and proximity forcing variables. Each file is saved with an acronym of 'sheep_goat' for small ruminants (Sheep & goat combined) and 'horse' for horses, followed by an underscore and a year. Missing data are represented by \"No data.\"
For detailed methodology, validation results, and further insights, please refer to the associated publication: \"Gridded livestock density database and spatial trends for Kazakhstan\" you can read the paper here and the dataset is available here.
"},{"location":"projects/gridded_livestock/#citation","title":"Citation","text":"Kolluru, V., John, R., Saraf, S. et al. Gridded livestock density database and spatial trends for Kazakhstan. Sci Data 10, 839 (2023).\nhttps://doi.org/10.1038/s41597-023-02736-5\nKOLLURU, VENKATESH; John, Ranjeet; Saraf, Sakshi; Chen, Jiquan; Hankerson, Brett; Robinson, Sarah; et al. (2023). Gridded livestock density database and spatial trends for\nKazakhstan. figshare. Dataset. https://doi.org/10.6084/m9.figshare.23528232.v3\nvar sheep_goat_collection = ee.Image(\"projects/sat-io/open-datasets/GRIDDED-LIVESTOCK/KZ_SHEEP_GOAT_DENSITY_DB\");\nvar horse_collection = ee.Image(\"projects/sat-io/open-datasets/GRIDDED-LIVESTOCK/KZ_HORSE_DENSITY_DB\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/KZ-GRIDDED-LIVESTOCK
"},{"location":"projects/gridded_livestock/#license","title":"License","text":"The datasets are available under a Creative Commons Attribution 4.0 International license.
Created by: Kolluru et al 2023
Curated in GEE by: Kolluru et al 2023 and Samapriya Roy
Keywords: livestock, machine learning, random forest, population, small ruminant, grazing, vegetation, grasslands, kazakhstan
Last updated in GEE: 2024-10-21
"},{"location":"projects/gridded_ppt/","title":"High-resolution gridded precipitation dataset for Peruvian and Ecuadorian watersheds (1981-2015)","text":"RAIN4PE is a novel daily gridded precipitation dataset obtained by merging multi-source precipitation data (satellite-based Climate Hazards Group InfraRed Precipitation, CHIRP (Funk et al. 2015), reanalysis ERA5 (Hersbach et al. 2020), and ground-based precipitation) with terrain elevation using the random forest regression method. Furthermore, RAIN4PE is hydrologically corrected using streamflow data in catchments with precipitation underestimation through reverse hydrology. Hence, RAIN4PE is the only gridded precipitation product for Peru and Ecuador, which benefits from maximum available in-situ observations, multiple precipitation sources, elevation data, and is supplemented by streamflow data to correct the precipitation underestimation over p\u00e1ramos and montane catchments.
Currently included layers are:
"},{"location":"projects/gridded_ppt/#earth-engine-snippet-annual-mean","title":"Earth Engine Snippet: Annual mean","text":"var rain4pe_clim = ee.ImageCollection('users/csaybar/rainpe/annual_mean')\nvar rain4pe_clim = ee.ImageCollection('users/csaybar/rainpe/monthly_clim')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-MONTHLY-CLIM
"},{"location":"projects/gridded_ppt/#earth-engine-snippet-monthly-data","title":"Earth Engine Snippet: Monthly data","text":"var rain4pe_clim = ee.ImageCollection('users/csaybar/rainpe/monthly')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-MONTHLY
"},{"location":"projects/gridded_ppt/#earth-engine-snippet-daily-data","title":"Earth Engine Snippet: Daily data","text":"var rain4pe_daily = ee.ImageCollection('projects/sat-io/open-datasets/rainpe/daily')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-DAILY
"},{"location":"projects/gridded_ppt/#resolution-01-or-roughly-10km-x-10km","title":"Resolution: 0.1\u00b0 (or roughly 10km x 10km)","text":""},{"location":"projects/gridded_ppt/#citation","title":"citation","text":"When using the data please cite:
Fernandez-Palomino, C. A.; Hattermann, F. F.; Krysanova, V.; Lobanova, A.; Vega-J\u00e1come, F.; Lavado, W.;\nSantini, W.; Aybar, C.; Bronstert, A. (2021). Rain for Peru and Ecuador (RAIN4PE). V. 1.0. GFZ Data\nServices. https://doi.org/10.5880/pik.2020.010\nThe data are supplementary material to:
Fernandez-Palomino, C. A.; Hattermann, F. F.; Krysanova, V.; Lobanova, A.; Vega-J\u00e1come, F.; Lavado, W.;\nSantini, W.; Aybar, C.; Bronstert, A. (2021). A novel high-resolution gridded precipitation dataset for\nPeruvian and Ecuadorian watersheds \u2013 development and hydrological evaluation. Journal of\nHydrometeorology. https://doi.org/10.1175/jhm-d-20-0285.1\nThis work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Cesar Aybar & Samapriya Roy
Keywords: precipitation, streamflow, Peru, Ecuador, random forest, SWAT, reverse hydrology, satellite data, Earth observation, GIS.
"},{"location":"projects/grip/","title":"Global Roads Inventory Project global roads database","text":"The Global Roads Inventory Project (GRIP) dataset was developed to provide a more recent and consistent global roads dataset for use in global environmental and biodiversity assessment models like GLOBIO.
The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km).
The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a Creative Commons License (CC-BY 4.0) and is free to use. Read about the methodology here
Download the dataset here
Use the following credit when these datasets are cited:
Meijer, Johan R., Mark AJ Huijbregts, Kees CGJ Schotten, and Aafke M. Schipper. \"Global patterns of current and future road infrastructure.\" Environmental Research Letters 13, no. 6 (2018): 064006.\nvar grip4_africa = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Africa\");\nvar grip4_central_south_america = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Central-South-America\");\nvar grip4_europe = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Europe\");\nvar grip4_north_america = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/North-America\");\nvar grip4_oceania = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Oceania\");\nvar grip4_south_east_asia = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/South-East-Asia\");\nvar grip4_middle_east_central_asia = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRIP4/Middle-East-Central-Asia\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-ROADS-INVENTORY-PROJECT
Total features: 25,758,453
Shared License: This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: global, road map, infrastructure, global roads inventory project (GRIP), SSP scenarios
Last updated: 2021-04-03
"},{"location":"projects/grn_wrz/","title":"Global river networks & Corresponding Water resources zones","text":"River networks and water resources zones (WRZ) are critical for planning, utilization, development, conservation and management of water resources. Currently, the river network and WRZ of world are most obtained based on digital elevation model data automatically, which are not accurate enough, especially in plains. In addition, the WRZ code is inconsistent with the river network. The authors proposed a series of methods and generated a higher resolution and consistent high-precision global river network and corresponding WRZs at level 1 to 4. This dataset provides an important basis and support for reasonable use of water resources and sustainable social development in the world. You can read the full paper here
Level Categorization for Global River Networks(GRN) and Water Resources Zone(WRZ)
The river at level 1 (L1 river) refers to the river that flows into the sea or lake.
The river at level 2 (L2 river) refers to the river that flows into the L1 river, and its confluence area is larger than one hundredth of the L1 river or 10,000 km2.
The river at level 3 (L3 river) refers to the river that flows into the L2 river, and its confluence area is larger than one hundredth of the L2 river or 1000 km2.
The river at level 4 (L4 river) refers to the river that flows into the L3 river, and its confluence area is large than one hundredth of the L3 river or 100 km2.
The tributaries that do not satisfy the above conditions were neglected.
The WRZ correspond to River Levels
"},{"location":"projects/grn_wrz/#paper-citation","title":"Paper Citation","text":"Yan, D., Wang, K., Qin, T. et al. A data set of global river networks and corresponding water resources zones divisions.\nSci Data 6, 219 (2019). https://doi.org/10.1038/s41597-019-0243-y\nYan, Denghua; Wang, Kun; Qin, Tianling; Weng, Baisha; wang, Hao; Bi, Wuxia; et al. (2019): A data set of global river networks and corresponding\nwater resources zones divisions. figshare. Dataset. https://doi.org/10.6084/m9.figshare.8044184.v6\nThe river networks are ingested for each continent and as provided by the author. The water resources zone on the other hands were available as level based subsets for each continent so a total of 24 files. To make this accessible as large feature collections Levels across multiple continents were merged into single feature collections.
Global River Network Levels Asia Level 1,2,3,4 Africa Level 1,2,3,4 Australia Level 1,2,3,4 Europe Level 1,2,3,4 North America Level 1,2,3,4 South America Level 1,2,3,4 Water Resources Zone Levels Asia Level 1,2,3,4 Africa Level 1,2,3,4 Australia Level 1,2,3,4 Europe Level 1,2,3,4 North America Level 1,2,3,4 South America Level 1,2,3,4 Combined Water Resources Zones Locations Level 1 All Continents Level 2 All Continents Level 3 All Continents Level 4 All Continents
"},{"location":"projects/grn_wrz/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var af_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/af_river\");\nvar as_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/as_river\");\nvar au_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/au_river\");\nvar eu_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/eu_river\");\nvar na_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/na_river\");\nvar sa_river = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRN/sa_river\");\nvar WRZ_L1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/WRZ/WRZ_L1\");\nvar WRZ_L2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/WRZ/WRZ_L2\");\nvar WRZ_L3 = ee.FeatureCollection(\"projects/sat-io/open-datasets/WRZ/WRZ_L3\");\nvar WRZ_L4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/WRZ/WRZ_L4\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-NETWORKS-WATER-RESOURCE-ZONES
"},{"location":"projects/grn_wrz/#data-subsets","title":"Data subsets","text":"The Water Resources Zones are also available as level based extracts for each countinent. Use the prefix and the level to get to each feature collection. The format is
projects/sat-io/open-datasets/WRZ/(Level)/(Prefix)_(Level)
Here are the prefix list and some examples
Country Prefix Path Asia as projects/sat-io/open-datasets/WRZ/L1/as_wrz1 Africa af projects/sat-io/open-datasets/WRZ/L2/af_wrz2 Australia au projects/sat-io/open-datasets/WRZ/L3/au_wrz3 Europe eu projects/sat-io/open-datasets/WRZ/L4/eu_wrz4 North America na projects/sat-io/open-datasets/WRZ/L1/na_wrz1 South America sa projects/sat-io/open-datasets/WRZ/L3/sa_wrz3
"},{"location":"projects/grn_wrz/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created by: Yan, D., Wang, K., Qin, T. et al.
Curated by: Samapriya Roy
Keywords: River networks, Water Resources, Hydrology
Last updated: 2019-09-28
"},{"location":"projects/grod/","title":"Global River Obstruction Database (GROD)","text":"GROD v1.1 (filename: GROD_v1.1.csv), or Global River Obstruction Database version 1.1, contains 30549 manually identified human-made structures that obstructing river longitudinal flow. Obstructions have been identified on Google Earth Engine satellite map for all rivers mapped in the Global River Widths from Landsat (GRWL) database. Each obstruction has assigned one of the six types\u2014Dam, Lock, Low head dam, Channel dam, Partial dam 1, Partial dam 2. Details of the mapping process and data quality can be found in the publication and the dataset can be accessed here.
"},{"location":"projects/grod/#citation","title":"Citation","text":"Yang, X., Pavelsky, T.M., Ross, M.R., Januchowski\u2010Hartley, S.R., Dolan, W., Altenau, E.H., Belanger, M., Byron, D., Durand, M., Van Dusen, I. and Galit, H., 2022. Mapping flow\u2010obstructing structures on global rivers. Water Resources Research, 58(1), p.e2021WR030386.\nYang, X., Pavelsky, T. M., Ross, M. R. V., Januchowski-Hartley, S. R., Dolan, W., Altenau, E. H., Belanger, M., Byron, D., Durand, M., Van Dusen,\nI., Galit, H., Jorissen, M., Langhorst, T., Lawton, E., Lynch, R., Mcquillan, K. A., Pawar, S., & Whittemore, A. (2021). Global River Obstruction\nDatabase v1.1 (v1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5793918\nvar grod = ee.FeatureCollection(\"projects/sat-io/open-datasets/GROD/GROD_V11\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-OBSTRUCTION-DATABASE
"},{"location":"projects/grod/#license","title":"License","text":"The datasets are provided under a Creative Commons 4.0 International License.
Provided by: Yang et al 2021
Curated in GEE by: Samapriya Roy
Keywords: river obstruction, dam, lock, low head dam, weir, partial dam, wing dam, dataset, fragmentation, SWOT
Last updated: 2024-04-15
"},{"location":"projects/grwl/","title":"Global River Width from Landsat (GRWL)","text":"The Global River Width from Landsat (GRWL) layers are the major output from the GRWL paper and is extremely large with over 64 million features after joining all the subparts and this is a combination from the subpart files provided by the author. You can read the paper here
The repository consists of 5 total files with each files having subparts
1) Simplified GRWL Vector Product: grwl_SummaryStats_v01_01
The shapefile contains the following attributes:
Index Attribute Description 1 width_min the minimum of river width measurements along the segment at mean discharge (meters) 2 width_med the median of river width measurements along the segment at mean discharge (meters) 3 width_mean the mean of river width measurements along the segment at mean discharge (meters) 4 width_max the maximum of river width measurements along the segment at mean discharge (meters) 5 width_sd the standard deviation of river width measurements along the segment at mean discharge (meters) 6 lakeflag integer specifying if segment is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3) 8 nSegPx number of pixels within the segment (N pixels) 9 Shape_Leng length of the segment (kilometers)2) GRWL Mask (raster): water_mask_v01_01
The file contains the following values:
DN Value Classification DN = 256 No Data DN = 255 River DN = 180 Lake/reservoir DN = 126 Tidal rivers/delta DN = 86 Canal3) GRWL Vector Product: water_vector_v01_01
The shapefile contains the following attributes:
Index Attribute Description 1 utm_east UTM Easting (UTM Zone is given in tile file name; meters) 2 utm_north UTM Northing (UTM Zone is given in tile file name; meters) 3 width_m wetted width of river (meters) [note: width_m == 1 indicates NA (no width data along the centerline) ] 4 nchannels braiding index (-) 5 segmentID unique ID of river segment in each tile 6 segmentInd Index of each observation in each segment. Not sorted by upstream or downstream 7 lakeflag integer specifying if observation is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3). 8 lon Longitude (decimal degrees) 9 lat Latitude (decimal degrees) 10 elev Elevation (meters) \u2013 sampled from the Hydro1k DEMand
4) Location map of the individual GRWL tiles: grwl_tiles
5) River and stream surface area totals by drainage basin (Fig. 4 in Allen & Pavelsky, 2018): rssa_basins
GRWL vector product has a feature Count: 64,572,998 features.
Currently included layers are
"},{"location":"projects/grwl/#earth-engine-snippet","title":"Earth Engine Snippet:","text":"var grwl_summary = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRWL/grwl_SummaryStats_v01_01\");\nvar water_mask = ee.ImageCollection(\"projects/sat-io/open-datasets/GRWL/water_mask_v01_01\");\nvar grwl_water_vector = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRWL/water_vector_v01_01\");\nvar grwl_tiles = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRWL/grwl_tiles\");\nvar grwl_rssa_basins = ee.FeatureCollection(\"projects/sat-io/open-datasets/GRWL/rssa_basins\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-WIDTH-LANDSAT
Resolution: approx 30m
"},{"location":"projects/grwl/#cite-the-dataset-using","title":"Cite the dataset using","text":"Allen, George H., & Pavelsky, Tamlin M. (2018). Global River Widths from Landsat (GRWL) Database (Version V01.01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1297434\nAllen, George H., and Tamlin M. Pavelsky. \"Global extent of rivers and streams.\"\nScience 361, no. 6402 (2018): 585-588.\nShared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: :\"GRWL, Fluvial Geomorphology, Hydrology, Rivers, River Width, Landsat, MNDWI\"
Last updated: 2021-04-17
"},{"location":"projects/gsa/","title":"Global Solar Atlas Datasets","text":"The current version of Global Solar Atlas is v 2.6 released in July 2021. The Global Solar Atlas version 2.0 is an enhancement of the online platform, originally published in 2016 in version 1.0, that offers access to data needed for preliminary assessment of solar energy projects and sites through use of GIS data layers and maps in Download section. This Technical report summarizes delivery of the GSA 2.0 version and compares version 2.0 with previous version 1.0 in terms of enhancement in methodology, data layers and Solargis approach to PV electricity simulation. GSA 2.0 provides an access to long-term averaged yearly (for selected parameters monthly) solar, air temperature, PV power potential data and map products for almost any site on Earth.
The atlas provides an access to long-term averaged yearly (for selected parameters monthly) solar, air temperature, PV power potential data and map products for almost any site on Earth.
"},{"location":"projects/gsa/#attribution-and-license","title":"Attribution and License","text":"If you get the data or use the dataset within the GSA app attribution below, the Works (datasets) themselves are under are licensed under the Creative Commons Attribution 4.0 International license, CC BY 4.0, except where expressly stated that another license applies.
[Data/information/map] obtained from the \u201cGlobal Solar Atlas 2.0, a free, web-based application is developed and\noperated by the company Solargis s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding\nprovided by the Energy Sector Management Assistance Program (ESMAP).\nFor additional information: https://globalsolaratlas.info\nYou can find the Global Solar Atlas here and you can interact and download the datasets here
"},{"location":"projects/gsa/#data-structure","title":"Data Structure","text":"Delivered GIS data include eight parameters in the form of a raster data layers, providing the information on solar resource, photovoltaic power potential, air temperature and terrain elevation on global scale
Data layers are provided in Geographical coordinate system (EPSG:4326) and calculated in 30 arc-sec (nominally 1 km) resolution. On top of this, for more detailed analysis solar resource data (GHI, DIF, GTI and DNI) is also provided in 9 arc-sec (nominally 250 m) resolution. Finally, auxiliary data layer of Optimum angle features with 2 arcmin (nominally 4 km)resolution.
Atlas Variable Atlas GEE Variable Short Name Description DIF dif_LTAy_AvgDailyTotals Diffuse horizontal irradiation Longterm average of diffuse horizontal irradiation DNI dni_LTAy_AvgDailyTotals Direct normal irradiation Longterm average of direct normal irradiation ELE ele_asl Terrain elevation above sea level Terrain elevation GHI ghi_LTAy_AvgDailyTotals Global horizontal irradiation Longterm average of global horizontal irradiation GTI gti_LTAy_AvgDailyTotals Global irradiation for optimally tilted surface Longterm average of global irradiation at optimum tilt OPTA opta_LTAy_AvgDailyTotals Optimum tilt to maximize yearly yield Optimum tilt of photovoltaic modules PVOUT_LTAm pvout_LTAm_AvgDailyTotals Photovoltaic power potential Average Monthly Longterm monthly average of daily totals of potential photovoltaic electricity production PVOUT_LTAy pvout_LTAy_AvgDailyTotals Photovoltaic power potential Average daily Longterm yearly average of daily totals of potential photovoltaic electricity production TEMP temp_2m_agl Temperature at 2m above ground Longterm yearly average of air temperature (1994-2018) "},{"location":"projects/gsa/#earth-engine-datasets","title":"Earth Engine Datasets","text":"var dif = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/dif_LTAy_AvgDailyTotals');\nvar dni = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/dni_LTAy_AvgDailyTotals');\nvar elevation_asl = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/ele_asl');\nvar ghi = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/ghi_LTAy_AvgDailyTotals');\nvar gti = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/gti_LTAy_AvgDailyTotals');\nvar opta = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/opta_LTAy_AvgDailyTotals');\nvar pvout_ltam = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/pvout_LTAm_AvgDailyTotals');\nvar pvout_ltay = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/pvout_LTAy_AvgDailyTotals');\nvar temp_agl = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_solar_atlas/temp_2m_agl');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-SOLAR-ATLAS
Produced and maintained by the Global Solar Atlas, ESMAP, Solargis and the World Bank Group (consisting of The World Bank and the International Finance Corporation, or IFC)
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: : Solar, energy, photovoltaic capacity, irradiation, optimally tilted surface, Photovoltaic power potential
Last updated: 2021-10-30
"},{"location":"projects/gshtd/","title":"Global Seamless High-resolution Temperature Dataset (GSHTD)","text":"The Global Seamless High-resolution Temperature Dataset (GSHTD) presented in this study offers a comprehensive and valuable resource for researchers across various fields. Covering the period from 2001 to 2020, this dataset focuses on land surface temperature (Ts) and near-surface air temperature (Ta). A unique feature of GSHTD is its incorporation of seven types of temperature data, including clear-sky daytime and nighttime Ts, all-sky daytime and nighttime Ts, and mean, maximum, and minimum Ta. Notably, the dataset achieves global coverage with an impressive 30 arcsecond or 1km spatial resolution.
The development of GSHTD involves the innovative Estimation of Temperature Difference (ETD) method, enabling the reconstruction of both clear- and cloudy-sky Ts. The dataset is seamless, eliminating missing values, and employs a Cubist machine learning algorithm to enhance accuracy in creating monthly averages of mean, maximum, and minimum Ta data. GSHTD exhibits high accuracy, outperforming existing methods with average mean absolute errors (MAEs) that are significantly lower. This dataset's accessibility at the Middle Yangtze River Geoscience Data Center provides a valuable tool for studies related to climate change, environmental science, ecology, epidemiology, and human health. You can find additional information in the paper here including links to the dataset.
These temperature datasets are not valid over open oceans.
"},{"location":"projects/gshtd/#citation","title":"Citation","text":"Yao, Rui, Lunche Wang, Xin Huang, Qian Cao, Jing Wei, Panxing He, Shaoqiang Wang, and Lizhe Wang. \"Global seamless and high-resolution temperature\ndataset (GSHTD), 2001\u20132020.\" Remote Sensing of Environment 286 (2023): 113422.\nvar all_sky_day = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/ALL_SKY_DAY\");\nvar all_sky_night = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/ALL_SKY_NIGHT\");\nvar clear_sky_day = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/CLEAR_SKY_DAY\");\nvar clear_sky_night = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/CLEAR_SKY_NIGHT\");\nvar tmax = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/TMAX\");\nvar tmean = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/TMEAN\");\nvar tmin = ee.ImageCollection(\"projects/sat-io/open-datasets/GSHTD/TMIN\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GSHTD
"},{"location":"projects/gshtd/#license","title":"License","text":"The dataset is distributed under the Creative Commons Attribution 4.0 International as requested by the authors.
Provided by: Yao et al 2023
Curated in GEE by: Samapriya Roy
Keywords: MODIS, High Resolution Temperature, Seamless, Gap filled, Global dataset
Last updated in GEE: 2024-02-04
"},{"location":"projects/gssr/","title":"Global Storm Surge Reconstruction (GSSR) database","text":"The Global Storm Surge Reconstruction (GSSR) database includes daily maximum surge values for the past at 882 tide gauges distributed along the global coastline. The data-driven models employed for the surge reconstruction were developed by Tadesse et al. (2020). The authors use five different atmospheric reanalysis products with different spatial and temporal resolution to produce surge information for the periods covered by the different reanalyses. The reanalysis that leads to the best validation results is marked with \"best reconstruction\" (note that in some locations data is not available for all reanalyses as there is no overlap in the periods covered by the tide gauges and the reanalysis). You can read the full paper here The full surge reconstruction for each reanalysis (comprised of 882 compressed individual .csv files for the different tide gauges) can be downloaded from the following links:
Tadesse, M.G., Wahl, T. A database of global storm surge reconstructions. Sci Data 8, 125 (2021).\nhttps://doi.org/10.1038/s41597-021-00906-x\nThe combined merged download daily maximum surge values for individual tide gauges and reanalysis products for different sites were merged into master feature collections while still maintaining different reanalyses products. Since site names included special characters like + or # or spaces which are not allowed in GEE naming convention we applied a consistent find and replace strategy to provide some level of consistency between locations. However in any case we hope the lat long provides a more accurate representation of a site. Another thing to note is that the CSVs seemed to have been exported with the index column which is not very useful and especially since it was missing a header, so the index column was removed from all CSVs before being renamed and ingested.
Reanalysis Type GEE Feature Collection name 20-CR Surge Reconstruction [1836 - 2015] 20-CR_surge_reconstruction ERA-20C Surge Reconstruction [1900 - 2010] era-20C_surge_reconstruction ERA-Interim Surge Reconstruction [1979 - 2019] era-Interim_surge_reconstruction MERAA-2 Reconstruction [1980 - 2019] merra-2_surge_reconstruction ERA-Five Reconstruction [1979 - 2019] era-5_surge_reconstruction "},{"location":"projects/gssr/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var surge_20_cr = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/20-CR_surge_reconstruction\");\nvar surge_era_20c = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/era-20C_surge_reconstruction\");\nvar surge_era_interim = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/era-Interim_surge_reconstruction\");\nvar surge_merra_2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/merra-2_surge_reconstruction\");\nvar surge_era_5 = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/global_storm_surge_reconstruction/era-5_surge_reconstruction\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL-STORM-SURGE-RC
"},{"location":"projects/gssr/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Tadesse, M.G., Wahl, T.
Curated by: Samapriya Roy
Keywords: : 20-CR, ERA-20C, ERA-Interim, MERAA-2, ERA-Five, reanalysis, storm-surge, surge-reconstruction, NCEP, extreme-sea-level
Last updated: 2022-01-04
"},{"location":"projects/gue/","title":"Global urban extents from 1870 to 2100","text":"Long term, global records of urban extent can help evaluate environmental impacts of anthropogenic activities. Remotely sensed observations can provide insights into historical urban dynamics, but only during the satellite era. Here, we develop a 1\u2009km resolution global dataset of annual urban dynamics between 1870 and 2100 using an urban cellular automata model trained on satellite observations of urban extent between 1992 and 2013. Hindcast (1870\u20131990) and projected (2020\u20132100) urban dynamics under the five Shared Socioeconomic Pathways (SSPs) were modeled. We find that global urban growth under SSP5, the fossil-fuelled development scenario, was largest with a greater than 40-fold increase in urban extent since 1870. The high resolution dataset captures grid level urban sprawl over 200 years, which can provide insights into the urbanization life cycle of cities and help assess long-term environmental impacts of urbanization and human\u2013environment interactions at a global scale. You can read the paper here
The dataset includes hindcast urban extent from 1870 to 1990 with a 10-year interval, satellite observed urban extent from 1992 to 2013 at an annual interval, and projected urban extent from 2020 to 2100 under five SSP scenarios with a 10-year interval. The datasets and entire collection are available at Figshare
"},{"location":"projects/gue/#citation","title":"Citation","text":"Li, Xuecao, Yuyu Zhou, Mohamad Hejazi, Marshall Wise, Chris Vernon, Gokul Iyer, and Wei Chen. \"Global urban growth between 1870 and 2100 from\nintegrated high resolution mapped data and urban dynamic modeling.\" Communications Earth & Environment 2, no. 1 (2021): 1-10.\nLi, Xuecao; Zhou, Yuyu (2020): High resolution mapping of global urban extents from 1870 to 2100 by integrating data and model driven approaches.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.9696218\nDates are added to the images and the start and end date are given one year periods to be consistent with a snapshot approach rather than a continuity approach. For the projected extent scenario is added as a metadata to the images for easy sort and use as needed.
"},{"location":"projects/gue/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var hindcast_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/global-urban-extents/hindcast_urban_extent\");\nvar observed_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/global-urban-extents/observed_urban_extent\");\nvar projected_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/global-urban-extents/project_urban_scenarios\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-URBAN-EXTENTS
"},{"location":"projects/gue/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by: Li, Xuecao, Yuyu Zhou, Mohamad Hejazi, Marshall Wise, Chris Vernon, Gokul Iyer, and Wei Chen
Curated in GEE by: Samapriya Roy
Keywords: Cellular Automata, Urban sprawl, temporal trend, Nighttime lights, hindcasts, Forecast
Last updated: 2021-11-09
"},{"location":"projects/gwa/","title":"Global Wind Atlas Datasets","text":"The Global Wind Atlas is a free, web-based application developed to help policymakers, planners, and investors identify high-wind areas for wind power generation virtually anywhere in the world, and then perform preliminary calculations. The Global Wind Atlas facilitates online queries and provides freely downloadable datasets based on the latest input data and modeling methodologies. Users can additionally download high-resolution maps of the wind resource potential, for use in GIS tools, at the global, country, and first-administrative unit (State/Province/Etc.) levels. You can read more about the methods used here
The modeling process is made up of a WAsP calculation of local wind climates for every 250 m at five heights: 10 m; 50 m; 100 m; 150 m and; 200 m. On a 250 m grid, there is a local wind climate estimate for every node. Power density data takes into account geographical variations of air density. Includes variables in the Google Earth Engine collection include, wind speed, air density and power density. Surface roughness length is a property of the surface which can be used to determine the way the horizontal wind speed varies with height. The wind speed at a given height decreases with increasing surface roughness.
Most of the data are named as follows: gwa_{variable}_{height}.tif with GEE collections, where variable is one of and this description below is obtained here
wind-speed - The mean wind speed at the location for the 10 year period
power-density - The mean power density of the wind, which is related to the cube of the wind speed, and can provide additional information about the strength of the wind not found in the mean wind speed alone.
air-density - The air density is found by interpolating the air density from the CFSR reanalysis to the elevation used in the global wind atlas following the approach described in WAsP 12.
RIX - The RIX (Ruggedness IndeX) is a measure of how complex the terrain is. It provides the percent of the area within 10 km of the position that have slopes over 30-degrees. A RIX value greater than 5 suggests that you should use caution when interpreting the results.
The files which do not follow the naming convention above are the capacity-factor layers. The capacity factor layers were calculated for 3 distinct wind turbines, with 100m hub height and rotor diameters of 112, 126, and 136m, which fall into three IEC Classes (IEC1, IEC2, and IEC3). Capacity factors can be used to calculate a preliminary estimate of the energy yield of a wind turbine (in the MW range), when placed at a location. This can be done by multiplying the rated power of the wind turbine by the capacity factor for the location (and the number of hours in a year):
AEP = Prated * CF * 8760 hr/year,
where AEP is annual energy production, Prated is rated power, and CF is capacity factor.
Variable Name Version Heights(in m) Wind Speed 3 10,50,100,150,200 Power Density 3 10,50,100,150,200 Air Density 3 10,50,100,150,200 Capacity Factor IEC1 3 NA Capacity Factor IEC2 3 NA Capacity Factor IEC3 3 NA Ruggedness Index 3 NA
"},{"location":"projects/gwa/#attribution-and-license","title":"Attribution and License","text":"If you get the data or use the dataset within the GWA app attribution below, the Works (datasets) themselves are under are licensed under the Creative Commons Attribution 4.0 International license, CC BY 4.0, except where expressly stated that another license applies.
[Data/information/map obtained from the] \u201cGlobal Wind Atlas 3.0, a free, web-based application developed,\nowned and operated by the Technical University of Denmark (DTU). The Global Wind Atlas 3.0 is released in partnership\nwith the World Bank Group, utilizing data provided by Vortex, using funding provided by\nthe Energy Sector Management Assistance Program (ESMAP). For additional information: https://globalwindatlas.info\u201d\nYou can also find the Global Wind Atlas here and you can interact and download the datasets here
"},{"location":"projects/gwa/#data-preprocessing-for-gee","title":"Data Preprocessing for GEE","text":"Capacity Factors were added onto a single collection and rotor diameter and hub height was added as metadata property for filtering. For variables that have height gradients, height was added as a metadata for filtering.
"},{"location":"projects/gwa/#earth-engine-datasets","title":"Earth Engine Datasets","text":"var air_density = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/air-density');\nvar capacity_factor = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/capacity-factor');\nvar power_density = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/power-density');\nvar rix= ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/ruggedness-index');\nvar wind_speed= ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/global_wind_atlas/wind-speed');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-WIND-ATLAS
Produced and maintained by the Global Wind Atlas, Department of Wind Energy at the Technical University of Denmark (DTU Wind Energy) and the World Bank Group (consisting of The World Bank and the International Finance Corporation, or IFC)
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: : Wind, energy, ruggedness index, capacity factor, wind speed, power density
Last updated: 2021-07-11
"},{"location":"projects/gwl_fcs/","title":"Global 30 m Wetland Map with a Fine Classification System","text":"GWL_FCS30 is a global wetland map with a resolution of 30 meters, designed to provide detailed information on wetland ecosystems worldwide. This dataset spans from 2000 to 2022 and includes a fine classification system that distinguishes between eight specific wetland subcategories. These subcategories encompass both coastal tidal wetlands and inland wetlands, offering a comprehensive view of wetland types across the globe.
The coastal tidal wetlands in this dataset are categorized into mangroves, salt marshes, and tidal flats. For inland wetlands, the classification includes permanent water, swamps, marshes, flooded flats, and saline wetlands. This level of detail is crucial for understanding and managing different wetland types and their ecological functions.
The dataset was created using a novel approach that combines automatic sample extraction from various existing global wetland products with multi-temporal satellite imagery, including Landsat reflectance data and Sentinel-1 SAR imagery. This method allows for capturing the complex temporal dynamics and spectral variations of wetlands. Additionally, a stratified classification strategy was employed, utilizing local adaptive random forest models to ensure precise classification at a high spatial resolution.
The GWL_FCS30 dataset offers a spatial resolution of 30 meters and covers the entire globe. It provides valuable insights into wetland areas and their distribution over the past two decades, with the data presented in square kilometers. This dataset is an important resource for ecological studies, wetland management, and conservation efforts, providing essential information for understanding and preserving wetland ecosystems.
You can read the paper here, and find the complete dataset here
"},{"location":"projects/gwl_fcs/#dataset-preprocessing","title":"Dataset Preprocessing","text":"Yearly images are distributed as zipped files consisting of tiles for global regions which are merged into a single image per year. These are then ingested into a single image collection in GEE.
"},{"location":"projects/gwl_fcs/#citation","title":"Citation","text":"Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., & Liu, W. (2023). GWL_FCS30: a global 30\u2009m wetland map with a fine classification\nsystem using multi-sourced and time-series remote sensing imagery in 2020. *Earth Syst. Sci. Data*, 15, 265\u2013293.\nhttps://doi.org/10.5194/essd-15-265-2023\nLiangyun, L., & Xiao, Z. (2023). Time-series global 30 m wetland maps from 2000 to 2022 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10068479\nClass Code Wetland Subcategory 0 Non-wetland 180 Permanent Water 181 Swamp 182 Marsh 183 Flooded Flat 184 Saline 185 Mangrove Forest 186 Salt Marsh 187 Tidal Flat
"},{"location":"projects/gwl_fcs/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var gwl_fcs30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GWL_FCS30\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GWL-FCS30-WETLANDS
"},{"location":"projects/gwl_fcs/#license","title":"License","text":"These datasets are made available under the Creative Commons Attribution 4.0 International.
Provided by: Zhang et al 2023
Curated in GEE by: Samapriya Roy
Keywords: fine classification system, land cover, wetland, wetland ecosystem
Last updated: 2024-07-28
"},{"location":"projects/habitat/","title":"Global maps of habitat types","text":"We provide a global, spatially explicit characterization of terrestrial and marine habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme, which is widely used in ecological analyses, including for quantifying species\u2019 Area of Habitat. We produced this novel habitat map for the years 2015-2019 by creating a global decision tree that intersects the best currently available global data on elevation and bathymetry, land and ocean cover, climate and land use.
"},{"location":"projects/habitat/#citation","title":"Citation","text":"Jung, M., Dahal, P.R., Butchart, S.H.M. et al. A global map of terrestrial habitat types.\nSci Data 7, 256 (2020). https://doi.org/10.1038/s41597-020-00599-8\n// Level 1 and level 2 for the year 2015\nvar lvl1 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/iucn_habitatclassification_composite_lvl1_ver004\")\nvar lvl2 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/iucn_habitatclassification_composite_lvl2_ver004\")\n\n// Note: Colour code is random\nMap.addLayer(lvl2.randomVisualizer(),{},'Habitat Classification Level 1')\n\n// Changemask for later.\n// Replace year in folder and mask to get a different year (for years 2016-2019)\n//for example 2017 would be var change2017_lvl1 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/changemasks/iucn_habitatclassification_2017changemask_lvl1_ver004\")\nvar change2019_lvl1 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/changemasks/iucn_habitatclassification_2019changemask_lvl1_ver004\").select('comp2019').unmask(0)\nvar change2019_lvl2 = ee.Image(\"projects/sat-io/open-datasets/IUCN_HABITAT/changemasks/iucn_habitatclassification_2019changemask_lvl2_ver004\").select('comp2019').unmask(0)\nprint(change2019_lvl1)\n\n//Level 1 and level 2 for the year 2019\nvar lvl12019 = change2019_lvl1.firstNonZero(lvl1)\nvar lvl22019 = change2019_lvl2.firstNonZero(lvl2)\n\nMap.addLayer(lvl12019.randomVisualizer(),{},'Level 1 2019')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GLOBAL-HABITAT-TYPES
Extra Info: Code to reproduce the maps can be found here and be visualized here. Default Maps are for the year 2015. Change maps are also available for later years (2016-2019) based on Copernicus only. Note that provided changemasks are cumulative (e.g. the year 2019 includes changes up to 2019). They can be used to updateMask the 2015 image.
https://zenodo.org/record/4058819
"},{"location":"projects/habitat/#source-code-for-dataset","title":"Source Code for dataset","text":"https://github.com/Martin-Jung/Habitatmapping
Created by : Jung, M., Dahal, P.R., Butchart, S.H.M. et al
Curated by: Martin Jung
Keywords: Global habitats, Ecosystems, Integrated map, IUCN, Biodiversity, Species
Last updated: 2020-09-01
"},{"location":"projects/hand/","title":"Global 30m Height Above the Nearest Drainage","text":"Read about the methodology here
Or get it from https://gena.users.earthengine.app/view/global-hand
Use the following credit when these data are cited:
Donchyts, Gennadii, Hessel Winsemius, Jaap Schellekens, Tyler Erickson, Hongkai Gao, Hubert Savenije, and Nick van de Giesen. \"Global 30m Height Above the Nearest Drainage (HAND)\",\nGeophysical Research Abstracts, Vol. 18, EGU2016-17445-3, 2016, EGU General Assembly (2016).\nvar hand30_100 = ee.ImageCollection(\"users/gena/global-hand/hand-100\").mosaic()\nvar hand30_1000 = ee.Image(\"users/gena/GlobalHAND/30m/hand-1000\")\nvar hand90_1000 = ee.Image(\"users/gena/GlobalHAND/90m-global/hand-1000\")\n30 and 90 - cell resolution, 100 and 1000 - number of river head threshold cells
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-HEIGHT-ABV-NEAREST-DRAINAGE
Shared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Source and Curated by: Donchyts/Deltares
Keywords: Global Hand, Hydrology, drainage
Last updated: ~2017
"},{"location":"projects/harvest/","title":"NASA Harvest Layers","text":"This page includes studies and geospatial layers made available as a result of publications from the NASA Harvest group members and made available in Google Earth Engine. This will be updated as newer and updated studies get published.
"},{"location":"projects/harvest/#rapid-response-crop-maps-in-data-sparse-regions","title":"Rapid Response Crop Maps in Data Sparse Regions","text":"We present a method for rapid mapping of croplands in regions where little to no ground data is available. We present results for this method in Togo, where we delivered a high-resolution (10 m) cropland map in under 10 days to facilitate rapid response to the COVID-19 pandemic by the Togolese government. This demonstrated a successful transition of machine learning applications research to operational rapid response in a real humanitarian crisis. All maps, data, and code are publicly available to enable future research and operational systems in data-sparse regions. Read the paper here
"},{"location":"projects/harvest/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var togo_cropland_binary = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/togo_cropland_binary\");\nvar togo_cropland_probability = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/togo_cropland_probability\");\nHannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe,Brian Barker, Blake Munshell,\nMadhava Paliyam, and Mehdi Hosseini. 2020.Rapid Response Crop Maps in Data Sparse Regions.\nKDD \u201920: ACMSIGKDDConference on Knowledge Discovery and Data Mining Workshops, August22\u201327, 2020, San Diego, CA.\nSample code:https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/NASA-HARVEST-CROPLAND
"},{"location":"projects/harvest/#annual-and-in-season-mapping-of-cropland-at-field-scale-with-sparse-labels","title":"Annual and in-season mapping of cropland at field scale with sparse labels","text":"Previously, we developed a method for binary classification of cropland that learns from sparse local labels and abundant global labels using a multi-headed LSTM and timeseries multispectral satellite inputs over one year. In this work, we present a new method that uses an autoregressive LSTM to classify cropland during the growing season (i.e., partially-observed time series). We used these methods to produce publicly-available 10m-resolution cropland maps in Kenya for the 2019-2020 and 2020-2021 growing seasons. These are the highest-resolution and most recent cropland maps publicly available for Kenya. These methods and associated maps are critical for scientific studies and decision-making at the intersection of food security and climate change. Read the paper here
"},{"location":"projects/harvest/#earth-engine-snippet_1","title":"Earth Engine Snippet","text":"var kenya_cropland_binary = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/kenya_cropland_binary\");\nvar kenya_cropland_probability = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/kenya_2019_cropland_probability\");\nvar busia_cropland_probability = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/busia_cropland_probability\");\nvar busia_cropland_binary = ee.Image(\"projects/sat-io/open-datasets/nasa-harvest/busia_cropland_binary\");\nTseng, Gabriel, Hannah Kerner, Catherine Nakalembe, and Inbal Becker-Reshef.\n\"Annual and in-season mapping of cropland at field scale with sparse labels.\"\nSample code:https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/NASA-HARVEST-CROPLAND
"},{"location":"projects/harvest/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: agriculture, Africa, Togo, crops, crop classification, food security, satellite data, Earth observation, GIS
Last updated: 2021-04-25
"},{"location":"projects/health_sites/","title":"Global Healthsites Mapping Project","text":"Healthsites.io and the Global Healthsites Mapping Project's mission is to help supply governments, NGOs, and the private sector with accurate and up-to-date health facility information. Health facility registers are the building blocks of a well-functioning health information system within a country. Accurate and up-to-date data provides the basic data that helps drive activities like service availability planning, monitoring and evaluation, and disaster risk preparedness.
The data is shared on both https://healthsites.io and expected a monthly updated on the Humanitarian Data Exchange. Expected update Frequency for now is every month. Read the Healthsites concept note http://bit.ly/2ocL2KY
The healthsites.io datasets are served as nodes (defining points in space) and ways (defining linear features and area boundaries) based on open street map object relations.
"},{"location":"projects/health_sites/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var node = ee.FeatureCollection(\"projects/sat-io/open-datasets/health-site-node\");\nvar way = ee.FeatureCollection(\"projects/sat-io/open-datasets/health-site-way\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-HEALTHSITES-MAPPING-PROJECT
"},{"location":"projects/health_sites/#license","title":"License","text":"Open Database License (ODbL)
You are free to
Conditions
Compiled by : Global Healthsites Mapping Project
Curated by: Samapriya Roy
Keywords: :\"Global Healthsites Mapping Project, Healthsites, Health, GLC\"
Last updated: 2024-01-16
"},{"location":"projects/heat-hazard/","title":"Global Extreme Heat Hazard","text":"Published by the World Bank (2017), this is a global data layer for extreme heat hazard, which is classified based on an existing and widely accepted heat stress indicator, the Wet Bulb Globe Temperature (WBGT, in \u00b0C) \u2013 more specifically the daily maximum WGBT. The WBGT has an obvious relevance for human health, but it is relevant in all kinds of projects and sectors, including infrastructure related, as heat stress affects personnel and stakeholders, and therefore the design of buildings and infrastructure. Heat stress studies in the scientific literature that make use of the WBGT apply thresholds of 28\u00b0C and 32\u00b0C to categorise heat stress risk. The damaging intensity thresholds are applied following this definition of slight/low (<28\u00b0C), moderate/high (28-32\u00b0C) and severe/very high (>32\u00b0C) heat stress. This dataset is licensed under Creative Commons Attribution 4.0. You can download the report here. Point of contact: sfraser@worldbank.org
Extra Info: There are three global GeoTIFF files in total, which can be combined into one single collection 5 year, 20 year and 100 year return period.
"},{"location":"projects/heat-hazard/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var global_extreme_heat_hazard = ee.ImageCollection('projects/sat-io/open-datasets/WORLD-BANK/global-ext-heat-hazard');\nSample code: : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-EXTREME-HEAT-HAZARD
"},{"location":"projects/heat-hazard/#license","title":"License","text":"This dataset is classified as Public under the Access to Information Classification Policy. Users inside and outside the Bank can access this dataset under a Creative Commons Attribution 4.0
Curated by: Koen De Ridder, Dirk Lauwaet, Hans Hooyberghs and Filip Lefebre from VITO (Author)
Keywords: Hazard Assessment, Extreme Heat, Climatology, Climate Change
Last updated: Mar 1, 2017
"},{"location":"projects/hihydro_soil/","title":"HiHydroSoil v2.0 layers","text":"In May 2020, ISRIC has released the latest version (v2.0) of its Soilgrids250m product. This release has made it possible for FutureWater to update its HiHydroSoil v1.2 database with newer, more precise and with a higher resolution soil data, which resulted in the development and release of HiHydroSoil v2.0.
Soil information is the basis for all environmental studies. Since local soil maps of good quality are often not available, global soil maps with a low resolution are used. Furthermore, soil maps do not include information about soil hydraulic properties, which are of importance in, for example, hydrological modeling, erosion assessment and crop yield modelling. HiHydroSoil v2.0 can fill this data gap. HiHydroSoil v2.0 includes the following data and additional information along with links to download the data can be found here
The Hydrologic Soil Group (HSG) determines the Runoff Curve Number which is often used in hydrological modelling to estimate the direct runoff from rainfall. Four hydrologic soil groups and three dual hydrologic soil groups. The data layers originally consisting of float data type were multiplied by a factor of 10,000 and subsequently converted to integer type. It is therefore required to translate the data to the proper units by multiplying with 0.0001.
"},{"location":"projects/hihydro_soil/#citation-related-publications","title":"Citation & Related Publications","text":"Simons, G.W.H., R. Koster, P. Droogers. 2020. HiHydroSoil v2.0 - A high resolution soil map of global hydraulic properties.\nFutureWater Report 213.\nYou can download the report here
Variable Unit Description Range Assets on GEE Organic Matter Content (ORMC) % Soil organic matter\u00a0(SOM) is the\u00a0organic matter\u00a0component of\u00a0soil, consisting of plant and animal\u00a0detritus\u00a0at various stages of\u00a0decomposition, cells and tissues of\u00a0soil microbes, and substances that soil microbes synthesize. 0 - 50 ormc Soil Texture Class (STC) O (Organic), VF\u00a0(Very\u00a0Fine), F\u00a0(Fine), MF (Medium Fine), C\u00a0(Coarse), M (Medium) Soil texture\u00a0is a\u00a0classification\u00a0instrument used both in the field and laboratory to determine\u00a0soil\u00a0classes based on their physical texture. 1- 6 (see column Unit) stc Alpha parameter for Mualem Van Genuchten Equation (ALPHA) 1/cm The shape of water retention curves can be characterized by several models, one of them known as the van Genuchten model. The Alpha parameter in this model is related to the inverse of the air entry suction. 0 - 0.2 alpha N parameter for Mualem Van Genuchten Equation (N) - The shape of water retention curves can be characterized by several models, one of them known as the van Genuchten model. The N parameter in this model is a measure of the pore-size distribution. 1 - 2.3 N Saturated Water Content (Wcsat) m3/m3 Saturated water content is\u00a0the maximum amount of water a soil can store and which is equivalent to the porosity of the soil. 0.25 - 0.85 wcsat Residual Water Content (Wcres) m3/m3 The residual volumetric water content\u00a0\u00a0represents the volumetric water content of a soil where a further increase in negative pore-water\u00a0pressure does not produce significant changes in water content. 0 - 0.02 wcres Saturated Hydraulic Conductivity (Ksat) cm/d Saturated hydraulic conductivity is a quantitative measure of a saturated soil's ability to transmit water when subjected to a hydraulic gradient. It can be thought of as the ease with which pores of a saturated soil permit water movement. 0 - 1500 ksat Water content at pF2 (field capacity) (WCpF2) m3/m3 Field Capacity is the amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. It's the upper limit of the rapidly available water for plants at a matric potential of -100 cm or pF2. 0 - 0.8 wcpf2 Water content at pF3 (critical point) (WCpF3) m3/m3 Critical point: lower limit of rapidly available water for plants. Upper limit of slowly available water for plants. This is at a matric potential of -1000 cm or pF3. 0 - 0.7 wcpf3 Water content at pF4.2 (permanent wilting point) (WCpF4.2) m3/m3 Plants can - on average - produce a suction till 16 x atmospheric pressure before a plants starts to permanently wilt. This atmosperic pressures is similar to a matrix potentential of -16000 cm, or pF 4.2. 0 - 0.7 wcpf4-2 Available water content (Wcavail) m3/m3 The amount of water between field capacity (pF2) and permanent wilting point (pF4.2). This value should be used with caution. First, plants will start wilting with subsequent yield losses well before the permanent wilting point. Secondly, plant available soil water is replenished by capillary rise, rainfall and irrigation water. 0 - 0.6 wcavail Water content between saturation point and field capacity (pF2) (SAT_FIELD) m3/m3 Water content between saturation point and field capacity (pF2) \u2026 sat-field Water content between field capacity (pF2) and critical point (pF3) (FIELD-CRIT) m3/m3 Water content between field capacity (pF2) and critical point (pF3) 0 - 0.4 field-crit Water content between critical point (pF3) and permanent wilting point (pF4.2) (CRIT-WILT) m3/m3 Water content between critical point (pF3) and permanent wilting point (pF4.2) 0 - 0.25 crit-wilt Hydrologic Soil Group A (low runoff potential), A/D, B\u00a0(moderately low runoff potential), B/D, C (moderately high runoff potential), C/D, D\u00a0(high runoff potential) Along with land use, land management practices and soil hydrologic conditions the Hydrologic Soil Group (HSG) determines the Runoff Curve Number which is often used in hydrological modelling to estimate the direct runoff from rainfall. Four hydrologic soil groups and three dual hydrologic soil groups are described by the USDA (2009) hydrologic-soil-group"},{"location":"projects/hihydro_soil/#earth-engine-snippet-hihydro-layers-hydrologic_soil_group_250m","title":"Earth Engine Snippet: HiHydro Layers (Hydrologic_Soil_Group_250m)","text":"var hydrologic_soil_group = ee.Image('projects/sat-io/open-datasets/HiHydroSoilv2_0/Hydrologic_Soil_Group_250m');\nSample Code: https://code.earthengine.google.com/4da512c4c0785ef2767f159028579fc6
"},{"location":"projects/hihydro_soil/#earth-engine-snippet-hihydro-additional-layers","title":"Earth Engine Snippet: HiHydro Additional Layers","text":"var ksat = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/ksat\");\nvar satfield = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/sat-field\");\nvar N = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/N\");\nvar alpha = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/alpha\");\nvar crit_wilt = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/crit-wilt\"),\nvar field_cirt = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/field-crit\");\nvar ormc = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/ormc\");\nvar stc = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/stc\");\nvar wcavail = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcavail\");\nvar wcpf2 = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcpf2\");\nvar wcpf3 = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcpf3\");\nvar wcpf4_2 = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcpf4-2\");\nvar wcres = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcres\");\nvar wcsat = ee.ImageCollection(\"projects/sat-io/open-datasets/HiHydroSoilv2_0/wcsat\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/HiHYDRO-SOIL-LAYERS
"},{"location":"projects/hihydro_soil/#license-information","title":"License Information","text":"HiHydroSoil v2.0 can be used freely and redistributed with attribution. No additional information made available by authors.
Curated by: William Ouellette and Samapriya Roy
Keywords: Global Hydrologic Soil Group, Hydrology, Hydrological, Soil, Hydraulic, Conductivity, Runoff, Run-off, Water, Water Cycle
Last updated dataset: October 2020 Last curated: 2021-05-05
"},{"location":"projects/histarfm/","title":"HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) database","text":"The HISTARFM database is a high spatial resolution monthly reflectance temporal series corrected from cloud data gaps. The dataset was created at 30 meters resolution through the fusion of the Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) temporal series. The method involves using two estimators that work together to eliminate random noise and minimize the bias of Landsat spectral reflectances. The first estimator is an optimal interpolator that generates Landsat reflectance estimates using Landsat historical data and fused MODIS and Landsat reflectances from the nearest overpasses. The fusion process employs a linear regression model at a pixel level. The second estimator is a Kalman filter that corrects any bias in the reflectance produced by the first estimator. HISTARFM provides improved reflectance values and a unique and useful side product, the reflectance uncertainties, which is helpful for realistic error calculation (e.g., computing error bars of Vegetation Indexes or biophysical variables). For a more detailed explanation of the HISTARFM algorithm, please refer to the Moreno-Martinez et al. 2020 manuscript.
Example of a mosaic ofHIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) database the HISTARFM data [right bottom], the red band uncertainty [left bottom], and the derivate leaf area index [top] over a large area in continental East Asia for a given year (2021).
"},{"location":"projects/histarfm/#dataset-access","title":"Dataset Access","text":"To access the HISTARFM dataset, you need to join the HISTARFM google group once you have access to the google group you can access the dataset using the code snippets and paths below. The method to find and add yourself to the group is fairly simple. Go to groups.google.com use the drop down to select all groups rather than my groups and search for keyword HISTARFM collection then click on join group and follow along. The steps are also captured below
"},{"location":"projects/histarfm/#citation","title":"Citation","text":"Moreno-Mart\u00ednez, \u00c1lvaro, Emma Izquierdo-Verdiguier, Marco P. Maneta, Gustau Camps-Valls, Nathaniel Robinson, Jordi Mu\u00f1oz-Mar\u00ed, Fernando Sedano,\nNicholas Clinton, and Steven W. Running. \"Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud.\" Remote Sensing of\nEnvironment 247 (2020): 111901.\nDifferent versions and study areas are already processes:
var histarfm_conus = ee.ImageCollection(\"projects/KalmanGFwork/GFLandsat_V1\")\nvar histarfm_ic = ee.ImageCollection(\"projects/ee-kalman-gap-filled/assets/histarfm_v5\")\nSample code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/HISTARFM-V5-EXAMPLE
For more information about how to work with HISTARFM and some examples of how to improve your research and applications with the HISTARFM database, visit the tutorial here.
The HISTARFM database was used in the following papers
Mart\u00ednez-Ferrer, L., et al. \"Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning.\" Remote Sensing of Environment 280 (2022): 113199.
Salerno, L., et al. \"Satellite Analyses Unravel the Multi-Decadal Impact of Dam Management on Tropical Floodplain Vegetation.\" Frontiers in Environmental Science (2022): 357.
Kushal, K. C., and Sami Khanal. \"Agricultural productivity and water quality tradeoffs of winter cover crops at a landscape scale through the lens of remote sensing.\" Journal of Environmental Management 330 (2023): 117212.
The dataset is licensed under a Creative Commons Attribution NonCommercial 4.0 International license.
Curated by: \u00c1lvaro Moreno-Mart\u00ednez, Emma Izquierdo-Verdiguier, Jordi Mu\u00f1oz-Mar\u00ed and Nicolas Clinton.
Keywords: MODIS, Landsat, Land reflectance images, gap-filled temporal series, vegetation
Last updated: 06-03-2023
"},{"location":"projects/historical_us/","title":"USGS Historical Imagery Western US","text":"This dataset contains an imagery base layer representing conditions from the mid-1950s across the western United States. We sourced the imagery from over 160,000 aerial images in the USGS EROS Archive taken between 1940 and 1970, with the median acquisition date being 1954. The imagery provides complete coverage for 17 western U.S. states: Arizona, California, Colorado, Idaho, Kansas, Montana, North Dakota, Nebraska, New Mexico, Nevada, Oklahoma, Oregon, South Dakota, Texas, Utah, Washington, and Wyoming. Explore the dataset visually through our easy-to-use web map application at LandscapeExplorer.org.
Find alternative methods of downloading and utilizing the imagery on our data download page.
We preprocessed the imagery in MATLAB to reduce image vignetting and improve image contrast. Orthorectification was performed in Metashape. The compiled imagery had varying Ground Sampling Distance values, ranging from 0.6 to 1.7 meters. The GEE dataset was written out at 1 meter GSD. Dive deeper into our data processing methods at our LandscapeExplorer.org development page.
"},{"location":"projects/historical_us/#additional-preprocessing","title":"Additional Preprocessing","text":"The feature collection had Dates in different formats everything from simply
Format YYYY YYYY-MM/dd YYYYs YYYY-MM-dd
An approach was taken to convert and standardize these dates and add the corresponding dates as epoch system:time_start to features in the overall feature collection. This was then merged back into a feature collection with additional properties system:time_start, year, standardized_date. Based on the year metadata you can now get counts across multiple time periods I am summarizing a 5 year range distribution
Year Range Data Total 1935-1939 2175 1940-1944 4380 1945-1949 31176 1950-1954 29966 1955-1959 25657 1960-1964 12347 1965-1969 12360 1970-1974 19623 1975-1979 14290 1980-1984 675 1985-1989 67
"},{"location":"projects/historical_us/#citation","title":"Citation","text":"Morford, S.L., Allred, B.W., Jensen, E.R., Maestas, J.D., Mueller, K.R., Pacholski, C.L., Smith, J.T., Tack, J.D., Tackett, K.N. and Naugle, D.E.\n(2023), Mapping tree cover expansion in Montana, U.S.A. rangelands using high-resolution historical aerial imagery. Remote Sens Ecol Conserv.\n[https://doi.org/10.1002/rse2.357]( https://doi.org/10.1002/rse2.357)\nvar conusWest_imagery = ee.ImageCollection(\"projects/wlfw-um/assets/historical-imagery/conus-west\");\nvar conusWest_metadata = ee.FeatureCollection(\"projects/wlfw-um/assets/historical-imagery/conus-west-seamlines\");\nvar conusWest_metadata_with_date = ee.FeatureCollection(\"projects/sat-io/open-datasets/wlfm-um-extra/wlfm-um-seamlines\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/USGS-HISTORICAL-AERIAL-IMAGERY
Sample app: https://sat-io.earthengine.app/view/landscape-explorer
"},{"location":"projects/historical_us/#license","title":"License","text":"These datasets are available under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Keywords: Aerial imagery, United States, Great Plains, Great Basin, Historical, photogrammetry
Provided by: University of Montana, Lands for Wildlife, Montana NRCS and Intermountain West Joint Venture
Curated in GEE by: University of Montana, Lands for Wildlife, Montana NRCS and Intermountain West Joint Venture
Last updated: 2023-09-24
"},{"location":"projects/hitisae/","title":"High-spatial-resolution Thermal-stress Indices over South and East Asia (HiTiSAE)","text":"This newly developed dataset is a high-spatial-resolution (0.1\u00b0\u00d70.1\u00b0) gridded product that contains the daily values of the indoor, outdoor shaded and outdoor unshaded UTCI, the MRT and eight other widely adopted human thermal-stress indices (ESI, HI, Humidex, WBGT, WBT, WCT, AT, NET), derived from the newly available ECMWF ERA5-Land and ERA5 reanalysis products, over South and East Asia from Jan 3, 1981 to Dec 31, 2019. You can read the complete article here. This high-spatial-resolution database of human thermal stress indices over South and East Asia (HiTiSEA), which contains the daily mean, maximum, and minimum values of UTCI, MRT, and eight other widely adopted indices, is suitable for both indoor and outdoor applications and allows researchers and practitioners to investigate the spatial and temporal evolution of human thermal stress and its impacts on densely populated regions over South and East Asia at a fner scale. The dataset is available for download via a Figshare collection which can be found here
"},{"location":"projects/hitisae/#paper-citation","title":"Paper Citation","text":"Yan, Yechao, Yangyang Xu, and Shuping Yue. \"A high-spatial-resolution dataset of human thermal stress indices over South and East Asia.\"\nScientific Data 8, no. 1 (2021): 1-14.\nYan, Yechao; Xu, Yangyang; Yue, Shuping (2021): A High-spatial-resolution Dataset of Human Thermal Stress Indices over South and East Asia.\nfigshare. Collection. https://doi.org/10.6084/m9.figshare.c.5196296\nThe dataset contains 14242 daily NetCDF files which are archived by month and compressed into tar.gz files with a total volume of 450 GB. The netcdf files for each subvariable was converted into Geotifs with Minimum, Mean and Maximum value for each parameter. To reduce the overall index size, a band order was constructed with b1, b2,b3 for each variable corresponding to Min, Mean and Maximum value for the same parameter.
For example HiTiSea_1981-01-03_AT contains 3 bands with b1 with AT_min, b2 as AT_mean and b3 as AT_max
Included indices, names and GEE Variable are included in the table below
Termal Indices Full Name of the Indices GEE Variable Variable Stats UTCI universal thermal climate index UTCI Min,Mean, Max indoor UTCI (UTCI2) UTCI for indoor environment UTCI2 Min,Mean, Max outdoor shaded(UTCI3) UTCI UTCI for outdoor shaded space UTCI3 Min,Mean, Max MRT mean radiant temperature MRT Min,Mean, Max ESI environment stress index ESI Min,Mean, Max HI heat index HI Min,Mean, Max Humidex humidity index Humidex Min,Mean, Max WBGT \u00a0wet-bulb globe temperature WBGT Min,Mean, Max WBT wet bulb temperature WBT Min,Mean, Max WCT wind chill temperature WCT Min,Mean, Max AT apparent temperature AT Min,Mean, Max NET net effective temperature NET Min,Mean, Max "},{"location":"projects/hitisae/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var AT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/AT\");\nvar ESI = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/ESI\");\nvar MRT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/MRT\");\nvar UTCI = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/UTCI\");\nvar UTCI2 = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/UTCI2\");\nvar UTCI3 = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/UTCI3\");\nvar HI = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/HI\");\nvar Humidex = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/Humidex\");\nvar WBGT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/WBGT\");\nvar WBT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/WBT\");\nvar WCT = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/WCT\");\nvar NET = ee.ImageCollection(\"projects/sat-io/open-datasets/HITISEA/NET\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/HIGHRES-THERMAL-STRESS-INDICES
"},{"location":"projects/hitisae/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by: Yechao Yan; Yangyang Xu; Shuping Yue
Preprocessed and Curated in GEE by : Samapriya Roy
Keywords: thermal-stress indices, south and southeast asia, heat index, humidity index, wind chill, apparent temperature
Last updated: 2021-05-20
Last updated on GEE: 2021-11-08
"},{"location":"projects/hntl/","title":"Harmonized Global Night Time Lights (1992-2021)","text":"In this study, the authors generated an integrated and consistent NTL dataset at the global scale by harmonizing the inter-calibrated NTL observations from the DMSP data and the simulated DMSP-like NTL observations from the VIIRS data. The generated global DMSP NTL time-series data (1992\u20132018) show consistent temporal trends. This temporally extended DMSP NTL dataset provides valuable support for various studies related to human activities such as electricity consumption and urban extent dynamics. The dataset contains
Spatial resolution: 30 arc-seconds (~1km)
The authors suggest using pixels with DN values greater than 7.
You can read the paper here
You can download the datasets here
"},{"location":"projects/hntl/#data-citation","title":"Data Citation","text":"Li, Xuecao; Zhou, Yuyu; zhao, Min; Zhao, Xia (2020): Harmonization of DMSP and VIIRS nighttime\nlight data from 1992-2020 at the global scale. figshare. Dataset.\nhttps://doi.org/10.6084/m9.figshare.9828827.v5\nLi, Xuecao, Yuyu Zhou, Min Zhao, and Xia Zhao. \"A harmonized global nighttime light dataset 1992\u20132018.\" Scientific data 7, no. 1 (2020): 1-9.\nvar dmsp = ee.ImageCollection(\"projects/sat-io/open-datasets/Harmonized_NTL/dmsp\");\nvar viirs = ee.ImageCollection(\"projects/sat-io/open-datasets/Harmonized_NTL/viirs\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/HARMONIZED-GLOBAL-NTL
"},{"location":"projects/hntl/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Xuecao Li et al
Curated by: Samapriya Roy
Keywords: : DMSP/OLS data, VIIRS, nighttime light, calibration, consistent, global
"},{"location":"projects/hntl/#changelog","title":"Changelog","text":"Last updated: 2023-09-22
"},{"location":"projects/hrdem/","title":"Canada High Resolution Digital Elevation Model (HRDEM)","text":"NoteThis dataset is currently only available to those in the insiders program
The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps.
The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. You can find more information here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or providers of the dataset and their works.
"},{"location":"projects/hrdem/#preprocessing","title":"Preprocessing","text":"Most of the 2m resolution datasets are generated from ArcticDEM project and as such not ingested in this effort and only 1m resolution tiles were ingested. Since tile edges were not matched and datasets were from various sources and dates a simple approach was used for tiles with same names where the largest file size replaced any file of that name. This was a decision to help deconflict tiles with similar names and was done programatically.
"},{"location":"projects/hrdem/#citation","title":"Citation","text":"openCanada.ca; High Resolution Digital Elevation Model (HRDEM) - CanElevation Series : Last accessed date\nvar dsm = ee.ImageCollection(\"projects/sat-io/open-datasets/OPEN-CANADA/CAN_ELV/HRDEM_1M_DSM\");\nvar dtm = ee.ImageCollection(\"projects/sat-io/open-datasets/OPEN-CANADA/CAN_ELV/HRDEM_1M_DTM\");\nvar footprint = ee.FeatureCollection(\"projects/sat-io/open-datasets/OPEN-CANADA/CAN_ELV/dataset_footprints\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/OPEN-CANADA-HRDEM
"},{"location":"projects/hrdem/#license","title":"License","text":"This work is licensed under a Open Government Licence - Canada.
Created by: CanElevation Series, Gov of Canada
Curated in GEE by: Samapriya Roy
keywords: digital terrain model, digital surface model, bare-earth, terrain, remote sensing, lidar,dsm,dtm
Last updated in GEE: 2022-12-27
"},{"location":"projects/hrdpa/","title":"High Resolution Deterministic Precipitation Analysis (HRDPA)","text":"The High Resolution Deterministic Precipitation Analysis (HRDPA) is a best estimate of 6 and 24 hour precipitation amounts. This objective estimate integrates data from in situ precipitation gauge measurements, radar QPEs and a trial field generated by a numerical weather prediction system. CaPA produces four analyses of 6 hour amounts per day, valid at synoptic hours (00, 06, 12 and 18 UTC) and two 24 hour analyses valid at 06 and 12 UTC. HRDPA is provided by the Meterological Service of Canada (MSC), a part of Environment and Climate Change Canada (ECCC). The MSC provides weather forecasts and warnings 24 hours a day, 365 days a year. MSC also provides federal department, agencies and other levels of government with information to support emergency preparedness and response to events such as storms, floods, wildfires and other weather-related emergencies. The model is based on the Canadian Precipitation Analysis (CaPA) system. You can find additional information here and information about the dataset can also be found on climate engine org data page here.
"},{"location":"projects/hrdpa/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Canada Spatial resolution 2.5 km grid (1/24 deg) Temporal resolution Daily Time span 2018-03-01 to present Update frequency Updated daily with 1 day lag timeVariables
Variable Details Precipitation ('Precipitation') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/hrdpa/#citation","title":"Citation","text":"- [Canadian Precipitation Analysis (CaPA)](https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/capa_information_leaflet_20141118_en.pdf) Methodology system\n\n- Fortin, V., Roy, G., Stadnyk, T., Koenig, K., Gasset, N., & Mahidjiba, A. (2018). Ten years of science based on the Canadian precipitation analysis: A CaPA system overview and literature review. Atmosphere-Ocean, 56(3), 178-196.\n// Read in Image Collections and get single image\nvar hrdpa_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-hrdpa-daily')\nvar hrdpa_i = hrdpa_ic.first()\n\n// Print single image to see bands\nprint(hrdpa_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(hrdpa_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CE-HRDPA-DAILY
"},{"location":"projects/hrdpa/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: precipitation, Canada, near real-time, daily, climate
Dataset provided by: Environment and Climate Change Canada
Dataset curated in GEE by: Climate Engine Org
"},{"location":"projects/hrdps/","title":"High Resolution Deterministic Prediction System (HRDPS)","text":"The High Resolution Deterministic Prediction System (HRDPS) provides useful numerical simulations of temperature over large areas. Climate Engine is ingesting only the band containing temperature at 2m above ground level, but HRDPS also produces bands for precipitation, cloud cover, wind speed and direction, humidity, and others. These numerical simulations can be used for air quality modeling and forecasting, climate and wildfire modeling, and extreme weather forecasting. Users who will benefit most from using these new data are those for whom a detailed forecast of surface temperatures and winds is important. The 2.5 km forecasts could add much value especially during the change of seasons and in wintertime when rapid changes in temperature and winds cause phase transitions of precipitation (freezing rain to snow to rain for example). HRDPS is the high resolution counterpart to the RDPS dataset. You can additional information here and on the climate engine org dataset page.
"},{"location":"projects/hrdps/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Canada Spatial resolution 2.5 km grid (1/24 deg) Temporal resolution Daily Time span 2015-04-23 to present Update frequency Updated daily with 1 day lag timeVariables
Variable Details Mean temperature ('Tavg') - Units: Degrees Celsius - Scale factor: 1.0"},{"location":"projects/hrdps/#citation","title":"Citation","text":"Milbrandt, J. A., B\u00e9lair, S., Faucher, M., Vall\u00e9e, M., Carrera, M. L., & Glazer, A. (2016). The pan-Canadian high resolution (2.5 km) deterministic\nprediction system. Weather and Forecasting, 31(6), 1791-1816.\n// Read in Image Collection and get first image\nvar hrdps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-hrdps-daily')\nvar hrdps_i = hrdps_ic.first()\n\n// Print first image to see bands\nprint(hrdps_i)\n\n// Visualize temperature from first image\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(hrdps_i.select('Tavg'), {min: -10, max: 20, palette: temp_palette}, 'Tavg')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CE-HRDPS-DAILY
"},{"location":"projects/hrdps/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: climate, temperature, daily, Canada, near real-time
Dataset Provider: Environment and Climate Change Canada
Curated in GEE by: Climate Engine Org
"},{"location":"projects/hrsl/","title":"High Resolution Settlement Layer","text":"In partnership with the Center for International Earth Science Information Network (CIESIN) at Columbia University, Facebook used state-of-the-art computer vision techniques to identify buildings from publicly accessible mapping services to create the world's most accurate population datasets. You can read about their project here. These are the datasets available for download on the Humanitarian Data Exchange for nearly every country in the world:
To reference this data, please use the following citation:
Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL Copyright 2016 DigitalGlobe. Accessed DAY MONTH YEAR. Data shared under: Creative Commons Attribution International.\nYou can get methodology here:
https://dataforgood.fb.com/docs/methodology-high-resolution-population-density-maps-demographic-estimates/
and step by step download here
https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/
License: Creative Commons Attribution International
"},{"location":"projects/hrsl/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var HRSL = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrslpop\");\nvar HRSL_men = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_men\");\nvar HRSL_women = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_women\");\nvar HRSL_youth = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_youth\");\nvar HRSL_children_under_five = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_children_under_five\");\nvar HRSL_women_reproductive_age = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_women_reproductive_age\");\nvar HRSL_elderly_over_sixty = ee.ImageCollection(\"projects/sat-io/open-datasets/hrsl/hrsl_elderly_over_sixty\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/FACEBOOK-HRSL-30m
Extra Info: Medium Article here
Download Tool/Code snippets if any: hdxpop
Curated by: Samapriya Roy
Keywords: High Density Population, Population, Facebook
"},{"location":"projects/hrsl/#changelog","title":"Changelog","text":"Last updated: 2022-08-27
"},{"location":"projects/hwsd/","title":"Harmonized World Soil Database (HWSD) version 2.0","text":"The Harmonized World Soil Database version 2.0 (HWSD v2.0) is a unique global soil inventory providing information on the morphological, chemical and physical properties of soils at approximately 1 km resolution. Its main objective is to serve as a basis for prospective studies on agro-ecological zoning, food security and climate change.
This updated version (HWSD v2.0) is built on the previous versions of HWSD with several improvements on (i) the data source that now includes several national soil databases, (ii) an enhanced number of soil attributes available for seven soil depth layers, instead of two in HWSD v1.2, and (iii) a common soil reference for all soil units (FAO1990 and the World Reference Base for Soil Resources). This contributes to a further harmonization of the database.
You can download the files and tutorials for the datasets here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hwsd/#dataset-preprocessing","title":"Dataset preprocessing","text":"A multiband image containing all the attributes of the FAO Harmonized World Soil Database v2.0 Soil Mapping Units (SMUs). The dataset was processed with special thanks to William Ouellette.
The band order is the following:
HWSD2_ID (numeric)WISE30s_ID (string converted to numeric in ascending alphanumeric order)COVERAGE (numeric)SHARE (numeric) - unit: %WRB4 (string converted to numeric in ascending alphanumeric order)WRB_PHASES (string converted to numeric in ascending alphanumeric order)WRB2_CODE (numeric) -- WRB2 is skipped, as it is redundant with WRB2_CODEFAO90 (string converted to numeric in ascending alphanumeric order)KOPPEN (string converted to numeric in ascending alphanumeric order)TEXTURE_USDA (numeric)REF_BULK_DENSITY (numeric) - unit: g/cm\u00b3BULK_DENSITY (numeric) - unit: g/cm\u00b3DRAINAGE (numeric)ROOT_DEPTH (numeric)AWC (numeric) - unit: mm/mPHASE1 (numeric)PHASE2 (numeric)ROOTS (numeric)IL (numeric)ADD_PROP (numeric)FAO & IIASA. 2023. Harmonized World Soil Database version 2.0. Rome and Laxenburg. https://doi.org/10.4060/cc3823en\nvar hwsd2 = ee.Image(\"projects/sat-io/open-datasets/FAO/HWSD_V2_SMU\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/HWSD-V2-SMU
"},{"location":"projects/hwsd/#license","title":"License","text":"This dataset is made available under Attribution-NonCommercial-ShareAlike 3.0 International (CC BY-NC-SA 3.0)
Created by: FAO & IIASA
Curated in GEE by: William Ouellette & Samapriya Roy
Keywords: Soil, World Soil properties, Texture, USDA, FAO, IIASA
Last updated in GEE: 23/03/2023
"},{"location":"projects/hydra_water/","title":"Tensor Flow Hydra Flood Models","text":"This dataset is a surface water output image from the Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods) system utilizing a Deep Learning TensorFlow approach. Specifically, this Joint Research Centre (JRC) Adjusted Learning Rate Binary Cross-Entropy (BCE) Dice model and methodology are discussed in detail in the recent Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine publication.
"},{"location":"projects/hydra_water/#citation","title":"Citation","text":"Mayer, T., Poortinga, A., Bhandari, B., Nicolau, A.P., Markert, K., Thwal, N.S., Markert, A., Haag, A., Kilbride, J., Chishtie, F. and Wadhwa, A.,\n2021. Deep Learning approach for Sentinel-1 Surface Water Mapping leveraging Google Earth Engine. ISPRS Open Journal of Photogrammetry and Remote\nSensing, p.100005.\nFor greater detail on the HYDRAFloods open-source Python application for downloading, processing, and delivering surface water maps derived from remote sensing data. Please see the HYDRAFloods Documentation.
"},{"location":"projects/hydra_water/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var HYDRAFloods = ee.Image(\"users/tjm0042/Hydrafloods_Outputs/TensorFlow_Surface_Water_Model_Mosaic\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/TENSORFLOW-HYDRA-FLOOD-MODELS
"},{"location":"projects/hydra_water/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Tim Mayer, Kel Markert, Biplov Bhandari, Ate Poortinga
Keywords: Surface Water Mapping, Floods, Deep Learning TensorFlow, SERVIR
Last updated: 2021-10-20
"},{"location":"projects/hydro90/","title":"Hydrography 90m Layers","text":"The Hydrography 90 layers uses the MERIT Hydro digital elevation model at 3 arcsec (\u223c 90 m at the Equator) to derive a globally seamless, standardised hydrographic network, the \"Hydrography90m\", with corresponding stream topographic and topological information. A central feature of the network is the minimal upstream contributing area, i.e. flow accumulation, of 0.05 km2 (or 5 ha) to initiate a stream channel, which allowed us to extract headwater stream channels in great detail.
The data validation procedures confirmed Hydrography90m as a more accurate representation of stream networks compared to HydroRIVERS, GRWL, and MERIT Hydro\u2013Vector. Improved accuracy was achieved principally by employing a higher resolution DEM, the MD8 flow routing algorithm, and a markedly smaller flow accumulation threshold to initiate stream channels. With these characteristics, Hydrography90m provides a valuable basis for supporting a variety of freshwater-related research disciplines. Find additional details in the paper here. The datasets can be downloaded here. This is one of the highest resolution global hydrography datasets and has multiple applications.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hydro90/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The hydrography datasets can downloaded here. The folders were renamed based on the descriptions of the papers and are included in the tables below. The parameter name were kept consistent and additional information is provided as need along with units for said parameters.
"},{"location":"projects/hydro90/#paper-citation","title":"Paper Citation","text":"Amatulli, Giuseppe, Jaime Garcia Marquez, Tushar Sethi, Jens Kiesel, Afroditi Grigoropoulou, Maria M. \u00dcblacker, Longzhu Q. Shen, and Sami Domisch.\n\"Hydrography90m: A new high-resolution global hydrographic dataset.\" Earth System Science Data 14, no. 10 (2022): 4525-4550.\nBase and network layers of Hydrography90m: flow accumulation, flow direction, drainage basins, outlets, stream segments, subcatchments, regional units, and depression
Output map description Unit GEE Collection Name Flow accumulation (raster) km2 accumulation=acc flow_accumulation Flow direction (raster) NE\u2013N\u2013NW\u2013W\u2013SW\u2013S\u2013SE\u2013E correspond to 1\u20132\u20133\u20134\u20135\u20136\u20137\u20138 flow__direction Drainage basin (raster) IDs from 1 to 1 676 628 drainage_basin Outlets (raster) ID=1 stream_vector=stream threshold=0.05; v.to.rast input=stream outlet Depression (raster) ID = 1 depression Stream segment (raster) IDs from 1 to 726 723 221 segment Sub-catchment (raster) IDs from basins=sub_catchment sub_catchment Regional unit (raster) IDs from 1 to 116 IDs from 150 to 200 regional_unit "},{"location":"projects/hydro90/#earth-engine-snippet-basin-network-layers","title":"Earth Engine snippet: basin-network-layers","text":"var flow_accumulation = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/flow_accumulation\");\nvar flow_direction = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/flow_direction\");\nvar depression = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/depression\");\nvar drainage_basin = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/drainage_basin\");\nvar outlet = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/outlet\");\nvar regional_unit = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/regional_unit\");\nvar segment = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/segment\");\nvar sub_catchment = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/base-network-layers/sub_catchment\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-BASE-NETWORK
"},{"location":"projects/hydro90/#data-structure-stream-slope","title":"Data structure: stream-slope","text":"Curvature, gradient (elevation difference divided by distance), and elevation difference raster maps.
Output raster map description Unit GEE Collection Name Maximum curvature between highest upstream cell, focal cell, and downstream cell m^-1 (scale factor 10^6) slope_curv_max_dw_cel Minimum curvature between highest upstream cell, focal cell, and downstream cell m^-1 (scale factor 10^6) slope_curv_min_dw_cel Elevation difference between focal cell and downstream cell m slope_elv_dw_cel Focal cell gradient Unitless (scale factor 10^6) slope_grad_dw_cel "},{"location":"projects/hydro90/#earth-engine-snippet-stream-slope","title":"Earth Engine Snippet: stream-slope","text":"var slope_curv_max_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-slope/slope_curv_max_dw_cel\");\nvar slope_curv_min_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-slope/slope_curv_min_dw_cel\");\nvar slope_elv_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-slope/slope_elv_dw_cel\");\nvar slope_grad_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-slope/slope_grad_dw_cel\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-STREAM-SLOPE
"},{"location":"projects/hydro90/#data-structure-stream-outlet-distance","title":"Data structure: stream-outlet-distance","text":"Stream or outlet distance and elevation difference raster maps.
Output raster map description Unit GEE collection name Shortest upstream distance between focal grid cell and the nearest sub-catchment drainage divide m stream_dist_up_near Longest upstream distance between focal grid cell and the nearest sub-catchment drainage divide m stream_dist_up_farth Distance between focal grid cell and its nearest downstream stream grid cell m stream_dist_dw_near Distance between focal grid cell and the outlet grid cell in the network m outlet_dist_dw_basin Distance between focal grid cell and the downstream stream node grid cell m outlet_dist_dw_scatch Euclidean distance between focal grid cell and the stream network m stream_dist_proximity Elevation difference of the shortest path from focal grid cell to the sub-catchment drainage divide m stream_diff_up_near Elevation difference of the longest path from focal grid cell to the sub-catchment drainage divide m stream_diff_up_farth Elevation difference between focal grid cell and its nearest downstream stream pixel m stream_diff_dw_near Elevation difference between focal grid cell and the outlet grid cell in the network m outlet_diff_dw_basin Elevation difference between focal grid cell and the downstream stream node grid cell m outlet_diff_dw_scatch "},{"location":"projects/hydro90/#earth-engine-snippet-stream-outlet-distance","title":"Earth Engine snippet: stream-outlet-distance","text":"var outlet_diff_dw_basin = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/outlet_diff_dw_basin\");\nvar outlet_diff_dw_scatch = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/outlet_diff_dw_scatch\");\nvar outlet_dist_dw_basin = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/outlet_dist_dw_basin\");\nvar outlet_dist_dw_scatch = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/outlet_dist_dw_scatch\");\nvar stream_diff_dw_near = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_diff_dw_near\");\nvar stream_diff_dw_far = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_diff_up_farth\");\nvar stream_diff_up_near = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_diff_up_near\");\nvar stream_dist_dw_near = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_dist_dw_near\");\nvar stream_dist_proximity = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_dist_proximity\");\nvar stream_dist_up_farth = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_dist_up_farth\");\nvar stream_dist_up_near = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-outlet-distance/stream_dist_up_near\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-STREAM-OUTLET-DIST
"},{"location":"projects/hydro90/#data-structure-flow-index","title":"Data structure: flow-index","text":"The compound topographic index (cti), stream power index (spi), and stream transportation index (sti) derived from flow accumu- lation (\u03b1) and terrain slope (\u03b2)
Output raster map description Unit GEE collection name Stream power index (spi) Unitless (scale factor 10^3) spi Stream transportation index (sti) Unitless (scale factor 10^3) sti Compound topographic index (cti) Unitless (scale factor 10^8) cti "},{"location":"projects/hydro90/#earth-engine-snippet-flow-index","title":"Earth Engine Snippet: flow-index","text":"var cti = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/flow_index/cti\");\nvar spi = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/flow_index/spi\");\nvar sti = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/flow_index/sti\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-FLOW-INDEX
"},{"location":"projects/hydro90/#data-structurestream-order","title":"Data structure:stream-order","text":"Stream order rasters
Output map description GEE collection name Strahler\u2019s stream order (raster) order_strahler Shreve\u2019s stream magnitude (raster) order_shreve Horton\u2019s stream order (raster) order_horton Hack\u2019s stream order (raster) order_hack Topological dimension of streams (raster) order_topo "},{"location":"projects/hydro90/#earth-engine-snippet-stream-order","title":"Earth Engine snippet: stream-order","text":"var order_hack = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_hack\");\nvar order_horton = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_horton\");\nvar order_shreve = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_shreve\");\nvar order_strahler = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_strahler\");\nvar order_topo = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-order/order_topo\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-STREAM-ORDER
"},{"location":"projects/hydro90/#data-structurecurvature-gradient","title":"Data structure:curvature-gradient","text":"Curvature, gradient (elevation difference divided by distance), and elevation change raster maps.
Output raster map description Unit GEE collection name Segment downstream mean gradient between focal cell and the node or outlet Unitless (scale factor 10^6) channel_grad_dw_seg Segment upstream mean gradient between focal cell and the init or node Unitless (scale factor 10^6) channel_grad_up_seg Upstream gradient between focal cell and the next cell Unitless (scale factor 10^6) channel_grad_up_cel Cell stream course curvature of the focal cell m^-1 (scale factor 10^6) channel_curv_cel Segment downstream elevation difference between focal cell and the node or outlet m channel_elv_dw_seg Segment upstream elevation difference between focal cell and the init or node m channel_elv_up_seg Upstream elevation difference between focal cell and the next cell m (outlet cell value = 99 999) channel_elv_up_cel Downstream elevation difference between focal cell and the next cell m channel_elv_dw_cel Segment downstream distance between focal cell and the node or outlet m channel_dist_dw_seg Segment upstream distance between focal cell and the init or node m channel_dist_up_seg Upstream distance between focal cell and next cell m channel_dist_up_cel "},{"location":"projects/hydro90/#earth-engine-snippet-curvature-gradient","title":"Earth Engine Snippet: curvature-gradient","text":"var channel_curv_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_curv_cel\");\nvar channel_dist_dw_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_dist_dw_seg\");\nvar channel_dist_up_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_dist_up_cel\");\nvar channel_dist_up_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_dist_up_seg\");\nvar channel_elv_dw_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_elv_dw_cel\");\nvar channel_elv_dw_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_elv_dw_seg\");\nvar channel_elv_up_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_elv_up_cel\");\nvar channel_elv_up_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_elv_up_seg\");\nvar channel_grad_dw_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_grad_dw_seg\");\nvar channel_grad_up_cel = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_grad_up_cel\");\nvar channel_grad_up_seg = ee.ImageCollection(\"projects/sat-io/open-datasets/HYDROGRAPHY90/stream-channel/channel_grad_up_seg\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROGRAPHY90-STREAM-CHANNEL
"},{"location":"projects/hydro90/#license","title":"License","text":"The dataset is licensed under a Creative Commons \"CC BY-NC 4.0\" license.
Created by: Amatulli, Giuseppe, Jaime Garcia Marquez, Tushar Sethi, Jens Kiesel, Afroditi Grigoropoulou, Maria M. \u00dcblacker, Longzhu Q. Shen, and Sami Domisch
Curated by: Samapriya Roy
Keywords: Hydrology, Hydrography, Slope, Channel, Stream, Runoff, Flow accumulation, Flow direction
Last updated in GEE: 2022-11-14
"},{"location":"projects/hydroatlas/","title":"HydroATLAS v1.0","text":"HydroATLAS offers a global compendium of hydro-environmental characteristics for all sub-basins of HydroBASINS, all river reaches of HydroRIVERS, and all lake polygons of HydroLAKES. The HydroATLAS database is divided into three distinct sub-datasets: BasinATLAS, RiverATLAS, and LakeATLAS which represent sub-basin delineations (polygons), the river network (lines), and lake shorelines (polygons), respectively. In total, HydroATLAS contains 1.0 million sub-basins, 8.5 million river reaches, and 1.4 million lakes.
HydroATLAS has been created by compiling and re-formatting a wide range of hydro-environmental attributes derived from existing global datasets in a consistent and organized manner. The resulting data compendium offers attributes grouped in seven categories: hydrology; physiography; climate; land cover & use; soils & geology; and anthropogenic influences. For each of the three sub-datasets, HydroATLAS contains 56 hydro-environmental variables, partitioned into 281 individual attributes. You can download the files here
The HydroATLAS database is distributed in large file sizes due to the enriched attribute information. Users who only need geometric information and digital vector maps of sub-basin boundaries, river network lines, and lake shorelines may prefer to download the HydroBASINS, HydroRIVERS, or HydroLAKES products instead.
"},{"location":"projects/hydroatlas/#technical-documentation","title":"Technical Documentation","text":"For more information on HydroATLAS please refer to hydrosheds page on hydroatlas and the HydroATLAS Technical Documentation.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hydroatlas/#citations","title":"Citations","text":"The development of BasinATLAS and RiverATLAS is fully described in Linke et al. (2019) and should be cited as:
Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V.,\nMaxwell, S., Moidu, H., Tan, F., Thieme, M. (2019). Global hydro-environmental sub-basin and river reach\ncharacteristics at high spatial resolution. Scientific Data 6: 283. doi: https://doi.org/10.1038/s41597-019-0300-6\nLehner, B., Messager, M.L., Korver, M.C., Linke, S. (2022). Global hydro-environmental lake characteristics at\nhigh spatial resolution. Scientific Data 9: 351. doi: https://doi.org/10.1038/s41597-022-01425-z\nThe HydroATLAS database is licensed under a Creative Commons Attribution (CC-BY) 4.0 International License. Please also refer to the HydroATLAS Technical Documentation for more details on the license and requested citations. By downloading and using the data the user agrees to the terms and conditions of this license.
ou can read the paper here : https://www.nature.com/articles/ncomms13603?origin=ppub
"},{"location":"projects/hydroatlas/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var riveratlas = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/RiverAtlas_v10\");\nvar lakeatlas_pt = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/LakeAtlas/LakeAtlas_v10_point\");\nvar lakeatlas_poly = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/LakeAtlas/LakeAtlas_v10_polygon\");\nvar basin_l01 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev01\");\nvar basin_l02 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev02\");\nvar basin_l03 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev03\");\nvar basin_l04 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev04\");\nvar basin_l05 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev05\");\nvar basin_l06 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev06\");\nvar basin_l07 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev07\");\nvar basin_l08 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev08\");\nvar basin_l09 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev09\");\nvar basin_l10 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev10\");\nvar basin_l11 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev11\");\nvar basin_l12 = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroAtlas/BasinAtlas/BasinATLAS_v10_lev12\");\nCreated by: Linke et al and Lehner et al
Curated by: Samapriya Roy
Keywords: water,hydrology, lakes, global lake surface, discharge, depth, volume, area, hydrolakes, hydrobasins, hydrorivers
Last updated: 2022-07-10
"},{"location":"projects/hydrolakes/","title":"HydroLAKES v1.0","text":"Lakes are key components of biogeochemical and ecological processes, thus knowledge about their distribution, volume and residence time is crucial in understanding their properties and interactions within the Earth system. However, global information is scarce and inconsistent across spatial scales and regions. Here we develop a geo-statistical model to estimate the volume of global lakes with a surface area of at least 10\u2009ha based on the surrounding terrain information.
The HydroLAKES database was designed as a digital map repository to include all lakes with a surface area of at least 10 ha with a total surface area of 2.67 \u00d7 106\u2009km2 (1.8% of global land area), a total shoreline length of 7.2 \u00d7 106\u2009km (about four times longer than the world\u2019s ocean coastline) and a total volume of 181.9 \u00d7 103\u2009km3 (0.8% of total global non-frozen terrestrial water stocks). HydroLAKES aims to be as comprehensive and consistent as possible at a global scale and contains both freshwater and saline lakes, including the Caspian Sea, as well as human-made reservoirs and regulated lakes.
HydroLAKES is publicly available for download at http://www.hydrosheds.org and is free for scientific, educational, and other uses.
"},{"location":"projects/hydrolakes/#datasets-used-for-creation-of-hydrolakes","title":"Datasets used for creation of HydroLAKES","text":"Original dataset Region Original format and resolution Reference Number of lakes Canadian hydrographic dataset (CanVec) Canada (entire country) Vector; 1:50,000 Natural Resources Canada (2013) 863550 Shuttle Radar Topographic Mission (SRTM) Water Body Data (SWBD) 56\u00b0 South to 60\u00b0 North Raster; 1 arc-second (~30 m at the equator); vectorized and smoothed Slater et al. (2006) 282571 MODerate resolution Imaging Spectro-radiometer (MODIS) MOD44W water mask Russia above 60\u00b0 North Raster; 250 m; vectorized and smoothed Carroll et al. (2009) 167435 US National Hydrography Dataset (NHD) Alaska (entire state) Vector; 1:24:000 U.S. Geological Survey (2013) 58496 European Catchments and Rivers Network System (ECRINS) Europe above 60\u00b0 North and entire Norway Vector; varying resolutions (~1:250,000) European Environment Agency (2012) 50699 Global Lakes and Wetlands Database (GLWD) World Vector; 1:1 million Lehner and D\u00f6ll (2004) 3023 Global Reservoir and Dam database (GRanD) World Vector; varying resolutions (1:1 million or better) Lehner et al. (2011) 1133 Other (own mapping) World Vector; varying resolutions (1:1 million or better) n/a 781 Total 1427688"},{"location":"projects/hydrolakes/#paper-citation","title":"Paper Citation","text":"Messager, Mathis Lo\u00efc, Bernhard Lehner, G\u00fcnther Grill, Irena Nedeva, and Oliver Schmitt. \"Estimating the volume and\nage of water stored in global lakes using a geo-statistical approach.\"\nNature communications 7, no. 1 (2016): 1-11.\nYou can read the paper here : https://www.nature.com/articles/ncomms13603?origin=ppub
"},{"location":"projects/hydrolakes/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var lake_poly = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroLakes/lake_poly_v10\");\nvar lake_points = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroLakes/lake_points_v10\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROLAKES
"},{"location":"projects/hydrolakes/#attribute-table-of-hydrolakes-polygon-and-point-layers","title":"Attribute table of HydroLAKES polygon and point layers","text":"Property Description Hylak_id Unique lake identifier. Values range from 1 to 1,427,688. Lake_name Name of lake or reservoir. This field is currently only populated for lakes with an area of at least 500 km2; for large reservoirs where a name was available in the GRanD database; and for smaller lakes where a name was available in the GLWD database. Country Country that the lake (or reservoir) is located in. International or transboundary lakes are assigned to the country in which its corresponding lake pour point is located and may be arbitrary for pour points that fall on country boundaries. Continent Continent that the lake (or reservoir) is located in. Geographic continent: Africa, Asia, Europe, North America, South America, or Oceania (Australia and Pacific Islands) Poly_src Source of original lake polygon: CanVec; SWBD; MODIS; NHD; ECRINS; GLWD; GRanD; or Other More information on these data sources can be found in Table 1. Lake_type Indicator for lake type: 1: Lake 2: Reservoir 3: Lake control (i.e. natural lake with regulation structure) Note that the default value for all water bodies is 1, and only those water bodies explicitly identified as other types (mostly based on information from the GRanD database) have other values; hence the type \u2018Lake\u2019 also includes all unidentified smaller human-made reservoirs and regulated lakes. Grand_id ID of the corresponding reservoir in the GRanD database, or value 0 for no corresponding GRanD record. This field can be used to join additional attributes from the GRanD database. Lake_area Lake surface area (i.e. polygon area), in square kilometers. Shore_len Length of shoreline (i.e. polygon outline), in kilometers. Shore_dev Shoreline development, measured as the ratio between shoreline length and the circumference of a circle with the same area. A lake with the shape of a perfect circle has a shoreline development of 1, while higher values indicate increasing shoreline complexity. Vol_total Total lake or reservoir volume, in million cubic meters (1 mcm = 0.001 km3). For most polygons, this value represents the total lake volume as estimated using the geostatistical modeling approach by Messager et al. (2016). However, where either a reported lake volume (for lakes \u2265 500 km2) or a reported reservoir volume (from GRanD database) existed, the total volume represents this reported value. In cases of regulated lakes, the total volume represents the larger value between reported reservoir and modeled or reported lake volume. Column \u2018Vol_src\u2019 provides additional information regarding these distinctions. Vol_res Reported reservoir volume, or storage volume of added lake regulation, in million cubic meters (1 mcm = 0.001 km3). 0: no reservoir volume Vol_src 1: \u2018Vol_total\u2019 is the reported total lake volume from literature 2: \u2018Vol_total\u2019 is the reported total reservoir volume from GRanD or literature 3: \u2018Vol_total\u2019 is the estimated total lake volume using the geostatistical modeling approach by Messager et al. (2016) Depth_avg Average lake depth, in meters. Average lake depth is defined as the ratio between total lake volume (\u2018Vol_total\u2019) and lake area (\u2018Lake_area\u2019). Dis_avg Average long-term discharge flowing through the lake, in cubic meters per second. This value is derived from modeled runoff and discharge estimates provided by the global hydrological model WaterGAP, downscaled to the 15 arc-second resolution of HydroSHEDS (see section 2.2 for more details) and is extracted at the location of the lake pour point. Note that these model estimates contain considerable uncertainty, in particular for very low flows. -9999: no data as lake pour point is not on HydroSHEDS landmask Res_time Average residence time of the lake water, in days. The average residence time is calculated as the ratio between total lake volume (\u2018Vol_total\u2019) and average long-term discharge (\u2018Dis_avg\u2019). Values below 0.1 are rounded up to 0.1 as shorter residence times seem implausible (and likely indicate model errors). -1: cannot be calculated as \u2018Dis_avg\u2019 is 0 -9999: no data as lake pour point is not on HydroSHEDS landmask Elevation Elevation of lake surface, in meters above sea level. This value was primarily derived from the EarthEnv-DEM90 digital elevation model at 90 m pixel resolution by calculating the majority pixel elevation found within the lake boundaries. To remove some artefacts inherent in this DEM for northern latitudes, all lake values that showed negative elevation for the area north of 60\u00b0N were substituted with results using the coarser GTOPO30 DEM of USGS at 1 km pixel resolution, which ensures land surfaces \u22650 in this region. Note that due to the remaining uncertainties in the EarthEnv-DEM90 some small negative values occur along the global ocean coastline south of 60\u00b0N which may or may not be correct. Slope_100 Average slope within a 100 meter buffer around the lake polygon, in degrees. This value is derived from the EarthEnv-DEM90 digital elevation model at 90 m pixel resolution. Slopes for each pixel were computed with latitudinal corrections for the distortion in the XY spacing of geographic coordinates by approximating the geodesic distance between cell centers. For 12 lakes located above the northern limit of the EarthEnv-DEM90 digital elevation model (83\u00b0N), slopes were computed from the GTOPO30 DEM of USGS at 1 km pixel resolution. -1: slope values were not calculated for the largest lakes (polygon area \u2265 500 km2) Wshd_area Area of the watershed associated with the lake, in square kilometers. The watershed area is calculated by deriving and measuring the upstream contribution area to the lake pour point using the HydroSHEDS drainage network map at 15 arc-second resolution. -9999: no data as lake pour point is not on HydroSHEDS landmask Pour_long Longitude of the lake pour point, in decimal degrees. Pour_lat Latitude of the lake pour point, in decimal degrees."},{"location":"projects/hydrolakes/#license","title":"License","text":"The data is licensed under a Creative Commons Attribution 4.0 International License (see section 4). By downloading and using the data the user agrees to the terms and conditions of this license.
Created by: Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O
Curated by: Samapriya Roy
Keywords: water,hydrology, lakes, global lake surface, discharge, depth, volume, area, hydrolakes
Last updated: 2021-09-05
"},{"location":"projects/hydrowaste/","title":"HydroWASTE v1.0","text":"HydroWASTE is a spatially explicit global database of 58,502 wastewater treatment plants (WWTPs) and their characteristics. This database was developed by combining national and regional datasets with auxiliary information to derive or complete missing characteristics, including the amount of people served, the flow rate of effluents, and the level of treatment of processed wastewater. The HydroSHEDS river network with streamflow estimates was used to geo-reference plant outfall locations and to assess the distribution of wastewaters at a global scale. All wastewater treatment plants are co-registered to the global river network of the HydroRIVERS database via their estimated outfall locations. You can find the datasets page here
For more information on HydroATLAS please refer to hydrosheds page on hydroatlas and technical information is included in the paper
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hydrowaste/#citations","title":"Citations","text":"The development of BasinATLAS and RiverATLAS is fully described in Linke et al. (2019) and should be cited as:
Ehalt Macedo, H., Lehner, B., Nicell, J., Grill, G., Li, J., Limtong, A., Shakya, R. (2022). Distribution and characteristics of wastewater\ntreatment plants within the global river network. Earth System Science Data, 14(2): 559\u2013577. https://doi.org/10.5194/essd-14-559-2022\nvar hydrowaste = ee.FeatureCollection(\"projects/sat-io/open-datasets/HydroWaste/HydroWASTE_v10\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HYDROWASTE
"},{"location":"projects/hydrowaste/#license","title":"License","text":"The data is licensed under a Creative Commons Attribution 4.0 International License (see section 4). By downloading and using the data the user agrees to the terms and conditions of this license.
Created by: Ehalt Macedo, H., Lehner, B., Nicell, J., Grill, G., Li, J., Limtong, A., Shakya, R.
Curated in GEE by: Samapriya Roy
Keywords: water,hydrology, rivers, discharge, depth, volume, area, hydrowaste, wastewater
Last updated: 2022-07-09
"},{"location":"projects/hyspecnet/","title":"HySpecNet-11K Hyperspectral Benchmark dataset","text":"The HySpecNet-11k dataset is a large-scale hyperspectral benchmark dataset constructed by the Remote Sensing Image Analysis (RSiM) group at TU Berlin and the Big Data Analytics in Earth Observation group at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). It consists of 11,483 nonoverlapping image patches acquired by the EnMAP satellite, with each patch being a portion of 128 \u00d7 128 pixels and containing 224 spectral bands. These patches have a ground sample distance of 30 m. The dataset was constructed using a total of 250 EnMAP tiles acquired between 2 November 2022 and 9 November 2022, during the routine operation phase. Only tiles with less than 10% cloud and snow cover were considered. These tiles underwent radiometric, geometric, and atmospheric corrections to generate the L2A water & land product. Subsequently, the tiles were divided into nonoverlapping image patches, eliminating the cropped patches at the tile borders. This process resulted in more than 45 patches per tile, totaling 11,483 patches for the complete HySpecNet-11k dataset. You can read details in the paper here and find information on the dataset and more here.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/hyspecnet/#dataset-preprocessing","title":"Dataset preprocessing","text":"The datset was split into multiple patch files including the Spectral images and the quality files. Single band quality files were selected and added as bands to the original spectral file with 224 bands. As suggested by the authors 22 bands were invalid and an example cope is included to remove the invalid bands. The single bands QL files and their values are included below. A custom manifest was used to achieve the band names of choice and to make sure the pyramiding schema for QL and spectral bands were Mode and Mean accordingly.
Quality Layer 0 1 2 3 QL_QUALITY_CIRRUS.TIF None Thin Medium Thick QL_QUALITY_CLASSES.TIF None Land Water Background QL_QUALITY_CLOUD.TIF None Cloud QL_QUALITY_CLOUDSHADOW.TIF None Cloud Shadow QL_QUALITY_HAZE.TIF None Haze QL_QUALITY_SNOW.TIF None Snow"},{"location":"projects/hyspecnet/#citation","title":"Citation","text":"Fuchs, Martin Hermann Paul, and Beg\u00fcm Demir. \"HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image\nCompression Methods.\" arXiv preprint arXiv:2306.00385 (2023).\nvar hyspecnet = ee.ImageCollection(\"projects/sat-io/open-datasets/HySpecNet/HYSPECNET-11K\");\nprint(hyspecnet.size())\n\n//Remove invalid bands\nvar invalid_bands = ['B126', 'B127', 'B128', 'B129', 'B130', 'B131', 'B132', 'B133', 'B134', 'B135', 'B136', 'B137', 'B138', 'B139', 'B140', 'B160', 'B161', 'B162', 'B163', 'B164', 'B165', 'B166']\n\n//Select an image\nvar image = hyspecnet.sort('system:time_start',false).first()\nimage = image.select(image.bandNames().removeAll(invalid_bands))\nprint('Resolution',image.select(['B1']).projection().nominalScale())\nprint('Band Names',image.bandNames())\n\n//Add image as layer\nMap.centerObject(image,12)\nMap.addLayer(image,{\"opacity\":1,\"bands\":[\"B3\",\"B2\",\"B1\"],\"min\":-154,\"max\":934,\"gamma\":1},'Sample HYSPECNET Image Chip')\nSample code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/HYSPECNET-11K
"},{"location":"projects/hyspecnet/#license","title":"License","text":"This dataset is available under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Curated by: Fuchs, Martin Hermann Paul, and Beg\u00fcm Demir
Keywords: Hyperspectral, Enmap, Benchmark, Tile
Last updated: June 29, 2023
"},{"location":"projects/iceland_dem/","title":"\u00cdslandsDEM v1.0 10m","text":"Since 2015, elevation data from the Arctic (north of 60\u00b0N, including Iceland) started to be openly available through the ArcticDEM project, led by the Polar Geospatial Center, University of Minnesota (https://www.pgc.umn.edu/data/arcticdem/).
This data consists of a large amount of Digital Elevation Models (DEMs) repeatedly acquired (multitemporal), typically from 2012-present, and the oldest data reaching back to 2008. The DEMs are derived from satellite sub-meter stereo imagery, particularly from WorldView 1-3 and GeoEye-1. The processing of the DEMs was done using SETSM, an open-source digital photogrammetric software, in the Bluewaters supercomputer (University of Ilinois). Each DEM has 2x2m resolution and a footprint of ~18x100km.
In a collaborative effort between the National Land Survey of Iceland, the Icelandic Meteorological Office and the Polar Geospatial Center, we developed methods to handle and process a large amount of data available for Iceland. The methods developed consisted of
Spatial adjustment of all the available DEMs, for homogeneity and consistency in the location of each individual DEM.
Robust mosaicking into one single DEM of Iceland, by taking advantage of the multi-temporal coverage of DEMs. Each pixel of the mosaic corresponds to a median elevation value from the possible elevations available from the ArcticDEM. More details on the dataset available here. This DEM is resampled for 10x10m resolution.
var DEM_10m_isn93 = ee.Image(\"projects/ee-landmaelingar/assets/IslandsDEMv1_10m_isn93\")\nProjection used: EPSG 3057 (ISN93/Lambert 1993)
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/ICELAND-DEM-10m
"},{"location":"projects/iceland_dem/#license","title":"License","text":"The datasets are distributed under a Attribution 4.0 International (CC BY 4.0) license.
Produced by : National Land Survey of Iceland & PGC
Curated in GEE by : National Land Survey of Iceland
Keywords: : Elevation, DEM, ArticDEM, Iceland, Geophysical
Last updated on GEE: 2022-03-29
"},{"location":"projects/india_river_trends/","title":"Temporal trends of Surface water across Indian Rivers & Basins","text":"This dataset quantifies the extent and rate of annual change in surface water area (SWA) across India's rivers and basins over 30 years from 1991 to 2020. It does so by season (annual dry, wet and permanent water, based on India's seasons) and at two spatial scales: the river basin scale (1516 level-7 basins from HydroBASINS) and the finer river reach scale (68,367 reaches). This dataset is derived from the historical time series of monthly surface water occurrence by JRC's Global Surface Water Explorer. You can read additional details about the dataset in the paper and access the dataset here.
The authors have also provided a dataset page and an earth engine app to analyze the dataset further.
These are available as the following GEE assets
Annual rate of change of surface water area, by season
projects/sat-io/open-datasets/indian_rivers/riverchanges/txsTrendsprojects/sat-io/open-datasets/indian_rivers/riverchanges/basinsTrendsAttribute Description HYBAS_ID or txId Feature's unique identifier.
Time series of annual surface water area, by season
projects/sat-io/open-datasets/indian_rivers/riverchanges/mainlandIndia_areasTs_txsprojects/sat-io/open-datasets/indian_rivers/riverchanges/mainlandIndia_areasTs_basinsL7Attribute Description HYBAS_ID or txId Feature's unique identifier. - HYBAS_ID is for basins. It is the basin's identifier HYBAS_ID in the HydroBASINS dataset. - txId is for transects. It is the '' concatenated string derived from the longitude and latitude values, truncated to 4 decimals, of the transect's median point. Specifically, it is \"_xx.xxxx_yy.yyyy\" where xx.xxxx and yy.yyyy are the median's longitude and latitude values truncated to 4 decimals. season Denotes the season, in \"sss_mmm\" format. - \"sss\" denotes the season: \"dry\" for dry, \"wet\" for wet, and \"prm\" for permanent. - \"mmm\" denotes the span of the season in calendar months: \"fma\" is for the dry season of February-March-April, \"ond\" is for the wet (post-monsoon) season of October-November-December, and \"DnW\" is for permanent which is dry AND wet season. year Year. water_ha Area of water pixels in the feature, in hectares. notwater_ha Area of notwater pixels in the feature, in hectares. nodata_ha Area of nodata pixels in the feature, in hectares. nodataFrac Proportion of the feature's area with nodata pixels. system:index GEE system-generated unique identifier.
projects/sat-io/open-datasets/indian_rivers/riverchanges/waterOccSeasComps Bands Description drySeasCompos_fma Each pixel in these bands have one of 3 integer values (following the convention in the JRC water dataset, Pekel et al. 2016) wetSeasCompos_ond * 2: a pixel with valid data and containing water (denoting a \"water\" pixel) prmSeasCompos_DnW * 1: a pixel with valid data and not containing water (denoting a \"notwater\" pixel) * 0: a pixel with no valid data (denoting a \"nodata\" pixel) Expand to show attributes for Time series of annual surface water image collection
Properties Description year year of the image. monsoonYearStartMonth Number (between 1-12) of the month when monsoon (or, hydrological) year starts. It is 6, indicating June, and is the same for all images. A year is taken to be June to May in this analysis. drySeasMonthsOffset Number of months after monsoonYearStartMonth when dry season starts. It is 8, indicating February. drySeasMonthsTag Suffix tag, in names of image bands, table columns, etc., indicating the 3 months of the dry season. wetSeasMonthsOffset Number of months after monsoonYearStartMonth when wet season starts. It is 4, indicating October. wetSeasMonthsTag Suffix tag, in names of image bands, table columns, etc., indicating the 3 months of the wet season.
More details and resources:
Published data repository (excluding the time series of annual surface water occurrence) https://doi.org/10.5281/zenodo.7803903 Published Earth Engine code behind the analysis https://doi.org/10.5281/zenodo.7839588 Published data description https://doi.org/10.1016/j.dib.2023.109991 Interactive visualization, and more https://sites.google.com/view/surface-water-trends-india/"},{"location":"projects/india_river_trends/#citation","title":"Citation","text":"Koulgi P, Jumani S. Dataset of temporal trends of surface water area across India's rivers and basins. Data Brief. 2023 Dec 19;52:109991.\ndoi: 10.1016/j.dib.2023.109991. PMID: 38235174; PMCID: PMC10792741.\nvar reachTrends = ee.FeatureCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/txsTrends');\nvar reachAreaTimeseries = ee.FeatureCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/mainlandIndia_areasTs_txs');\nvar basTrends = ee.FeatureCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/basinsTrends');\nvar basAreaTimeseries = ee.FeatureCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/mainlandIndia_areasTs_basinsL7');\nvar annualWaterOccSeasComps = ee.ImageCollection('projects/sat-io/open-datasets/indian_rivers/riverchanges/waterOccSeasComps');\n\nvar brewer7ClPuOr = ['b35806', 'f1a340', 'fee0b6', 'f7f7f7', 'd8daeb', '998ec3', '542788'];\nvar empty = ee.Image().byte();\n\nvar reachTrendsDrySeason = reachTrends.filter(ee.Filter.eq('season', 'dry_fma'));\nvar fillsreach = empty.paint(reachTrendsDrySeason, 'sl_perYr');\nMap.addLayer(fillsreach, {palette: brewer7ClPuOr, min: -0.02, max: 0.02}, 'dry_fma_reach');\nMap.setCenter(79.49959, 16.63471, 14);\n\nvar basTrendDrySeason = basTrends.filter(ee.Filter.and(ee.Filter.eq('HYBAS_ID', 4071092530), ee.Filter.eq('season', 'dry_fma')));\nvar fillsBas = empty.paint(basTrendDrySeason, 'sl_perYr');\nMap.addLayer(fillsBas, {palette: brewer7ClPuOr, min: -75, max: 75}, 'dry_fma_bas', false);\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/TEMPORAL-TRENDS-INDIAN-RIVERS-BASINS
Earth Engine app: Access the Earth Engine app here and the data page here
"},{"location":"projects/india_river_trends/#license","title":"License","text":"These datasets are provided under a CC-BY-4.0 license.
Provided by: Koulgi and Jumani 2023
Curated in GEE by: Pradeep Koulgi and Samapriya Roy
Keywords : surface water, river reaches, river basins, time series,india
Last updated on GEE: 2024-02-16
"},{"location":"projects/irc/","title":"Irrecoverable carbon in Earth\u2019s ecosystems","text":"These datasets provide global maps of carbon density (aboveground, belowground biomass carbon and soil organic carbon stocks) for the year 2010 and 2018 at ~300-m spatial resolution in Mg ha-1 (Coordinate System: WGS 1984, float format). Input maps were collected from published literature, and where necessary, updated to cover the focal time period. These updates were applied to the manageable carbon, vulnerable carbon and irrecoverable carbon maps. Manageable carbon is carbon in terrestrial and coastal ecosystems that could experience an anthropogenic land-use conversion event . Vulnerable carbon is the carbon that would be that would be released in a typical land-use conversion. Irrecoverable carbon is the carbon that, if lost, would not recover by mid-century. Datasets are disaggregated for carbon density in biomass or soils. To view these datasets, go to: https://irrecoverable.resilienceatlas.org/map. You can read the open sourced paper here
"},{"location":"projects/irc/#preprocessing","title":"Preprocessing","text":"All datasets tif files were ingested in Google Earth Engine, Ecosystem layers were ingested after removing the no data value to avoid conflict with a -128 no data value. The ecosystem categorical layers were also ingested with a mode sampling as recommended by GEE.
"},{"location":"projects/irc/#paper-citation","title":"Paper Citation","text":"Noon, M.L., Goldstein, A., Ledezma, J.C. et al. Mapping the irrecoverable carbon in Earth\u2019s ecosystems.\nNat Sustain (2021). https://doi.org/10.1038/s41893-021-00803-6\nNoon, Monica, Goldstein, Allie, Ledezma, Juan Carlos, Roehrdanz, Patrick, Cook-Patton, Susan C., Spawn-Lee, Seth A., Wright, Timothy Maxwell,\nGonzalez-Roglich, Mariano, Hole, David G., Rockstr\u00f6m, Johan, & Turner, Will R. (2021). Mapping the irrecoverable carbon in Earth's ecosystems\n(1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4091029\nvar irrecoverable_carbon_total = ee.ImageCollection(\"projects/sat-io/open-datasets/irrecoverable_carbon/carbon_total\");\nvar irrecoverable_carbon_soil = ee.ImageCollection(\"projects/sat-io/open-datasets/irrecoverable_carbon/carbon_soil\");\nvar irrecoverable_carbon_biomass = ee.ImageCollection(\"projects/sat-io/open-datasets/irrecoverable_carbon/carbon_biomass\");\nvar vulnerable_carbon_total = ee.ImageCollection(\"projects/sat-io/open-datasets/vulnerable_carbon/carbon_total\");\nvar vulnerable_carbon_soil = ee.ImageCollection(\"projects/sat-io/open-datasets/vulnerable_carbon/carbon_soil\");\nvar vulnerable_carbon_biomass = ee.ImageCollection(\"projects/sat-io/open-datasets/vulnerable_carbon/carbon_biomass\");\nvar manageable_carbon_total = ee.ImageCollection(\"projects/sat-io/open-datasets/manageable_carbon/carbon_total\");\nvar manageable_carbon_soil = ee.ImageCollection(\"projects/sat-io/open-datasets/manageable_carbon/carbon_soil\");\nvar manageable_carbon_biomass = ee.ImageCollection(\"projects/sat-io/open-datasets/manageable_carbon/carbon_biomass\");\nvar ecosystem_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/ecosystem_extent\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GLOBAL-IRRECOVERABLE-CARBON
"},{"location":"projects/irc/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Noon et al
Curated by: Samapriya Roy
Keywords: : irrecoverable carbon, vulnerable carbon, manageable carbon, global map, ecosystem
Last updated: 2021-12-14
"},{"location":"projects/isccp_hxg/","title":"International Satellite Cloud Climatology Project: HXG Cloud Cover","text":"The data consists of one variable 'cloud' from the ISCCP HXG, dataset as retrieved from the NCEI in September of 2021. Quoting from the ISCCP website (isccp.giss.nasa.gov) :
The International Satellite Cloud Climatology Project (ISCCP) was established in 1982 as part of the World Climate Research Program (WCRP) to collect weather satellite radiance measurements and to analyze them to infer the global distribution of clouds, their properties, and their diurnal, seasonal and interannual variations. The resulting datasets and analysis products are being used to study the role of clouds in climate, both their effects on radiative energy exchanges and their role in the global water cycle.
The \"H\" series of data products is a high spatial resolution (0.1 degree) version of the ISCCCP dataset which is documented at: https://data.amerigeoss.org/es/dataset/97db2b39-9602-4501-a3de-421ab2375027
The ISCCP H-Series Climate Data Record consists of several parts
ISCCP H Series data The full ISCCP dataset consists of netCDF files containing various derived cloud parameters. The H-Series data includes several products. These include: HXS (H-series pixel level single satellite - not in netcdf), HXG (H-series pixel level gridded), HGG (H-series Gridded Global), HGH (H-series gridded monthly by hour), and * HGM ( H-series Gridded Monthly). The netCDF files are not structured with CF-standard names. Data variables are unitless and rely on data tables that are needed to represent each geophysical variable. Keeping ISCCP H-Series in this native format ensures that existing \"power users\" will be able to continue using the data.ISCCP Basic H Series ISCCP Basic files contains a subset of the cloud variables and products available in the full ISCCP dataset. It consists of remapped, calibrated, and subsetted variables following CF-conventions. In addition, the netCDF files follow full netCDF CF and ACDD Conventions. These files are intended to be use by new and/or less advanced users that may want to use cloud data, but do not need the full ISCCP dataset. These were converted to Geotiff files for use from the netCDF files.
The values in the file are as follows: 0 (no cloud) 1 (cloud) and 255 (NoData)
"},{"location":"projects/isccp_hxg/#citation","title":"Citation","text":"Rossow, WB., RA Schiffer, 1999: Advances in understanding clouds from ISCCP. BULLETIN OF THE AMERICAN\nMETEOROLOGICAL SOCIETY, 80, 2261-2287.\nvar isccp = ee.ImageCollection('projects/sat-io/open-datasets/isccp/hxg');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/HXG-CLOUD-COVER
"},{"location":"projects/isccp_hxg/#license","title":"License","text":"Public Domain/No restrictions (CC0): Under the terms of this license you are free to use the material for any purpose without any restrictions.
Preprocessed by: Michael Lefsky
Curated by: Samapriya Roy & Michael Lefsky
Keywords: ISCCP, Clouds, International Satellite Cloud Climatology Project, WCRP, World Climate Research Program
"},{"location":"projects/isric/","title":"Soil Grids 250m v2.0","text":"SoilGrids is designed as a globally consistent, data-driven system that predicts soil properties and classes using global covariates and globally fitted models. If you are looking for soil information on national and/or local levels we advise to compare SoilGrids predictions with soil maps derived from national and local soil geographical databases. National soil maps are usually based on more detailed input soil information and therefore are often more accurate than SoilGrids (within the local coverage area). For an overview of national and regional soil databases, please refer to the Soil Geographic Databases compendium. The \u2018mean\u2019 and \u2018median (0.5 quantile)\u2019 may both be used as predictions of the soil property for a given cell. The mean represents the \u2018expected value\u2019 and provides an unbiased prediction of the soil property.
Name Description Mapped units Conversion factor Conventional units Assets on GEE bdod Bulk density of the fine earth fraction cg/cm\u00b3 100 kg/dm\u00b3 bdod_mean cec Cation Exchange Capacity of the soil mmol\u00a9/kg 10 cmol\u00a9/kg cec_mean cfvo Volumetric fraction of coarse fragments (> 2 mm) cm3/dm3 (vol\u2030) 10 cm3/100cm3 (vol%) cfvo_mean clay Proportion of clay particles (< 0.002 mm) in the fine earth fraction g/kg 10 g/100g (%) clay_mean nitrogen Total nitrogen (N) cg/kg 100 g/kg nitrogen_mean phh2o Soil pH pHx10 10 pH phh2o_mean sand Proportion of sand particles (> 0.05 mm) in the fine earth fraction g/kg 10 g/100g (%) sand_mean silt Proportion of silt particles (\u2265 0.002 mm and \u2264 0.05 mm) in the fine earth fraction g/kg 10 g/100g (%) silt_mean soc Soil organic carbon content in the fine earth fraction dg/kg 10 g/kg soc_mean ocd Organic carbon density hg/dm\u00b3 10 kg/dm\u00b3 ocd_mean ocs Organic carbon stocks t/ha 10 kg/m\u00b2 ocs_mean"},{"location":"projects/isric/#citation","title":"Citation","text":"Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217\u2013240, https://doi.org/10.5194/soil-7-217-2021, 2021.\nvar isric_bdod_mean = ee.Image(\"projects/soilgrids-isric/bdod_mean\");\nvar isric_cec = ee.Image(\"projects/soilgrids-isric/cec_mean\");\nvar isric_cfvo = ee.Image(\"projects/soilgrids-isric/cfvo_mean\");\nvar isric_clay = ee.Image(\"projects/soilgrids-isric/clay_mean\");\nvar isric_sand = ee.Image(\"projects/soilgrids-isric/sand_mean\");\nvar isric_silt = ee.Image(\"projects/soilgrids-isric/silt_mean\");\nvar isric_nitrogen = ee.Image(\"projects/soilgrids-isric/nitrogen_mean\");\nvar isric_phh20 = ee.Image(\"projects/soilgrids-isric/phh2o_mean\");\nvar isric_soc = ee.Image(\"projects/soilgrids-isric/soc_mean\");\nvar isric_ocd = ee.Image(\"projects/soilgrids-isric/ocd_mean\");\nvar isric_ocs = ee.Image(\"projects/soilgrids-isric/ocs_mean\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/ISRIC-SOIL-GRID-250
"},{"location":"projects/isric/#data-available-from","title":"Data available from","text":"www.soilgrids.org.
"},{"location":"projects/isric/#publication-date","title":"Publication date","text":"2020-05-04
"},{"location":"projects/isric/#period","title":"Period","text":"Fri Mar 31 1905 19:00:00 GMT-0500 Mon Jul 04 2016 20:00:00 GMT-0400
"},{"location":"projects/isric/#provided-by","title":"Provided by :","text":"International Soil Reference and Information Centre (ISRIC)
"},{"location":"projects/isric/#license","title":"License","text":"Attribution 4.0 International (CC BY 4.0)
"},{"location":"projects/isric/#doi","title":"DOI","text":"https://doi.org/10.17027/isric-soilgrids.713396fa-1687-11ea-a7c0-a0481ca9e724
Created and Curated by: International Soil Reference and Information Centre (ISRIC)
Keywords: For example Global Soilgrid, Sandy Soil, ISRIC
Last updated: 2020-10-20
"},{"location":"projects/japan_eq2024/","title":"Emergency Observation Data for the 2024 Sea of Japan Earthquake","text":"The 2024 Sea of Japan earthquake occurred on January 1, 2024, after 4:00 PM (Japan time), resulting in significant damage, including building collapses, landslides, and fires at various locations. In response to requests from domestic disaster prevention agencies, JAXA conducted emergency observations using ALOS-2 from the night of the disaster. The released data includes Level 2.1 (GeoTIFF) and archive data, facilitating interference analysis and change detection to contribute to disaster reduction and prevention. Notably, this publicly released data is intended for non-commercial purposes, including government and local authority use, as well as research by universities.
"},{"location":"projects/japan_eq2024/#dataset-preprocessing","title":"Dataset preprocessing","text":"Additional metadata was added to the images in the collection. Field names such as system:time_start and system:time_end were added to make the collection filterable in Google Earth Engine. Custom code was written for ingest into Google Earth Engine and a no data value of 0 was used for masking.
"},{"location":"projects/japan_eq2024/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var notoPeninsula = ee.ImageCollection(\"projects/sat-io/open-datasets/disaster/japan-earthquake-2024_ALOS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/SEA-OF-JAPAN-EQ-2024
"},{"location":"projects/japan_eq2024/#license","title":"License","text":"This publicly released data is intended for non-commercial purposes, including government and local authority use, as well as research by universities.
Please note the terms of use: https://global.jaxa.jp/policy.html
Provided by: Japan Aerospace Exploration Agency (JAXA)
Year: 2024
URL: https://www.eorc.jaxa.jp/ALOS/jp/dataset/alos_open_and_free_j.htm#Noto2024
For citation details, please refer to the above URL
Curated in GEE by Samapriya Roy and Keiko Nomura
Keywords: Emergency Data, ALOS, JAPAN, Earthquake
Last updated on: 2024-01-06
"},{"location":"projects/land_subsidence/","title":"Global Land subsidence mapping","text":"This dataset centers on the creation of a global land subsidence dataset through the use of advanced geospatial and modeling techniques. The study investigates the relationships between groundwater stress, aquifer depletion, and land subsidence on a worldwide scale. Employing remote sensing data and model-based datasets, a machine learning model has been developed to predict land subsidence at a remarkably high spatial resolution of approximately 2 kilometers. The outcomes of this study include a comprehensive estimation of global land subsidence magnitude, a first-order assessment of aquifer storage loss due to consolidation, and the quantification of key factors driving subsidence. Notably, a significant portion of the observed subsidence is concentrated in cropland and urban areas, underscoring the urgency of adopting sustainable groundwater management practices in these regions. This dataset is invaluable for understanding the spatial distribution of subsidence in both known and previously unidentified groundwater-stressed areas worldwide.
The global land subsidence dataset is a pioneering effort in characterizing the complex interplay between groundwater dynamics, land subsidence, and aquifer storage loss. By leveraging machine learning and comprehensive datasets, this study contributes to a deeper understanding of the environmental challenges posed by excessive groundwater pumping and highlights the need for proactive measures to safeguard water resources and mitigate land subsidence impacts, particularly in regions facing water scarcity and population growth. You can read the full paper here. You can find additional information in this GitHub repository.
"},{"location":"projects/land_subsidence/#citation","title":"Citation","text":"Hasan, M.F., Smith, R., Vajedian, S. et al. Global land subsidence mapping reveals widespread loss of aquifer storage capacity.\nNat Commun 14, 6180 (2023). https://doi.org/10.1038/s41467-023-41933-z\nHasan, M. F., R. Smith, S. Vajedian, R. Pommerenke, S. Majumdar (2023). Global Land Subsidence Mapping Reveals Widespread Loss of Aquifer Storage\nCapacity Datasets, HydroShare, https://doi.org/10.4211/hs.dc7c5bfb3a86479b889d3b30ab0e4ef7\nvar subsidence_prediction_probability = ee.Image(\"projects/sat-io/open-datasets/global_subsidence/Final_subsidence_proba_greater_1cm_2013_2019_recoded\");\nvar subsidence_prediction_recoded = ee.Image(\"projects/sat-io/open-datasets/global_subsidence/Final_subsidence_prediction_recoded\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GLOBAL-LAND-SUBSIDENCE
"},{"location":"projects/land_subsidence/#license","title":"License","text":"This resource is shared under the Creative Commons Attribution CC BY
Created by: Hasan, M. F., R. Smith, S. Vajedian, R. Pommerenke, S. Majumdar
Curated in GEE by: Samapriya Roy
Keywords: machine learning,global groundwater,groundwater monitoring,land subsidence, InSAR
Last updated: 2023-11-08
"},{"location":"projects/landfire/","title":"Landfire Mosaics LF","text":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture's Forest Service, U.S. Department of the Interior's Geological Survey, and The Nature Conservancy.
LANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees. You can read about the Landfire 2022 updates v2.3.0 here
The LANDFIRE (LF) 2022 Update represents another step in moving towards an annual update. This update is the first time in LANDFIRE history in which disturbances from the year before are represented in current year products. LF 2022 includes adjustments to vegetation and fuels in disturbed areas for disturbances recorded in 2021 and 2022. LF 2022 disturbance layers contain comprehensive polygon treatment data (disturbance events) obtained from national and local sources and fire program data including:
Disturbances are also identified with LF's remote sensing of landscape change (RSLC), which identifies spectral change in vegetation using automated algorithms and image analyst review of the entire country.
Both vegetation cover and height, as well as fuels, will be 2023 capable in disturbed areas. This means that in mapped disturbances, vegetation and fuels represent current year conditions. Transition rulesets for vegetation account for disturbances from 2017 to 2022 since they were designed to use LF 2016 Remap vegetation data as inputs. Fuel updates utilize 2013\u20132022 disturbances because fuels transition rules encompass ten years of disturbance and can use pre-disturbance fuel inputs.
Important changes featured in the LF 2022 update include:
Currently included layers are
"},{"location":"projects/landfire/#earth-engine-snippet-fire-regime-v230","title":"Earth Engine Snippet: Fire Regime v2.3.0","text":"var sclass = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fire-regime/sclass\");\nvar vcc = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fire-regime/vcc\");\nvar vdep = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fire-regime/vdep\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-FIRE-REGIME
"},{"location":"projects/landfire/#earth-engine-snippet-disturbance-230","title":"Earth Engine Snippet: Disturbance 2.3.0","text":"var fdist = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/disturbance/FDIST\");\nvar hdist = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/disturbance/HDIST\");\nvar distyear = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/disturbance/DISTYEAR\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-DISTURBANCE
"},{"location":"projects/landfire/#earth-engine-snippet-topographic-220","title":"Earth Engine Snippet: Topographic 2.2.0","text":"var elevation = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/topographic/ELEV\");\nvar aspect = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/topographic/ASP\");\nvar slope_degrees = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/topographic/SLP\");\nvar slope_perc_rise = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/topographic/SlpP\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-TOPOGRAPHIC
"},{"location":"projects/landfire/#earth-engine-snippet-fuel-230","title":"Earth Engine Snippet: Fuel 2.3.0","text":"var cbd = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CBD\");\nvar cbh = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CBH\");\nvar cc = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CC\");\nvar cffdrs = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CFFDRS\");\nvar ch = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/CH\");\nvar fbfm13 = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FBFM13\");\nvar fbfm40 = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FBFM40\");\nvar fvc = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FVC\");\nvar fvh = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FVH\");\nvar fvt = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FVT\");\nvar fccs = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/fuel/FCCS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-FUEL
"},{"location":"projects/landfire/#earth-engine-snippet-vegetation-230","title":"Earth Engine Snippet: Vegetation 2.3.0","text":"var evc = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/vegetation/EVC\");\nvar evh = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/vegetation/EVH\");\nvar evt = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/vegetation/EVT\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-VEGETATION
"},{"location":"projects/landfire/#earth-engine-snippet-transportation-220","title":"Earth Engine Snippet: Transportation 2.2.0","text":"var roads = ee.ImageCollection(\"projects/sat-io/open-datasets/landfire/transportation/ROADS\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LANDFIRE-TRANSPORTATION
Resolution: approx 30m
"},{"location":"projects/landfire/#citation","title":"Citation","text":"LANDFIRE spatial data products
Homepage title: Data product.(Last update). Agency. [Online].Available: URL [Access date].
LANDFIRE: LANDFIRE Existing Vegetation Type layer.(2013, June - last update). U.S. Department of Interior, Geological Survey.[Online]. Available: http://landfire.cr.usgs.gov/viewer/ [2013,May 8].\nLANDFIRE data are public domain data with no use restrictions, though if modifications or derivatives of the product(s) are created, then please add some descriptive modifier to the data set to avoid confusion.
Curated in GEE by: Samapriya Roy
Keywords: doi, fire, landfire, nature-conservancy, usda, usgs, vegetation, wildfire
Last updated: 2024-01-14
"},{"location":"projects/landfire/#changelog","title":"Changelog","text":"The LandScan Program was initiated at Oak Ridge National Laboratory (ORNL) in 1997 to address the need for improved estimates of population for consequence assessment. For example, natural and manmade disasters across the globe place vast populations at risk, often with little or no advance warning. It was critical to develop highly resolved estimates so that they were useful to evaluate to events across multiple geographic scales. This has been an annual product since 1998.
Building on the modeling approach developed for LandScan Global, and taking advantage of higher quality data available for the U.S., we improved on both the spatial and the temporal resolution with our first version of LandScan USA in 2004. LandScan USA was created with the goal of capturing the diurnal variation of population that is critical for a variety of analyses and actions including emergency preparedness and response. In 2016, the original LandScan USA model was re-engineered to incoroporate advances in geospatial technology, machine learning approaches, and new input data sources. Since then, we have made annual improvements to the underlying model and released a new version of the dataset each year.
Around the time LandScan USA was first initiated, ORNL was also pioneering work in machine learning and computer vision, specifically to identify anthropogenic signals apparent in overhead imagery. This work ultimately enabled rapid, large-scale detection of human settlements from high resolution imagery and became the basis for early efforts to develop an improved resolution population distribution outside the U.S. known as Landscan HD. LandScan HD model employs a mixture of multi-modal data fusion, spatial data science, big data resources, and satellite imagery exploitation. The first country-scale LandScan HD dataset was created in 2014 and a continuous stream of new country-scale datasets have been developed ever since.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/landscan/#paper-citation","title":"Paper Citation","text":"LandScan Global 2021 for other years find citation information here
Sims, K., Reith, A., Bright, E., McKee, J., & Rose, A. (2022). LandScan Global 2021 [Data set]. Oak Ridge National Laboratory. https://doi.org/10.\n48690/1527702\nLandScan USA 2021 for other years find citation information here
Weber, E., Moehl, J., Weston, S., Rose, A., Brelsford, C., & Hauser, T. (2022). LandScan USA 2021 [Data set]. Oak Ridge National Laboratory. https://\ndoi.org/10.48690/1527701\nLandScan HD 2021 for find citation information for each country here
"},{"location":"projects/landscan/#earth-engine-snippet-landscan-global","title":"Earth Engine Snippet: LANDSCAN GLOBAL","text":"var landscan_global = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/LANDSCAN-GLOBAL
"},{"location":"projects/landscan/#earth-engine-snippet-landscan-usa","title":"Earth Engine Snippet: LANDSCAN USA","text":"var landscan_usa_night = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_USA_NIGHT\");\nvar landscan_usa_day = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_USA_DAY\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/LANDSCAN-USA
"},{"location":"projects/landscan/#earth-engine-snippet-landscan-hd","title":"Earth Engine Snippet: LANDSCAN HD","text":"var landscan_hd = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_HD\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/LANDSCAN-HD
"},{"location":"projects/landscan/#user-contributed-code","title":"User Contributed Code","text":"The code snippet shows how to use the Landscan Global population dataset to plot a time series chart comparing changes in the yearly population of two regions from 2000-2020.
Code link: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/LANDSCAN-POPULATION-COMPARE
Code Attribution Source: Ujaval Gandhi
"},{"location":"projects/landscan/#license","title":"License","text":"These datasets are offered under the Creative Commons Attribution 4.0 International License. Users are free to use, copy, distribute, transmit, and adapt the data for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
"},{"location":"projects/landscan/#changelog","title":"Changelog","text":"Created by: Oakridge National Laboratory
Curated in GEE by : Samapriya Roy
keywords: Global Population, Population count, Diurnal population, remote sensing, machine learning
Last modified: 2023-07-05
Last updated on GEE: 2023-07-20
"},{"location":"projects/landslide/","title":"Global Landslide Catalog :NASA Goddard (1970-2019)","text":"The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This is a unique data set with the ID tag \u201cGLC\u201d in the landslide editor.
You can find information about the project here and find source information for the dataset here
"},{"location":"projects/landslide/#citation","title":"Citation","text":"Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog\nfor hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561\u2013575.\ndoi:10.1007/s11069-009-9401-4.\n\nKirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global\nLandslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016.\nvar glc = ee.FeatureCollection(\"projects/sat-io/open-datasets/events/global_landslide_1970-2019\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/GLOBAL-LANDSLIDE-CATALOG
"},{"location":"projects/landslide/#license","title":"License","text":"This dataset is intended for public access and use.
Compiled by : NASA Goddard Space Flight Center
Curated by: Samapriya Roy
Keywords: :\"landslide, rainfall, NASA, GLC\"
Last updated: 2021-05-01
"},{"location":"projects/lcmap/","title":"Land Change Monitoring, Assessment, and Projection (LCMAP) v1.3","text":"Land Change Monitoring, Assessment, and Projection (LCMAP) represents a new generation of land cover mapping and change monitoring from the U.S. Geological Survey\u2019s Earth Resources Observation and Science (EROS) Center. LCMAP answers a need for higher quality results at greater frequency with additional land cover and change variables than previous efforts. LCMAP Collection 1.3 products were released in August 2022, including LCMAP products for 1985-2021
LCMAP science product documentation contain details, descriptions and caveats for the products and it can be downloaded here. Collection 1.0 for Hawaii was made available Jan 19 and more information can be found here
Additional Resources Links LCMAP website https://www.usgs.gov/core-science-systems/eros/lcmap Algorithm description document https://www.usgs.gov/media/files/lcmap-ccdc-add RSS Feed https://www.usgs.gov/core-science-systems/eros/lcmap/news Validation (these are tables) https://www.sciencebase.gov/catalog/item/5f86f28682cebef40f170771 Reference data (these are points) https://www.sciencebase.gov/catalog/item/5e42e54be4b0edb47be84535 USGS LCMAP Publications https://www.usgs.gov/core-science-systems/eros/lcmap/publications
"},{"location":"projects/lcmap/#lcmap-products","title":"LCMAP Products","text":"LCMAP provides 10 science products based on the USGS implementation of CCDC. The science products provide annual products for the years 1985-2019 for each CONUS ARD tile and CONUS level downloads are available which is used for the GEE collection. Land surface change products, denoted by the \u201cSC\u201d prefix in the short name, are produced directly from CCDC time series models. Land cover products, denoted by the \u201cLC\u201d prefix in the short name, are produced by the classification of the time series models. Note to optimize size GeoTiff files were run through a lossless LZW compression.
Product Name Short Name Product Description Time of Spectral Change SCTIME Indicator of a spectral change in the current year and the specific timing (day-of-year - DOY) within the year. Change Magnitude SCMAG Indicator of a spectral change in the current year and degree of change. Spectral Stability Period SCSTAB Time, in days, that the spectral time series has been in its current state. Time Since Last Change SCLAST Time, in days, since the last identified Spectral Change (SCTIME). Spectral Model Quality SCMQA Information regarding the type of time series model applied on July 1 of the current year. Primary Land Cover LCPRI The most likely Level 1 land cover class on July 1 of the current year Primary Land Cover Confidence LCPCONF Measure of confidence that the Primary Land Cover label matches the training data. Secondary Land Cover LCSEC The second most likely Level 1 land cover class on July 1 of the current year Secondary Land Cover Confidence LCSCONF Measure of confidence that the Secondary Land Cover label matches the training data. Annual Land Cover Change LCACHG Synthesis of Primary Land Cover of current and previous year identifying changes in land cover class."},{"location":"projects/lcmap/#product-specifications","title":"Product Specifications","text":"The product specifications allows for understanding categorical vs continuous datasets and informed pyramid policy for ingest into Google Earth Engine.
Short Name Data Type Units Range Valid Range Fill Value SCTIME UINT16 DOY 0-65535 0-366 0 SCMAG FLOAT32 Unitless -3.4e+38 - +3.4e+38 0 to +3.4e+38 0 SCSTAB UINT16 Days 0-65535 0-65535 0 SCLAST UINT16 Days 0-65535 0-65535 0 SCMQA UINT8 Discrete 0-255 0- 4,6,8,14,24,44,54 0 LCPRI UINT8 Discrete 0-255 0-8 0 LCPCONF UINT8 0-255 0-255 0 LCSEC UINT8 Discrete 0-255 0-8 0 LCSCONF UINT8 0-255 0-255 0 LCACHG UINT8 Discrete 0-255 0-87 0"},{"location":"projects/lcmap/#lcmap-level-1-land-cover-classes","title":"LCMAP Level 1 Land Cover Classes","text":"For classification of thematic land cover, LCMAP implements a Level 1 classification schema similar to an Anderson Level 1 (Anderson\u00a0et\u00a0al.,\u00a01976) representing dominant land cover classes most relevant to remotely monitoring land change.
Land Cover Class Description Developed Areas of intensive use with much of the land covered with structures (e.g., high-density residential, commercial, industrial, mining, or transportation), or less intensive uses where the land cover matrix includes vegetation, bare ground, and structures (e.g., low-density residential, recreational facilities, cemeteries, transportation/utility corridors, etc.), including any land functionality related to the developed or built-up activity. Cropland Land in either a vegetated or unvegetated state used in production of food, fiber, and fuels. This includes cultivated and uncultivated croplands, hay lands, orchards, vineyards, and confined livestock operations. Forest plantations are considered as forests or woodlands (Tree Cover class) regardless of the use of the wood products. Grass/Shrub Land predominantly covered with shrubs and perennial or annual natural and domesticated grasses (e.g., pasture), forbs, or other forms of herbaceous vegetation. The grass and shrub cover must comprise at least 10% of the area and tree cover is less than 10% of the area. Tree Cover Tree-covered land where the tree cover density is greater than 10%. Cleared or harvested trees (i.e., clearcuts) will be mapped according to current cover (e.g., Barren, Grass/Shrub). Water Areas covered with water, such as streams, canals, lakes, reservoirs, bays, or oceans. Wetland Lands where water saturation is the determining factor in soil characteristics, vegetation types, and animal communities. Wetlands are composed of mosaics of water, bare soil, and herbaceous or wooded vegetated cover. Ice/Snow Land where accumulated snow and ice does not completely melt during the summer period (i.e., perennial ice/snow). Barren Land comprised of natural occurrences of soils, sand, or rocks where less than 10% of the area is vegetated."},{"location":"projects/lcmap/#citation","title":"Citation","text":"There are no restrictions on the use of the LCMAP Reference Data Products. It is not a requirement of data use, but the following citation may be used in publication or presentation materials to acknowledge the USGS as a data source and to credit the original research.
LCMAP Reference Data products courtesy of the U.S. Geological Survey Earth Resources Observation and Science Center.
Brown, J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E., Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z.,\nPengra, B.W., Smith, K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant, A.L., Zelenak, D., Reker, R.R., and Rover, J.,\n2020 Lessons learned implementing an operational continuous United States national land change monitoring capability\u2014The Land Change Monitoring,\nAssessment, and Projection (LCMAP) approach: Remote Sensing of Environment, v. 238, article 111356, at https://doi.org/10.1016/j.rse.2019.111356.\n\nZhu, Z., and Woodcock, C.E., 2014, Continuous change detection and classification of land cover using all available Landsat data: Remote Sensing\nof Environment, v. 144, p. 152\u2013171, at https://doi.org/10.1016/j.rse.2014.01.011.\nvar lcachg = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCACHG\");\nvar lcpconf = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCPCONF\");\nvar lcpri = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCPRI\");\nvar lcsconf = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCSCONF\");\nvar lcsec = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/LCSEC\");\nvar sclast = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCLAST\");\nvar scmag = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCMAG\");\nvar scmqa = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCMQA\");\nvar scstab = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCSTAB\");\nvar sctime = ee.ImageCollection(\"projects/sat-io/open-datasets/LCMAP/SCTIME\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/LCMAP
I have also added the reference dataset to be used with the other 10 LCMAP products, which is about 25000 plot level datasets.
var reference = ee.FeatureCollection(\"projects/sat-io/open-datasets/LCMAP/LCMAP_CU_20200414_V01_REF\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/LCMAP-REFERENCE
"},{"location":"projects/lcmap/#reference-publications-find-additional-publications-here","title":"Reference Publications: Find additional publications here","text":"LCMAP data are freely available to the public (similar to a CC0 license) and are generated by leveraging other national programs including the Landsat satellite program
Created by: U.S. Geological Survey Center for Earth Resources Observation and Science (EROS)
Curated by: Samapriya Roy
Keywords: Landsat, ARD, Land Cover, Spectral Change, USGS, EROS
Last updated: 2023-04-04
"},{"location":"projects/lcmap/#changelog","title":"Changelog","text":"Updated v1.3
Collection 1.3 extends LCMAP\u2019s record to 37 years, characterizing the landscapes across CONUS at 30-meter resolution from 1985-2021. The 10-product\nsuite can be used to capture the dynamics of wetlands in growth or decline, characterize the ephemeral impacts of hurricanes or rapidly-shifting\nmining operations, track the pace of coastal erosion or urban growth, observe the progression of fires, monitor recovery from mudslides and\nwildfires, and much more. LCMAP products can also serve as a complement to other USGS Landsat-based mapping efforts, such as the National Land Cover\nDatabase, LANDFIRE, Monitoring Trends in Burn Severity, and others.\nUSGS posted a notification for v1.2, so datasets were reprocessed and ingested
A processing issue was found in LCMAP Conterminous United States (CONUS) Collection 1.2 product mosaics in March 2022. The CONUS Collection 1.2\nmosaics have been reprocessed as of April 14, 2022, and users who downloaded CONUS Collection 1.2 mosaics before that date are encouraged to\nredownload the reprocessed data. CONUS Collection 1.2 tiled data were unaffected and are available on EarthExplorer. LCMAP Conterminous United\nStates (CONUS) Collection 1.2 products are available on EarthExplorer, the LCMAP Web Viewer, and the LCMAP Mosaic Download website as of December\n2021. CONUS Collection 1.2 includes LCMAP products for 1985-2020. Previous LCMAP collections will remain available on EarthExplorer; however, user\nare encouraged to use the most recent release. LCMAP Hawaii (HI) Collection 1.0 products are also available on EarthExplorer, the LCMAP Web Viewer,\nand the LCMAP Mosaic Download website as of January 2022. HI Collection 1.0 includes LCMAP products for 2000-2020.\nLandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. Image chips of 256 x 256 pixels in LandCoverNet spanning across multiple tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.
One of the strongest feature of this dataset is Consensus labeling where each image chip was validated by three independent users. The accuracy of each user was assessed using chips that were separately labeled by experts from Radiant Earth\u2019s team. To generate the consensus label for each pixel a Bayesian model averaging approach was implemented taking into account the accuracy of each user. The resulting labels are accompanied by a \u201cconsensus score\u201d between 0 and 100 which indicates the degree of agreement among the three users. This forms b2 for the dataset while b1 is the class value.
You can read a sample detailed methodology here and you can go to the sample dataset page here. You can read about the approach in the paper here
Tutorials on this can be further accessed here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/lcnet/#citation","title":"Citation","text":"Alemohammad, Hamed, and Kevin Booth. \"LandCoverNet: A global benchmark land cover classification training dataset.\"\narXiv preprint arXiv:2012.03111 (2020).\nAlemohammad S.H., Ballantyne A., Bromberg Gaber Y., Booth K., Nakanuku-Diggs L., & Miglarese A.H. (2020) \"LandCoverNet Africa: A Geographically\nDiverse Land Cover Classification Training Dataset\", Version 1.0, Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.d2ce8i\nThe datasets are provided as raster chips with 256 x 256 pixel resolution meaning a total of 65,536 pixels. Overall dataset distribution per region is summarized below
Abbreviation Image Chip Count Ref ID Region Proportion Global AU 600 ref_landcovernet_au_v1_labels Australia 6.72 AS 2753 ref_landcovernet_as_v1_labels Asia 30.81 AF 1980 ref_landcovernet_af_v1_labels Africa 22.16 EU 840 ref_landcovernet_eu_v1_labels Europe 9.4 NA 1561 ref_landcovernet_na_v1_labels North America 17.47 SA 1200 ref_landcovernet_sa_v1_labels South America 13.43The datasets do consist of a STAC representation and while the command line tool is the way to access this data, I wrote some custom script for parsing the properties in STAC metadata as well as to download the raster objects and the source imagery CSVs for use as Google Earth Engine assset level property.
Retained metadata includes date which is used for both start and end date.
"},{"location":"projects/lcnet/#additional-metadata-fields","title":"Additional Metadata fields","text":"var au = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_au_v1_labels\");\nvar af = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_af_v1_labels\");\nvar as = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_as_v1_labels\");\nvar eu = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_eu_v1_labels\");\nvar na = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_na_v1_labels\");\nvar sa = ee.ImageCollection(\"projects/sat-io/open-datasets/LandCoverNet/LABELS/ref_landcovernet_sa_v1_labels\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/LANDCOVERNET_V1
"},{"location":"projects/lcnet/#license","title":"License","text":"The dataset is released under (CC BY 4.0) license. You can find license summary here
Produced by: Radiant Earth Foundation
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Remote Sensing, landsat-8, sentinel-1, sentinel-2, segmentation
Last updated on GEE: 2022-07-17
"},{"location":"projects/lghap/","title":"Long-term Gap-free High-resolution Air Pollutants (LGHAP)","text":"A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), the 21-year-long (2000\u20132020) gap free AOD, PM2.5 and PM10 grids with a 1-km resolution covering the land area of China was provided. You can read the accepted preprint here
Specifically, data gaps in daily AOD imageries from MODIS aboard Terra were reconstructed based on a set of AOD data tensors acquired from satellites, numerical analysis, and in situ air quality data via integrative efforts of spatial pattern recognition for high dimensional gridded image analysis and knowledge transfer in statistical data mining.
"},{"location":"projects/lghap/#citation","title":"Citation","text":"Bai, K., Li, K., Ma, M., Li, K., Li, Z., Guo, J., Chang, N.-B., Tan, Z., and Han, D.: LGHAP: a Long-term Gap-free High-resolution Air Pollutants concentration dataset derived\nvia tensor flow based multimodal data fusion, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-404, in review, 2021.\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Daily 1-km gap-free AOD grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5652257\n\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Daily 1-km gap-free PM2.5 grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5652265\n\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Daily 1-km gap-free PM10 grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5652263\n\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Monthly averaged 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5655797\n\nKaixu Bai, Ke Li, Zhuo Tan, Di Han, & Jianping Guo. (2021). Annual mean 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000\u20132020). [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5655807\nAll datasets were provided as netCDF file formats and the authors did provide some code for conversation to geotiff. Their code was modified to support multithreaded batch processing along with the addition of LZW compression. Overall uncompressed size was approximately 4 TB which post ingestion across all assets was brought down to 246.65 GB. The code was also adjusted to handle tiling for optimizing output files. Additionally date were added to the GEE assets for quick filter and sorting.
Collection Name Collection Path AOD_daily projects/sat-io/open-datasets/LGHAP/AOD_daily AOD_monthly_avg projects/sat-io/open-datasets/LGHAP/AOD_monthly_avg AOD_yearly_avg projects/sat-io/open-datasets/LGHAP/AOD_yearly_avg PM10_daily projects/sat-io/open-datasets/LGHAP/PM10_daily PM10_monthly_avg projects/sat-io/open-datasets/LGHAP/PM10_monthly_avg PM10_yearly_avg projects/sat-io/open-datasets/LGHAP/PM10_yearly_avg PM25_daily projects/sat-io/open-datasets/LGHAP/PM25_daily PM25_monthly_avg projects/sat-io/open-datasets/LGHAP/PM25_monthly_avg PM25_yearly_avg projects/sat-io/open-datasets/LGHAP/PM25_yearly_avg
"},{"location":"projects/lghap/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aod_daily = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/AOD_daily\");\nvar aod_monthly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/AOD_monthly_avg\");\nvar aod_yearly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/AOD_yearly_avg\");\nvar pm10_daily = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM10_daily\");\nvar pm10_monthly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM10_monthly_avg\");\nvar pm10_yearly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM10_yearly_avg\");\nvar pm25_daily = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM25_daily\");\nvar pm25_monthly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM25_monthly_avg\");\nvar pm25_yearly_avg = ee.ImageCollection(\"projects/sat-io/open-datasets/LGHAP/PM25_yearly_avg\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/LONG-TERM-HIGHRES-AIR-POLLUTANTS
"},{"location":"projects/lghap/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created by: Kaixu Bai; Ke Li; Zhuo Tan; Di Han; Jianping Guo
Curated by: Samapriya Roy
Keywords: AOD, PM2.5, PM10, Gap free
"},{"location":"projects/lgrip30/","title":"GFSAD Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP)","text":"The Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP) provides high resolution, global cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data (GFSAD) project, LGRIP maps the world\u2019s agricultural lands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas for every country in the world. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2014-2017 time period to create a nominal 2015 product.
Each LGRIP 30 meter resolution GeoTIFF file contains a contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also available.
The datasets are coded as follows and are an enhacement to the GFSAD GCEP30 dataset which does not differentiate between Irrigated and rainfed croplands. You can find the dataset and links to download here
Class Label Name Description 0 Ocean Ocean and Water bodies 1 Non-croplands Land with other land use 2 Irrigated croplands Agricultural croplands that are irrigated 3 Rainfed croplands Agricultural croplands that are rainfedDisclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.
"},{"location":"projects/lgrip30/#citation","title":"Citation","text":"Teluguntla, P., Thenkabail, P., Oliphant, A., Gumma, M., Aneece, I., Foley, D., and Mccormick,\nR. (2023). The GFSAD Landsat-derived Global Rainfed and Irrigated-Cropland Product at nominal 30m of the World (GFSADLGRIP30WORLD). NASA EOSDIS Land\nProcesses DAAC. IP148728. DOI: https://doi.org/10.5067/Community/LGRIP/LGRIP30.001\nvar lgrip30 = ee.ImageCollection(\"projects/sat-io/open-datasets/GFSAD/LGRIP30\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/LGRIP-30-CROPLAND-EXTENT
"},{"location":"projects/lgrip30/#license","title":"License","text":"GFSAD LGRIP 30 data are freely available to the public (similar to a CC0 license) and are generated by leveraging other national programs including the Landsat satellite program.
Created by: U.S. Geological Survey Center for Earth Resources Observation and Science (EROS)
Curated by: Samapriya Roy
Keywords: Landsat, Global Food, Cropland Extent, GEE, USGS, EROS
Last updated in GEE: 2023-03-01
"},{"location":"projects/ltrait/","title":"Global Leaf trait estimates for land modelling","text":"At the organismal level, plant traits, which are measurable morphological, anatomical, physiological, and phenological characteristics, can influence individuals' establishment, fitness, and survival. These measurable characteristics provide essential information to explain long-term (e.g., annual) patterns underlying carbon, water, energy fluxes, and biodiversity globally. We provide the only global remotely sensed-based maps of leaf traits at 1km spatial resolution. In particular, we present global maps of specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen content per dry mass (LNC), and leaf phosphorus content per dry mass (LPC). The methodology combines MODIS and Landsat data, climatological data (Worldclim), the largest traits database (TRY), and machine learning algorithms.
The following figure shows a flowchart of the methodology for providing our traits estimates. The numbered boxes indicate the three main components of the methods: (1) gap filling the traits database; (2) calculating the community weighted mean (CWM) trait values at the canopy level for MODIS pixels with nearby trait observations; and (3) spatialization of CWMs to global trait maps at 500\u202fm resolution.
The full information about the methodology can be found here. The users can also explore the dataset in GEE with the following app.
The data is also available at two spatial resolutions, 3km and 1km. It can be downloaded from these links 1, 2.
"},{"location":"projects/ltrait/#additional-information-about-v3","title":"Additional Information about v3","text":"Version 3.0 of the processing chain prevents extrapolation and uses an updated categorical trait table. To prevent extrapolations, this updated version of the processing chain uses the random forest algorithm (RF) with surrogates for the estimation of trait values. RF with surrogates allows obtaining an ensemble of models inside the convex hull of the input data for the predictions. Additionally, the use of an updated and more extensive categorical trait table allowed increasing the amount of training samples to produce the maps.
"},{"location":"projects/ltrait/#citation","title":"Citation","text":"Moreno-Mart\u00ednez, \u00c1., Camps-Valls, G., Kattge, J., Robinson, N., Reichstein, M., Bodegom, P. V., Kramer, K., Cornelissen, J. H. C., Reich, P. B.,\nBahn, M., Niinemets, \u00dc., Pe\u00f1uelas, J., Craine, J., Cerabolini, B., Minden, V., Laughlin, D. C., Sack, L., Allred, B., Baraloto, C., Byun, C.,\nSoudzilovskaia, N. A., Running, S. W. (2018). A methodology to derive global maps of leaf traits using remote sensing and climate data.\nRemote Sensing of Environment, 218, 69-88. [doi](https://doi.org/10.1016/j.rse.2018.09.006)\n// SLA (mm2/g)\nvar SLA=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/SLA_1km_v3').select([0],['SLA']);\nvar SLA_SD = ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/SLA_sd_1km_v3').select([0],['SLA_sd']);\n\n// LNC (mg/g)\nvar LNC=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LNC_1km_v3').select([0],['LNC']);\nvar LNC_SD = ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LNC_sd_1km_v3').select([0],['LNC_sd']);\n\n// LPC (mg/g)\nvar LPC=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LPC_1km_v3').select([0],['LPC']);\nvar LPC_SD=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LPC_sd_1km_v3').select([0],['LPC_sd']);\n\n// Leaf dry matter content LDMC (g/g)\nvar LDMC=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LDMC_1km_v3').select([0],['LDMC']);\nvar LDMC_SD = ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LDMC_sd_1km_v3').select([0],['LDMC_sd']);\n\n//let's mask unprocessed data (Positive values correspond with natural vegetated areas)\nSLA = SLA.mask(SLA.gt(0));\nLNC = LNC.mask(LNC.gt(0));\nLPC = LPC.mask(LPC.gt(0));\nLDMC = LDMC.mask(LDMC.gt(0));\n\nvar vis_vi = {min:7 , max: 22, palette: [\"ffffd9\", \"edf8b1\", \"c7e9b4\", \"7fcdbb\", \"41b6c4\", \"1d91c0\", \"225ea8\", \"253494\", \"081d58\"]};\nMap.addLayer(SLA, vis_vi, 'SLA (mm2 / g)',true)\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-LEAF-TRAITS
"},{"location":"projects/ltrait/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
"},{"location":"projects/ltrait/#contact-information","title":"Contact information","text":"If you have any further questions or doubts, please don't hesitate to contact us.
Curated by: Alvaro Moreno-Mart\u00ednez, Gustau Camps-Valls, Jens Kattge, Nathaniel Robinson, Markus Reichstein, Peter van Bodegom, Josep Pe\u00f1uelas, Brady Allred, Steve W. Running
Curated copy in GEE by: Samapriya Roy
Keywords: Plant traits, Machine learning, Remote sensing, Plant ecology, Climate, MODIS, Landsat
Last updated: Nov 2021
Last updated in GEE: 2022-12-18
"},{"location":"projects/mangrove/","title":"Global Mangrove Watch","text":"This study has used L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA) for 11 epochs from 1996 to 2020 to develop a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. This approach was applied between all image-date pairs producing 10 maps for each epoch, which were summarised to produce the global mangrove time-series. The resulting mangrove extent maps had an estimated accuracy of 87.4 % (95th conf. int.: 86.2 - 88.6 %), although the accuracies of the individual gain and loss change classes were lower at 58.1 % (52.4 - 63.9 %) and 60.6 % (56.1 - 64.8 %), respectively.
Sources of error included a mis-registration in the SAR mosaic datasets, which could only be partially corrected for, but also confusion in fragmented areas of mangroves, such as around aquaculture ponds. Overall, 152,604 km2 (133,996 - 176,910) of mangroves were identified for 1996, with this decreasing by -5,245 km2 (-13,587 - 3686) resulting in a total extent of 147,359 km2 (127,925 - 168,895) in 2020, and representing an estimated loss of 3.4 % over the 24-year time period. The Global Mangrove Watch Version 3.0 represents the most comprehensive record of global mangrove change achieved to date and is expected to support a wide range of activities, including the ongoing monitoring of the global coastal environment, defining and assessments of progress towards conservation targets, protected area planning and risk assessments of mangrove ecosystems worldwide.
You can download the dataset here and read the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/mangrove/#preprocessing","title":"Preprocessing","text":"Raster tiles were mosaiced so that all extents and allied rasters can fit into single collections. Date ranges were added later to the raster and the vector layers.
"},{"location":"projects/mangrove/#citation","title":"Citation:","text":"Bunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R.M.; Thomas, T.; Tadono, T.; Worthington, T.A.; Spalding, M.; Murray, N.J.; Rebelo, L-M. Global\nMangrove Extent Change 1996 \u2013 2020: Global Mangrove Watch Version 3.0. Remote Sensing. 2022\nBunting, Pete, Rosenqvist, Ake, Hilarides, Lammert, Lucas, Richard, Thomas, Nathan, Tadono , Takeo, Worthington, Thomas, Spalding , Mark, Murray,\nNicholas, & Rebelo, Lisa-Maria. (2022). Global Mangrove Watch (1996 - 2020) Version 3.0 Dataset (3.0) [Data set]. Zenodo. https://doi.org/10.5281/\nzenodo.6894273\nvar extent_raster = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/GMW_V3\");\nvar extent_1996 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_1996_vec\");\nvar extent_2007 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2007_vec\");\nvar extent_2008 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2008_vec\");\nvar extent_2009 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2009_vec\");\nvar extent_2010 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2010_vec\");\nvar extent_2015 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2015_vec\");\nvar extent_2016 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2016_vec\");\nvar extent_2017 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2017_vec\");\nvar extent_2018 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2018_vec\");\nvar extent_2019 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2019_vec\");\nvar extent_2020 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2020_vec\");\nvar change_f1996_raster = ee.ImageCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/change_f1996\");\nvar change_f1996_2007 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2007_vec\");\nvar change_f1996_2008 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2008_vec\");\nvar change_f1996_2009 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2009_vec\");\nvar change_f1996_2010 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2010_vec\");\nvar change_f1996_2015 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2015_vec\");\nvar change_f1996_2016 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2016_vec\");\nvar change_f1996_2017 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2017_vec\");\nvar change_f1996_2018 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2018_vec\");\nvar change_f1996_2019 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2019_vec\");\nvar change_f1996_2020 = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2020_vec\");\nSingle layer of pixels which were mangroves at any date in the time series
var gmw_union_raster = ee.Image(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/union/gmw_v3_mng_union\");\nvar gmw_union_vector = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/union/gmw_v3_union_vec\");\nSingle layer of pixels which were mangroves at all dates within the time series
var gmw_core_raster = ee.Image(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/core/gmw_v3_mng_core\");\nvar gmw_core_vector = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/core/gmw_v3_core_vec\");\nVector layer with the 1x1 degree tiles used for the analysis
var tiles = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/gmw_v3_tiles\");\nResolution: approx 30m
"},{"location":"projects/mangrove/#global-mangrove-watch-annual-mangrove-extent-4019","title":"Global Mangrove Watch: Annual Mangrove Extent 4.0.19","text":"To improve the resolution and local relevance of the Global Mangrove Watch (GMW) baseline, a new layer has been created for 2020. Using Copernicus Sentinel-2 satellite imagery, processed to a pixel resolution of 10 m, the mangrove extent has been completely remapped and revised with many areas which were not previously mapped now included within the new map. This has increased the spatial resolution of the mapping from a pixel resolution of 25 m to 10 m, allowing finer features to be mapped, such as fringing and riverine mangroves.
"},{"location":"projects/mangrove/#earth-engine-snippet-sentinel-raster-and-vector-baseline-v4019","title":"Earth Engine Snippet: Sentinel Raster and Vector Baseline v4.0.19","text":"//Extent v4.0.19\nvar raster_extent = ee.ImageCollection(\"projects/sat-io/open-datasets/GMW/annual-extent/GMW_MNG_2020\");\nvar vector_extent = ee.FeatureCollection(\"projects/sat-io/open-datasets/GMW/annual-extent/GMW_MNG_VEC_2020\");\nMap.addLayer(raster_extent.median(),{\"opacity\":1,\"bands\":[\"b1\"],\"min\":1,\"max\":1,\"palette\":[\"228B22\"]},'GMW Raster Extent 2020 v4.0.19')\nMap.addLayer(ee.Image().paint(vector_extent,0,3), {\"palette\":[\"red\"]}, 'GMW Vector Extent 2020 v4.0.19')\nResolution: approx 10m
Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-MANGROVE-WATCH
"},{"location":"projects/mangrove/#license-usage","title":"License & Usage","text":"Attribution 4.0 International CC BY 4.0.
Curated in GEE by: Samapriya Roy
Keywords: Global, Mangrove, GMW, 1996, 2020
Last updated: 2024-09-08
"},{"location":"projects/mangrove/#changelog","title":"Changelog","text":""},{"location":"projects/mangrove/#updated-2024-09-08","title":"Updated 2024-09-08","text":"This paper is currently in review with Nature Scientific Data and the citation will be updated once the paper has been published. Please keep this into consideration while using this dataset
This dataset provides global mangrove canopy height maps for 2015 at a 12-meter resolution. Canopy height estimates were derived from the TanDEM-X digital surface models, calibrated and validated with GEDI Lidar data. The dataset covers a circum-equatorial band from 34 degrees north to 39 degrees south latitude, encompassing the majority of mangrove ecosystems globally. The dataset includes 1443 GeoTIFF files containing global mangrove canopy height maps organized into 1\u00b0 by 1\u00b0 tiles. Each GeoTIFF file represents a single tile and is named as follows: TDM1_DEM__10_Y##X###_DEM_EGM08_GMW314_2015_WM_hcap_cal.tif, where Y##X### represents the latitude (Y = \"N\" or \"S\") and longitude (X = \"W\" or \"E\") coordinates of the tile's southwest corner.
These canopy height maps are useful for assessing local-scale geophysical and environmental conditions that regulate forest structure and carbon cycle dynamics in mangrove ecosystems. These canopy height maps are instrumental for assessing local-scale geophysical and environmental conditions that regulate forest structure and carbon cycle dynamics in mangrove ecosystems. You can find the dataset on ORNAL DAAC here
"},{"location":"projects/mangrove_ht_tandemx/#data-acquisition-and-materials","title":"Data acquisition and materials","text":"For data acquisition and materials, the sources included Digital Elevation Model (DEM) data from the German Aerospace Agency (DLR) TanDEM-X mission, as reported by Rizzoli et al. in 2017. The Global Mangrove Watch (GMW) map provided a global mangrove extent map, as detailed by Bunting et al. in 2022. Additionally, Global Ecosystems Dynamics Investigation (GEDI) L2A data was used, as documented by Dubayah et al. in 2020.
"},{"location":"projects/mangrove_ht_tandemx/#citation","title":"Citation","text":"Simard, M., L. Fatoyinbo, N.M. Thomas, A.E. Stovall, A. Parra, A. Barenblitt, P. Bunting, and I. Hajnsek. 2024. A new global mangrove height map with a 12-meter spatial resolution. In review 2024, Nature Scientific Data.\nSimard, M., L. Fatoyinbo, N. Thomas, A. Stovall, A. Parra, M.W. Denbina, D. Lagomasino, and I. Hajnsek. 2024. CMS: Global Mangrove Canopy Height\nMaps Derived from TanDEM-X, 2015. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2251\nThe GEDI_MANGROVE_HT, layer contains mangrove heights for individual GEDI L2A tiles used to generate the GeoTIFF files.
Variable GEDI L2A variable name Units Description GEDI_file_name - - Name of the GEDI file beam Beam ID - Beam number (0-11) delta_time delta_time YYYY-MM-DD HH:MM:SS.SSSSSS+00:00 Transmit time of the shot shot_number shot_number 1 Unique shot ID lat_lowestmode lat_lowestmode degrees Latitude of center of lowest mode lon_lowestmode lon_lowestmode degrees Longitude of center of lowest mode channel channel 1 Channel number (0-7) degrade_flag degrade_flag flag Non-zero values indicate the shot occurred during a degraded period digital_elevation_model digital_elevation_model m Digital elevation model height above the WGS84 ellipsoid. Interpolated at latitude_bin0 and longitude_bin0 from the TandemX 90m product. digital_elevation_model_srtm digital_elevation_model_srtm m Shuttle Radar Topography Mission (SRTM) elevation at GEDI footprint location elev_highestreturn elev_highestreturn m Elevation of highest detected return relative to reference ellipsoid elev_lowestmode elev_lowestmode m Elevation of lowest mode elevation_bias_flag elevation_bias_flag flag Elevations potentially affected by 4bin (~60 cm) ranging error energy_total energy_total 1 Integrated counts in the return waveform relative to the mean nise level landsat_treecover landsat_treecover percent Tree cover in the year 2010, defined as canopy closure for all L2A vegetation taller than 5 m in height (Hansen et al., 2013). Encoded as a percentage per output grid cell. landsat_water_persistence landsat_water_persistence percent The percent UMD GLAD Landsat observations with classified Derived surface water between 2018 and 2019. Values >80 usually represent permanent water, while values <10 represent permanent land. urban_proportion urban_proportion percent The percentage proportion of land area within a focal area surrounding each shot that is urban land cover. Urban land cover is derived from the DLR 12 m resolution TanDEM-X Global Urban Footprint Product. mean_sea_surface mean_sea_surface m Mean sea surface height above the WGS84 ellipsoid, includes the geoid. Interpolated at latitude_bin0 and longitude_bin0 from DTU15. num_detectedmodes num_detectedmodes 1 Number of detected modes in rxwaveform quality_flag quality_flag flag Flag simplifying selection of most useful data rh rh m Relative height metrics at 98% interval rx_energy rx_energy 1 total energy f rxwaveform, mean noise removed selected_algorithm selected_algorithm - ID of algorithm selected as identifying the lowest non-noise mode sensitivity sensitivity degrees Maximum canopy cover that can be penetrated considering th NR of the waveform solar_elevation solar_elevation degrees The elevation of the sun position vector from the laser bounce point position in the local ENU frame. The angle is measured from the East-North plane and is positive Up. surface_flag surface_flag flag Indicates elev_lowestmode is within 300 m of DEM or MSS egm_08 m Elevation over the EGM 2008 geoid tdx_max m Maximum TanDEM-X DEM value from the pixels overlapping the GEDI footprint tdx_std m Standard deviation of TanDEM-X DEM values from the pixels overlapping the GEDI footprint tdx_mean m Mean TanDEM-X DEM value from the pixels overlapping the GEDI footprint tdx_min m Minimum TanDEM-X DEM value from the pixels overlapping the GEDI footprint pixel_count 1 Number of TanDEM-X pixels overlapping the GEDI footprint
"},{"location":"projects/mangrove_ht_tandemx/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var mangrove_tandemx_12 = ee.ImageCollection(\"projects/sat-io/open-datasets/GLOBAL_MANGROVE_HT_TANDEMX\");\nvar mangrove_gedi = ee.FeatureCollection(\"projects/space-geographer/assets/GEDI_MANGROVE_HT\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-MANGROVE-CANOPY-HT-TANDEMX
"},{"location":"projects/mangrove_ht_tandemx/#license","title":"License","text":"Data hosted by the ORNL DAAC is openly shared, without restriction, in accordance with NASA's Earth Science program Data and Information Policy
Provided by: Simard et al 2024
Curated in GEE by: Samapriya Roy
Keywords: Mangrove, Tandem-X, Canopy Height, GEDI
Last updated : 2024-07-26
"},{"location":"projects/mapbiomas/","title":"Mapbiomas Annual land cover and use maps","text":"The Brazilian Annual Land Use and Land Cover Mapping Project is an initiative that involves a collaborative network of biomes, land use, remote sensing, GIS and computer science experts that rely on Google Earth Engine platform and its cloud processing and automated classifiers capabilities to generate Brazil\u2019s annual land use and land cover time series. MapBiomas Project - is a multi-institutional initiative to generate annual land cover and use maps using automatic classification processes applied to satellite images. The complete description of the project can be found here.
Other regions such as Pan Amazonia, Indonesia, Bolivia , Peru and others were spun out of the work in Mapbiomas Brazil and as such these are also included in the current release
Scale: 30 m to 10m Data Type: Multiple raster datasets and types
"},{"location":"projects/mapbiomas/#citation","title":"Citation","text":"Souza at. al. (2020) - Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine -\nRemote Sensing, Volume 12, Issue 17, 10.3390/rs12172735 doi: 10.3390/rs12172735 https://doi.org/10.3390 /rs12172735\n\"Project MapBiomas - Collection [version] of [region] Land Cover & Use Map Series, accessed on [date] through the link: [LINK]\"\n//From collection 8\nassets: {\n integration: 'projects/mapbiomas-workspace/public/collection8/mapbiomas_collection80_integration_v1',\n transitions: 'projects/mapbiomas-workspace/public/collection8/mapbiomas_collection80_transitions_v1',\n vectors: [\n 'projects/mapbiomas-workspace/AUXILIAR/areas-protegidas',\n 'projects/mapbiomas-workspace/AUXILIAR/municipios-2016',\n 'projects/mapbiomas-workspace/AUXILIAR/estados-2017',\n 'projects/mapbiomas-workspace/AUXILIAR/biomas-2019',\n 'projects/mapbiomas-workspace/AUXILIAR/bacias-nivel-1',\n 'projects/mapbiomas-workspace/AUXILIAR/bacias-nivel-2',\n ]\n},\nAdd repo link: https://code.earthengine.google.com/?accept_repo=users/mapbiomas/user-toolkit
Extra Info: GitHub Tutorial
Curated by: MapBiomas
"},{"location":"projects/mapbiomas/#license","title":"License","text":"All these datasets are shared under Creative Commons Attribution-Share Alike 4.0 International License
Keywords: Mapbiomas, Land Use, Land Cover
Last updated: Refer to webpage
Last updated on GEE community datasets: 2023-10-14
"},{"location":"projects/mapbiomas/#changelog","title":"Changelog","text":"Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, monitoring of staging areas and emergency response, damage assessment, and recovery. These images are generated using the Maxar ARD pipeline, tiled on an organized grid in analysis-ready cloud-optimized formats. New data is released in response to activations. Older data may be migrated to the ARD format as needed. You can find additional details and datasets here
"},{"location":"projects/maxar_opendata/#preprocessing","title":"Preprocessing","text":"The metadata tags were parsed from the existing metadata json files and available properties were parsed to confirm to property names for GEE. The datetime property was added as system:time_start for easy filtering of datasets. Not all datasets have been added and this will be continuously updated to include additional datasets/disasters
"},{"location":"projects/maxar_opendata/#citation","title":"Citation","text":"Maxar Open Data Program was accessed on DATE from [url].\nvar afghanistan_earthquake_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/afghanistan_earthquake_2022\");\nvar gambia_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/gambia_flooding_2022\");\nvar hurricane_fiona_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/hurricane_fiona_2022\");\nvar hurriance_ian_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/hurricane_ian_2022\");\nvar kentucky_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/kentucky_flooding_2022\");\nvar pakistan_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/pakistan_flooding_2022\");\nvar southafrica_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/southafrica_flooding_2022\");\nvar sudan_flooding_2022 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/sudan_flooding_2022\");\nvar turkey_earthquake_2023 = ee.ImageCollection(\"projects/sat-io/open-datasets/MAXAR-OPENDATA/earthquake_turkey_2023\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/MAXAR-OPENDATA-MS
"},{"location":"projects/maxar_opendata/#license","title":"License","text":"This work is licensed under Creative Commons Attribution Non Commercial 4.0.
Data provided by: MAXAR
Curated in GEE by : Samapriya Roy
Keywords: disaster, maxar, high resolution, flooding, hurriance, earthquake
Last updated on GEE: 2022-10-30
"},{"location":"projects/merrav2/","title":"Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2)","text":"NASAs Global Modeling and Assimilation Office (GMAO) produces the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) which is a 30+ year global climate reanalysis dataset.This dataset complements existing MERRA2 Earth Engine assets: https://developers.google.com/earth-engine/datasets/tags/merra. You can find additional information on this dataset here and read more about the dataset in the climate org data page
"},{"location":"projects/merrav2/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Global Spatial resolution ~50-km (0.5-deg x 0.625-deg) Temporal resolution Daily Time span 1980-04-02 to present Update frequency Updated every 1-2 monthsVariables
Variable Details Minimum temperature, 2m ('T2MMIN') - Units: Degrees Kelvin - Scale factor: 1.0 Maximum temperature, 2m ('T2MMAX') - Units: Degrees Kelvin - Scale factor: 1.0 Precipitation ('PRECTOTCORR') - Units: Millimeters - Scale factor: 1.0 Wind speed, 10m ('WIND2M') - Units: Meters/second - Scale factor: 1.0 - NOTE: Windspeed outputs are based on the standard 10m measurement height, despite the erroneous \u20182M\u2019 suffix. ASCE Grass Reference Evapotranspiration - Units: Millimeters ('ETo_ASCE') - Scale factor: 1.0 ASCE Alfalfa Reference Evapotranspiration - Units: Millimeters ('ETr_ASCE') - Scale factor: 1.0 Specific humidity, 2m ('QV2M') - Units: kg kg-1 - Scale factor: 1.0 Surface pressure ('PS') - Units: Pa - Scale factor: 1.0 Surface incoming shortwave flux ('SWGDN') - Units: W m-2 - Scale factor: 1.0 Surface incoming shortwave flux assuming - Units: W m-2 clear sky ('SWGDNCLR') - Scale factor: 1.0"},{"location":"projects/merrav2/#citation","title":"Citation","text":"MERRA-2 Overview: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Ronald Gelaro, et al., 2017, J. Clim.,\ndoi: 10.1175/JCLI-D-16-0758.1\n// Read in Image Collection and get first image\nvar merra2_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/climate-engine/merra2/daily')\nvar merra2_i = merra2_ic.first()\n\n// Print first image to see bands\nprint(merra2_i)\n\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nvar eto_palette = [\"#ffffb2\", \"#fed976\", \"#feb24c\", \"#fd8d3c\", \"#fc4e2a\", \"#e31a1c\", \"#b10026\"]\nMap.addLayer(merra2_i.select('PRECTOTCORR'), {min: 0, max: 10, palette: prec_palette}, 'PRECTOTCORR')\nMap.addLayer(merra2_i.select('T2MMAX').subtract(273.15), {min: -10, max: 30, palette: temp_palette}, 'T2MMAX')\nMap.addLayer(merra2_i.select('T2MMIN').subtract(273.15), {min: -10, max: 30, palette: temp_palette}, 'T2MMIN')\nMap.addLayer(merra2_i.select('ETo_ASCE'), {min: 0, max: 10, palette: eto_palette}, 'ETo_ASCE')\nMap.addLayer(merra2_i.select('ETr_ASCE'), {min: 0, max: 10, palette: eto_palette}, 'ETr_ASCE')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/MERRA-2
"},{"location":"projects/merrav2/#license","title":"License","text":"NASA promotes the full and open sharing of all data with research and applications communities, private industry, academia, and the general public.
Keywords: MERRA2, NASA, global, climate, reanalysis, temperature, precipitation, evapotranspiration, evaporative demand, wind
Datasets provided by: NASA
Dataset curated in GEE by: Climate Engine Org
"},{"location":"projects/meta_trees/","title":"High Resolution 1m Global Canopy Height Maps","text":"The Global Canopy Height Maps dataset offers comprehensive insights into tree canopy heights worldwide, providing an overview of tree canopy presence and height for the analysed period (2009-2020), with eighty per cent of the data obtained from imagery acquired between 2018 and 2020. This baseline can be used as a reference for supplementing field-based measurements of carbon in carbon credit monitoring and verification schema. When newer imagery is available, the publicly shared model can be used to detect changes in canopy heights. Developed through a collaboration between Meta and the World Resources Institute, this dataset stands as a cornerstone for understanding forest structure and dynamics. This dataset achieves an unparalleled level of detail through the fusion of state-of-the-art satellite imagery and advanced artificial intelligence techniques. By analyzing satellite imagery spanning from 2009 to 2020, with a focus on data from 2018 to 2020, it provides extensive temporal coverage for tracking changes in canopy height over time across the entire landmass of the planet. Using AI models such as DiNOv2, the dataset enables precise prediction of canopy height with a mean absolute error of 2.8 meters, empowering accurate assessment of carbon stocks and the effectiveness of mitigation strategies.
Moreover, its integration into conservation initiatives, carbon credit monitoring, and climate agreements underscores its significance in guiding sustainable forest management practices, afforestation, reforestation efforts, and biodiversity conservation. Complemented by the accessibility of the AI model used to generate the data on GitHub, this dataset catalyzes further research and development in forest monitoring and carbon sequestration, contributing to global efforts to combat climate change. You can read the blogpost from meta here and the associated paper here.
"},{"location":"projects/meta_trees/#citation","title":"Citation","text":"Tolan, J., Yang, H.I., Nosarzewski, B., Couairon, G., Vo, H.V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J. and Moutakanni, T.,\n2024. Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial\nlidar. Remote Sensing of Environment, 300, p.113888.\nHigh Resolution Canopy Height Maps by WRI and Meta was accessed on DATE from Google Earth Engine. Meta and World Resources Institude (WRI) - 2023.\nHigh Resolution Canopy Height Maps (CHM). Source imagery for CHM \u00a9 2016 Maxar. Accessed DAY MONTH YEAR.\nvar canopy_ht = ee.ImageCollection(\"projects/meta-forest-monitoring-okw37/assets/CanopyHeight\")\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-1m-CANOPY-HEIGHT
GEE app link: https://meta-forest-monitoring-okw37.projects.earthengine.app/view/canopyheight
"},{"location":"projects/meta_trees/#license","title":"License","text":"This dataset is made available under a Creative Commons Attribution 4.0 International License
Dataset provider: Meta and WRI, Tolan et al 2023
Curated in GEE by: Meta & WRI
Keywords: DiNOv2, Maxar, Self Supervised Learning (SSL), Canopy height, Global dataset, Meta, WRI
Last updated on GEE: 2024-04-13
"},{"location":"projects/mlab_extracts/","title":"Measurement Lab Network Extracts (M-Lab)","text":"Measurement Lab (M-Lab) is the largest open source Internet measurement effort in the world. The M-Lab Network Diagnostic Tool (NDT) dataset is a valuable resource for researchers and network engineers interested in understanding internet performance. It consists of a massive collection of test results gathered from users running the NDT tool. These tests measure various aspects of a user's internet connection, including download and upload bandwidth, latency (signal delay), and packet loss.
This is a very small sample extract for 15,000 download and upload extracts from a single days worth of extract 2024-06-01
The unique aspect of the NDT dataset lies in its user-initiated nature. Unlike traditional network monitoring conducted by service providers, the NDT data reflects real-world user experiences. Users often run the NDT tool when they encounter internet issues, resulting in a surge of tests during network outages or periods of poor performance. This makes the NDT dataset a rich source for analyzing trends in internet health, identifying bottlenecks, and understanding how network problems manifest for end-users. By studying the characteristics of NDT tests, researchers can gain valuable insights into the overall quality and performance of the internet.
"},{"location":"projects/mlab_extracts/#citation","title":"Citation","text":"Measurement Lab. \"The M-Lab NDT Data Set.\" (February 11, 2009 -- December 21, 2015).\nAccessed July 2, 2024. https://measurementlab.net/tests/ndt.\nvar mlab_download_extract = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/MLAB/mlab_download_extract\");\nvar mlab_upload_extract = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/MLAB/mlab_upload_extract\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/MLAB-EXTRACTS-NETWORK-SPEED
"},{"location":"projects/mlab_extracts/#license_1","title":"License","text":"All data collected by M-Lab tests are available to the public without restriction under a No Rights Reserved Creative Commons Zero Waiver.
Dataset accessed: 2024-07-01
Dataset provided by: Measurement Lab (M-Lab)
Curated in GEE by: Samapriya Roy
Keywords: : analytics,network speed,cities,civic,infrastructure,internet,network traffic, telecommunications,isp
Last updated: 2024-07-02
"},{"location":"projects/modis_8day_snow/","title":"MOD10A2061 Snow Cover 8-Day L3 Global 500m","text":"MOD10A2 is a snow cover data set from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. The data set consists of 1200 km by 1200 km tiles of 500 m resolution data gridded in a sinusoidal map projection. The data set reports the maximum snow cover extent during an eight-day period. The snow cover algorithm identifies snow-covered land and snow-covered ice on inland water. The algorithm uses a Normalized Difference Snow Index (NDSI) and other criteria tests. The eight-day compositing period was chosen because that is the exact ground track repeat period of the Terra and Aqua platforms.
Parameter Description Values Maximum_Snow_Extent Maximum snow extent observed over an eight-day period. 0: missing data1: no decision11: night25: no snow37: lake39: ocean50: cloud100: lake ice200: snow254: detector saturated255: fill"},{"location":"projects/modis_8day_snow/#dataset-details","title":"Dataset details","text":"Title: MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid Author: Hall, D. K. and G. A. Riggs. Publisher: NASA NSIDC DAAC: NASA National Snow and Ice Data Center Distributed Active Archive Center Publication date: 2021-03-30T12:00:00Z Publication place: Boulder, Colorado USA Series: MOD10A2 Edition: 61 DOI: 10.5067/MODIS/MOD10A2.061 URL: https://doi.org/10.5067/MODIS/MOD10A2.061
"},{"location":"projects/modis_8day_snow/#citation","title":"Citation","text":"Hall, D. K., V. V. Salomonson, and G. A. Riggs. \"MODIS/Terra snow cover 8-day l3 global 500m grid, version 5.\" Tile h12v12]. Boulder, Colorado USA:\nNational Snow and Ice Data Center (2006).\nHall, D. K. and G. A. Riggs. 2021. MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid, Version 61. [Indicate subset used]. Boulder, Colorado USA.\nNASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD10A2.061. [Date Accessed]\nvar MOD10A261 = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS/MOD10A261\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-MOD10A261-Snow-Cover-8-Day
"},{"location":"projects/modis_8day_snow/#license","title":"License","text":"You may download and use photographs, imagery, or text from the NSIDC web site, unless limitations for its use are specifically stated. For more information on usage and citing NSIDC datasets, please visit the NSIDC Use and Copyright page.
Curated in GEE by: Michael Lefsky and Samapriya Roy
Keywords: albedo, eight-day, 8-day, geophysical, global, modis, nasa, nsidc, snow, terra, mod10a2
Last updated: Last date the dataset was updated (if known)
"},{"location":"projects/monoculture/","title":"Aboveground carbon accumulation in global monoculture plantation forests","text":"Restoring forest cover is a key action for mitigating climate change. Although monoculture plantations dominate existing commitments to restore forest cover, we lack a synthetic view of how carbon accumulates in these systems. Here, we assemble a global database of 4756 field-plot measurements from monoculture plantations across all forested continents. With these data, we model carbon accumulation in aboveground live tree biomass and examine the biological, environmental, and human drivers that influence this growth.
This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). You can read the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/monoculture/#dataset-preprocessing","title":"Dataset Preprocessing","text":"To connect site id lat long to studies a code was written to iterate through all sites and studies and link with lat long. This was then ingested as a combined table.
"},{"location":"projects/monoculture/#paper-citation","title":"Paper Citation","text":"Bukoski, J.J., Cook-Patton, S.C., Melikov, C. et al. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests.\nNat Commun 13, 4206 (2022). https://doi.org/10.1038/s41467-022-31380-7\nBukoski, Jacob, Cook-Patton, Susan C., Melikov, Cyril, Ban, Hongyi, Chen, Jessica Liu, Goldman, Elizabeth D., Harris, Nancy L., & Potts, Matthew D.\n(2022). Global Plantation Forest Carbon database (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6555216\nvar global_fertilizer_use = ee.FeatureCollection(\"projects/sat-io/open-datasets/global-monoculture-plantations\");\nSample code: https://code.earthengine.google.com/243774c7f7cdbec21c3450c4fa8a64fb
"},{"location":"projects/monoculture/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International license.
Created by: Bukoski, Jacob et al 2022
Curated in GEE by : Samapriya Roy
keywords: Forests, Aboveground carbon stocks, Climate change, Reforestation, Plantations, Aboveground biomass
Last modified: 2022-05-16
Last updated on GEE: 2022-09-05
"},{"location":"projects/mpw/","title":"Mismanaged Plastic Waste Dataset in Rivers","text":"This dataset shows the exposure of global rivers to mismanaged plastic waste (MPW) in 2015 and its projected impact in 2060 based on three scenarios. The different scenarios for 2060 are A: business as usual, B: improved plastic recycling, and C:improved plastic recycling and reduced plastic use projection.
Four global datasets are available that include
Datasets are described in further detailed in the paper published in Science of the Total Environment, at the Zenodo data repository or using the interactive map available here.
Mismanaged Plastic Waste Dataset
Values for MPW datasets
Values for FFPW datasets
River Type Dataset
River Migration Dataset
River Impact Dataset
Nyberg, Bj\u00f6rn, Peter T. Harris, Ian Kane, and Thomas Maes. \"Leaving a plastic legacy: Current and future scenarios for mismanaged plastic waste in\nrivers.\" Science of the Total Environment 869 (2023): 161821.\nNyberg, Bjorn. (2022). Legacy of MPW in Rivers (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6894684\nvar change = ee.Image('projects/sat-io/open-datasets/MPW/changeMap'); //River Change\nvar ffr = ee.Image('projects/sat-io/open-datasets/MPW/riverImpact'); //Free flowing rivers\nvar env = ee.Image('projects/sat-io/open-datasets/MPW/Plastics_Env'); //River Types\nvar mpw = ee.Image('projects/sat-io/open-datasets/MPW/MPW_data'); // Mismanaged plastic waste\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/MISMANAGED-PLASTIC-WASTE
"},{"location":"projects/mpw/#license","title":"License","text":"The dataset is made available under the terms of the Creative Commons Attribution 4.0 International license
Curated by: Bj\u00f6rn Nyberg
Keywords: Rivers, Plastic, Mismanaged plastic waste
Last updated: July 24th 2022
"},{"location":"projects/msbuildings/","title":"Global ML Building Footprints","text":"Bing Maps is releasing open building footprints around the world. We have detected 777M buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. For the sake of completeness datasets from earlier releases were included in this dataset and included. You can find the Github repo and more information about the methodology here. Datasets are zipped and available as GeoJSON and GeoJSONL files from different regions. Additional information on preprocessing and some more context is available on the blog here
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/msbuildings/#data-preprocessing","title":"Data preprocessing","text":"The MSBuildings dataset that I have ingested into Google Earth Engine includes earlier releases apart from the 777 Million Global building footprints from Microsoft and in its final state stands at 1 Billion+ footprint (1,069,059,359). There are some interesting performance behaviors across Ingest.\u00a0
All datasets are in the format
var country = ee.FeatureCollection('projects/sat-io/open-datasets/MSBuildings/{country_name}');\nfor a list of all countries and assets use this
var ee_folder = ee.data.listAssets(\"projects/sat-io/open-datasets/MSBuildings\");\nHere are some example setups for two countries
var australia = ee.FeatureCollection('projects/sat-io/open-datasets/MSBuildings/Australia');\nvar chile = ee.FeatureCollection('projects/sat-io/open-datasets/MSBuildings/Chile')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GLOBAL-ML-BUILDINGS
"},{"location":"projects/msbuildings/#license","title":"License","text":"The datasets are released under the Open Data Commons Open Database License.
Created by: Microsoft
Curated in GEE by: Samapriya Roy
Keywords: building footprint, machine learning, remote sensing, global
Last updated in GEE: 2022-05-30
"},{"location":"projects/msroads/","title":"Microsoft Bing Global Mined Roads","text":"NoteThis dataset is currently only available to those in the insiders program
Bing Maps is releasing mined roads around the world. We have detected 47.8M km of all roads and 1165K km of roads missing from OSM. Mining is performed with Bing Maps imagery between 2020 and 2022 including Maxar and Airbus. Datasets were provided in tsv formats and additional steps were used to convert them into GEE ready formatting.
"},{"location":"projects/msroads/#data-generation-details","title":"Data generation details","text":"The road extraction is done in four stages (full drop went through two stages and OSM missing set went through all four)
You can find additional information here.
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/msroads/#data-preprocessing","title":"Data preprocessing","text":"To get the datasets ready the TSV files were converted to GeoJSON format and then to shapefile splitting up large GeoJSON that would exceed the 4 GB limit for shapefiles. To allow for consistency checks checks were performed to exclude the point datasets that were part of the US data extract but encoded as line strings.The larger datasets like Europe and US were then merged back, flattened and exported into a single GEE asset for ease of use.
"},{"location":"projects/msroads/#citation","title":"Citation","text":"Microsoft Road Detection - Mined Roads : Last accessed date\nvar africa_center = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaCenter\");\nvar africa_east = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaEast\");\nvar africa_north = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaNorth\");\nvar africa_south = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaSouth\");\nvar africa_west = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Africa/AfricaWest\");\nvar america_center = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/AmericaCenter\");\nvar asia_center = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Asia/AsiaCenter\");\nvar asia_north = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Asia/AsiaNorth\");\nvar asia_south = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Asia/AsiaSouth\");\nvar asia_southeast = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Asia/AsiaSouthEast\");\nvar canada = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Canada\");\nvar caribbean_islands = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/CaribbeanIslands\");\nvar eu = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/EU\");\nvar middle_east = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/MiddleEast\");\nvar oceania = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/Oceania\");\nvar south_america = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/SouthAmerica\");\nvar united_states = ee.FeatureCollection(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/MSRoads/US\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/MS-GLOBAL-ROADS
"},{"location":"projects/msroads/#license","title":"License","text":"The datasets are released under the Open Data Commons Open Database License.
Created by: Microsoft
Curated in GEE by: Samapriya Roy
Keywords: Mined Roads, Machine Learning, Classification, linestring, Global roads, OSM
Last updated in GEE: 2022-12-30
"},{"location":"projects/mtbs/","title":"Monitoring Trends in Burn Severity (MTBS) 1984-2019","text":"Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map the burn severity and extent of large fires across all lands of the United States from 1984 to present. This includes all fires 1000 acres or greater in the western United States and 500 acres or greater in the eastern Unites States. The extent of coverage includes the continental U.S., Alaska, Hawaii and Puerto Rico.
The program is conducted by the U.S. Geological Survey Center for Earth Resources Observation and Science (EROS) and the USDA Forest Service Geospatial Technology and Applications Center (GTAC).
The two datasets included in this package include
The fire occurrence location dataset is a vector point ESRI shapefile of the centroids of all currently completed MTBS fires occurring in the continental United States, Alaska, Hawaii and Puerto Rico.
The burned area boundaries dataset is a vector polygon ESRI shapefile of the extent of the burned areas of all currently completed MTBS fires for the continental United States, Alaska, Hawaii and Puerto Rico.
You can read the MTBS overview paper here
In the MTBS project (from the FAQ page ), \"burn severity\" refers specifically to fire effects on above-ground biomass. The definition is drawn from the reference: NWCG Glossary of Wildland Fire Terms and is based on the term Fire Severity, which is defined as: \"Degree to which a site has been altered or disrupted by fire; loosely, a product of fire intensity and residence time.\"
The following additional statements further clarify the nature of the products developed by this project:
The area occurrence layer is now part of official GEE catalog offering, you can find it here
"},{"location":"projects/mtbs/#paper-citation","title":"Paper citation","text":"Eidenshink, Jeff, Brian Schwind, Ken Brewer, Zhi-Liang Zhu, Brad Quayle, and Stephen Howard. \"\nA project for monitoring trends in burn severity.\" Fire ecology 3, no. 1 (2007): 3-21.\nvar area_boundaries = ee.FeatureCollection(\"projects/sat-io/open-datasets/MTBS/burned_area_boundaries\");\nvar fire_occurrence = ee.FeatureCollection(\"projects/sat-io/open-datasets/MTBS/fire_occurrence\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/MONITORING-TRENDS-BURN-SEVERITY
"},{"location":"projects/mtbs/#license","title":"License","text":"MTBS data are freely available to the public (similar to a CC0 license) and are generated by leveraging other national programs including the Landsat satellite program, jointly developed and managed by the USGS and NASA. Landsat data are analyzed through a standardized and consistent methodology, generating products at a 30 meter resolution dating back to 1984.
Created by: U.S. Geological Survey Center for Earth Resources Observation and Science (EROS) and the USDA Forest Service Geospatial Technology and Applications Center (GTAC)
Curated by: Samapriya Roy
Keywords: burned area,fire occurrence, fire area, burn severity,MTBS, EROS, GTAC, USGS, USDA
Last updated: 2021-09-05
"},{"location":"projects/nadm/","title":"North American Drought Monitor (NADM)","text":"The North American Drought Monitor (NADM) raster dataset is produced by the National Centers for Environmental Information (NCEI) and the National Oceanic and Atmospheric Administration's (NOAA) National Integrated Drought Information System (NIDIS). This dataset is a gridded version of the North American Drought Monitor (NADM) produced by Canadian, Mexican and US authors where for each 2.5-km gridcell, the value is given by the current NADM drought classification for that region is:
Drought categories are coded as the following values in the images:
Additional details can be found here and information about this dataset is also available at climate engine org.
"},{"location":"projects/nadm/#dataset-details","title":"Dataset details","text":"Spatial extent North America Spatial resolution 2.5-km (0.025 deg) Temporal resolution Monthly Time span 2001-11-01 to present Update frequency Updated Monthly
Variables
Variable Drought category ('nadm') Units Drought classification Scale factor 1.0
"},{"location":"projects/nadm/#citation","title":"Citation","text":"Heim, Jr., R. R., 2002. A review of Twentieth-Century drought indices used in the United States. Bulletin of the American Meteorological Society, 83, 1149-1165.\n\nLawrimore, J., et al., 2002. Beginning a new era of drought monitoring across North America. Bulletin of the American Meteorological Society, 83, 1191-1192.\n\nLott, N., and T. Ross, 2000. NCDC Technical Report 2000-02, A Climatology of Recent Extreme Weather and Climate Events. [Asheville, N.C.]: National Climatic Data Center.\n\nSvoboda, M., et al., 2002. The Drought Monitor. Bulletin of the American Meteorological Society, 83, 1181-1190.\n// Read in Image Collection and mosaic to single image\nvar nadm_ic = ee.ImageCollection('projects/climate-engine/nadm/monthly')\nvar nadm_i = nadm_ic.first()\n\n// Print image to see bands\nprint(nadm_i)\n\n// Visualize a single image\nvar nadm_palette = [\"#ffffff\", \"#ffff00\", \"#fcd37f\", \"#ffaa00\", \"#e60000\", \"#730000\"]\nMap.addLayer(nadm_i, {min:-1, max:4, palette: nadm_palette}, 'nadm_i')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/NADM-MONTHLY
"},{"location":"projects/nadm/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: drought, NADM, North America, United States, Canada, Mexico
Created & provided by: NOAA, NIDIS, NCEI
Curated by: Climate Engine Org
"},{"location":"projects/nafd/","title":"NAFD Forest Disturbance History 1986-2010","text":"The North American Forest Dynamics (NAFD) products provided in this data set consist of 25 annual and two time-integrated forest disturbance maps for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. Each annual map has classified pixels showing water, no forest cover, forest cover, no data available (data gaps) in present year, and forest disturbances that occurred in that year. The time-integrated maps are similarly classified, but over the entire 1986-2010 period with the first and last forest disturbance years identified and provided as separate maps.
Maps have a nominal spatial resolution of 30 m, with forest disturbances mapped at an annual time step . These products are referred to as the NAFD-NEX data set to acknowledge the collaboration with the supercomputing facilities at the NASA Ames Research Center provided by the NASA Earth Exchange (NEX: Nemani et al. 2011) to process the large volume of Landsat imagery used in this study. You can find details about the dataset including the Vegetation Change Tracker (VCT) algorithm here
"},{"location":"projects/nafd/#data-structure","title":"Data structure","text":"The North American Forest Dynamics (NAFD) products provided in this data set consist of 25 annual and two time-integrated forest disturbance maps for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. Each annual map has classified pixels showing water, no forest cover, forest cover, no data available (data gaps) in present year, and forest disturbances that occurred in that year. The time-integrated maps are similarly classified, but over the entire 1986-2010 period with the first and last forest disturbance years identified and provided as separate maps.
Maps have a nominal spatial resolution of 30 m, with forest disturbances mapped at an annual time step (Fig. 1). These products are referred to as the NAFD-NEX data set to acknowledge the collaboration with the supercomputing facilities at the NASA Ames Research Center provided by the NASA Earth Exchange (NEX: Nemani et al. 2011) to process the large volume of Landsat imagery used in this study.
"},{"location":"projects/nafd/#data-file-naming","title":"Data File Naming","text":"The annual forest disturbance map GeoTIFF files are named as follows: VCT_Annual_30m_YYYY.tif
The two time-integrated forest disturbance GeoTIFF maps are named VCT_30m_first.tif and VCT_30m_last.tif.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/nafd/#data-citation","title":"Data Citation","text":"Goward, S.N., C. Huang, F. Zhao, K. Schleeweis, K. Rishmawi, M. Lindsey, J.L. Dungan, and A. Michaelis. 2016. NACP NAFD Project:\nForest Disturbance History from Landsat, 1986-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1290\nvar nafd_annual = ee.ImageCollection(\"projects/sat-io/open-datasets/NAFD/vct_annual\");\nvar nafd_first = ee.Image(\"projects/sat-io/open-datasets/NAFD/VCT_30m_first\");\nvar nafd_last = ee.Image(\"projects/sat-io/open-datasets/NAFD/VCT_30m_last\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/NAFD-FOREST-DISTURBANCE
"},{"location":"projects/nafd/#license","title":"License","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
Created by: Goward, S.N., C. Huang, F. Zhao, K. Schleeweis, K. Rishmawi, M. Lindsey, J.L. Dungan, and A. Michaelis
Curated in GEE by : Samapriya Roy
keywords: NAFD, Forest Dynamics, NAFD-NEX, Landsat
Last modified: 2016-03-03
Last updated on GEE: 2022-09-05
"},{"location":"projects/native/","title":"Native Land (Indigenous Land Maps)","text":"Land acknowledgements are a way that people insert an awareness of Indigenous presence and land rights in everyday life. This is often done at the beginning of ceremonies, lectures, or, in this case, education guides. It can be an explicit yet limited way to recognize the history of colonialism and first nations as well as a need for change in settler-colonial societies. In this context, we\u2019re looking to acknowledge the existence of Indigenous bodies in geography and how they occupy land. You can visit the actual map here https://native-land.ca/
Native-Land.ca offers an online platform where users can interact with maps of Indigenous territories, treaties, and languages, and locate themselves and their favorite places on the map. Fundamentally, the maps aim to visualize the complexity and diversity of Indigenous peoples, nations, and cultures across the Americas, Australia, and increasingly the world, so that nonIndigenous and Indigenous people alike can increase their understanding and knowledge of the breadth and depth of Indigenous history in these places. Some of the studies in the systematic review describe Indigenous populations within administrative boundaries (i.e. states and countries), for which data is relatively easy to obtain as it is often available through government sources. Other studies described Indigenous groups, lands and territories, for which data isn\u2019t readily available for various reasons (colonial legacies and land tenure and governance, with factors such as changing boundaries and non-digitized records).
"},{"location":"projects/native/#native-land-disclaimer","title":"Native Land Disclaimer","text":"This map does not represent or intend to represent official or legal boundaries of any Indigenous nations. To learn about definitive boundaries, contact the nations in question. Also, this map is not perfect -- it is a work in progress with tons of contributions from the community. Please send us fixes if you find errors. If you would like to read more about the ideas behind Native Land or where we are going, check out the blog. You can also see the roadmap. Also something to keep in mind
(dataset) Native Land Territories map. (2021). Native Land CA. https://native-land.ca/. Accessed 2021-09-19.
"},{"location":"projects/native/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var territories = ee.FeatureCollection(\"projects/sat-io/open-datasets/native-land/indigenousTerritories\");\nvar languages = ee.FeatureCollection(\"projects/sat-io/open-datasets/native-land/indigenousLanguages\");\nvar treaties = ee.FeatureCollection(\"projects/sat-io/open-datasets/native-land/indigenousTreaties\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/INDIGENOUS-LAND-MAPS
"},{"location":"projects/native/#license","title":"License","text":"The Native Land Maps are under a Creative Commons International Attribution License and the datasets are publicly available resource.
Created by: Native Land CA
Curated by: Samapriya Roy
Keywords: native lands, indigenousTreaties, indigenousLanguages, indigenousTerritories, Indigenous, land rights
Last updated: 2023-12-14
"},{"location":"projects/nawbd/","title":"High-Res water body dataset for tundra and boreal forests North America","text":"This represents a water body dataset for the North American high latitudes (WBD-NAHL). Nearly 6.5 million water bodies were identified, with approximately 6 million (\u223c90\u2009%) of them smaller than 0.1\u2009km2. The dataset provides area and morphological attributes for every water body. During this study, we developed an automated approach for detecting surface water extent and identifying water bodies in the 10\u2009m resolution Sentinel-2 multispectral satellite data to enhance the capability of delineating small water bodies and their morphological attributes. The approach was applied to the Sentinel-2 data acquired in 2019 to produce the water body dataset for the entire tundra and boreal forests in North America. The dataset provided a more complete representation of the region than existing regional datasets for North America, e.g., Permafrost Region Pond and Lake (PeRL). The total accuracy of the detected water extent by the WBD-NAHL dataset was 96.36\u2009% through comparison to interpreted data for locations randomly sampled across the region. The original data source is from the National Tibetan Plateau/Third Pole Environment Data Center.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/nawbd/#citation","title":"Citation","text":"Sui, Yijie, Min Feng, Chunling Wang, and Xin Li. \"A high-resolution inland surface water body dataset for the tundra and boreal forests of North\nAmerica.\" Earth System Science Data 14, no. 7 (2022): 3349-3363.\nvar wbd = ee.FeatureCollection(\"projects/sat-io/open-datasets/HYDRO/wbd_fixed_geoms\");\nSample Code : https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/HRES-INLAND-WB-NA
"},{"location":"projects/nawbd/#license-and-usage","title":"License and Usage","text":"This dataset is shared under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.
Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).
Curated by: Ethan D. Kyzivat & Samapriya Roy
Keywords: Hydrology, Boreal , Tundra, water body
Last updated: 2022-02-21
Last updated in GEE: 2023-02-22
"},{"location":"projects/nbac/","title":"Canada National Burned Area Composite (NBAC)","text":"The National Burned Area Composite (NBAC) is a GIS database and system that calculates the area of forest burned on a national scale for each year since 1986. The data are used to help estimate carbon emissions in Canada. The burned area is determined by evaluating a number of available sources of data, which use different techniques to map any given fire. The system chooses the best available source of data for each burned area and builds a national composite picture.
The NBAC is part of the Fire Monitoring, Accounting and Reporting System (FireMARS), jointly developed by the Canada Centre for Mapping and Earth Observation (formerly the Canada Centre for Remote Sensing) of Natural Resources Canada and the Canadian Forest Service. Initially, FireMARS was developed with funding support from the Canadian Space Agency Government Related Initiatives Program through a collaboration of those in fire research, forest carbon accounting and remote sensing.
Data are provided for NBAC from: \u2022 Natural Resources Canada, and \u2022 Provincial, Territorial, and Parks Canada agencies.
The NBAC can be used for spatial and temporal analyses of landscape-scale fire impacts. You can download the datasets here
"},{"location":"projects/nbac/#supplemental-information","title":"Supplemental Information","text":"NBAC is a national product compiled annually since 1986 by the FireMARS system which tracks forest fires for annual estimates of carbon emissions and to help identify National Forest Inventory plots that may have been disturbed by fire. See the FireMARS website at (http://www.nrcan.gc.ca/forests/fire/13159) and carbon accounting - disturbance monitoring website (http://www.nrcan.gc.ca/forests/climate-change/13109) for additional information.
When using these data for mapping activities and analysis, research, evaluation or display, please acknowledged the source using the following citation:
Canadian Forest Service. National Burned Area Composite (NBAC). Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta. https://cwfis.cfs.nrcan.gc.ca/.
"},{"location":"projects/nbac/#citation","title":"Citation","text":"Skakun, R.; Castilla, G.; Metsaranta, J.; Whitman, E.; Rodrigue, S.; Little, J.; Groenewegen, K.; Coyle, M. (2022). Extending the National Burned\nArea Composite Time Series of Wildfires in Canada. Remote Sensing, 14, 3050. DOI: https://doi.org/10.3390/rs14133050\n\nSkakun, R.S.; Whitman, E.; Little, J.M.; and Parisien, M.-A. (2021). Area burned adjustments to historical wildland fires in Canada. Environmental\nResearch Letters 16 064014. DOI: https://doi.org/10.1088/1748-9326/abfb2c\n\nHall, R.J.; Skakun, R.S.; Metsaranta, J.M.; Landry, R.; Fraser, R.H.; Raymond, D.A.; Gartrell, J.M.; Decker, V. and Little, J.M. (2020). Generating\nannual estimates of forest fire disturbance in Canada: the National Burned Area Composite. International Journal of Wildland Fire. 10.1071/WF19201.\nDOI: https://doi.org/10.1071/WF19201\nvar nbac_raster8622 = ee.Image(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/NBAC_MRB_1986_to_2022\");\nvar nbac8622 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/nbac_1986_to_2022_20230630\");\nvar nbac_1986_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1986_r9_20210810\");\nvar nbac_1987_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1987_r9_20210810\");\nvar nbac_1988_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1988_r9_20210810\");\nvar nbac_1989_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1989_r9_20210810\");\nvar nbac_1990_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1990_r9_20210810\");\nvar nbac_1991_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1991_r9_20210810\");\nvar nbac_1992_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1992_r9_20210810\");\nvar nbac_1993_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1993_r9_20210810\");\nvar nbac_1994_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1994_r9_20210810\");\nvar nbac_1995_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1995_r9_20210810\");\nvar nbac_1996_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1996_r9_20210810\");\nvar nbac_1997_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1997_r9_20210810\");\nvar nbac_1998_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1998_r9_20210810\");\nvar nbac_1999_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_1999_r9_20210810\");\nvar nbac_2000_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2000_r9_20210810\");\nvar nbac_2001_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2001_r9_20210810\");\nvar nbac_2002_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2002_r9_20210810\");\nvar nbac_2003_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2003_r9_20210810\");\nvar nbac_2004_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2004_r9_20210810\");\nvar nbac_2005_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2005_r9_20210810\");\nvar nbac_2006_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2006_r9_20210810\");\nvar nbac_2007_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2007_r9_20210810\");\nvar nbac_2008_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2008_r9_20210810\");\nvar nbac_2009_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2009_r9_20210810\");\nvar nbac_2010_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2010_r9_20210810\");\nvar nbac_2011_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2011_r9_20210810\");\nvar nbac_2012_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2012_r9_20210810\");\nvar nbac_2013_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2013_r9_20210810\");\nvar nbac_2014_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2014_r9_20210810\");\nvar nbac_2015_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2015_r9_20210810\");\nvar nbac_2016_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2016_r9_20210810\");\nvar nbac_2017_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2017_r9_20210810\");\nvar nbac_2018_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2018_r9_20210810\");\nvar nbac_2019_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2019_r9_20210810\");\nvar nbac_2020_r9_20210810 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2020_r9_20210810\");\nvar nbac_2021_r9_20220624 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2021_r9_20220624\");\nvar nbac_2022_r12_20230630 = ee.FeatureCollection(\"projects/sat-io/open-datasets/CA_FOREST/NBAC/YEARLY/nbac_2022_r12_20230630\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-NATIONAL-BURNED-AREA-COMPOSITE
"},{"location":"projects/nbac/#license","title":"License","text":"Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada). When using these data for mapping activities and analysis, research, evaluation or display, please acknowledged the source using the following citation: Canadian Forest Service. National Burned Area Composite (NBAC). Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta. https://cwfis.cfs.nrcan.gc.ca/.
Created by: Natural Resources Canada,Canadian Wildland Fire Information System
Curated in GEE by : Samapriya Roy
Keywords: canada,burned area,forestry,forest fire,wildfire
Last updated in GEE: 2024-04-02
"},{"location":"projects/nclim_grid/","title":"NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid)","text":"The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) dataset is available as either a daily (NClimGrid-d) or a monthly (NClimGrid-m) dataset. The datasets contain gridded fields and area averages of maximum, minimum, and mean temperature and precipitation amounts for the contiguous United States. NClimGrid consists of gridded fields covering the land area between approximately 24\u00b0N and 49\u00b0N and between 67\u00b0W and 125\u00b0W at a resolution of 1/24 of a degree (0.041667\u00b0). The primary purpose of these products is to support applications such as drought monitoring that require time series of spatially and/or temporally aggregated gridpoint values. Reliance on single-day values and individual points is discouraged due to the significant uncertainty that is inherent in such a product, as a result of the spatial distribution of the underlying observations, differences in observation time between neighboring stations, and interpolation errors. Spatial and temporal averaging tends to reduce the effect of these uncertainties, and time series of such aggregated values can be shown to be suitable for climatological applications. You can find addtional informationabout the dataset here and climate engine org page here.
"},{"location":"projects/nclim_grid/#dataset-description","title":"Dataset description","text":"Spatial Information
Parameter Value Spatial extent Conterminous United States Spatial resolution 4.6-km (1/24-deg x 1/24-deg) Temporal resolution Daily and monthly Time span 1951-01-01 to present (daily data); 1895-01-01 to present (monthly data) Update frequency Updated daily with 3 day lag (daily data); Updated monthly with 1 month lag (monthly data)Variables
Variable Details Minimum temperature, 2m - Units: Degrees Celsius - Scale factor: 1.0 Maximum temperature, 2m - Units: Degrees Celsius ('tmin') - Scale factor: 1.0 Mean temperature ('tavg') - Units: Degrees Celsius - Scale factor: 1.0 Precipitation ('precip') - Units: Millimeters - Scale factor: 1.0"},{"location":"projects/nclim_grid/#citation","title":"Citation","text":"Vose, Russell S., Applequist, Scott, Squires, Mike, Durre, Imke, Menne, Matthew J., Williams, Claude N. Jr., Fenimore, Chris, Gleason, Karin, and\nArndt, Derek (2014): NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid), Version 1. [indicate subset used]. NOAA National Centers for\nEnvironmental Information. DOI:10.7289/V5SX6B56 [access date].\n// Read in daily and monthly Image Collections and get single image from each collection\nvar nclimgrid_m_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-ncei-nclimgrid/monthly')\nvar nclimgrid_m_i = nclimgrid_m_ic.first()\nvar nclimgrid_d_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-ncei-nclimgrid/daily')\nvar nclimgrid_d_i = nclimgrid_d_ic.first()\n\n// Print each single image to see bands\nprint(nclimgrid_m_i)\nprint(nclimgrid_d_i)\n\n// Visualize each band from each single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(nclimgrid_m_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip, monthly')\nMap.addLayer(nclimgrid_m_i.select('tmin'), {min: -20, max: 20, palette: temp_palette}, 'tmin, monthly')\nMap.addLayer(nclimgrid_m_i.select('tmax'), {min: -20, max: 20, palette: temp_palette}, 'tmax, monthly')\nMap.addLayer(nclimgrid_m_i.select('tavg'), {min: -20, max: 20, palette: temp_palette}, 'tavg, monthly')\nMap.addLayer(nclimgrid_d_i.select('precip'), {min: 0, max: 10, palette: prec_palette}, 'precip, daily')\nMap.addLayer(nclimgrid_d_i.select('tmin'), {min: -20, max: 20, palette: temp_palette}, 'tmin, daily')\nMap.addLayer(nclimgrid_d_i.select('tmax'), {min: -20, max: 20, palette: temp_palette}, 'tmax, daily')\nMap.addLayer(nclimgrid_d_i.select('tavg'), {min: -20, max: 20, palette: temp_palette}, 'tavg, daily')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/NOAA-NCLIM-GRID
"},{"location":"projects/nclim_grid/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site. NClimGrid Data Use And Access Constraints: https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332#Constraints
Keywords: NOAA, nclim, CONUS, United Stated, daily, near real-time, temperature, precipitation
Provider: NOAA
Curated in Earth Engine by: Climate Engine Org
"},{"location":"projects/nhd/","title":"National Hydrography Dataset (NHD)","text":"The National Hydrography Dataset (NHD) represents the water drainage network of the United States with features such as rivers, streams, canals, lakes, ponds, coastline, dams, and streamgages. The National Hydrography Dataset (NHD) is mapped at 1:24,000 or larger scale (1:63,360 or larger scale in Alaska). These data are updated and maintained through Stewardship partnerships with states and other collaborative bodies (source)
The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all. You can find the dataset and additional links here
"},{"location":"projects/nhd/#data-citation","title":"Data Citation","text":"You can find citation information here. The USGS recommends the user to follow guidelines from the journal in question.
Citation example
U.S. Geological Survey, 2022, National Hydrography Dataset (ver. USGS National Hydrography Dataset Best Resolution (NHD) for Hydrologic Unit (HU) 4 - 2001 (published 20191002)), accessed April 29, 2022 at URL https://www.usgs.gov/national-hydrography/access-national-hydrography-products\nindividual shapefiles pertaining to each dataset within a state is ingested to Google Earth Engine. It seems that the flowline features were split for large areas and as such to avoid the use having to combine multiple collections and flatten, a flattened joined NHDFlowline layer has been provided for each state. Each state has it's own sub folder and as such we recommend using the state abbreviations to get to the state of choice. Overall list of all states can be generated by simply using the earthengine command line
earthengine ls \"projects/sat-io/open-datasets/NHD\"\nThe datasets are arranged by state abbreviations, so to get to a specific state simply replace state abbreviation
Example Path: projects/sat-io/open-datasets/NHD/NHD_AK
"},{"location":"projects/nhd/#earth-engine-snippet","title":"Earth Engine Snippet","text":"Sample example for state Missouri with state abbreviation MO
var nhd_area = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDArea\");\nvar nhd_flowline = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDFlowline\");\nvar nhd_line = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDLine\");\nvar nhd_point = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDPoint\");\nvar nhd_point_event_fc = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDPointEventFC\");\nvar nhd_waterbody = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/NHDWaterbody\");\nvar nhd_wbdhu10 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU10\");\nvar nhd_wbdhu12 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU12\");\nvar nhd_wbdhu14 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU14\");\nvar nhd_wbdhu2 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU2\");\nvar nhd_wbdhu4 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU4\");\nvar nhd_wbdhu6 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU6\");\nvar nhd_wbdhu8 = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDHU8\");\nvar nhd_wbdline = ee.FeatureCollection(\"projects/sat-io/open-datasets/NHD/NHD_MO/WBDLine\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-HYDROGRAPHY-DATASET
"},{"location":"projects/nhd/#license","title":"License","text":"The NHD data is distributed under a license similar to Public domain license and distributed by United States Geological Survey (USGS)
Created by: United States Geological Survey (USGS)
Curated in GEE by : Samapriya Roy
Keywords: Hydrography, Hydrologic, United States, USGS, NHD, National Hydrography Dataset
Last updated on GEE: 2022-05-04
"},{"location":"projects/nhd/#data-changelog","title":"Data changelog","text":"The ACIS Climate Maps are produced daily using data from the Applied Climate Information System (ACIS) at a 5-km (0.04-deg x 0.04-deg) spatial resolution from 1951-present, updated every 1-2 weeks.You can find additiona information here and the Climate Engine org dataset page. Station data in ACIS primarily come from the following networks:
Note: All near-real-time data are considered preliminary and subject to change.
"},{"location":"projects/noaa_acis/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent Conterminous United States Spatial resolution 5-km (0.04-deg x 0.04-deg) Temporal resolution Daily Time span 1951-01-01 to present Update frequency 1-2 weeksVariables
Variable Details Minimum temperature, 2m ('tmax') - Units: Degrees Fahrenheit - Scale factor: 1.0 Maximum temperature, 2m ('tmin') - Units: Degrees Fahrenheit - Scale factor: 1.0 Precipitation ('precip') - Units: Inches - Scale factor: 1.0"},{"location":"projects/noaa_acis/#earth-engine-snippet-if-dataset-already-in-gee","title":"Earth Engine Snippet if dataset already in GEE","text":"// Read in Image Collection and get first image\nvar acis_nrcc_nn_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-nrcc-acis-nn/daily')\nvar acis_nrcc_nn_i = acis_nrcc_nn_ic.first()\n\n// Print first image to see bands\nprint(acis_nrcc_nn_i)\n\n// Visualize each band from first image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(acis_nrcc_nn_i.select('precip'), {min: 0, max: 0.5, palette: prec_palette}, 'precip')\nMap.addLayer(acis_nrcc_nn_i.select('tmin'), {min: -10, max: 50, palette: temp_palette}, 'tmin')\nMap.addLayer(acis_nrcc_nn_i.select('tmax'), {min: -10, max: 50, palette: temp_palette}, 'tmax')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/NOAA-NRCC-ACIS
"},{"location":"projects/noaa_acis/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: climate, precipitation, temperature, NOAA, reanalysis, CONUS, daily, near real-time
Dataset provider: NOAA
Curated in GEE by: Climate Engine Org
"},{"location":"projects/npp_viirs_ntl/","title":"Global NPP-VIIRS-like nighttime light (2000-2022)","text":"The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process.
The extended data NPP-VIIRS-like NTL data (2000\u20132022) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000\u20132018) of nighttime light data.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/npp_viirs_ntl/#citation","title":"Citation","text":"Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., and Wu, J.: An extended time series (2000\u20132018) of global NPP-VIIRS-like nighttime\nlight data from a cross-sensor calibration, Earth Syst. Sci. Data, 13, 889\u2013906, https://doi.org/10.5194/essd-13-889-2021, 2021.\nvar viirs_ntl = ee.ImageCollection(\"projects/sat-io/open-datasets/npp-viirs-ntl\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-NPP-VIIRS-LIKE-NTL
"},{"location":"projects/npp_viirs_ntl/#license","title":"License","text":"These datasets are made available under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information below.
Created by: Chen, Zuoqi et al
Curated in GEE by : Samapriya Roy
keywords: Nighttime light, VIIRS, NTL, NPP-VIIRS
Last updated on GEE: 2023-08-13
"},{"location":"projects/nsi/","title":"National Structures Inventory","text":"NoteThese will be made available primarily in the insiders only dataset before being made generally available to all users of the community catalog
The National Structure Inventory (NSI) is a system of databases containing structure inventories of varying quality and spatial coverage. The purpose of the NSI databases is to facilitate storage and sharing of point-based structure inventories used in the assessment and analysis of natural hazards. Flood risk is the primary usage, but sufficient data exists on each structure to compute damages and life safety risk due to other hazard types. This document describes the NSI data structure and the processes utilized to produce the 2022 NSI base data.
The National Structure Inventory Base layer was created and is maintained by the U.S. Army Corps of Engineers (USACE). The USACE base data layer was created to simplify the GIS pre-processing workflow for the USACE Modeling Mapping and Consequence center, but the data has gone on to see use in a variety of USACE, FEMA, and other agency applications. The NSI is a repository of point structure inventories with a structured RESTful API service, and the inventory contains a series of required attributes or fields that describe each point in the inventory.
"},{"location":"projects/nsi/#table-of-public-fields","title":"Table of Public Fields","text":"The NSI attributes available to the general public areField Name Description Attribute Type Limits fd_id A number that should be unique for all structures. Integer bid A building ID, represented as the centroid (in grid reference system format) and four cardinal extents. String x X coordinate of each structure in the Geographic Coordinate System (GCS) WGS84. Double y Y coordinate of each structure in GCS WGS84. Double cbfips Census Block that contains the structure. Currently, the NSI refers to 2010 census blocks. String 15 Characters st_damcat Damage category of the structure. Aggregated categories include Residential, Commercial, Industrial, or Public. String occtype Damage Function or Occupancy Type of the structure, related to depth-damage relationships. String bldgtype Building type of the structure (e.g., Masonry, Wood, Manufactured, Steel). String source The source of the initial iteration of the structure (e.g., Parcel, ESRI, HIFLD). String sqft Estimated square footage of the structure. Double ftprntid Identifier of the building footprint record used for estimating fields such as sqft and num_story. String ftprntsrc The source of the utilized footprint (e.g., Bing, Oak Ridge National Labs, NGA). String found_type Type of foundation (e.g., Crawl, Basement, Slab, Pier). String found_ht Foundation height of the structure in feet from the ground elevation. Double num_story The number of stories of the structure. Double val_struct Value in dollars of the structure. The base NSI estimates depreciated replacement value. Double val_cont Value in dollars of the contents of the structure. Double val_vehic Value in dollars of the cars at the structure. Double med_yr_blt Median year built of structures within the Census Block. Integer pop2amu65 Population at night for people under the age of 65. Integer pop2amo65 Population at night for people over the age of 65. Integer pop2pmu65 Population during the day for people under the age of 65. Integer pop2pmo65 Population during the day for people over the age of 65. Integer students Number of students attending the school as estimated by NCES data. Integer o65disable Percent of the county population over the age of 65 with an ambulatory disability. Double u65siable Percent of the county population under the age of 65 with an ambulatory disability. Double firmzone Estimated 2021 flood zone for the structure. String grnd_elv_m Ground elevation (in meters, NAVD88) at the structure. Double ground_elv Ground elevation (in feet, NAVD88) at the structure. Double
"},{"location":"projects/nsi/#structure-processing-and-valuation","title":"Structure Processing and Valuation","text":"The National Structure Inventory (NSI) involves several key processes for managing and analyzing structures:
Structure Placement and Aggregation: Initially, structure locations are based on source data such as parcel centroids or business addresses. The NSI Generator refines these locations by aligning structures with building footprints within the same parcel. Commercial structures outside their original parcel are relocated based on distance and use code similarity. Structures are placed in unpaired footprints until all footprints are matched. Stacked structures are partially or completely merged based on their occupancy type, with residential units combined as multi-family structures if stacked. Commercial structures are merged only if they share the same occupancy type and similar characteristics, like number of stories and construction material.
Population Distribution: The NSI-2022 estimates population levels for 2020 using data from 2010 block information and 2020 county data. This population is distributed to structures according to housing units and block-level estimates. For commercial structures, worker population estimates are derived from the U.S. Census Bureau\u2019s LEHD database.
Structure Valuation: Depreciated replacement values for structures are estimated based on a dollar per square foot metric, with depreciation at 1% per year for the first 20 years. Content values are calculated using specific ratios tied to occupancy types.
Occupancy types help determine structure valuation and damage criteria. They are based on FEMA definitions with further classification.
The table below shows occupancy types and their content-to-structure value ratiosDamage Category Occupancy Type Name Description Content to Structure Value Ratio Residential RES1-1SNB Single Family Residential, 1 story, no basement 0.5 Residential RES1-1SWB Single Family Residential, 1 story, with basement 0.5 Residential RES1-2SNB Single Family Residential, 2 story, no basement 0.5 Residential RES1-2SWB Single Family Residential, 2 story, with basement 0.5 Residential RES1-3SNB Single Family Residential, 3 story, no basement 0.5 Residential RES1-3SWB Single Family Residential, 3 story, with basement 0.5 Residential RES1-SLNB Single Family Residential, split-level, no basement 0.5 Residential RES1-SLWB Single Family Residential, split-level, with basement 0.5 Residential RES2 Manufactured Home 0.5 Residential RES3A Multi-Family housing 2 units 0.5 Residential RES3B Multi-Family housing 3-4 units 0.5 Residential RES3C Multi-Family housing 5-10 units 0.5 Residential RES3D Multi-Family housing 10-19 units 0.5 Residential RES3E Multi-Family housing 20-50 units 0.5 Residential RES3F Multi-Family housing 50 plus units 0.5 Commercial COM1 Light Commercial, Office, Retail 1.0 Commercial COM2 General Commercial 1.0 Commercial COM3 Heavy Commercial, Manufacturing 1.0 Commercial COM4 Industrial 1.0 Public PUB1 Institutional 1.0 Public PUB2 Education 1.0 Public PUB3 Healthcare 1.0 Public PUB4 Government 1.0
"},{"location":"projects/nsi/#main-data-sources","title":"Main Data Sources","text":"The table contains main data sources of dataSource Database Dataset Description HAZUS Bndrygbs.mdb hzCensusBlock Provides the structure building schemes and block type. flSchemeCoastal, flSchemeRiverine, flSchemeGLakes Provides information on foundation type and height. MSH.mdb flGenBldgScheme Provides the construction type distributions and NFIP entry year for structures. USACE NSI 2015 Base layer Used in any Census Block that lacks ESRI or CoreLogic data. Homeland Infrastructure Foundation-Level Data Lightbox County Level Data Parcel polygons and associated data tables; used for initial spatial location and occupancy type, and may influence structure attributes (square feet, foundation type, etc) of single-family structures. Nursing Home Point data indicating the presence of a nursing home and its number of beds. Hospital Point data indicating the presence of a hospital and its number of beds. Mobile Home Point data indicating the presence of a mobile home park and the number of units associated with the park (either exact units, or a range). Map Building Layer Nationwide building footprint parcel. Largely restricted to central business districts. Often indicating the height of the building to the nearest meter. Used to improve structure locations, square foot estimates and number of stories estimates. Esri Business Layer InfoGroup Provides initial structure location; NAICS code informs occupancy type and the number of employee field influences population weighting and square footage estimates. Microsoft Building Footprints State level polygons Paired with parcel polygons to improve structure location and to inform structure aggregation and square footage estimates. FEMA Geospatial Resource Center USA Structures State level polygons Includes both ORNL and NGA generated footprint polygons. Paired with parcel polygons to improve structure location and to inform structure aggregation and square footage estimates. NGA based footprints include heights in meters and help inform number of stories estimates. U.S. Census Bureau American Community Survey Population, Demographics Informs population growth estimates, disability rates, and age distribution. Characteristics of New Housing Annual, Various Provides structure characteristic data such as number of stories and square feet. Longitudinal Employer-Household Dynamic Database Population Data Contains worker counts by origin and destination census blocks. Used to decrease residential populations (primarily in the day) and to create a population pool for commercial workers. NCES Schools Database School Data Contains the locations of schools, number of teachers and students per school. U.S. Geological Survey National Elevation Dataset 10 Meter Dataset Provides raster ground elevation data.
"},{"location":"projects/nsi/#citation","title":"Citation","text":"U.S. Army Corps of Engineers (Year). National Structure Inventory (NSI) Base Data. U.S. Army Corps of Engineers. URL or DOI.\nThe datasets are available by state using the two alphabet state abbreviation for example, for WYOMING (WY)
var nsi_wy = ee.FeatureCollection('projects/sat-io/open-datasets/NSI/nsi_2022_WY');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/NSI
"},{"location":"projects/nsi/#license","title":"License","text":"The National Structure Inventory (NSI) from the U.S. Army Corps of Engineers (USACE) has a licensing structure that allows for public access to its primary fields, as these fields are curated to have less restrictive license agreements.
Provided by: USACE
Curated in GEE by: Samapriya Roy
Keywords: Buildings, Structures Inventory, US
Last updated: 2024-08-07
"},{"location":"projects/nwi/","title":"National Wetland Inventory (Surface Water and Wetlands)","text":"The US Fish and Wildlife Service (FWS) is the principal US Federal agency tasked with providing information to the public on the status and trends of our Nation's wetlands. Wetlands provide a multitude of ecological, economic and social benefits. They provide habitat for fish, wildlife and plants - many of which have a commercial or recreational value - recharge groundwater, reduce flooding, provide clean drinking water, offer food and fiber, and support cultural and recreational activities. Unfortunately, over half of America\u2019s wetlands have been lost since 1780, and wetland losses continue today. This highlights the urgent need for geospatial information on wetland extent, type, and change. The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US wetlands. NWI data are used by natural resource managers, within the US FWS and throughout the Nation, to promote the understanding, conservation and restoration of wetlands. You can download the dataset here.
"},{"location":"projects/nwi/#dataset-and-gee-reference","title":"Dataset and GEE reference","text":"Layer Name GEE_Folder_Prefix Description NWI Historic Wetlands historic_wetlands This data set represents the extent and approximate location of historic wetland habitats in certain areas of the conterminous United States NWI Historic Wetlands Project Metadata hwpm This data set represents the extent, status, and location of current NWI historic wetland mapping projects. NWI Wetlands wetlands This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories NWI Wetlands Project Metadata wpm This data set represents the extent, status, and location of National Wetland Inventory wetland mapping projects for NWI Version 2, Surface Waters and Wetlands NWI Riparian Areas riparian This data set represents the extent, approximate location and type of riparian habitats in the western United States. These data delineate the areal extent of riparian habitats as defined by a System For Mapping Riparian Areas in the United States (USFWS 2009) NWI Riparian Project Metadata rpm This data set represents the extent, status, and location of current NWI riparian mapping projects NWI Wetlands Historic Map Info hmi This data set represents the extent and location of historic wetland map reports generated by the U.S. Fish and Wildlife Service, cooperators, and contractors"},{"location":"projects/nwi/#data-preprocessing","title":"Data Preprocessing","text":"The datasets are provided by states and some states are split into multiparts. The shapefile consists of multiple geometry types including but not limited to points an linestring apart from polygons and multipolygons. Attempt was made to combine multiple parts from each state into a single feature collection within earthengine, since GEE will not work with a zero area object during a featurecollection export, a filter was created to tag each feature type and calculate area. Also zero area features were then excluded. Currently the wetlands datasets is the only one where this transformation was applied.
Currently these wetlands files are complete and are present in the folder with naming State-Abbreviation_Wetlands so for example for Florida , FL_Wetlands and so on. Other datasets are not present for all states and you can get a list of assets by simply running an earthengine ls on the objects or by using the catalog. Since this is an immensely large dataset collection, no attempt was made to create a country wide composite. A single JSON file is also created to allow you to asses which datasets contain which state as an easy reference, you can find it here.
(dataset) U.S. Fish & Wildlife Service. (2018). National Wetlands Inventory. U.S. Fish & Wildlife Service. https://data.nal.usda.gov/dataset/national-wetlands-inventory. Accessed 2021-09-19.
"},{"location":"projects/nwi/#earth-engine-snippet","title":"Earth Engine Snippet","text":"The dataset templates underneath can be simply replaced by the state code/territory code to get to the state/region of interest.
var wetlands = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/wetlands/FL_Wetlands\");\nvar historic_wetland = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/historic_wetlands/FL_Historic_Wetlands\");\nvar historic_wetland_project_metadata = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/hwpm/FL_Historic_Wetlands_Project_Metadata\");\nvar historic_map_info = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/hmi/FL_Wetlands_Historic_Map_Info\");\nvar co_riparian = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/riparian/CO_Riparian\");\nvar co_riparian_metadata = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/rpm/CO_Riparian_Project_Metadata\");\nvar wetlands_metadata = ee.FeatureCollection(\"projects/sat-io/open-datasets/NWI/wpm/FL_Wetlands_Project_Metadata\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-WETLANDS-INVENTORY
"},{"location":"projects/nwi/#wetlands-layer-legend","title":"Wetlands Layer Legend","text":"Wetland types are displayed on the Wetlands Mapper in groups of similar classifications (e.g. all freshwater emergent wetlands are displayed as a single color category). The display categories are shown in the table below. Display color codes are provided for those looking to create their own maps using the Mapper color scheme.
#008837 Freshwater- Forested and Shrub wetland #7FC31C Freshwater Emergent wetland #688CC0 Freshwater pond #66C2A5 Estuarine and Marine wetland #0190BF Riverine #13007C Lakes #007C88 Estuarine and Marine Deepwater #B28653 Other Freshwater wetland
"},{"location":"projects/nwi/#license","title":"License","text":"The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US. NWI datasets are freely available to the public (similar to a CC0 license) and the U.S. Public Domain license.
Created by: U.S. Fish and Wildlife Service
Curated by: Samapriya Roy
Keywords: wetlands, conservation areas, habitats, fish, wildlife, drinking water, recreation, U.S. Fish and Wildlife Service
Last updated: 2021-09-19
"},{"location":"projects/oam/","title":"Open Aerial Map Subset","text":"OpenAerialMap (OAM) was created as a set of tools and portal for searching, sharing, and using openly licensed satellite and unmanned aerial vehicle (UAV) imagery. Built on top of the Open Imagery Network (OIN), OAM is an open service that provides search and access to this imagery. While Open Aerial Map is excellent and with plug and play capability coming in the future this will only evolve as a resouce for most users the goal of bringing a subset in Google Earth Engine was to explore current capabilities of the platform with ultra high resolution datasets and to allow for labeling classification and potential use of the collection as teaching and training tool via plugins like Collect Earth online and other providers who might be interested in leveraging this further including applications like Machine learning models.
While a lot of preprocessing steps were applied the datasets are initially housed withing the analysis ready datasets snippets to reflect these images are often post processed by providers and are effectively ready to use to some extent.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/oam/#data-preprocessing","title":"Data preprocessing","text":"For creating a collection subset a automated script was created to fetch all images with a valid link and those that are not related to the platform satellite. This was to keep the overall platforms limited to other forms of platforms. The datasets were upload into a GCS bucket before ingest and while the uncompressed size for the overall collection was only 1.9 TB , GEE uncompressed quota usage exceed over 7+ TB. I am hoping to do more event specific updates in the future to allow for exploration across different use cases. Since a lot of the no data values for 8 bit imagery were coded incorrectly 0 and 255 were chosen and default nodata values list but results will vary depending on the individual images.
"},{"location":"projects/oam/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var oam_subset = ee.ImageCollection(\"projects/sat-io/open-datasets/open-aerial-map\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/OPEN-AERIAL-MAP
"},{"location":"projects/oam/#license","title":"License","text":"All imagery is publicly licensed and made available through the Humanitarian OpenStreetMap Team's Open Imagery Network (OIN) Node. All imagery contained in OIN is licensed CC-BY 4.0, with attribution as contributors of Open Imagery Network. Each imagery has their own license type and it is included in the image metadata.
Provided by: Open Aerial Map Community providers
Curated in GEE by : Samapriya Roy
keywords: Open Aerial Map, OAM, HOTOSM, Drones, UAV, High resolution
Last updated on GEE: 2023-04-20
"},{"location":"projects/ogim/","title":"Oil and Gas Infrastructure Mapping (OGIM) database","text":"The Oil and Gas Infrastructure Mapping (OGIM) dataset, available in the Awesome Google Earth Engine (GEE) Community Catalog, is a comprehensive global repository of spatially explicit information on oil and gas infrastructure. Developed by the Environmental Defense Fund (EDF) (www.edf.org), the OGIM database is meticulously curated to facilitate the quantification and characterization of methane emissions from oil and gas sources.
This dataset is a result of extensive efforts at EDF, involving the acquisition, analysis, curation, integration, and quality assurance of public-domain datasets sourced from official government reports, industry publications, academic studies, and various non-government entities. The primary objective of the OGIM database is to provide valuable support for research and analysis related to oil and gas methane emissions. The dataset spans across the globe, offering a comprehensive view of oil and gas infrastructure.It provides detailed information on the locations and attributes of various oil and gas infrastructure types known to be significant sources of methane emissions. This includes data on oil and gas production wells, offshore production platforms, natural gas compressor stations, processing facilities, liquefied natural gas facilities, crude oil refineries, pipelines, and more.
You can read more about the OGIM dataset here and you can find the dataset here.
"},{"location":"projects/ogim/#citation","title":"Citation","text":"Omara, Mark, Ritesh Gautam, Madeleine O'Brien, Anthony Himmelberger, Alex Franco, Kelsey Meisenhelder, Grace Hauser et al.\n\"Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at\nhigh resolution.\" Earth System Science Data Discussions 2023 (2023): 1-35.\nRitesh Gautam. (2023). Oil and Gas Infrastructure Mapping (OGIM) database (OGIM_v1.1) [Data set]. Zenodo.\nhttps://doi.org/10.5281/zenodo.7922117\nOGIM geospatial data layer Additional information Geometry type Oil and natural gas wells Includes active, inactive, and plugged and abandoned oil and natural gas wells. POINT Natural gas compressor stations Facilities for natural gas compression in the gathering, transmission, and distribution sector. POINT Gathering and processing facilities Includes natural gas processing plants, natural gas dehydration and other treatment facilities, and oil gathering and processing facilities. POINT Tank battery Can be collocated with well sites; typical equipment includes oil and natural gas separation equipment and an arrangement of storage tanks. POINT Offshore platforms Oil and natural gas drilling, production, and processing platforms in offshore areas. POINT LNG facilities Includes both liquefaction and regasification facilities. POINT Crude oil refineries - POINT Petroleum terminals Includes tank farms and petroleum bulk storage tanks and terminals. POINT Injection, disposal, and underground storage facilities - POINT Stations - Other Includes metering and regulating stations and POL (petroleum, oil, and lubricants) pumping stations. POINT Equipment and components Includes point locations for dehydrators, separators, tanks, and valves. POINT Oil and natural gas production Includes reported well-level, facility-level, and field-level oil and natural gas production, as reported for 2021. POINT Natural gas flaring detections Based on VIIRS natural gas flaring detections in 2021. POINT
"},{"location":"projects/ogim/#earth-engine-snippet","title":"Earth Engine Snippet","text":"
var crude_oil_refineries = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/crude_oil_refineries\");\nvar equipment_and_components = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/equipment_and_components\");\nvar gathering_and_processing = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/gathering_and_processing\");\nvar injection_disposal_and_underground_storage = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/injection_disposal_and_underground_storage\");\nvar lng_facilities = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/lng_facilities\");\nvar natural_gas_compressor_stations = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/natural_gas_compressor_stations\");\nvar natural_gas_flaring_detections = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/natural_gas_flaring_detections\");\nvar offshore_platforms = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/offshore_platforms\");\nvar oil_and_natural_gas_production = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/oil_and_natural_gas_production\");\nvar oil_and_natural_gas_wells = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/oil_and_natural_gas_wells\");\nvar petroleum_terminals = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/petroleum_terminals\");\nvar stations_other = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/stations_other\");\nvar tank_battery = ee.FeatureCollection(\"projects/sat-io/open-datasets/OGIM/tank_battery\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/OGIM
"},{"location":"projects/ogim/#license","title":"License","text":"These datasets are provided under a Creative Commons Attribution 4.0 International Public License, unless otherwise noted.
Created by: Omara et al
Curated in GEE by: Samapriya Roy
Keywords: OGIM dataset, Methane emissions, Environmental Defense Fund (EDF), infrastructure types, oil and gas wells, offshore platforms, Compressor stations, Liquefied natural gas facilities, Crude oil refineries
Last updated: 2024-01-19
"},{"location":"projects/oil-palm/","title":"Oil Palm Plantation Layers","text":""},{"location":"projects/oil-palm/#1-oil-palm-plantation-indonesia-malaysia-thailand-1984-2017","title":"1) Oil Palm Plantation (Indonesia, Malaysia, Thailand) 1984-2017","text":"This paper used a landsat time series approach coupled with other datasets to determine the year in which the oil palm plantations are first detected, at which point they are 2 to 3 years of age. From this, the approximate age of the oil palm plantation in 2017 was generated.
Read the paper here
"},{"location":"projects/oil-palm/#data-records","title":"Data Records","text":"The data set is publicly accessible for download from the permanent DARE repository housed by the International Institute for Applied Systems Analysis (IIASA) (http://dare.iiasa.ac.at/85/)33. It consists of a 16-bit GeoTIFF at a resolution of 30\u2009m with a single attribute value, i.e., the year in which the oil palm plantation was first detected. At this point, the plantation is 2 to 3 years of age. The data values range from 0 to 37 where 0 is the No Data value. Values 1 to 3 are not present and a value of 4 corresponds to the year 1984, the first year oil palm was detected, and each consecutive number represents the next year, i.e., 5 is 1985, while the maximum value of 37 corresponds to 2017.
Use the following credit when these datasets or paper is cited:
Danylo, O., Pirker, J., Lemoine, G. et al. A map of the extent and year of detection of oil palm plantations in Indonesia\nMalaysia and Thailand. Sci Data 8, 96 (2021). https://doi.org/10.1038/s41597-021-00867-1\nSince the dataset was categorical the no data value was use for masking and a Mode pyramiding policy was applied for ingestion into Google Earth Engine.
var oil_palm = ee.ImageCollection(\"projects/sat-io/open-datasets/landcover/oil-palm-plantation-1984_2017\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/OIL-PALM-PLANTATION-LAYERS
App Website: App link here
Source Code to App: https://code.earthengine.google.com/b569003eec6dc5d60dd6a187a9213f06
"},{"location":"projects/oil-palm/#shared-license","title":"Shared License","text":"This work is licensed under a Creative Commons Attribution 3.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Olga Danylo, et al, International Institute for Applied Systems Analysis
Curated in GEE by: Samapriya Roy
Keywords: Oil palm plantations, Indonesia, Malaysia, Thailand, Landsat, Sentinel-1
Last updated: 2021-06-19
"},{"location":"projects/oil-palm/#2-high-resolution-global-industrial-and-smallholder-oil-palm-map-for-2019","title":"2) High resolution global industrial and smallholder oil palm map for 2019","text":"The dataset contains 634 100x100 km tiles, covering areas where oil palm plantations were detected. The classified images (\u2018oil_palm_map\u2019 folder, in geotiff format) are the output of the convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. The images have a spatial resolution of 10 meters and contain three classes: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm.
You can find the paper here and download the datasets here
Use the following credit when these datasets or paper is cited:
Descals, Adri\u00e0, Serge Wich, Erik Meijaard, David LA Gaveau, Stephen Peedell, and Zoltan Szantoi.\n\"High-resolution global map of smallholder and industrial closed-canopy oil palm plantations.\"\nEarth System Science Data 13, no. 3 (2021): 1211-1231.\nCite the Data using
Adri\u00e0, Descals, Serge, Wich, Erik, Meijaard, David, Gaveau, Stephen, Peedell, & Zoltan, Szantoi. (2021).\nHigh resolution global industrial and smallholder oil palm map for 2019 (Version v1) [Data set].\nZenodo. http://doi.org/10.5281/zenodo.4473715\nSince the dataset was categorical the no data value was use for masking and a Mode pyramiding policy was applied for ingestion into Google Earth Engine.
var oil_palm = ee.ImageCollection(\"projects/sat-io/open-datasets/landcover/oil_palm_industrial_smallholder_2019\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/GLOBAL-INDUSTRIAL-SMALLHOLDER-OIL-PALM
"},{"location":"projects/oil-palm/#shared-license_1","title":"Shared License","text":"This work is licensed under a Creative Commons Attribution 4.0 International. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Adri\u00e0 Descals et al 2021
Curated in GEE by: Samapriya Roy
Keywords: industrial, smallholder, oil palm, deep learning, global, remote sensing, Sentinel-1, Sentinel-2, convolutional neural network
Last updated: 2021-06-19
"},{"location":"projects/ookla_5g/","title":"Ookla 5G Map Data","text":"The Ookla 5G Map\u2122 was launched in May 2019 to provide a comprehensive view of the global expansion of 5G technology. At its inception, the map highlighted 300 deployments across 17 countries, illustrating the initial rollout of this transformative technology. Over time, the map has grown significantly, now encompassing data from 233 providers with over 145,000 deployments in 142 countries. This extensive coverage underscores the rapid adoption and widespread implementation of 5G networks worldwide.
As 5G technology has become prevalent in many regions, the Ookla 5G Map\u2122 is evolving its focus. With the widespread presence of 5G, the emphasis is shifting towards analyzing the latest emerging technologies and identifying markets where connectivity is lagging. This pivot aims to spotlight new technological advancements and address the digital divide, ensuring that connectivity improvements continue to progress globally. The Ookla 5G Map\u2122 remains an essential tool for understanding the current landscape and future trends of global connectivity.
"},{"location":"projects/ookla_5g/#citation","title":"Citation","text":"\"Ookla\u00ae 5G Map Data was provided by Ookla and accessed on [DAY MONTH YEAR] from [https://www.speedtest.net/ookla-5g-map].\nBased on [LICENSEE\u2019S] analysis of Ookla\u00ae 5G Map Data. Ookla trademarks used under license and reprinted with permission.\"\nvar ookla_5g_map = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/ookla_5g_map\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/OOKLA-5G-MAP
"},{"location":"projects/ookla_5g/#license","title":"License","text":"The dataset is made available under a CC BY-NC-SA 4.0
Dataset provided by: Ookla
Curated in GEE by: Samapriya Roy
Keywords: : analytics,operator,cities,status,civic,infrastructure,internet,telecommunications
Last updated: 2024-07-01
"},{"location":"projects/osm_water/","title":"OSM Water Layer Surface Waters in OpenStreetMap","text":"OSM Water Layers is a global surface water data, generated by extracting surface water features from OpenStreetMap. The OSM water layer rasterized map is referenced to WGS84. The data is prepared as 5 degree x 5 degree tiles (6000 pixel x 6000 pixel). Filename represents the center of the lower left pixel of the data domain; e.g. the file \"n30w120.tif\" is for the domain N30-N35, W120-W115. (more accurately, N29.99958333-N34.99958333,W120.0004167-W115.0004167)
Scale: 90m
"},{"location":"projects/osm_water/#raster-values","title":"Raster Values","text":"Citation
Yamazaki, Dai, Daiki Ikeshima, Jeison Sosa, Paul D. Bates, George H. Allen, and Tamlin M. Pavelsky. \"MERIT Hydro: a high\u2010resolution global hydrography map based on latest topography dataset.\" Water Resources Research 55, no. 6 (2019): 5053-5073.\nLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)
"},{"location":"projects/osm_water/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var mainlands = ee.ImageCollection(\"projects/sat-io/open-datasets/OSM_waterLayer\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/OSM-WATER-SURFACE
Extra Info: Go to the OSM Water Layer webpage
Curated by: Samapriya Roy and Erin Trochim
Keywords: Global water layer, Open Street Map, OSM
Last updated: 2020-04-28
"},{"location":"projects/overture_buildings/","title":"Overture Foundation Building Footprints","text":"NoteThis dataset will be updated in batches owing to the extent and size of the feature collections. These will be made available primarily in the insiders only dataset while they are ingested and tested. Once all areas have been ingested this will be made available to all users of the community catalog
The Overture Foundation's building dataset is part of the 2024-07-22.0 data release and v1.0.0 of the schema, now available. The base, buildings, divisions, and places themes have reached General Availability (GA). The transportation theme remains in beta, and users can anticipate additional breaking changes to the transportation schema. Currently, the dataset only includes data extracted for the CONUS region.
"},{"location":"projects/overture_buildings/#overview","title":"Overview","text":"The Overture Maps buildings theme describes human-made structures with roofs or interior spaces that are permanently or semi-permanently in one place (source: OSM building definition). The theme includes two feature types:
has_parts that describes whether there are any associated building parts. (Currently only building layers are being added to the catalog)building_id.The Overture buildings dataset is a combination of the following open building datasets:
Source Type Conflation Priority Count OpenStreetMap Community-contributed 1 ~600 Million Esri Community Maps Community-contributed 2 ~14 Million Google Open Buildings ML-derived roofprints (>90% precision) 3 ~400 Million Microsoft ML-derived roofprints 4 ~600 Million Google Open Buildings ML-derived roofprints (<90% precision) 5 ~700 Million "},{"location":"projects/overture_buildings/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var buildings = ee.FeatureCollection('projects/sat-io/open-datasets/OVERTURE/BUILDINGS/CONUS-EXTRACT');\nMap.centerObject(buildings.first(),12)\nMap.addLayer(buildings, {}, 'Buildings CONUS Extract');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/OVERTURE-BUILDINGS-EXTRACT
"},{"location":"projects/overture_buildings/#license","title":"License","text":"The data from the Overture Foundation Building Dataset is available under the Open Data Commons Open Database License (ODbL).
Provided by: Overture Foundation
Curated in GEE by: Samapriya Roy
Keywords: Building Data, Overture Foundation, OpenStreetMap, Esri, Google, Microsoft
Dataset release date: 2024-07-22
Last updated in GEE: 2024-08-01
"},{"location":"projects/peatland/","title":"Global Peatland Database","text":"The Global Peatland Map 2.0, launched by the Global Peatlands Initiative partners at the Global Peatland Pavilion during UNFCCC COP26, enhances our understanding of the location and extent of peatlands worldwide. This dataset integrates data on location, extent, and drainage status of peatlands and organic soils globally, covering 268 countries and regions. It combines external data with mapping contributions from the Greifswald Mire Centre, providing a composite map of global peatlands, organic soils, and proxy data. The dataset supports map production, statistics, and analysis for the Global Peatland Assessment 2022.
You can download Global Peatland Map 2.0 here and additional information about about Global Peatland Database can be found here
"},{"location":"projects/peatland/#dataset-characteristics","title":"Dataset Characteristics","text":"Property Value Format GeoTiff Projection WGS 84 Pixel Values 1 = peat dominated, 2 = peat in soil mosaic Spatial Resolution 1x1 km"},{"location":"projects/peatland/#citation","title":"Citation","text":"Greifswald Mire Centre (2022). Global Peatland Map 2.0. Underlying dataset of the UNEP Global Peatland Assessment - The State of the World\u2019s Peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands, Global Peatlands Initiative, United Nations Environment Programme, Nairobi.\n// Load administrative boundaries for Indonesia\nvar admin1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM1\");\nvar geometry = admin1.filter(ee.Filter.eq('shapeGroup', 'IDN'));\n\nMap.centerObject(geometry, 4);\nMap.setOptions(\"Hybrid\");\n\nvar peat = ee.Image(\"projects/sat-io/open-datasets/GLOBAL-PEATLAND-DATABASE\")\n .clip(geometry)\n .unmask();\n\n// Display the results\nMap.addLayer(peat.clip(geometry),\n {min: 0, max: 1, palette: ['#f7fcf5', '#c7e9c0', '#74c476', '#238b45', '#00441b']},\n 'Peatland Distribution', true\n );\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-PEATLAND-FRACTIONAL-COVER
"},{"location":"projects/peatland/#license","title":"License","text":"This dataset is made available under a Creative Commons NonCommercial-ShareAlike 4.0 International.
"},{"location":"projects/peatland/#keywords","title":"Keywords","text":"peatland, wetland, organic soil, soil carbon, ecosystem
Provided by: Greifswald Mire Centre (2022)
Curated in GEE by: Ka Hei and Samapriya Roy
Last updated in GEE: 2024-07-14
"},{"location":"projects/peatland_ml/","title":"Global Peatland Fractional Coverage","text":"Peatlands, as waterlogged terrestrial wetland ecosystems, store vast amounts of soil carbon and freshwater, playing a crucial role in the global carbon and hydrologic cycles. The Peat-ML dataset is a spatially continuous global map of peatland fractional coverage generated using machine learning models trained with climate, geomorphological, soil data, and remotely-sensed vegetation indices. Available peatland coverage maps from 14 regions, along with non-peatland ecoregions, were used to develop a statistical model with an average r-squared of 0.73 and errors of 9.11% (root mean square) and -0.36% (bias). The dataset is available in NetCDF format and published in 2021. For more information, you can access the associated research paper here.
The original datasets are available as NetCDF with a model accuracy with R\u00b2 = 0.73, RMSE = 9.11%, MBE = -0.36%. You can download the Peat-ML Dataset (2021) here. Additional details are available in the paper Melton et al., 2022
Workflow for dataset generation (Joe R. Melton et al., 2022)
Example data visualization of peatland distribution in Indonesia
"},{"location":"projects/peatland_ml/#citation","title":"Citation","text":"Melton, J. R., Chan, E., Millard, K., Fortier, M., Winton, R. S., Mart\u00edn-L\u00f3pez, J. M., Cadillo-Quiroz, H., Kidd, D., and Verchot, L. V.: A map of\nglobal peatland extent created using machine learning (Peat-ML), Geosci. Model Dev., 15, 4709\u20134738, https://doi.org/10.5194/gmd-15-4709-2022, 2022.\nMelton, J. R., Chan, E., Millard, K., Fortier, M., Winton, R. S., Mart\u00edn-L\u00f3pez, J. M., Cadillo-Quiroz, H., Kidd, D., & Verchot, L. V. (2021). A map\nof global peatland extent created using machine learning (Peat-ML) [Data set]. In Geoscientific Model Development (1.0).\nZenodo. https://doi.org/10.5281/zenodo.7352284\n// Load administrative boundaries for Indonesia\nvar admin1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM1\");\nvar geometry = admin1.filter(ee.Filter.eq('shapeGroup', 'IDN'));\n\nMap.centerObject(geometry, 4);\nMap.setOptions(\"Hybrid\");\n\nvar peat = ee.Image(\"projects/sat-io/open-datasets/ML-GLOBAL-PEATLAND-EXTENT\")\n .clip(geometry)\n .unmask();\n\n// Display the results\nMap.addLayer(peat.clip(geometry),\n {min: 0, max: 100, palette: ['#f7fcf5', '#c7e9c0', '#74c476', '#238b45', '#00441b']},\n 'Peatland Distribution', true\n );\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-PEATLAND-FRACTIONAL-COVER
"},{"location":"projects/peatland_ml/#license","title":"License","text":"These datasets are provided under a Creative Commons Attribution 4.0.
"},{"location":"projects/peatland_ml/#keywords","title":"Keywords","text":"peatland, soil carbon, wetland, ecosystem
Provided by: Melton et al 2022
Curated in GEE by: Samapriya Roy
Last updated in GEE: 2024-07-14
"},{"location":"projects/piscoeo/","title":"Reference ET gridded database based on FAO Penman-Monteith for Peru (PISCOeo_pm)","text":"PISCOeo_pm has been developed for the 1981\u20132016 period at ~1 km (0.01\u00b0) spatial resolution for the entire continental Peruvian territory. The framework for the development of PISCOeo_pm is based on previously generated gridded data of meteorological subvariables such as air temperature (maximum and minimum), sunshine duration, dew point temperature, and wind speed.
Different steps, i.e., (i) quality control, (ii) gap-filling, (iii) homogenization, and (iv) spatial interpolation, were applied to the subvariables. PISCOeo_pm is useful for better understanding the terrestrial water and energy balances in Peru as well as for its application in fields such as climatology, hydrology, and agronomy, among others. Read the full paper here
"},{"location":"projects/piscoeo/#citation","title":"Citation","text":"Huerta, A., Bonnesoeur, V., Cuadros-Adriazola, J., Gutierrez, L. F., Ochoa-Tocachi, B. F., Rom\u00e1n-Da\u00f1obeytia, F., & Lavado-Casimiro, W.. (2022). PISCOeo_pm, a\nreference evapotranspiration gridded database based on FAO Penman-Monteith in Peru. Nature Scientific Data. https://doi.org/10.1038/s41597-022-01373-8\nHuerta, A., Bonnesoeur, V., Cuadros-Adriazola, J., Gutierrez, L. F., Ochoa-Tocachi, B. F., Rom\u00e1n-Da\u00f1obeytia, F., & Lavado-Casimiro, W.. (2022). Reference\nevapotranspiration gridded database based on FAO Penman-Monteith for Peru (PISCOeo_pm) V.1.0. SENAMHI-Per\u00fa. https://doi.org/10.6084/m9.figshare.c.5633182.v3\nCurrently included layers are:
"},{"location":"projects/piscoeo/#earth-engine-snippet-yearly-mean-data","title":"Earth Engine Snippet: Yearly mean data","text":"var PISCOeo_pm_yearly = ee.ImageCollection('users/lgutierrezlf/PISCOeo_pm/yearly')\nvar PISCOeo_pm_climatology = ee.ImageCollection('users/lgutierrezlf/PISCOeo_pm/climatology')\nvar PISCOeo_pm_monthly = ee.ImageCollection('users/lgutierrezlf/PISCOeo_pm/monthly')\nvar PISCOeo_pm_daily = ee.ImageCollection('users/lgutierrezlf/PISCOeo_pm/daily')\nhttps://lgutierrezlf.users.earthengine.app/view/piscoeopmts
Resolution: 0.01\u00b0 (or roughly 1km x 1km)
app code : https://code.earthehttps://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/REFERENCE-ET-GRIDDED-PERU
"},{"location":"projects/piscoeo/#license","title":"License:","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Gutierrez Leonardo & Samapriya Roy
Keywords: reference evapotranspiration, FAO Penman Monteith, Peru, hydrology, satellite data, Earth observation, GIS.
Last updated: 27/06/2022
"},{"location":"projects/pk_nssed/","title":"National-Scale Soil Erosion Dataset for Pakistan (2005 and 2015)","text":"This dataset offers a comprehensive assessment of soil erosion dynamics in Pakistan from 2005 to 2015, utilizing the Revised Universal Soil Loss Equation (RUSLE) and considering six key influencing factors: rainfall erosivity (R), soil erodibility (K), slope-length (L), slope-steepness (S), cover management (C), and conservation practice (P). Soil erosion maps, categorized into four classes ranging from low to very high erosion rates, provide insights into the spatial distribution and changes in soil erosion patterns over the study period. Transition analyses among erosion classes reveal shifts in erosion intensity, while spatial patterns and dynamics are evaluated across seven administrative units of Pakistan. The dataset highlights a national-scale increase in soil erosion from 1.79 \u00b1 11.52 ton ha\u207b\u00b9 yr\u207b\u00b9 in 2005 to 2.47 \u00b1 18.14 ton ha\u207b\u00b9 yr\u207b\u00b9 in 2015, driven by land cover and land use changes induced by population growth, infrastructural development, and natural resource exploitation. Comprehensive assessment of soil erosion dynamics in Pakistan for 2005 and 2015 at 1 km\u00b2 spatial resolution using the Revised Universal Soil Loss Equation (RUSLE) model and six influencing factors. Soil erosion maps are categorized into four classes: low, medium, high, and very high, revealing an increase from 1.79 to 2.47 ton ha\u207b\u00b9 yr\u207b\u00b9 on the national level. You can read the full paper here
The national-scale soil erosion dataset for Pakistan (2005 and 2025) at 1km spatial resolution data is available here
"},{"location":"projects/pk_nssed/#citation","title":"Citation","text":"Gilani, H., Ahmad, A., Younes, I., & Abbas, S. (2021). Impact assessment of land cover and land use changes on soil erosion changes (2005\u20132015) in\nPakistan. Land Degradation & Development, 33(1):204\u2013217. [doi.org/10.1002/ldr.4138](https://doi.org/10.1002/ldr.4138)\nGilani, Hammad, Ahmad, Adeel, Younes, Isma, & Abbas, Sawaid. (2021). National-scale soil erosion dataset for Pakistan (2005 and 2025) at 1km spatial resolution (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10694225\nvar pk_soil_erosion_2005 = ee.Image('projects/sat-io/open-datasets/NSSED-PAKISTAN/Pakistan_soil_erosion_2005_1km');\nvar pk_soil_erosion_2015 = ee.Image('projects/sat-io/open-datasets/NSSED-PAKISTAN/Pakistan_soil_erosion_2015_1km');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/NATIONAL-SOIL-ERODABILITY-DATASET-PK
"},{"location":"projects/pk_nssed/#license","title":"License","text":"The datasets are licensed under a Creative Commons Attribution (CC-BY) 4.0 International License.
Created by: Gilani et al. 2021
Curated in GEE by : Adeel Ahmad and Samapriya Roy
Keywords: soil erosion, soil conservation, RUSLE, Pakistan, temporal soil erosion
Last updated in GEE: 2024-02-20
"},{"location":"projects/plastic/","title":"Plastic Inputs from Rivers into Oceans","text":"This dataset shows a global estimate of plastic inputs from rivers into the oceans for 2010, expressed in kilograms per year. The authors used data on waste management, population density, and hydrological information to create this model. The dataset includes information on 40,760 watersheds and 182 different countries. The data is presented in a vector format.
Plastic pollution in our oceans and on our coastlines have become a major threat to ocean health worldwide. A better understanding and quantification of marine plastic sources can help in implementing mitigation strategies to alleviate the issue. The dataset can help in identifying places that require higher attention in terms of plastic waste monitoring and mitigation plans. This data can also be used as a baseline measurement for ocean plastic mass balance exercises.
This data was developed by researchers funded by The Ocean Cleanup Foundation.
"},{"location":"projects/plastic/#methodology","title":"Methodology","text":"The amount of plastic inputs from rivers into the oceans was estimated by using data on mismanaged plastic waste production (MPW) per country, population density, topographic elevation, and location of artificial barriers (weirs and dams).
For each catchment area mismanaged plastic waste production (MPW) rates per day were calculated by combining data on waste generation by inhabitant per day and population density for the area. This data was combined with water flow per river catchment area to provide a final value for the mass of plastic released at the river\u2019s mouth. This data was extrapolated using seasonal variations in water flow to create a year dataset. Data on population density was derived from the dataset Global 15 x 15 Minute Grids of the Downscaled Population by the Socioeconomic Data and Applications Center (SEDAC) for 182 countries. Data used to calculate MPW rates were collected from seven peer reviewed studies. Topographic information was taken from Global Land Data Assimilation System (GLDAS) hydrological model for surface/subsurface runoff and location of artificial barriers was taken from AquaStat and Global Reservoir and Dam Database (GRanD).
In total the dataset includes information for 40,760 watersheds worldwide. For the full documentation, please see the source methodology.
"},{"location":"projects/plastic/#citation","title":"Citation","text":"lebreton, laurent; Reisser, Julia (2018): Supplementary data for 'River plastic emissions to the world's oceans'. figshare. Dataset. https://doi.org/10.6084/m9.figshare.4725541\nvar plastic = ee.FeatureCollection(\"projects/sat-io/open-datasets/open-ocean/river_plastic_emissions\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/PLASTIC-INPUT-RIVERS
"},{"location":"projects/plastic/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Ocean Cleanup Foundation
Curated by: Samapriya Roy
Keywords: : Pollution, Society, Coral Reefs, SDG 14, Life below Water, Cities, Reefs Water, Oceans Waste, hydrology, waste management, marine plastic
Last updated: 2022-01-05
"},{"location":"projects/polaris/","title":"Polaris 30m Probabilistic Soil Properties US","text":"Probabilistic Remapping of SSURGO (POLARIS) soil properties\u2014a database of 30-m probabilistic soil property maps over the contiguous United States (CONUS). The mapped variables over CONUS include soil texture, organic matter, pH, saturated hydraulic conductivity, Brooks-Corey and Van Genuchten water retention curve parameters, bulk density, and saturated water content.
Variable Description Units silt silt percentage % sand sand percentage % clay clay percentage % bd bulk density g/cm3 theta_s saturated soil water content m3/m3 theta_r residual soil water content m3/m3 ksat saturated hydraulic conductivity log10(cm/hr) ph soil pH in H20 N/A om organic matter log10(%) lambda pore size distribution index (brooks corey) N/A hb bubbling pressure (brooks corey) log10(kPa) n measure of the pore size distribution (van genuchten) N/A alpha scale parameter inversely proportional to mean pore diameter (van genuchten) log10(kPa-1)"},{"location":"projects/polaris/#citation-related-publications","title":"Citation & Related Publications","text":"Read the original paper here and cite the work using
Chaney, Nathaniel W., Budiman Minasny, Jonathan D. Herman, Travis W. Nauman, Colby W. Brungard, Cristine LS Morgan\nAlexander B. McBratney, Eric F. Wood, and Yohannes Yimam. \"POLARIS soil properties: 30\u2010m probabilistic maps of soil properties\nover the contiguous United States.\" Water Resources Research 55, no. 4 (2019): 2916-2938.\nPOLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons.
The dataset is available at varying depth from surface, while the statistics provided include mean, mode , median and percentile values, only median values have been included as part of the collection created.
Depth from Surface 0-5 cm 5-15 cm 15-30 cm 30-60 cm 60-100 cm 100-200 cm
Overall datasets include processing approximately 80,000 files which have been converted into individual images within a collection per property at varying depth. So for example collection bd_mean includes bd_0_5 and represents a single image for contiguous US with bd value at surface depth of 0-5 cm from surface.
"},{"location":"projects/polaris/#notes-from-data-providers","title":"Notes from Data providers","text":"05/01/2019 - The variables hb, alpha, ksat, om are in log10 space.
05/01/2019 - Due to file size constraints, the 1 arcsec database is split into 1x1 degree tiffs. Each variable/layer/statistic has its own virtual raster that acts as the \"glue\" of all the smaller 1x1 degree chunks. For more information on virtual rasters see https://www.gdal.org/gdal_vrttut.html.
06/02/2019 - The variables hb and alpha were originally reported to have the units of log10(cm) and log10(cm-1) respectively. This was a typo. The correct units are log10(kPa) and log10(kPa-1) respectively.
var bd_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/bd_mean');\nvar clay_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/clay_mean');\nvar ksat_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/ksat_mean');\nvar n_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/n_mean');\nvar om_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/om_mean');\nvar ph_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/ph_mean');\nvar sand_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/sand_mean');\nvar silt_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/silt_mean');\nvar theta_r_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/theta_r_mean');\nvar theta_s_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/theta_s_mean');\nvar lambda_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/lambda_mean');\nvar hb_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/hb_mean');\nvar alpha_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/alpha_mean');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/POLARIS-PROBABILISTIC-SOIL-PROPERTIES-30
You can download the datasets here: http://hydrology.cee.duke.edu/POLARIS/
"},{"location":"projects/polaris/#license","title":"License","text":"POLARIS is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Curated by: Samapriya Roy
Keywords: Digital soil mapping, Soil, Environmental modeling, High performance computing
Last updated dataset: 2019-05-04
Last curated: 2022-03-05
"},{"location":"projects/pomelo/","title":"POMELO Model Population Density Maps","text":"POMELO is a deep learning model addressing the need for fine-grained population maps in urban planning, environmental monitoring, public health, and humanitarian operations. It uses coarse census data and open geospatial data to create high-resolution population maps with a 100-meter ground sampling distance. POMELO can estimate populations even in regions lacking census data, achieving accuracy in sub-Saharan Africa experiments. It evaluates performance in three scenarios: coarse supervision, fine supervision, and transfer tasks, highlighting the practicality of fine supervision. POMELO estimates building occupancy rates and computes populations by multiplying them with building counts. It relies on free sources like the Open Buildings dataset but acknowledges potential errors. Dependence on high-resolution images and data availability is a concern. Crowdsourced data is essential in data-scarce regions. POMELO uses various geospatial data layers, with nightlight and settlement layers being predictive. Future improvements may include additional covariates from open geospatial sources, but handling incomplete and biased data remains a challenge. This dataset presents a fine-grained population map of Tanzania, Mozambique, Uganda, Zambia, and Rwanda with a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details. Each pixel contains a floating point number specifying the number of inhabitants of the respective pixel (i.e. People/100m).
Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.
"},{"location":"projects/pomelo/#key-features","title":"Key Features","text":"Metzger, Nando, John E. Vargas-Mu\u00f1oz, Rodrigo C. Daudt, Benjamin Kellenberger, Thao Ton-That Whelan, Ferda Ofli, Muhammad Imran, Konrad Schindler,\nand Devis Tuia. \"Fine-grained population mapping from coarse census counts and open geodata.\" Scientific Reports 12, no. 1 (2022): 20085.\n// load the population density\nvar popDensity = ee.Image(\"projects/sat-io/open-datasets/POMELO/POMELOv1\");\n\n// Define the inferno color palette\nvar infernoPalette = [\n '#000004', '#1b0c41', '#4a0c6b', '#781c81', '#a52c7a', '#cf4446',\n '#ed721c', '#fb9b06', '#f7d03c', '#fcffa4'\n];\n\n// Define visualization parameters.\nvar visParams = {\n min: 0,\n max: 450,\n palette: infernoPalette,\n opacity: 0.7 // 70% transparent\n};\n\n// Add the population density layer to the map.\nMap.addLayer(popDensity, visParams, 'Population Density');\n\n// Center map\nMap.setCenter(39.2026, -6.1659, 12);\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/POMELO-POP-DENSITY
"},{"location":"projects/pomelo/#license","title":"License","text":"Creative Commons Attribution 4.0 International (CC-BY-4.0)
Keywords: population mapping, developing countires, population density, humanitarian actions
Provided by: Metzger et al 2022
Curated in GEE by: Metzger et al 2022 and Samapriya Roy
"},{"location":"projects/popcorn/","title":"POPCORN Scalable Population Mapping with Sentinel-1 & Sentinel-2","text":"Popcorn (POPulation from COaRrse census Numbers) is a population mapping method designed to address the challenges of generating accurate population maps, especially in data-scarce regions. By using only free, globally available satellite imagery from Sentinel-1 and Sentinel-2, along with a small number of aggregate census counts, Popcorn surpasses the accuracy of many traditional population mapping approaches that rely on high-resolution building footprints. For example, Popcorn produced 100m resolution population maps for Rwanda with fewer than 400 regional census counts, achieving an accuracy score of 66% in Kigali with an average error of just 10 inhabitants per hectare.
Popcorn's method retrieves explicit maps of built-up areas and local building occupancy rates, providing additional insights into the distribution of unpopulated built-up areas, such as industrial warehouses. This makes the method interpretable and practical for urban planning and humanitarian efforts. Popcorn aims to democratize access to high-resolution population maps, making them available to regions without the resources for extensive census campaigns. You can find the full paper here and find a lot more information about the model and files here on Popcorn Population Mapping Project page
"},{"location":"projects/popcorn/#data-characteristics","title":"Data Characteristics","text":"Category Details Data Inputs - Satellite imagery from Sentinel-1 and Sentinel-2 - Coarse regional population counts Key Features - 100m ground sampling distance (GSD) - Built-up area and building occupancy rate maps - Scalable and timely mapping for urban planning and humanitarian use Example Use Case - Rwanda population mapping: 66% accuracy in Kigali with minimal census data"},{"location":"projects/popcorn/#citation","title":"Citation","text":"Metzger, Nando, Rodrigo Caye Daudt, Devis Tuia, and Konrad Schindler. \"High-resolution population maps derived from Sentinel-1 and Sentinel-2.\"\nRemote Sensing of Environment 314 (2024): 114383.\nvar snazzy = require(\"users/aazuspan/snazzy:styles\");\nsnazzy.addStyle(\"https://snazzymaps.com/style/15/subtle-grayscale\", \"Greyscale\");\n\n\n// load the population density\nvar popDensity = ee.Image(\"projects/sat-io/open-datasets/POPCORN/POPCORNv1\");\n\n// Define the inferno color palette\nvar infernoPalette = [\n '#000004', '#1b0c41', '#4a0c6b', '#781c81', '#a52c7a', '#cf4446',\n '#ed721c', '#fb9b06', '#f7d03c', '#fcffa4'\n];\n\n// Define visualization parameters.\nvar visParams = {\n min: 0,\n max: 4,\n palette: infernoPalette,\n opacity: 0.8 // 70% transparent\n};\n\n\n// Mask out the lowest values (e.g., less than a certain threshold)\nvar threshold = 0.08;\nvar maskedPopDensity = popDensity.updateMask(popDensity.gt(threshold));\n\n// Add the masked population density layer to the map.\nMap.addLayer(maskedPopDensity, visParams, 'Population Density');\n\n// Create a legend\nvar legend = ui.Panel({\n style: {\n position: 'bottom-right',\n padding: '8px 15px',\n }\n});\n\n// Create legend title\nvar legendTitle1 = ui.Label({\n value: 'POPCORN',\n style: {\n fontWeight: 'bold',\n fontSize: '32px',\n margin: '0 0 4px 0',\n padding: '0'\n }\n});\n\nlegend.add(legendTitle1);\n\n// Create another legend title\nvar legendTitle2 = ui.Label({\n value: 'Population Density [People/ha]',\n style: {\n fontWeight: 'bold',\n fontSize: '18px',\n margin: '0 0 4px 0',\n padding: '0'\n }\n});\n\nlegend.add(legendTitle2);\n\n// Create a continuous color legend\nvar legendImage = ui.Thumbnail({\n image: ee.Image.pixelLonLat().select(0),\n params: {\n bbox: [0, 0, 1, 0.1],\n dimensions: '300x15',\n format: 'png',\n min: 0,\n max: 1,\n palette: infernoPalette,\n },\n style: { margin: '0 0 4px 0' },\n});\n\nlegend.add(legendImage);\n\n// Create labels for min and max values\nvar minLabel = ui.Label(visParams.min.toString(), { margin: '0 269px 4px 0' });\nvar maxLabel = ui.Label(visParams.max.toString(), { margin: '0 0 4px 0' });\n\n// Add labels to the legend\nvar labelsPanel = ui.Panel([minLabel, maxLabel], ui.Panel.Layout.flow('horizontal'));\nlegend.add(labelsPanel);\nMap.setControlVisibility({all: false});\n\n// Add the legend to the map\nMap.add(legend);\n\n// Center map for the rwanda/DRC boarder scene\nMap.setCenter(29.244453536522037, -1.6857641047022471, 13); // The third parameter is the zoom level.\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/POPCORN-POPULATION-DENSITY
Earth Engine App: https://ee-nandometzger.projects.earthengine.app/view/popcornv1-rwa
"},{"location":"projects/popcorn/#license","title":"License","text":"Creative Commons Attribution 4.0 International (CC-BY-4.0)
Keywords: population mapping, developing countries, population density, humanitarian actions, machine learning models
Provided by: Metzger et al 2024
Curated in GEE by: Metzger et al 2024 and Samapriya Roy
Last updated: 2024-09-08
"},{"location":"projects/pwplants/","title":"Global Power Plant Database","text":""},{"location":"projects/pwplants/#release-version-13-release-date-2021-06-02","title":"release version: 1.3, release date: 2021-06-02","text":"The Global Power Plant Database is an open-source open-access dataset of grid-scale (1 MW and greater) electricity generating facilities operating across the world.
The Database currently contains nearly 35000 power plants in 167 countries, representing about 72% of the world's capacity. Entries are at the facility level only, generally defined as a single transmission grid connection point. Generation unit-level information is not currently available.
You can find the dataset and related details at https://datasets.wri.org/dataset/globalpowerplantdatabase
"},{"location":"projects/pwplants/#citation","title":"Citation","text":"Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia,\nWorld Resources Institute. 2019. Global Power Plant Database.\nPublished on Resource Watch and Google Earth Engine. http://resourcewatch.org/ https://earthengine.google.com/\nsource field 14 geolocation_source geo_source attribution for geolocation information 15 wepp_id wepp_id a reference to a unique plant identifier in the widely-used PLATTS-WEPP database. 16 year_of_capacity_data yr_capacity year the capacity information was reported 17 generation_gwh_2013 gen_gwh2013 electricity generation in gigawatt-hours reported for the year 2013 18 generation_gwh_2014 gen_gwh2014 electricity generation in gigawatt-hours reported for the year 2014 19 generation_gwh_2015 gen_gwh2015 electricity generation in gigawatt-hours reported for the year 2015 20 generation_gwh_2016 gen_gwh2016 electricity generation in gigawatt-hours reported for the year 2016 21 generation_gwh_2017 gen_gwh2017 electricity generation in gigawatt-hours reported for the year 2017 22 generation_gwh_2018 gen_gwh2018 electricity generation in gigawatt-hours reported for the year 2018 23 generation_gwh_2019 gen_gwh2019 electricity generation in gigawatt-hours reported for the year 2019 24 generation_data_source gen_dat_src attribution for the reported generation information 25 estimated_generation_gwh_2013 est_gen_gwh2013 estimated electricity generation in gigawatt-hours for the year 2013 (see [2]) 26 estimated_generation_gwh_2014 est_gen_gwh2014 estimated electricity generation in gigawatt-hours for the year 2014 (see [2]) 27 estimated_generation_gwh_2015 est_gen_gwh2015 estimated electricity generation in gigawatt-hours for the year 2015 (see [2]) 28 estimated_generation_gwh_2016 est_gen_gwh2016 estimated electricity generation in gigawatt-hours for the year 2016 (see [2]) 29 estimated_generation_gwh_2017 est_gen_gwh2017 estimated electricity generation in gigawatt-hours for the year 2017 (see [2]) 30 estimated_generation_note_2013 est_gen_nt2013 label of the model/method used to estimate generation for the year 2013 (see section on this field below) 31 estimated_generation_note_2014 est_gen_nt2014 label of the model/method used to estimate generation for the year 2014 (see section on this field below) 32 estimated_generation_note_2015 est_gen_nt2015 label of the model/method used to estimate generation for the year 2015 (see section on this field below) 33 estimated_generation_note_2016 est_gen_nt2016 label of the model/method used to estimate generation for the year 2016 (see section on this field below) 34 estimated_generation_note_2017 est_gen_nt2017 label of the model/method used to estimate generation for the year 2017 (see section on this field below)"},{"location":"projects/pwplants/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var global_power_plants = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_power_plant_DB_1-3\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-POWERPLANT-DATABASE
"},{"location":"projects/pwplants/#license","title":"License","text":"The Global Power Planet Database is available under a CC BY 4.0 license
Data download page: Download v1.3 from here
Dataset created by: World Resources Institute
Curated in GEE by: Samapriya Roy
Keywords: : infrastructure, energy, climate, power, power-plants, wri
Last updated: 2021-07-16
Note: Older version of this dataset is available in GEE and might be updated to reflect in the public catalog
"},{"location":"projects/qdann/","title":"QDANN 30m Yield Map for Corn, Soy, and Winter Wheat in the U.S","text":"This dataset presents a novel scale transfer framework for estimating crop yields at subfield levels using satellite remote sensing and machine learning techniques. The framework, known as Quantile Loss Domain Adversarial Neural Networks (QDANN), utilizes knowledge from county-level datasets to accurately map yields at finer spatial resolutions, addressing the limitations posed by the scarcity of ground truth data for model training and evaluation.While broader scale yield mapping (e.g., state or county level) has become standard, finer-scale mapping has faced challenges due to the lack of subfield yield information. QDANN employs an unsupervised domain adaptation strategy, training on labeled county-level data while leveraging unlabeled subfield data, thus eliminating the need for yield information at the subfield level.
The dataset is based on Landsat imagery and Gridmet weather data, focusing on maize, soybean, and winter wheat fields across the United States. It is validated using yield monitor records from approximately one million field-year observations. QDANN's performance is benchmarked against various process-based and machine learning methods that utilize simulated yield records or county-level data.
Key results include: - R\u00b2 scores (RMSE) for maize, soybean, and winter wheat were 48% (2.29 t/ha), 32% (0.85 t/ha), and 39% (1.40 t/ha) respectively, demonstrating superior accuracy compared to benchmark methods. - When yields were aggregated to the county level, QDANN's performance improved significantly, achieving R\u00b2 scores (RMSE) of 78% (0.98 t/ha) for maize, 62% (0.37 t/ha) for soybean, and 53% (1.00 t/ha) for winter wheat.
This study illustrates the efficacy of the QDANN framework for reliable yield mapping at subfield levels, even in the absence of fine-scale yield data. The dataset includes publicly available 30-meter annual yield maps for major crop-producing states in the U.S., generated since 2008 with units kg/ha. You can find additional details in the paper here.
"},{"location":"projects/qdann/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The datasets were originally ingested by the authors as images into folders with State abbreviation and year to get to a specific image. These were now moved into two separate collections for corn-soybean and winter-wheat. State abbreviations were added as a property called \"state_abbv\" and dates are added to represent the time period. This allows for easy filtering of the collection by state and date.
Raster Band Info Unit Corn & Soybean b1: corn, b2: soybean kg/ha Winter Wheat b1: winter wheat kg/ha
"},{"location":"projects/qdann/#citation","title":"Citation","text":"Ma, Yuchi, Sang-Zi Liang, D. Brenton Myers, Anu Swatantran, and David B. Lobell. \"Subfield-level crop yield mapping without ground truth data:\nA scale transfer framework.\" Remote Sensing of Environment 315 (2024): 114427.\nvar corn_soybean = ee.ImageCollection(\"projects/sat-io/open-datasets/lobell-lab/VAE_QDANN_YIELD_MAP/CORN_SOY_MAP\");\nvar winter_wheat = ee.ImageCollection(\"projects/sat-io/open-datasets/lobell-lab/VAE_QDANN_YIELD_MAP/WINTER_WHEAT_MAP\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/QDANN-30M-YIELD-MAPS
"},{"location":"projects/qdann/#license","title":"License","text":"QDANN Yield Maps follow CC-BY-NC-SA 4.0. Thus, those compounds are freely available for academic purposes or individual research, but restricted for commercial use.
Created by: Ma,Yuchi et al. 2024, Lobell Lab
Curated in GEE by: Yuchi Ma & Samapriya Roy
Keywords : corn,soybean,winter wheat,yield
Last updated in GEE: 2024-09-22
"},{"location":"projects/radd/","title":"RADD Forest Disturbance Alert","text":"RADD - RAdar for Detecting Deforestation - Near real-time disturbances in humid tropical forest based on Sentinel-1 at 10m spatial scale. Primary humid tropical forest of South America (13 countries), Central America (6 countries), Africa (25 countries), insular Southeast Asia (5 countries) and Pacific (1 country). You can find more information here at University of Wageningen
The RADD (RAdar for Detecting Deforestation) alerts contribute to the World Resources Institute\u2019s Global Forest Watch initiative in providing timely and accurate information to support a wide range of stakeholders in sustainable forest management and law enforcement activities against illegal deforestation. The RADD alerts are implemented in Google Earth Engine. This dataset is also available on Global Forest Watch - Open Data Portal, Sepal.io and EarthMap
"},{"location":"projects/radd/#dataset-citation","title":"Dataset Citation","text":"Reiche J, Mullissa A, Slagter B, Gou Y, Tsendbazar N, Odongo-Braun C, Vollrath A, Weisse M, Stolle F, Pickens A, Donchyts G, Clinton N, Gorelick N &\nHerold M, (2021), Forest disturbance alerts for the Congo Basin using Sentinel-1, Environmental Research Letters\nhttps://doi.org/10.1088/1748-9326/abd0a8\nvar radd = ee.ImageCollection('projects/radar-wur/raddalert/v1');\nvar geography = 'sa';\n\n// forest baseline mask\nvar forest_baseline = ee.Image(radd.filterMetadata('layer','contains','forest_baseline')\n .filterMetadata('geography','equals',geography).first())\n\nMap.addLayer(forest_baseline, {palette:['black'], opacity: 0.3},'Forest baseline')\n\nvar latest_radd_alert = ee.Image(radd.filterMetadata('layer','contains','alert')\n .filterMetadata('geography','equals',geography)\n .sort('system:time_end', false).first());\n\nMap.addLayer(latest_radd_alert.select('Alert'), {min:2,max:3,palette:['blue','coral']}, 'RADD alert')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/RADD-FOREST-ALERT
Sample code to download RADD Alerts as GeoTIFF to Google Drive
"},{"location":"projects/radd/#license","title":"License","text":"The data is licensed under a Creative Commons Attribution 4.0 International License and may be used by anyone, anywhere, anytime without permission or royalty payment.
Curated by: J Reiche, J Balling, M Herold, B Slagter, NE Tsendbazar
Keywords: Forest, Deforestation, Alerts, NBS, Sentinel-1, Radar
Last updated: 2023-01-08 (updates with S1)
"},{"location":"projects/rai/","title":"Rural Access Index (RAI)","text":"The Rural Access Index (RAI) is one of the most important global development indicators in the transport sector. It is currently the only indicator for the SDGs that directly measures rural accessibility, and it does so by assessing rural populations\u2019 access to all-season roads. Following its adoption as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, the indicator received a new methodology taking advantage of geospatial techniques, published under the \u201cMeasuring rural access using new technologies\u201d report in 2016 (World Bank, 2016). The World Bank has since endorsed an additional Research for Community Access Partnership (ReCAP) funded project led by the Transport Research Laboratory (TRL)\u2014the RAI Supplemental Guidelines (Workman and McPherson, 2019)\u2014which provided detailed guidance for calculating the RAI, notably with an alternative approach to the all-season aspect of RAI, focusing on the changing accessibility profile of road networks rather than relying on road surface quality alone or scarce physical measurements for road conditions. Nevertheless, neither the 2016 nor the 2019 methodologies were implemented globally, with official implementations published by the World Bank being restricted to more in-depth studies for selected countries mostly in Africa and the Middle East (World Bank, 2023a) due to data source restrictions.
Here the SDG Transformation Center, part of the UN Sustainable Development Solutions Network (UN SDSN), seeks to fill in this gap by implementing the most up-to-date methodology endorsed by the World Bank\u2019s (World Bank\u2019s 2016 methodology supplemented by TRL\u2019s 2019 guidelines) at global scale with free remotely sensed datasets with global coverage. This dataset was produced by UN SDSN\u2019s SDG Transformation Center and is, to date, the only publicly available application of this particular method at a global scale.
The complete methodology is available here
"},{"location":"projects/rai/#citation","title":"Citation","text":"Iablonovski G, Drumm E, Fuller G and Lafortune G (2024) A global implementation of the rural access index.\nFront. Remote Sens. 5:1375476. doi: 10.3389/frsen.2024.1375476\n//Use the inaccessibility index to multiply your gridded rural population dataset to obtain the\n//distribution of rural population with access to all-season roads\nvar inaccessibilityindex = ee.Image('projects/sat-io/open-datasets/RAI/raimultiplier');\nMap.addLayer(inaccessibilityindex,{min:0, max:1, 'palette': ['EFC2B3','ECB176','E9BD3A','E6E600','63C600','00A600']}, 'Inaccessibility index');\n\n//In order to get the Rural Access Index for any given set of boundaries, get zonal statistics\n//for the total rural population and the rural population with access to all-season roads\n\nvar ruralpopulation = ee.Image('projects/sat-io/open-datasets/RAI/ruralpop');\nMap.addLayer(ruralpopulation, {min:0, max:100,'palette': ['FFFFFF', 'ff0000']},'Rural Population');\n\nvar ruralpopulationwithaccess = ee.Image('projects/sat-io/open-datasets/RAI/ruralpopaccess');\nMap.addLayer(ruralpopulationwithaccess,{min:0, max:100,'palette': ['00A600','63C600','E6E600','E9BD3A','ECB176','EFC2B3']},'Rural Pop w/ Access');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/RURAL-ACCESS-INDEX
"},{"location":"projects/rai/#license","title":"License","text":"Creative Commons Attribution Noncommercial Share Alike license (CC BY-NC-SA-4.0) Most of our work is available in open source. Copyrights and licensing conditions for commercial reuse may vary across reports and studies. Should you have any questions on licensing and reuse of our work please reach out to: privacy@unsdsn.org .
Provided by: United Nations Sustainable Development Solutions Network
Curated in GEE by: UNSDSN and Samapriya Roy
Keywords: sdg, accessibility, rural
Last updated: 2024-06-15
"},{"location":"projects/rap/","title":"Rangeland Analysis Platform layers","text":"Rangeland Analysis Platform data products are available as GEE assets and are made publicly available for analysis through the web application at rangelands.app
Vegetation Cover: Vegetation Cover: Rangeland Analysis Platform (RAP) Vegetation Cover, version 3.0 consists of gridded fractional estimates of plant functional groups for rangelands in the continental United States. The estimates are produced at 30-meter spatial resolution for each year between 1984\u2013present. The six plant functional groups are Annual Forbs and Grasses, Perennial Forbs and Grasses, Shrubs, Trees, Litter, and Bare Ground. Cover values are reported as percentages on a pixel-by-pixel basis. The estimates were produced using a temporal convolutional network using field measures of plant functional groups collected by the Natural Resources Conservation Service Natural Resources Inventory (NRI) program, the Bureau of Land Management Assessment, Inventory, and Monitoring (AIM) program, and the National Park Service Northern Colorado Plateau Network (NCPN) alongside spatially continuous earth observations from Landsat TM, ETM+, and OLI Collection 2.
Rangeland Production: : Rangeland Analysis Platform (RAP) Rangeland Production, version 3.0 consists of gridded estimates of herbaceous aboveground biomass, partitioned into vegetation types for annual forbs and grasses and perennial forbs and grasses. The estimates are produced at 30m spatial resolution from 1986-present. Estimates are provided annually and at 16-day intervals. Values are reported in terms of net primary productivity which can be converted to pounds per acre of new growth of aboveground biomass using the function in the Google Earth Engine script below\u2013 estimates do not reflect standing biomass from previous years. Estimates are calculated using a light use efficiency model (to estimate net primary production in terms of carbon) which is then allocated to aboveground and belowground pools (based on mean annual temperature) and further converted to biomass using a carbon-to-dry matter ratio.
Dataset was updated based on specifications provided in changelog below. Updated validation statistics provided here: https://rangelands.app/products/rapV3/
"},{"location":"projects/rap/#earth-engine-asset-snippets","title":"Earth Engine Asset Snippets","text":"// Vegetation Cover\nvar RAP_veg = ee.ImageCollection(\"projects/rap-data-365417/assets/vegetation-cover-v3\")\n\n// Net Primary Production (annual)\nvar RAP_npp = ee.ImageCollection(\"projects/rap-data-365417/assets/npp-partitioned-v3\")\nCode Snippets: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/RANGELAND-ANALYSIS-PLATFORM-EXAMPLE
"},{"location":"projects/rap/#citation","title":"Citation","text":"Jones, M.O., N.P. Robinson, D.E. Naugle, J.D. Maestas, M.C. Reeves, R.W.\nLankston, and B.W. Allred. 2020. Annual and 16-day rangeland production\nestimates for the western United States. bioRxiv 2020.11.06.343038.\nhttp://dx.doi.org/10.1101/2020.11.06.343038\n\nRobinson, N. P., M. O. Jones, A. Moreno, T. A. Erickson, D. E. Naugle, and B. W.\nAllred. 2019. Rangeland productivity partitioned to sub-pixel plant functional\ntypes. Remote Sensing 11:1427. http://dx.doi.org/10.3390/rs11121427\n\nAllred, B. W., B. T. Bestelmeyer, C. S. Boyd, C. Brown, K. W. Davies, L. M.\nEllsworth, T. A. Erickson, S. D. Fuhlendorf, T. V. Griffiths, V. Jansen, M. O.\nJones, J. Karl, J. D. Maestas, J. J. Maynard, S. E. McCord, D. E. Naugle, H. D.\nStarns, D. Twidwell, and D. R. Uden. 2020. Improving Landsat predictions of\nrangeland fractional cover with multitask learning and uncertainty.\nbioRxiv:2020.06.10.142489. http://dx.doi.org/10.1101/2020.06.10.142489\nSample scripts are available on the RAP Support Site.
Extra Info: See any of the three herbaceous biomass scripts for the function to convert from net primary production to biomass.
Download Tool/Code snippets if any: Analysis can be performed on these datasets for your regions of interest through the GUI at rangelands.app
"},{"location":"projects/rap/#license-information","title":"License Information","text":"Public Domain-CC0
"},{"location":"projects/rap/#curated-by","title":"Curated by","text":"Sarah McCord, Point of Contact, and Jeb Williamson, Agricultural Research Service, U.S. Department of Agriculture
Keywords: rangelands, vegetation, time-series, machine learning, landsat
Last updated: 2022-01-06
"},{"location":"projects/rap/#changelog","title":"Changelog","text":"RAP v3 was released on 2022-01-01
Primary changes include:
The Regional Deterministic Precipitation Analysis (RDPA) based on the Canadian Precipitation Analysis (CaPA) system is on a domain that corresponds to that of the operational regional model, i.e. the Regional Deterministic Prediction System (RDPS-LAM3D) except for areas over the Pacific Ocean where the western limit of the RDPA domain is slightly shifted eastward with respect to the regional model domain. The resolution of the RDPA analysis is identical to the resolution of the operational regional system RDPS LAM3D. The fields in the RDPA GRIB2 dataset are on a polar-stereographic (PS) grid covering North America and adjacent waters with a 10 km resolution at 60 degrees north, 2003-present. You can find additional information on the dataset here, and also here apart from the climate engine org page.
Spatial extent Spatial resolution Temporal resolution Time span Update frequency United States and Canada 10.0 km grid (1/11 deg) Daily 2003-01-01 to present Updated daily with 5 month lag timeVariables
Variable Units Scale factor Precipitation ('precip') Millimeters 1.0"},{"location":"projects/rdpa/#citation","title":"Citation","text":"[Canadian Precipitation Analysis (CaPA)](https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/capa_information_leaflet_20141118_en.pdf) Methodology system\n\nFillion, Luc, Monique Tanguay, Ervig Lapalme, Bertrand Denis, Michel Desgagne, Vivian Lee, Nils Ek et al. \"The Canadian regional data assimilation and forecasting system.\" Weather and Forecasting 25, no. 6 (2010): 1645-1669.\n\nEnvironment and Climate Change Canada. (2023). Regional Deterministic Precipitation Analysis (RDPA) dataset [Version 2.0]. [Dataset]. Retrieved from https://open.canada.ca/data/en/dataset/fdd3446a-dc20-5bad-9755-0855e3ec9b19\n// Read in Image Collections and get single image\nvar rdpa_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-rdpa-daily')\nvar rdpa_i = rdpa_ic.first()\n\n// Print single image to see bands\nprint(rdpa_i)\n\n// Visualize precipitation for single image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(rdpa_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CE-RDPA-DATASETS
"},{"location":"projects/rdpa/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: climate, precipitation, Canada, United States, daily
Provided by: Environment and Climate Change Canada
Curated in GEE by: Climate Engine Org
"},{"location":"projects/rdps/","title":"Regional Deterministic Prediction System (RDPS)","text":"The Regional Deterministic Prediction System (RDPS) carries out physics calculations to arrive at deterministic predictions of atmospheric elements from the current day out to 48 hours into the future at a 10.0 km grid (1/11 deg) spatial resolution. The data for mean temperature covers North America and is provided by the Meterological Service of Canada (MSC), a part of Environment and Climate Change Canada (ECCC). The MSC provides weather forecasts and warnings 24 hours a day, 365 days a year. MSC also provides federal department, agencies and other levels of government with information to support emergency preparedness and response to events such as storms, floods, wildfires and other weather-related emergencies. You can find additional information here and also on the climate org data page.
"},{"location":"projects/rdps/#dataset-description","title":"Dataset Description","text":"Spatial Information
Parameter Value Spatial extent United States and Canada Spatial resolution 10.0 km grid (1/11 deg) Temporal resolution Daily Time span 2010-11-01 to present Update frequency Updated daily with 1 day lag timeVariables
Variable Details Mean temperature ('Tavg') - Units: Degrees Celsius - Scale factor: 1.0"},{"location":"projects/rdps/#citation","title":"Citation","text":"Fillion, L., Tanguay, M., Lapalme, E., Denis, B., Desgagne, M., Lee, V., ... & Pag\u00e9, C. (2010). The Canadian regional data assimilation and\nforecasting system. Weather and Forecasting, 25(6), 1645-1669.\n// Read in Image Collection and get first image\nvar rdps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-rdps-daily')\nvar rdps_i = rdps_ic.first()\n\n// Print first image to see bands\nprint(rdps_i)\n\n// Visualize temperature from first image\nvar temp_palette = [\"#b2182b\", \"#ef8a62\", \"#fddbc7\", \"#f7f7f7\", \"#d1e5f0\", \"#67a9cf\", \"#2166ac\"].reverse()\nMap.addLayer(rdps_i.select('Tavg'), {min: -10, max: 20, palette: temp_palette}, 'Tavg')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CE-RDPS-DAILY
"},{"location":"projects/rdps/#license","title":"License","text":"Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.
Keywords: climate, daily, United States, Canada, daily, near real-time
Dataset provided by: Environment and Climate Change Canada
Dataset curated in GEE by: Climate Org
"},{"location":"projects/realsat/","title":"RealSAT Global Dataset of Reservoir and Lake Surface Area","text":"RealSAT presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. The static subset of the dataset was ingested rather than timeseries since there was transformation issues however the dataset can be downloaded here.. A reattempt might be made in the future to ingest the timeseries files.
You can read the paper here : https://www.nature.com/articles/s41597-022-01449-5 and a viewer is made available to look at an online copy of the data
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/realsat/#citations","title":"Citations","text":"Khandelwal, A., Karpatne, A., Ravirathinam, P. et al. ReaLSAT, a global dataset of reservoir and lake surface area variations. Sci Data 9, 356\n(2022). https://doi.org/10.1038/s41597-022-01449-5\nvar realsat = ee.FeatureCollection(\"projects/sat-io/open-datasets/ReaLSAT/ReaLSAT-1_4\");\nThe database is licensed under a Creative Commons Attribution (CC-BY) 4.0 International License.
Created by: Khandelwal, A., Karpatne, A., Ravirathinam, P. et al.
Curated in GEE by: Samapriya Roy
Keywords: water,hydrology, lakes, global lake surface, ReaLSAT, Surface water monitoring, Lakes and reservoirs, Hydrology, Landsat
Last updated: 2022-07-10
"},{"location":"projects/rgi/","title":"Randolph Glacier Inventory","text":"The Randolph Glacier Inventory RGI is a globally complete inventory of glacier outlines (excluding the ice sheets in Greenland and Antarctica). It is a subset of the database compiled by the Global Land Ice Measurements from Space GLIMS initiative. While GLIMS is a multi-temporal database with an extensive set of attributes, the RGI is intended to be a snapshot of the world\u2019s glaciers at a specific target date, which in RGI 7.0 and all previous versions has been set as close as possible to the year 2000 (although in fact its range of dates can still be substantial in some regions). The RGI includes outlines of all glaciers larger than 0.01 km\u00b2, which is the recommended minimum of the World Glacier Inventory. You can read more about the dataset in the user guide here
"},{"location":"projects/rgi/#dataset-citation","title":"Dataset citation","text":"RGI Consortium, . (2023). Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 7 [Data Set]. Boulder,\nColorado USA. National Snow and Ice Data Center. https://doi.org/10.5067/F6JMOVY5NAVZ. Date Accessed 01-04-2024.\nvar glacier_ft = ee.FeatureCollection(\"projects/sat-io/open-datasets/RGI/RGI_VECTOR_MERGED_V7\");\nMap.centerObject(glacier_ft.first())\n\n// print a feature\nprint('first glacier feature', glacier_ft.first());\n\n// Make a raster image out of the land area attribute.\nvar glacier_img = glacier_ft.reduceToImage({\nproperties: ['area_km2'],\nreducer: ee.Reducer.first()\n});\n\n// Make a binary mask\nvar glacier_binary = glacier_img.gt(0).unmask();\n\n//Add layers\nMap.addLayer(glacier_binary, {min:0, max:1}, 'Glacier raster mask',false);\nMap.addLayer(glacier_ft,{color:'#368BC1'},'Glacier Features')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/RANDOLPH-GLACIER-INVENTORY
"},{"location":"projects/rgi/#license","title":"License","text":"The RGI is licensed for distribution under a CC-BY-4.0 license.
Curated in GEE by: Hendrik Wulf and Samapriya Roy
Keywords: glacier outlines, RGI, GLIMS
Last updated: 2024-01-04
"},{"location":"projects/river_deltas/","title":"Global River Deltas and vulnerability","text":"Global river delta dataset combines 2174 delta locations with polygons that define delta area. We define delta area as the extent of geomorphic activity created by deltaic channel movement, and delta progradation. We focus on channel network activity because it defines the most flood-prone zone and creates the resources and natural infrastructures that make deltas attractive sites for habitation. We define deltaic polygons with five points that encompass deltaic activity. These five points mark visible traces of deltaic activity with two points capturing the lateral extent of deposition along the shoreline, and with three points enclosing the up and downstream extent of deposition. The convex hull around these five points defines a delta polygon. You can read the open source paper here and you can download the data used to create the feature collection from the supplementary material here.
"},{"location":"projects/river_deltas/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var convex_hull = ee.FeatureCollection(\"projects/sat-io/open-datasets/delta/delta-convex-hull\");\nvar convex_hull_bound = ee.FeatureCollection(\"projects/sat-io/open-datasets/delta/delta-convex-bounds\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-RIVER-DELTAS-VULNERABILITY
"},{"location":"projects/river_deltas/#citation","title":"Citation","text":"Edmonds, Douglas A., Rebecca L. Caldwell, Eduardo S. Brondizio, and Sacha MO Siani.\n\"Coastal flooding will disproportionately impact people on river deltas.\"\nNature communications 11, no. 1 (2020): 1-8.\nThis work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: : Fluvial Geomorphology, Hydrology, Rivers, Coastal Rivers, Tidal, River Mouth, Vulnerability, Poverty
Last updated: 2021-04-24
"},{"location":"projects/rivermouth/","title":"Global coastal rivers and environmental variables","text":"A global dataset of 5399 coastal rivers and data on eight environmental variables. Of these rivers, 40\u2009% (n=2174) have geomorphic deltas defined either by a protrusion from the regional shoreline, a distributary channel network, or both. Globally, coastlines average one delta for every \u223c300\u2009km of shoreline, but there are hotspots of delta formation, for example in Southeast Asia where there is one delta per 100\u2009km of shoreline. Our analysis shows that the likelihood of a river to form a delta increases with increasing water discharge, sediment discharge, and drainage basin area. On the other hand, delta likelihood decreases with increasing wave height and tidal range. Delta likelihood has a non-monotonic relationship with receiving-basin slope: it decreases with steeper slopes, but for slopes\u2009>0.006 delta likelihood increases. This reflects different controls on delta formation on active versus passive margins.
"},{"location":"projects/rivermouth/#earth-engine-snippet","title":"Earth Engine Snippet:","text":"var global_costal_rivers = ee.FeatureCollection(\"projects/sat-io/open-datasets/delta/global-costal-rivers-points\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/GLOBAL-COASTAL-RIVERS-ENV-VARIABLES
"},{"location":"projects/rivermouth/#citation","title":"Citation","text":"Caldwell, R. L., Edmonds, D. A., Baumgardner, S., Paola, C., Roy, S., and Nienhuis, J. H.: A global delta dataset and the environmental variables that predict delta formation on marine coastlines, Earth Surf. Dynam., 7, 773\u2013787, https://doi.org/10.5194/esurf-7-773-2019, 2019.\nAdditional information
Property Match from Supplement
Properties Reference Property ID ID DL_Binary Delta Presence or Absence Region Region Latitude RM_Lat Longitude RM_Lon MF_matches M&F_matches MF_IDs M&F_ID(s) WV_HT_Hw_m Wave_Height_Hw (m) WV_HT_m Tidal_Range_Ht (m) Bslope_RM Bathymetric_Slope_from_RM_Sbr SLC_mm Sea_Level_Change (mm yr^-1)Region Code and Description Tables
Region Code Region Description AFR mainland Africa AUS Australia, New Zealand, New Guinea BLS Black Sea, Sea of Azov CAM Central America EAS East Asia EUR Europe MAD Madagascar MED Mediterranean MID Middle East NAM North America RUS Russia SAM South America SAS South Asia SEA Southeast Asia"},{"location":"projects/rivermouth/#license","title":"License","text":"Shared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Samapriya Roy
Keywords: :\"Fluvial Geomorphology, Hydrology, Rivers, Coastal Rivers, Tidal, River Mouth\"
Last updated: 2021-04-17
"},{"location":"projects/rwi/","title":"Relative Wealth Index (RWI)","text":"The Relative Wealth Index predicts the relative standard of living within countries using de-identified connectivity data, satellite imagery and other nontraditional data sources. This index is built using nontraditional data sources, including satellite imagery and de-identified Facebook connectivity data. The index is validated using ground truth measurements from the Demographic and Health Surveys. The data is provided for 93 low and middle-income countries at 2.4km resolution.
"},{"location":"projects/rwi/#extra-processing","title":"Extra processing","text":"The datasets are provided as CSV files with lat long Relative Wealth Index (RWI) and error. The CSV files are converted to Shapefiles and ingested as tables for each of the countries. A master feature collection is then created to combine all feature collections across low and middle-income countries (LMICs) countries. Currently only 92 countries are made available from Facebook. You can download the updated dataset from the Humanitarian Data Exchange website or from Facebook's data for Good website.
"},{"location":"projects/rwi/#steps","title":"Steps","text":"var rwi = ee.FeatureCollection(\"projects/sat-io/open-datasets/facebook/relative_wealth_index\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/RELATIVE-WEALTH-INDEX(RWI)
Interactive Map: http://beta.povertymaps.net/
"},{"location":"projects/rwi/#license","title":"License","text":"Public Domain/No restrictions (CC0): Under the terms of this license you are free to use the material for any purpose without any restrictions.
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: :\"Relative Wealth Index, RWI, Facebook, CIESIN, gridded\"
Last updated: 2021-04-18
"},{"location":"projects/s1gbm/","title":"Normalized Sentinel-1 Global Backscatter Model Land Surface","text":"This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation, within a dedicated project by the European Space Agency (ESA). The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016-17 by the mean C-band radar cross section in VV- and VH-polarization at a 10 m sampling, giving a high-quality impression on surface- structures and -patterns. The TU Wein center processed 0.5 million Sentinel-1 scenes totaling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects.
The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Supporting the design and verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states, as well as water body mapping. You can read the full paper which is open source here. The authors further introduce the use of Equi7Grid for distribution of the dataset which is a high resolution optimized global grid for distribution of data.
"},{"location":"projects/s1gbm/#citation","title":"Citation","text":"Bauer-Marschallinger, Bernhard, Senmao Cao, Claudio Navacchi, Vahid Freeman, Felix Reu\u00df, Dirk Geudtner, Bj\u00f6rn Rommen et al. \"The normalised\nSentinel-1 Global Backscatter Model, mapping Earth\u2019s land surface with C-band microwaves.\" Scientific Data 8, no. 1 (2021): 1-18.\nThe VV and VH mosaics are sampled at 10 m pixel spacing, georeferenced to the Equi7Grid and divided into six continental zones (Africa, Asia, Europe, North America, Oceania, South America), which are further divided into square tiles of 100 km extent (\"T1\"-tiles). With this setup, the S1GBM consists of 16071 tiles over six continents, for VV and VH each, totaling to a compressed data volume of 2.67 TB. The files were distributed as aggregated zipfiles with a total of 12 zip files.
The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.
"},{"location":"projects/s1gbm/#gee-pre-processing","title":"GEE Pre Processing","text":"The main file rather than the preview files are ingested and file name is used to create the complete metadata structure for each of these tiled images. While all attempts were made for completeness of tiles, the extremely large volume of each zipfile caused multiple failed attempts and broken link issues. However attempts were made to retry for failures at both download and ingest stages into GEE.
A filename of one tile of a mosaic may be for example:
M20160104_20171230_TMENSIG38_S1-IWGRDH1VH-_\u2014\u2014_B0104_NA010M_E064N036T1.tif\nIt defines the following:
\"M\" for the actual main data, or \u201cQ\u201d for the quicklook-file (for preview, see below).
start- and end-time of input data to this mosaic tile, in the format YYYYMMDD
the aggregated statistical parameter; for Version 1.0 this is always \u201cTMENSIG38\u201d, i.e. mean of backscatter normalised to 38\u00b0
relating to the input data, the satellite and sensor mode identifier \u201cS1-IWGRDH1\u201d, abbreviating Sentinel-1 Interferometric Wide Swath mode that is Ground Range Detected at High-resolution
the backscatter polarisation; so \"VV\" or \"VH\"
the version of TU Wien\u2019s internal processing engine, i.e. \"B0104\"
the identifier for Equi7Grid\u2019s continental grid, with pixel sampling in meters, e.g., \"NA010M\" for North America and 10 m pixel size
the identifier for Equi7Grid\u2019s tile within the continent, defined by the lower left coordinate, and the tile extent; e.g. \"E064N036\" for 6400\u2009km easting and 3600\u2009km northing, and \"T1\" for 100 km tile extent to the east and north
var AF_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_AF_GEOG_TILE_T1\");\nvar AN_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_AN_GEOG_TILE_T1\");\nvar AS_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_AS_GEOG_TILE_T1\");\nvar EU_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_EU_GEOG_TILE_T1\");\nvar NA_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_NA_GEOG_TILE_T1\");\nvar OC_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_OC_GEOG_TILE_T1\");\nvar SA_T1 = ee.FeatureCollection(\"projects/sat-io/open-datasets/equi7grid/EQUI7_V14_SA_GEOG_TILE_T1\");\nSample Code Equi7Grid: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/EQUI7-GRID
"},{"location":"projects/s1gbm/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var VH = ee.ImageCollection(\"projects/sat-io/open-datasets/S1GBM/normalized_s1_backscatter_VH\");\nvar VV = ee.ImageCollection(\"projects/sat-io/open-datasets/S1GBM/normalized_s1_backscatter_VV\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/S1-GLOBAL-BACKSCATTER
"},{"location":"projects/s1gbm/#web-based-data-viewer","title":"Web-Based Data Viewer","text":"The layer is also made available for visualization from Earth Observation Data Centre (EODC) under http://s1map.eodc.eu/.
"},{"location":"projects/s1gbm/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Bernhard et al, European Space Agency
Keywords: Mosaic, Sentinel-1, Backscatter, Normalized, VV, VH, polarization, S1GBM, European Space Agency, ESA
Last data update: 2021-10-26
Last updated on GEE: 2021-11-07
"},{"location":"projects/s2hswiss/","title":"S2 SR HARMONIZED SWITZERLAND","text":"Sentinel-2 (ESA) optical satellite data provides complete coverage of Switzerland approximately every three days. The effectiveness of this data relies significantly on meteorological factors like cloud cover, atmospheric correction, data registration, and delivery methods (projection). We've enhanced existing processing procedures and incorporated additional post-processing techniques to produce analysis-ready surface reflectance data specifically tailored for Switzerland. You can find additional information in geocat.ch - the swiss geographic catalogue and metadata information can be found here. Processing code can be found in the SATROMO GitHub Repository.
"},{"location":"projects/s2hswiss/#processing","title":"Processing","text":"Each overpass is mosaiced and there are two assets per overpass for the 10m and 20m spatial resolution.
Note from Data Providers
Please note that we are in commissioning mode until 01.01.2025. For 2024, we will have real-time data,\nand we will reprocess data from 2017 onward\nAsset Resolution Bands 10m - B2 (Blue) - B3 (Green) - B4 (Red) - B8 (NIR) - terrainShadowMask (binary mask for terrain shadow) - cloudAndCloudShadowMask (binary mask for clouds and cloud shadows) - reg_dx (offset in x direction from the co-registration) - reg_dy (offset in y direction from the co-registration) - reg_confidence (displacement confidence from the co-registration) - cloudProbability (percentage of cloud probability) 20m - B8A (NIR 2) - B11 (SWIR 1) - B5 (Red Edge 1)
"},{"location":"projects/s2hswiss/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var s2_sr_harmonized_swiss = ee.ImageCollection(\"projects/sat-io/open-datasets/SWISSTOPO/S2_SR_HARMONIZED_SWISS\");\nThe free geodata and geoservices of swisstopo may be used, distributed and made accessible. Furthermore, they may be enriched and processed and also used commercially. Terms of use for free geodata and geoservices(OGD) from swisstopo. Contains modified Copernicus Sentinel data.
The use of free geodata and geoservices from swisstopo is governed by the following legal bases
A reference to the source is mandatory. In the case of digital or analogue representations and publications, as well as in the case of dissemination, one of the following source references must be attached in any case:
Keywords: BGDI, optical satellite imagery, Sentinel-2, surface reflectance, Analysis ready data
Created and provided by: Federal Office of Topography swisstopo
Curated in GEE by: swisstopo and Samapriya Roy
Last updated: 2024-04-24
"},{"location":"projects/sa_nlc/","title":"South African National Land Cover (SANLC)","text":"The South African National Land Cover (SANLC) datasets are a series of land cover classification datasets for South Africa. The datasets are based on the gazetted land-cover classification standard (SANS 19144-2) and have 73 classes of information. New updates includes the 2020 SANLC apart from the 2018 dataset included earlier.The South African National Land-Cover 2018 dataset has been generated from 20 meter multi-seasonal Sentinel 2 satellite imagery. The imagery used represents the full temporal range of available imagery acquired by Sentinel 2 during the period 01 January 2018 to 31 December 2018. The SANLC 2018 dataset is based primarily on the new gazetted land-cover classification standard (SANS 19144-2) with 73 classes of information and is comparable, with the previous 1990 and 2013-14 South African National Land-Cover (SANLC) datasets. The previous land cover classes are also included for comparisons.
The SANLC 2018 data was launched on the 1st October 2019 and is now available for download from the E-GIS website, download link: https://egis.environment.gov.za/gis_data_downloads.
"},{"location":"projects/sa_nlc/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var sa_nlc2018 = ee.Image('projects/sat-io/open-datasets/landcover/SA_NLC_1990');\nvar sa_nlc2018 = ee.Image('projects/sat-io/open-datasets/landcover/SA_NLC_2018');\nvar sa_nlc2013_2014 = ee.Image('projects/sat-io/open-datasets/landcover/SA_NLC_2013_2014');\nvar sa_nlc_2020 = ee.Image('projects/sat-io/open-datasets/landcover/SA_NLC_2020');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/SOUTH-AFRICA-LULC
No. Legend Colour 2018 NLC Class Name 1 #F2F2F2 Contiguous (indigenous) Forest (combined very high, high, medium) 2 #065106 Contiguous Low Forest & Thicket (combined classes) 3 #005F00 Dense Forest & Woodland (35 - 75% cc) 4 #008500 Open Woodland (10 - 35% cc) 5 #F74006 Contiguous & Dense Planted Forest (combined classes) 6 #F9764D Open & Sparse Planted Forest 7 #F9906C Temporary Unplanted Forest 8 #B8ABD1 Low Shrubland (other regions) 9 #8FAB39 Low Shrubland (Fynbos) 10 #AC92C5 Low Shrubland (Succulent Karoo) 11 #AC9CDA Low Shrubland (Nama Karoo) 12 #85D285 Sparsely Wooded Grassland (5 - 10% cc) 13 #D2B485 Natural Grassland 14 #00009F Natural Rivers 15 #041FA7 Natural Estuaries & Lagoons 16 #0639AB Natural Ocean, Coastal 17 #0D50AC Natural Lakes 18 #125FAC Natural Pans (flooded @ obsv time) 19 #1373B4 Artificial Dams (incl. canals) 20 #1D81B6 Artificial Sewage Ponds 21 #1F8EB8 Artificial Flooded Mine Pits 22 #06DEDC Herbaceous Wetlands (currently mapped) 23 #06E0D0 Herbaceous Wetlands (previous mapped extent) 24 #9F1FEC Mangrove Wetlands 25 #ffffe0 Natural Rock Surfaces 26 #DCDAC5 Dry Pans 27 #F9E0E0 Eroded Lands 28 #F9F9C5 Sand Dunes (terrestrial) 29 #F9F9A7 Coastal Sand Dunes & Beach Sand 30 #CDD2E0 Bare Riverbed Material 31 #ffffe0 Other Bare 32 #A62C39 Cultivated Commercial Permanent Orchards 33 #B31F5C Cultivated Commercial Permanent Vines 34 #DB0000 Cultivated Commercial Sugarcane Pivot Irrigated 35 #9F3978 Commercial Permanent Pineapples 36 #FF0000 Cultivated Commercial Sugarcane Non-Pivot (all other) 37 #F64D6C Cultivated Emerging Farmer Sugarcane Non-Pivot (all other) 38 #381A12 Commercial Annuals Pivot Irrigated 39 #521F1C Commercial Annuals Non-Pivot Irrigated 40 #85402C Commercial Annuals Crops Rain-Fed / Dryland / Non-Irrigated 41 #C5735F Subsistence / Small-Scale Annual Crops 42 #C1436C Fallow Land & Old Fields (Trees) 43 #C55E82 Fallow Land & Old Fields (Bush) 44 #D27592 Fallow Land & Old Fields (Grass) 45 #E0AAB8 Fallow Land & Old Fields (Bare) 46 #DB90A9 Fallow Land & Old Fields (Low Shrub) 47 #ECDB0F Residential Formal (Tree) 48 #F6EC13 Residential Formal (Bush) 49 #F9F81F Residential Formal (low veg / grass) 50 #FFFF29 Residential Formal (Bare) 51 #EC82EC Residential Informal (Tree) 52 #F691E0 Residential Informal (Bush) 53 #F99FCF Residential Informal (low veg / grass) 54 #FFC5CF Residential Informal (Bare) 55 #ECC500 Village Scattered (bare only) 56 #FFD91F Village Dense (bare only) 57 #AC7879 Smallholdings (Tree) 58 #B89192 Smallholdings (Bush) 59 #C49C9E Smallholdings (low veg / grass) 60 #D2B8B8 Smallholdings (Bare) 61 #BFFF00 Urban Recreational Fields (Tree) 62 #33FF33 Urban Recreational Fields (Bush) 63 #66FF66 Urban Recreational Fields (Grass) 64 #99FF99 Urban Recreational Fields (Bare) 65 #C49F0D Commercial 66 #8F8506 Industrial 67 #F9DD03 Roads & Rail (Major Linear) 68 #FFFF00 Mines: Surface Infrastructure 69 #B30606 Mines: Extraction Sites: Open Cast & Quarries combined 70 #C50606 Mines: Extraction Sites: Salt Mines 71 #D21D1A Mines: Waste (Tailings) & Resource Dumps 72 #F95479 Land-fills 73 #6CE7DC Fallow Land & Old Fields (wetlands)"},{"location":"projects/sa_nlc/#license","title":"License","text":"The South African National Land-Cover 2018 dataset is available on an open licence agreement.
Created by: Department of Forestry, Fisheries and the Environment, Republic of South Africa
Curated by: Geethen Singh & Samapriya Roy
Keywords: : land use, South Africa, land cover, Sentinel-2, copernicus, sentinel, satellite
Last updated: 2023-09-07
"},{"location":"projects/salinity/","title":"Global Surface water and groundwater salinity measurements (1980-2019)","text":"This data contains a new global salinity database, compiled from electrical conductivity (EC) monitoring data of both surface water (rivers, lakes/reservoirs) and groundwater locations over the period 1980-2019. The database includes information from over 16.3 million samples, from 45,103 surface water locations and 208,550 groundwater locations around the world.
The database consists of three categories; 1. River Data 2. Lake/Reservoir Data 3. Groundwater Data
Each category have two associated data files in csv-format, one containing the full salinity data, and one summary file currently the full salinity datasets are ingested. You can download the dataset here. You can read the article here
"},{"location":"projects/salinity/#data-citation","title":"Data Citation","text":"Thorslund, Josefin; van Vliet, Michelle T H (2020): A global salinity dataset of surface water\nand groundwater measurements from 1980-2019. PANGAEA, https://doi.org/10.1594/PANGAEA.913939\nThorslund, Josefin, and Michelle TH van Vliet. \"a global dataset of surface water and groundwater\nsalinity measurements from 1980\u20132019.\" Scientific Data 7, no. 1 (2020): 1-11.\nvar groundwater = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_water_salinity/groundwaters_database\");\nvar rivers = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_water_salinity/rivers_database\");\nvar lakes_reservoir = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_water_salinity/lakes_reservoirs_database\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/GLOBAL-WATER-SALINITY
"},{"location":"projects/salinity/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Thorslund et al
Curated by: Samapriya Roy
Keywords: : electrical conductivity, groundwater monitoring, Salinity, surface water, lakes, reservoirs
Last updated: 2021-11-25
"},{"location":"projects/sarl/","title":"Surface Area of Rivers and Lakes (SARL)","text":"The Surface Area of Rivers and Lakes (SARL) dataset, developed by Nyberg et al. (2024), provides a comprehensive analysis of water surface area changes in rivers and lakes over a 38-year period (1984-2022). This global dataset, at a 30-meter resolution, offers valuable insights into the dynamics of surface water, particularly highlighting the increasing role of seasonality.
The SARL dataset categorizes water bodies into seven classes: - 0: Background Value: Represents areas without water. - 1: Permanent River: Areas with continuous water presence throughout the year. - 2: Permanent Lake: Areas with continuous water presence throughout the year. - 3: Seasonal River: Areas with water present for at least one month during the year. - 4: Seasonal Lake: Areas with water present for at least one month during the year. - 5: No Data Lakes: Areas with missing data for lakes. - 6: No Data Rivers: Areas with missing data for rivers.
The study, reveals that while the total permanent surface area of both rivers and lakes has remained relatively constant, the areas experiencing intermittent seasonal coverage have significantly increased. Specifically, seasonal river coverage has risen by 12%, and seasonal lake coverage has increased by 27%. These trends are statistically significant across over 84% of global water catchments. The open access article published in Hydrology and Earth System Sciences can be found here. In addition, the datasets are archived in a Zenodo repository available at this url.
"},{"location":"projects/sarl/#citation","title":"Citation","text":"Nyberg, B., Sayre, R., and Luijendijk, E.: Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022,\nHydrol. Earth Syst. Sci., 28, 1653\u20131663, 2024.\nNyberg, B. (2023). Surface Area of River and Lakes (SARL) (1.0) [Data set]. Zenodo.\nhttps://doi.org/10.5281/zenodo.6895820\nvar sarl = ee.Image(\"projects/sat-io/open-datasets/SARL\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/SURFACE-AREA-RIVER-LAKES
Earth Engine App: https://bjornburrnyberg.users.earthengine.app/view/waterchange
App Source Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/SARL-APP
"},{"location":"projects/sarl/#license","title":"License","text":"Creative Commons Attribution 4.0 International
Created by: Nyberg et al 2024
Curated in GEE by: Nyberg and Samapriya Roy
Keywords: water, water change, rivers, lakes
"},{"location":"projects/sci/","title":"Social Connectedness Index (SCI)","text":"The Social Connectedness Index measures the strength of connectedness between two geographic areas as represented by Facebook friendship ties. These connections can reveal important insights about economic opportunities, social mobility, trade and more. We use aggregated friendship connections on Facebook to measure social connectedness between geographies. Locations are assigned to users based on information they provide, connection information, and location services they have opted into (to learn more about how Facebook uses location data and how to control location privacy see Location Privacy Basics). We use these friendships to estimate the probability a pair of users in these countries are Facebook friends (we rescale based on the population of two regions) and map this to an index score called the Social Connectedness Index (SCI). If the SCI is twice as large between two pairs of regions, it means the users in the first region-pair. are about twice as likely to be connected than the second region-pair. More details on the methodology can be found by clicking Learn More below, or in the paper Social Connectedness: Measurement, Determinants, and Effects published in the Journal of Economic Perspectives.
"},{"location":"projects/sci/#features","title":"Features","text":"Friendship Data The Social Connectedness Index offers a new type of data to the research and non-profit community, measuring the frequency and density of friendship and social ties around the world, a type of data that has rarely been made available to those interested in understanding how relationships affect social outcomes.
Global Reach With over 2.5 billion active users globally, the Social Connectedness Index from Facebook provides the first comprehensive measure of social networks at an international level.
Privacy The index provides researchers with connectedness scores, but not the number of links between two places or any of the underlying data. The data set uses sampling, differential privacy noise, and normalization to protect privacy.
"},{"location":"projects/sci/#extra-processing","title":"Extra processing","text":"The datasets are provided as TSV files with lat long Relative Wealth Index (RWI) and error. The TSV files are then converted to spatial by joining with country boundary units and ingested as tables for each of the countries. For now only Country to Country SCIs are processed. Two datasets are created one using the User or First Location and the second using the FR_LOC or Second Location. You can download the source data here to process it as needed.
"},{"location":"projects/sci/#citation","title":"Citation","text":"M. Bailey, R. Cao, T. Kuchler, J. Stroebel, and A. Wong. Social connectedness: Measurements,\ndeterminants, and effects. Journal of Economic Perspectives, 32(3):259\u201380, 2018b.and the Facebook Data for Good Program, Social Connectedness Index (SCI). https://dataforgood.fb.com/, Accessed DAY MONTH YEAR.\"\nEach dataset has three columns
Column Name Description user_loc First Location fr_loc Second Location scaled_sci Scaled SCI"},{"location":"projects/sci/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var sci_user_loc = ee.FeatureCollection(\"projects/sat-io/open-datasets/facebook/sci_user_loc\");\nvar sci_fr_loc = ee.FeatureCollection(\"projects/sat-io/open-datasets/facebook/sci_fr_loc\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/SOCIAL-CONNECTEDNESS-INDEX(SCI)
Interactive Map: http://beta.povertymaps.net/
"},{"location":"projects/sci/#license","title":"License","text":"Public Domain/No restrictions (CC0): Under the terms of this license you are free to use the material for any purpose without any restrictions.
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: : \"Social Connectedness Index, SCI, Facebook, CIESIN, country,first location, second location\"
Last updated: 2021-04-18
"},{"location":"projects/scs/","title":"Soil carbon storage in terrestrial ecosystems of Canada","text":"This collection contains datasets with the spatial distribution of carbon stock in soil and plants of Canada and canopy heights. It is being made public to act as supplementary data for the publication 'Large soil carbon storage in terrestrial ecosystems of Canada', currently under review. The maps were produced in the Remote Sensing Lab, McMaster University, between January and December 2020. This research project was made possible by a grant from the World Wildlife Fund (WWF)- Canada. This project aimed to produce the first wall-to-wall estimate of carbon stocks in plants and soils of Canada at 250 m spatial resolution using multisource satellite, climate and topographic data and a machine-learning algorithm.
You can read the paper here and download the datasets:
Canopy Height Map The canopy height maps were built to be included as covariates in the model to predict AGB (and carbon stock) in forest areas of Canada. We created wall-to-wall height metrics using ATL08 LiDAR products from the ICESat-2 satellite. The data was download for one-year period (October 2018 to October 2019). Points were filtered regarding solar background noise and atmospheric scattering, totaling 49,959 points distributed over the entire Canada. These points were associated with 10 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, annual PALSAR-2 HH and HV polarization, annual clumping index, and also the MODIS NDVI summer season. Afterwards, the random forest algorithm was used to extrapolate ATL08 parameters and develop regression models with the abovementioned spatially continuous variables.
Soil Carbon Concentration To generate the soil carbon concentration maps, we used 6,533 ground soil samples, long-term climate data, multisource remote sensing data, topografic information, soil type, depth, and a 3D random forest regression model.
Forest Carbon This dataset contains the map with total carbon stored in plants of forested areas in Canada (AGB, BGB and dead plants) and carbon stock uncertainty. To estimate the carbon stored in plants of forest areas, we used 47,967 ground measurements of AGB and 58 covariates mainly composed of optical data, terrain parameters, structural parameters (e.g., SAR data, clump index, canopy heights \u2013 generated from satellite LiDAR- included in the other dataset), soil type map and radiation flux data. We used a random forest model for spatial prediction of AGB in forest areas while 1st and 3rd quantiles of RF quantile regression were used to build the uncertainty map. After generating the AGB map, the root biomass of forest areas was computed by its relationship to AGB according to forest type. The dead plant materials were computed by a linear regression between live and dead AGB defined with ground measurements. Ultimately, the AGB as well as dead plant materials and BGB were multiplied by 0.5 to provide the carbon maps. includes carbon stored in above and belowground biomass and dead plant materials
Soil Carbon Stock Map To generate the soil carbon stock map, we used 6,490 ground samples of soil organic carbon concentration (g/kg) and 2,973 ground samples of bulk density (kg/dm3), long-term climate data, multisource remote sensing data, topografic information, soil type, depth, and a 3D random forest regression model. The uncertainty map was generated using the random forest quantile regression approach difference between 95th and 5th quantiles (90% confidence interval) of soil organic carbon and bulk density predictions. Water and ice/snow areas were masked based on the 2015 Land Cover of Canada and SOC stock in permafrost areas was discounted according to ice abundance using the 'Ground ice map of Canada' (O'Neill et al., 2020).
GEE asset Variable name projects/sat-io/open-datasets/carbon_stocks_ca/ch Canopy Height projects/sat-io/open-datasets/carbon_stocks_ca/fc Forest Carbon projects/sat-io/open-datasets/carbon_stocks_ca/sc Soil Carbon Stock projects/sat-io/open-datasets/carbon_stocks_ca/scc Soil Carbon Concentration"},{"location":"projects/scs/#data-citation","title":"Data Citation","text":"Sothe, C., Gonsamo, A., Arabian, J., Kurz, W. A., Finkelstein, S. A., & Snider, J. (2022). Large soil carbon storage in terrestrial ecosystems of Canada. Global Biogeochemical Cycles, 36, e2021GB007213. https://doi.org/10.1029/2021GB007213\nvar ch = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon_stocks_ca/ch\");\nvar fc = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon_stocks_ca/fc\");\nvar sc = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon_stocks_ca/sc\");\nvar scc = ee.ImageCollection(\"projects/sat-io/open-datasets/carbon_stocks_ca/scc\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/SOIL-CARBON-STOCKS-CANADA
"},{"location":"projects/scs/#license","title":"License","text":"This work is licensed under and freely available to the public (similar to a CC0 license).
Created by: Sothe et al 2022
Curated in GEE by : Samapriya Roy
keywords: soil carbon density, soil carbon stock estimate, soil carbon storage, terrestrial ecosystem models, machine Learning Methods Enabled Predictive Modeling
Last updated on GEE: 2021-11-14
"},{"location":"projects/secondary_forest/","title":"Benchmark maps of 33 years of secondary forest age for Brazil v3 (1986-2019)","text":"The restoration and reforestation of 12 million hectares of forests by 2030 are amongst the leading mitigation strategies for reducing carbon emissions within the Brazilian Nationally Determined Contribution targets assumed under the Paris Agreement. Understanding the dynamics of forest cover, which steeply decreased between 1985 and 2018 throughout Brazil, is essential for estimating the global carbon balance and quantifying the provision of ecosystem services. To know the long-term increment, extent, and age of secondary forests is crucial; however, these variables are yet poorly quantified. Here we developed a 30-m spatial resolution dataset of the annual increment, extent, and age of secondary forests for Brazil over the 1986\u20132018 period. Land-use and land-cover maps from MapBiomas Project were used as input data for our algorithm, implemented in the Google Earth Engine platform. This dataset provides critical spatially explicit information for supporting carbon emissions reduction, biodiversity, and restoration policies, enabling environmental science applications, territorial planning, and subsidizing environmental law enforcement. Read the dataset paper and details here
"},{"location":"projects/secondary_forest/#citation","title":"Citation:","text":"Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G., Broggio, I.S., Rosan, T.M., Doblas, J.,\nAnderson, L.O., Rousseau, G.X., Shimabukuro, Y.E., Silva, C.A., House, J.I., Arag\u00e3o, L.E.O.C.\nBenchmark maps of 33 years of secondary forest age for Brazil. Scientific Data (2020).\nhttps://doi.org/10.1038/s41597-020-00600-4\nYou can access the dataset here: https://doi.org/10.5281/zenodo.3928660
The updated v3 of the dataset is available directly as GEE collections
"},{"location":"projects/secondary_forest/#dataset-citation","title":"Dataset Citation","text":"Celso H. L. Silva Junior, Viola H. A. Heinrich, Ana T. G. Freire, Igor S. Broggio, Thais M. Rosan, Juan Doblas,\nLuiz E. O. C. Arag\u00e3o. (2020). Benchmark maps of 33 years of secondary forest age for Brazil (Version v2.0.0)\n[Data set]. Scientific Data. Zenodo. http://doi.org/10.5281/zenodo.3928660\nvar forest_age = ee.Image('users/celsohlsj/public/secondary_forest_age_collection5_v3');\nvar forest_extent = ee.Image('users/celsohlsj/public/secondary_forest_extent_collection5_v3');\nvar forest_increment = ee.Image('users/celsohlsj/public/secondary_forest_increment_collection5_v3');\nvar forest_loss = ee.Image('users/celsohlsj/public/secondary_forest_loss_collection5_v3');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/BRAZIL-SECONDARY-FOREST-AGE
"},{"location":"projects/secondary_forest/#technical-validation","title":"Technical Validation","text":"This dataset was based on the Collection 4.1 of MapBiomas Project (Annual Land-Use and Land-Cover Maps of Brazil)1; thus, the accuracy of the secondary forest increment, extension and age maps presented here is anchored to the accuracy of the MapBiomas land-use and land-cover dataset. The MapBiomas analyses of accuracy were performed using the Pontius Jr and Millones (2011) method23. For the entire Brazil24, the MapBiomas dataset has an average of 86.40\u2009\u00b1\u20090.46% of overall accuracy, 11.06\u2009\u00b1\u20090.67% of allocation disagreement, and 2.5\u2009\u00b1\u20090.29% of area disagreement between 1985 and 2018, considering the land-use and land-cover classes from the legend level with the greatest detail (level 3).The accuracy assessment for the Brazilian biomes can be found in the MapBiomas accuracy statistics web page (https://mapbiomas.org/en/accuracy-analysis).
"},{"location":"projects/secondary_forest/#license-usage","title":"License & Usage","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Github Page: https://github.com/celsohlsj/gee_brazil_sv
Export Page for App: https://code.earthengine.google.com/13bfcedb77ac7bac9ea1fb962b587a54?hideCode=true
Zenodo Data download page: https://zenodo.org/record/3928660
Created and Curated by: Celso H. L. Silva Junior
Keywords: Deforestation, MapBiomas, Climate Change, Forest Restoration, Carbon Sequestration
Last updated: 2021-03-05
"},{"location":"projects/shd_sun_gpp/","title":"Global Sunlit and Shaded GPP for vegetation canopies (1992-2020)","text":"Gross primary production (GPP) is a vital component of the terrestrial carbon budget and plays a prominent role in the global carbon cycle. Accurate estimation of terrestrial GPP is critical for understanding the interaction between the terrestrial biosphere and the atmosphere in the context of global climate change projecting future change, and informing climate policy decisions. GPP is closely related to vegetation types meteorological factors, soil moisture, and other factors. In particular, GPP is affected by vegetation canopy structures e.g., sunlit and shaded leaves. Sunlit leaves can absorb direct and diffuse radiation simultaneously, and light saturation is easy to occur when the radiation is high, while shaded leaves can only absorb diffuse radiation and the absorbed radiation intensity is generally between the light compensation point and the light saturation point.
Here we produce a global 0.05\u00b0, 8-day dataset for GPP, GPPshade and GPPsun over 1992\u20132020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Such products can support exploring the similarities and differences of sunlit and shaded leaves contributing to GPP or SIF (Sun induced chlorophyll fluorescence), to further excavate the interior ecological mechanism of different carbon cycle processes and advance carbon cycle modelling.
You can download the dataset here and read the paper here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/shd_sun_gpp/#usage-notes-from-dataset-page","title":"Usage notes from Dataset page","text":"The units of three temporal resolutions (8-day, monthly, annual) are gC m-2 8day-1, gC m-2 month-1 and gC m-2 a-1, respectively. And the scale factor of the monthly data is 0.1, that of the 8-day data is 0.01. In the dataset, in order to ensure the authenticity, we did not delete or modify a small number of abnormally high values (caused by LAI). Therefore, when using this dataset, you can set thresholds to remove the anomalies.
"},{"location":"projects/shd_sun_gpp/#citation","title":"Citation:","text":"Bi, W., He, W., Zhou, Y. et al. A global 0.05\u00b0 dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020. Sci\nData 9, 213 (2022). https://doi.org/10.1038/s41597-022-01309-2\nWenjun, Bi; Yanlian, Zhou (2022), A global 0.05\u00b0 dataset for gross primary production of sunlit and shaded vegetation canopies (1992\u20132020), Dryad,\nDataset, https://doi.org/10.5061/dryad.dfn2z352k\nvar gpp_annual = ee.Image(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/gpp_yearly/GPP_v21_2020\");\nvar shaded_annual = ee.Image(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/shaded_yearly/Shade_GPP_v21_2020\");\nvar sunlit_annual = ee.Image(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/sunlit_yearly/Sun_GPP_v21_2020\");\nvar gpp_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/gpp_monthly\");\nvar shaded_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/shaded_monthly\");\nvar sunlit_monthly = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/sunlit_monthly\");\nvar gpp_8day = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/gpp_8day\");\nvar shaded_8day = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/shaded_8day\");\nvar sunlit_8day = ee.ImageCollection(\"projects/sat-io/open-datasets/GPP_SUNLIT_SHADED/sunlit_8day\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-SUNLIT-SHADED-GPP-VEG-CANOPIES
"},{"location":"projects/shd_sun_gpp/#license-usage","title":"License & Usage","text":"This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
Curated in GEE by: Samapriya Roy
Keywords: carbon flux, global changes, long-time series, shaded GPP, sunlit GPP
Last updated: 2022-09-16
"},{"location":"projects/shoreline/","title":"Global Shoreline Dataset","text":"A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1\u2005km2 (21,818), and islands smaller than 1\u2005km2 (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.
Publication URL: https://pubs.er.usgs.gov/publication/70202401
Scale: 30m
Please use Citation:
Sayre, R., S. Noble, S. Hamann, R. Smith, D. Wright, S. Breyer, K. Butler, K. Van Graafeiland, C. Frye, D. Karagulle, D. Hopkins, D. Stephens, K. Kelly, Z. Basher, D. Burton, J. Cress, K. Atkins, D. Van Sistine, B. Friesen, R. Allee, T. Allen, P. Aniello, I. Asaad, M. Costello, K. Goodin, P. Harris, M. Kavanaugh, H. Lillis, E. Manca, F. Muller-Karger, B. Nyberg, R. Parsons, J. Saarinen, J. Steiner, and A. Reed. 2019. A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units. Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: 10.1080/1755876X.2018.1529714\nShared Under: Creative Commons Attribution-Share Alike 4.0 International License
"},{"location":"projects/shoreline/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var mainlands = ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/mainlands');\nvar big_islands = ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/big_islands');\nvar small_islands = ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/small_islands');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL_SHORELINES
Extra Info: Over 100 Million+ vertices
Curated by: Samapriya Roy
Keywords: Global Shoreline, Shoreline, Oceans
Last updated: 2020-05-08
"},{"location":"projects/slrdem/","title":"NOAA Sea-Level Rise Digital Elevation Models (DEMs)","text":"The NOAA Coastal Services Center has developed high-resolution digital elevation models (DEMs) for use in the Center's Sea Level Rise and Coastal Flooding Impacts internet mapping application. These DEMs serve as source datasets used to derive data to visualize the impacts of inundation resulting from sea level rise along the coastal United States and its territories.
These data were created as part of the National Oceanic and Atmospheric Administration Coastal Services Center's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise (slr) and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed here
URL(s) of dataset description can be found here and the dataset can be downloaded here
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/slrdem/#preprocessing","title":"Preprocessing","text":"While the datasets were collected and made available from NOAA different collects do have varying nominal resolutions , different CRS and different no data values. While GEE collections will allow for variable values for all of those, the nominal resolution and native CRS was left intact was no data value was reprocessed to -9999 by simply using gdalwarp. I have added a function onto the example script which allows you to add the nominal scale as a property to the collection in case the user would like to split and apply different methods on top.
"},{"location":"projects/slrdem/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var slrdem = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/SLR_DEM\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/NOAA-SLR-DEM
"},{"location":"projects/slrdem/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. There are no restrictions on the use of data received from the U.S. Geological Survey's Earth Resources Observation and Science (EROS) Center or NASA's Land Processes Distributed Active Archive Center (LP DAAC), unless expressly identified prior to or at the time of receipt. Depending on the product source, we request that the following statements be used when citing, copying, or reprinting data: Data available from the U.S. Geological Survey.
Provider: NOAA
Curated by: Samapriya Roy
Keywords: Elevation, topography, topobathymetric, bathymetry, SLR, DEM, sea level rise
Last updated on GEE: 2022-02-27
"},{"location":"projects/snodas/","title":"Snow Data Assimilation System (SNODAS)","text":"The Snow Data Assimilation System (SNODAS) represents a comprehensive modeling and data assimilation system meticulously developed by the National Operational Hydrologic Remote Sensing Center (NOHRSC). Its primary objective is to provide highly accurate estimations of snow cover and associated parameters, serving as a crucial resource for hydrologic modeling and analysis. SNODAS achieves this by assimilating data from a diverse array of sources, including satellite observations, ground-based measurements, and numerical weather prediction models. These disparate data streams undergo thorough processing within a snow mass and energy balance model, ultimately yielding estimations of snow water equivalent (SWE), snow depth, snow cover extent, and snow albedo.
The SNODAS dataset boasts a spatial resolution of 1 km and a temporal resolution of 24 hours, ensuring precise and timely insights. Updated daily, the dataset encompasses the continental United States, Alaska, and Hawaii, offering comprehensive coverage for users across a spectrum of applications. SNODAS data caters to a wide-ranging audience, including water resource managers, emergency responders, and climate scientists. These invaluable data play a pivotal role in diverse applications, including estimating snowmelt runoff, forecasting snow avalanches, monitoring snowpack conditions for drought and flood management, and conducting studies on the influence of climate change on snow dynamics. SNODAS data is freely accessible through the National Snow and Ice Data Center (NSIDC), further enhancing its accessibility and utility for a broad user base.
This dataset description provides a comprehensive overview of SNODAS, emphasizing its significance in supporting hydrologic research and decision-making across various domains. You can find additional information here and you can also find link to the dataset in climate engine org here
"},{"location":"projects/snodas/#dataset-details","title":"Dataset details","text":"Spatial extent Conterminous US Spatial resolution 1000 m (1/120-deg) Temporal resolution Daily Time span 2003-10-01 to present Update frequency Updated daily with 1 day lag
Variables
Variable Units Scale Factor Snow Water Equivalent Meters 1.0 Snow Depth Meters 1.0
"},{"location":"projects/snodas/#citation","title":"Citation","text":"Barrett, Andrew. 2003. National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS) Products at NSIDC. NSIDC Special\nReport 11. Boulder, CO USA: National Snow and Ice Data Center. 19 pp.\n\nBarrett, A. P., R. L. Armstrong, and J. L. Smith. 2001. The Snow Data Assimilation System (SNODAS): An overview.\nJournal of Hydrometeorology 2(3):288-306.\n// Read in Image Collection and get image\nvar snodas_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/climate-engine/snodas/daily')\nvar snodas_i = snodas_ic.filterDate('2022-01-01', '2022-01-05').first()\n\n// Print first image to see bands\nprint(snodas_i)\n\n// Visualize select bands from first image\nvar prec_palette = [\"#ffffcc\", \"#c7e9b4\", \"#7fcdbb\", \"#41b6c4\", \"#1d91c0\", \"#225ea8\", \"#0c2c84\"]\nMap.addLayer(snodas_i.select('Snow_Depth'), {min: 0, max: 1, palette: prec_palette}, 'Snow_Depth')\nMap.addLayer(snodas_i.select('SWE'), {min: 0, max: 1, palette: prec_palette}, 'SWE')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/SNODAS-DAILY
"},{"location":"projects/snodas/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: snow, climate, near real-time, CONUS, United States, NOAA, daily
Created & provided by: NOAA, NSIDC
Curated by: Climate Engine Org
"},{"location":"projects/snow_cover/","title":"Global MODIS-based snow cover monthly values (2000-2020)","text":"The Global monthly snow cover repository contains multiple products (based on the MODIS/Terra MOD10A2), the description along with the datasets can be found here
Quantiles (probability either 0.05, 0.5, 0.9 and/or 0.95) have been derived by matching dates in the filenames (daily or weekly values). After deriving quantiles, gaps were filled using temporal neighbors (e.g. missing values for year 2002 were filled using average of values between year 2001 and 2003). The gaps were especially large for months of November, December, January and February, northern Hemisphere. Important note: maps still contain some artifacts due to high reflections of white-sands e.g. Salar de Uyuni desert in Bolivia and similar. Processing steps are available here. Antarctica is not included.
To access and visualize global datasets use: https://openlandmap.org
If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:
Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues All files provided as Cloud-Optimized GeoTIFFs / internally compressed using \"COMPRESS=DEFLATE\" creation option in GDAL. File naming convention:
Hengl, T. (2021). Global MODIS-based snow cover monthly long-term (2000-2012) at 500 m, and aggregated monthly values (2000-2020)\nat 1 km (v1.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.5774954\nvar lt_p90 = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/monthly_lt_p90\");\nvar lt_sd = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/monthly_lt_sd\");\nvar lt_snow_quantile = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/monthly_snow_quantile\");\nvar tmax_geom = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/midmonth_geom_tmax\");\nvar tmin_geom = ee.ImageCollection(\"projects/sat-io/open-datasets/MODIS_LT_SNOW/midmonth_geom_tmin\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-MODIS-SNOWCOVER
"},{"location":"projects/snow_cover/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Hengl 2021
Curated by: Samapriya Roy
Keywords: : snow cover, global, openlandmap
Last updated: 2021-12-18
"},{"location":"projects/soc/","title":"Soil Organic Carbon Stocks & Trends South Africa","text":"Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details
"},{"location":"projects/soc/#citation","title":"Citation","text":"Venter, Zander S., Heidi-Jayne Hawkins, Michael D. Cramer, and Anthony J. Mills. \"Mapping soil organic\ncarbon stocks and trends with satellite-driven high resolution maps over South Africa.\" Science of The\nTotal Environment 771 (2021): 145384.\nvar SOC30_mean = ee.ImageCollection(\"projects/sat-io/open-datasets/NINA/SOC30_SA_mean\");\nvar SOC30_trend = ee.ImageCollection(\"projects/sat-io/open-datasets/NINA/SOC30_SA_trend\");\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/SOIL-ORGANIC-CARBON-SA
"},{"location":"projects/soc/#dataset-details","title":"Dataset Details","text":"Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are:
NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.
"},{"location":"projects/soc/#dataset-citation","title":"Dataset Citation","text":"Venter, Zander S, Hawkins, Heidi-Jayne, Cramer, Michael D, & Mills, Anthony J. (2020). Soil organic\ncarbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas\nof South Africa (Version 01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4384692\nCreative Commons Attribution-Share Alike 4.0 International License
Created by: Venter, Zander S, Hawkins, Heidi-Jayne, Cramer, Michael D, & Mills, Anthony J
Curated by: Samapriya Roy
Keywords: : carbon stocks, land degradation, natural climate solutions, remote sensing, soil mapping, spatial prediction, soil carbon, carbon sequestration
Last updated: 2021-04-29
"},{"location":"projects/soil_bioclim/","title":"Global Soil bioclimatic variables","text":"Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts).
"},{"location":"projects/soil_bioclim/#citation","title":"Citation","text":"Lembrechts, Jonas J., Johan van den Hoogen, Juha Aalto, Michael B. Ashcroft, Pieter De Frenne, Julia Kemppinen, Martin Kopeck\u00fd et al. \"Global maps\nof soil temperature.\" Global Change Biology 28, no. 9 (2022): 3110-3144.\nvar SBIO_0_5cm = ee.Image(\"projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm\")\nvar SBIO_5_15cm = ee.Image(\"projects/crowtherlab/soil_bioclim/SBIO_v2_5_15cm\")\n// Load image\nvar SBIO_0_5cm = ee.Image('projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm')\n\n// Print bandNames\nprint(SBIO_0_5cm.bandNames())\n\n// Add to map\nMap.addLayer(SBIO_0_5cm.select('SBIO1_Annual_Mean_Temperature'),\n {min: -10, max: 30, palette: [\"2166AC\", \"4393C3\", \"92C5DE\", \"D1E5F0\", \"FDDBC7\", \"F4A582\", \"D6604D\", \"B2182B\"]},\n 'SBIO1_Annual_Mean_Temperature')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/SOIL-BIOCLIM
Extra Info: Each of the 11 soil bioclims is available for two soil depths: 0-5 cm and 5-15cm.
"},{"location":"projects/soil_bioclim/#license","title":"License","text":"Creative Commons Attribution 4.0 International License
Curated by: Jonas Lembrechts & Johan van den Hoogen
Keywords: Bioclim, soil, soil temperature, climate
Last updated: 2022-10-02
"},{"location":"projects/soil_nematode/","title":"Soil nematode abundance & functional group composition","text":"As the most abundant animals on earth, nematodes are a dominant component of the soil community. They play critical roles in regulating biogeochemical cycles and vegetation dynamics within and across landscapes and are an indicator of soil biological activity. Here, we present a comprehensive global dataset of soil nematode abundance and functional group composition. This dataset includes 6,825 georeferenced soil samples from all continents and biomes. For geospatial mapping purposes these samples are aggregated into 1,933 unique 1-km pixels, each of which is linked to 73 global environmental covariate data layers. This study uses direct measurements of soil nematode abundance from 6,825 georeferenced locations around the world, covering all continents and all terrestrial biomes. You can read the paper here
"},{"location":"projects/soil_nematode/#data-citation","title":"Data Citation","text":"Hoogen, Johan van den; Geisen, Stefan; Wall, Diana H.; Wardle, David A.; Traunspurger, Walter; Goede, Ron G. M. de;\net al. (2020): A global database of soil nematode abundance and functional group composition. figshare. Collection.\nhttps://doi.org/10.6084/m9.figshare.c.4718003.v1\nvan den Hoogen, J., Geisen, S., Wall, D.H. et al. A global database of soil nematode abundance and functional group\ncomposition. Sci Data 7, 103 (2020). https://doi.org/10.1038/s41597-020-0437-3\nvar nematode= ee.FeatureCollection(\"projects/sat-io/open-datasets/global-nematode\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/SOIL-NEMATODE-ABUNDANCE
Property Name GEE_Property Description Units Source Sample_ID \ufeffSample_ID Unique sample ID Bacterivores Bacterivores Number of bacterivorous nematodes individuals per 100g dry soil Fungivores Fungivores Number of fungivorous nematodes individuals per 100g dry soil Herbivores Herbivores Number of herbivorous nematodes individuals per 100g dry soil Omnivores Omnivores Number of omnivorous nematodes individuals per 100g dry soil Predators Predators Number of predatory nematodes individuals per 100g dry soil Unidentified Unidentified Number of unidentified nematodes individuals per 100g dry soil Total_Number Total_Number Total number of nematodes individuals per 100g dry soil Latitude Pixel_Lat Sample latitude Decimal degree in WGS84 Longitude Pixel_Long Sample longitude Decimal degree in WGS84 WWF_Biome WWF_Biome WWF Biome https://www.worldwildlife.org/biomes sampling method sampling method: Nematode extraction method sampling_ref sampling_ref Nematode extraction method, summarised sampling depth sampling depth Sampling Depth cm DOI/URL DOI/URL Reference to original publication, where applicable Data_provider Data_provider Name of co-author(s) who supplied data"},{"location":"projects/soil_nematode/#license","title":"License","text":"Creative Commons Attribution-Share Alike 4.0 International License
Created by: Hoogen et al
Curated by: Samapriya Roy
Keywords: : nematode,soil ecology,biogeographic studies,soil biotic community
Last updated: 2021-08-16
"},{"location":"projects/soilprop/","title":"Soil Properties 800m","text":"The data shown here were obtained by aggregating current USDA-NCSS soil survey data (SSURGO back-filled with STATSGO where SSURGO is not available) within 800m\u00b2 grid cells. This data aggregation technique results in maps that may not match the original data at any given point, and is intended to depict regional trends in soil properties at the statewide scale.
This app was developed by the California Soil Resource Lab at UC Davis and UC-ANR in collaboration with the USDA Natural Resources Conservation Service. Please use the following citation for this website and gridded data products:
"},{"location":"projects/soilprop/#citation","title":"Citation","text":"Walkinshaw, Mike, A.T. O'Geen, D.E. Beaudette. \"Soil Properties.\" California Soil Resource Lab, 1 Oct. 2020,\ncasoilresource.lawr.ucdavis.edu/soil-properties/.\nFor layers with depth profiles a _profile collection is created with a min_depth and max_depth property,this can then be used for filtering an allows the varying profiles to stay in single image collection for a single property like sand and sand_profile for example.
You can download the data layers here along with lookup tables for layers that are categorical instead of continuous. Also mode pyramding policy is applied to all categorical layers and you can find the lookup table for all categorical variable in the download page.
"},{"location":"projects/soilprop/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var caco3 = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/caco3');\nvar cec = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/cec');\nvar cec_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/cec_profile');\nvar ec = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/ec');\nvar ec_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/ec_profile');\nvar ph = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/ph');\nvar ph_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/ph_profile');\nvar sar = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/sar');\nvar som = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/som');\nvar som_max = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/chemical/som_max');\nvar hydrologic_group = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/hydrologic_group');\nvar kw_025 = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/kw_025');\nvar lcc_i = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/lcc_i');\nvar lcc_ni = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/lcc_ni');\nvar resdept = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/resdept');\nvar soil_depth = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/soil_depth');\nvar soil_order = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/soil_order');\nvar soil_temp_regime = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/soil_temp_regime');\nvar survey_type = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/survey_type');\nvar wind_erodibility_group = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/wind_erodibility_group');\nvar wind_erodibility_index = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/land_use/wind_erodibility_index');\nvar bulk_density = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/bulk_density');\nvar clay = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/clay');\nvar clay_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/clay_profile');\nvar drainage_class = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/drainage_class');\nvar ksat_05 = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/ksat_05');\nvar ksat_max = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/ksat_max');\nvar ksat_mean = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/ksat_mean');\nvar ksat_min = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/ksat_min');\nvar rf_025 = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/rf_025');\nvar sand = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/sand');\nvar sand_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/sand_profile');\nvar silt = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/silt');\nvar silt_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/silt_profile');\nvar soil_texture_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/soil_texture_profile');\nvar water_storage = ee.Image('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/water_storage');\nvar water_storage_profile = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/physical/water_storage_profile');\nvar soil_color = ee.ImageCollection('projects/earthengine-legacy/assets/projects/sat-io/open-datasets/CSRL_soil_properties/soil_color');\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/CRSL-SOIL-PROPERTIES-800
"},{"location":"projects/soilprop/#soil-texture-palette","title":"Soil Texture Palette","text":"Palettes have been created for variable types and a palette for Soil Texture is included below. Some palettes include rgb values and can be converted in hex codes for each variable type. These can be extracted from the app page and a few are included in the sample code.
#BEBEBE Sand #FDFD9E Loamy Sand #ebd834 Sandy Loam #307431 Loam #CD94EA Silt Loam #546BC3 Silt #92C158 Sandy Clay Loam #EA6996 Clay Loam #6D94E5 Silty Clay Loam #4C5323 Sandy Clay #E93F4A Silty Clay #AF4732 Clay
Sample Code with Soil Texture Class:https://code.earthengine.google.com/bb16ef5adbd5711d9fcb77ce7705618e
"},{"location":"projects/soilprop/#shared-license","title":"Shared License","text":"This work is licensed under a Creative Commons Attribution 4.0 International and is an open license unless otherwise indicated. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : California Soil Resource Lab at UC Davis and UC-ANR in collaboration with the USDA Natural Resources Conservation Service
Curated in GEE by: Samapriya Roy
Keywords: Soil grid, USDA, CSRL, UCANR,USDA, Soil, Coarse
Last updated: 2021-07-22
"},{"location":"projects/speedtest/","title":"Global fixed broadband and mobile (cellular) network performance","text":"Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy.
Available years of datasets: 2019,2020,2021,2022,2023 and 2024.Find the GitHub project and datasets here: https://github.com/teamookla/ookla-open-data You can also download the datasets from AWS Open data registry: https://registry.opendata.aws/speedtest-global-performance/
"},{"location":"projects/speedtest/#tiles","title":"Tiles","text":"Hundreds of millions of Speedtests are taken on the Ookla platform each month. In order to create a manageable dataset, we aggregate raw data into tiles. The size of a data tile is defined as a function of \"zoom level\" (or \"z\"). At z=0, the size of a tile is the size of the whole world. At z=1, the tile is split in half vertically and horizontally, creating 4 tiles that cover the globe. This tile-splitting continues as zoom level increases, causing tiles to become exponentially smaller as we zoom into a given region. By this definition, tile sizes are actually some fraction of the width/height of Earth according to Web Mercator projection (EPSG:3857). As such, tile size varies slightly depending on latitude, but tile sizes can be estimated in meters.
For the purposes of these layers, a zoom level of 16 (z=16) is used for the tiling. This equates to a tile that is approximately 610.8 meters by 610.8 meters at the equator (18 arcsecond blocks). The geometry of each tile is represented in WGS 84 (EPSG:4326) in the tile field.
Each tile contains the following adjoining attributes:
Field Name Type Description avg_d_kbps Integer The average download speed of all tests performed in the tile, represented in kilobits per second. avg_u_kbps Integer The average upload speed of all tests performed in the tile, represented in kilobits per second. avg_lat_ms Integer The average latency of all tests performed in the tile, represented in milliseconds tests Integer The number of tests taken in the tile. devices Integer The number of unique devices contributing tests in the tile. quadkey Text The quadkey representing the tile."},{"location":"projects/speedtest/#quadkeys","title":"Quadkeys","text":"Quadkeys can act as a unique identifier for the tile. This can be useful for joining data spatially from multiple periods (quarters), creating coarser spatial aggregations without using geospatial functions, spatial indexing, partitioning, and an alternative for storing and deriving the tile geometry.
"},{"location":"projects/speedtest/#citation","title":"Citation","text":"Speedtest\u00ae by Ookla\u00ae Global Fixed and Mobile Network Performance Maps.\nBased on analysis by Ookla of Speedtest Intelligence\u00ae data for [DATA TIME PERIOD].\nProvided by Ookla and accessed [DAY MONTH YEAR]. Ookla trademarks used under license\nand reprinted with permission.\nTwo layers are distributed as separate sets of files:
performance_mobile_tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a cellular connection type (e.g. 4G LTE, 5G NR).performance_fixed_tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a non-cellular connection type (e.g. WiFi, ethernet).Layers are generated based on a quarter year of data (three months) and files will be updated and added on a quarterly basis. A /year=2020/quarter=1/ period, the first quarter of the year 2020, would include all data generated on or after 2020-01-01 and before 2020-04-01.
Data is subject to be reaggregated regularly in order to honor Data Subject Access Requests (DSAR) as is applicable in certain jurisdictions under laws including but not limited to General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Lei Geral de Prote\u00e7\u00e3o de Dados (LGPD). Therefore, data accessed at different times may result in variation in the total number of tests, tiles, and resulting performance metrics.
"},{"location":"projects/speedtest/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var mobile_20210101 = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/mobile_tiles/2022-01-01_performance_mobile_tiles\");\nvar fixed_20210101 = ee.FeatureCollection(\"projects/sat-io/open-datasets/network/fixed_tiles/2022-01-01_performance_fixed_tiles\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-FIXED-MOBILE-NETWORK-PERFORMANCE
Earth Engine files for mobile and fixed tiles across different quarters are arranged in the format, since quarters are 3 month intervals replace month variable by 01,04,07,10 which represents 3 month intervals
* ee.FeatureCollection(\"projects/sat-io/open-datasets/network/mobile_tiles/Year-month-01_performance_mobile_tiles\")\n* ee.FeatureCollection(\"projects/sat-io/open-datasets/network/fixed_tiles/Year-month-01_performance_mobile_tiles\")\nAs part of processing this datasets I further converted these datasets into 32 bit float rasters , these are produced at 610m resolution and feature property such as avg_d_kbps,avg_u_kbps,avg_lat_ms,devices,tests are converted in Bands for these images. The start and end date for each quarter are further added to the images however the quad information is not retained from vector to raster conversion. The result are two image collections for fixed and mobile datasets.
"},{"location":"projects/speedtest/#earth-engine-snippet_1","title":"Earth Engine Snippet","text":"var fixed = ee.ImageCollection(\"projects/sat-io/open-datasets/network/raster_tiles/performance_fixed_tiles\");\nvar mobile = ee.ImageCollection(\"projects/sat-io/open-datasets/network/raster_tiles/performance_mobile_tiles\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-FIXED-MOBILE-NETWORK-PERF-RASTER
"},{"location":"projects/speedtest/#license","title":"License","text":"These datasets are made available under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Provided by: Ookla
Curated in GEE by: Samapriya Roy
Keywords: : analytics,broadband,cities,civic,infrastructure,internet,network traffic, telecommunications,tiles
Last updated: 2024-08-01
"},{"location":"projects/spring_indices/","title":"High Res Extended Spring Indices database","text":"The Extended Spring Indices (SI-x) provide a comprehensive dataset for studying the timing of spring onset and its relationship to climate change. These models, derived from daily minimum and maximum temperatures, track the first leaf and first bloom events for key plant species. By transforming temperature data into consistent indices, the SI-x enable the calculation of the frost damage index. This dataset offers a multi-decadal, high-resolution (1 km) analysis of spring phenology across North America (1980-2022) and Europe (1950-2020).
At the present, the 1 km SI-x products are available over two study areas
North and Central America (located between 14\u00b002'31.3\"N and 55\u00b037'04.1\"N latitude and 56\u00b005'50.7\"W 126\u00b022'06.1\"W longitude). Daymet version 4 from 1980 to 2022, was used to generate this dataset. The daily maximunim and minimum temperature and daylength are available in GEE. These SI-x products are an updated version of those presented in Izquierdo-Verdiguier et al.
Europe (located between 35\u00b055'48.7\"N and 73\u00b032'47.1\"N latitude and 10\u00b036'29.5\"W and 44\u00b050'29.5\"E longitude). The daily maximum and minimum temperature come from the Downscaled version of European Observations (E-OBS) version 3 from 1950 to 2020, which is available for download here. The daylength is calculated (modelled) once the data are ingested in GEE (ingested because this data is not directly available in GEE).
Izquierdo-Verdiguier, Emma, Ra\u00fal Zurita-Milla, Toby R. Ault, and Mark D. Schwartz. \"Development and analysis of spring plant phenology products: 36\nyears of 1-km grids over the conterminous US.\" Agricultural and forest meteorology 262 (2018): 34-41.\n\nIzquierdo-Verdiguier, Emma, and Ra\u00fal Zurita-Milla. \"A multi-decadal 1 km gridded database of continental-scale spring onset products.\"\nScientific Data 11.1 (2024): 905. (https://www.nature.com/articles/s41597-024-03710-5).\n// Define Image Collections\nvar bloomDaymet = 'projects/sat-io/open-datasets/SIx_products/BloomDaymetv4';\nvar bloomEurope = 'projects/sat-io/open-datasets/SIx_products/BloomEuropev3';\nvar diDaymet = 'projects/sat-io/open-datasets/SIx_products/DI_Daymetv4';\nvar diEurope = 'projects/sat-io/open-datasets/SIx_products/DI_Europev3';\nvar leafDaymet = 'projects/sat-io/open-datasets/SIx_products/LeafDaymetv4';\nvar leafEurope = 'projects/sat-io/open-datasets/SIx_products/LeafEuropev3';\nSample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/EXTENDED-SPRING-INDICES
Earth Engine App: https://emma.users.earthengine.app/view/spring-onset
"},{"location":"projects/spring_indices/#license","title":"License","text":"This work is licensed under a CC BY-NC 4.0 license.
Created by: Izquierdo-Verdiguier. 2024
Curated in GEE by : Samapriya Roy
Keyworks: spring onset, phenology, climate change
Last updated in GEE: 2024-08-29
"},{"location":"projects/srer_drone/","title":"Santa Rita Experimental Range Drone Imagery","text":"The technology around drones and image analysis is rapidly advancing which is making high volume workflows easier to implement. Larger quantities of monitoring data will significantly improve our understanding of the impact management actions have on land processes and ecosystem traits. This drone imagery data is used to support a research project investigating the ability to map ecological states across the Santa Rita Experimental Range (SRER) in southern Arizona. The imagery was collected in two campaigns: the first occurred in May 2019 and the second in Aug/Sept 2019. Imagery was collected using a DJI Phantom 4 RTK drone, flying 38 m above the ground and yielding ~1 cm ground sampling distance. The imagery is located at long-term transects and exclosures at SRER. The imagery was to be compared with field mapping of ecological states conducted by Dan Robbinett. The imagery and other data in this repository are connected with the 2021 Ecosphere publication Innovations to expand drone data collection and analysis for rangeland monitoring, which you can read here. You can find additional information about the researh site and experiments here Santa Rita Exprimental Range Website.
"},{"location":"projects/srer_drone/#citation","title":"Citation","text":"Gillan, Jeffrey K., Guillermo E. Ponce\u2010Campos, Tyson L. Swetnam, Alessandra Gorlier, Philip Heilman, and Mitchel P. McClaran.\n\"Innovations to expand drone data collection and analysis for rangeland monitoring.\" Ecosphere 12, no. 7 (2021): e03649.\nvar full_ortho_srer_may_2019_1cm = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_srer_may_2019_1cm\");\nvar full_ortho_srer_sept_2019_1cm = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_srer_sept_2019_1cm\");\nvar chm_sept_2019 = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/chm_sept_2019\");\nvar chm_may_2019 = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/chm_may_2019\");\n\n// Class mapping ['1', '2', '3', '4'] = ['Herb', 'Woody','Bareground','Shadow']\nvar class_sep_2019 = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_classified_sep_2019_5cm\");\nvar class_may_2019 = ee.Image(\"users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_classified_may_2019_5cm\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/SRER-HIGHRES-DRONE
Data visualization tool in GEE App
App code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/SRER-APP-CODE
"},{"location":"projects/srer_drone/#license","title":"License","text":"GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Curated by: Jeffrey K. Gillan, Guillermo E. Ponce Campos
Keywords: cloud computing, high-performance computing, monitor,real-time kinematic (RTK), unmanned aerial systems
Last updated: 11/21/2019
"},{"location":"projects/sstg/","title":"Global gridded sea surface temperature (SSTG)","text":"Sea surface temperature (SST) is an important geophysical parameter that is essential for studying global climate change. Although sea surface temperature can currently be obtained through a variety of sensors (MODIS, AVHRR, AMSR-E, AMSR2, WindSat, in situ sensors), the temperature values obtained by different sensors come from different ocean depths and different observation times, so different temperature products lack consistency.
The SSTG dataset is a global sea surface temperature data during the period of 2002-2019, in Celsius, in monthly temporal and 0.041\u00b0 spatial resolution. It is produced by combing daily in situ SST data and daily satellite SST retrieval data from two infrared (MODIS and AVHRR) and three passive microwave (AMSR-E, AMSR2, Windsat) radiometers after calibration by using a temperature depth and observation time correction model. The accuracy assessments indicate that the reconstructed dataset exhibits significant improvements and can be used for mesoscale ocean phenomenon analyses.
"},{"location":"projects/sstg/#paper-citation","title":"Paper Citation","text":"Cao, M., Mao, K., Yan, Y., Shi, J., Wang, H., Xu, T., Fang, S., and Yuan, Z.: A new global gridded sea surface temperature data product based on\nmultisource data, Earth Syst. Sci. Data, 13, 2111\u20132134, https://doi.org/10.5194/essd-13-2111-2021, 2021.\nMengmeng cao, Kebiao Mao, Yibo Yan, Jiancheng Shi, Han Wang, Tongren Xu, Shu Fang, & Zijin Yuan. (2021). A New Global Gridded Sea Surface\nTemperature Data Product Based on Multisource Data (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4762067\nvar sstg = ee.ImageCollection(\"projects/sat-io/open-datasets/sstg\")\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL-GRIDDED-SST
"},{"location":"projects/sstg/#license","title":"License","text":"This work is distributed under the Creative Commons Attribution 4.0 International License
Created by: Mengmeng cao, Kebiao Mao, Yibo Yan, Jiancheng Shi, Han Wang, Tongren Xu, Shu Fang, & Zijin Yuan
Curated by:Samapriya Roy
Keywords: Sea Surface Temperature, SST, Gridded
Last updated: 2021-01-05
"},{"location":"projects/streamflow_india/","title":"Streamflow reconstruction for Indian sub-continental river basins 1951\u20132021","text":"The Hydrological Model-Simulated Monthly Streamflow Dataset for Indian-Subcontinental (ISC) River Basins, spanning from 1951 to 2021, addresses a critical need for long-term streamflow observations in the ISC region. Given the essential role of streamflow data in water resources management, hydroclimatic analysis, and ecological assessments, this dataset provides a valuable resource for a wide range of applications. The dataset is constructed through a multi-faceted approach that combines meteorological data, sophisticated hydrological modeling, and a high-resolution vector-based routing model known as mizuRoute. By synthesizing these elements, the dataset yields monthly streamflow simulations for 9579 stream reaches within the ISC river basins.
Validation of the dataset against observed flows at gauge stations demonstrates its reliability, with a significant proportion of the gauge stations showing strong agreement, as evidenced by coefficient of determination (R\u00b2) and Nash-Sutcliffe efficiency (NSE) values exceeding 0.70. Such validation empowers the analysis of variability in low, average, and high flow within the stream networks. The dataset's significance extends to identifying long-term changes in streamflow patterns. Notably, it highlights a substantial decline in flow within the Ganga basin and an increase in semi-arid western India and the Indus basin, findings of great relevance for water management planning and climate change adaptation in the Indian subcontinent. Moreover, this resource alleviates the challenge posed by limited streamflow observations, especially in the case of the three major transboundary basins (Ganga, Indus, and Brahmaputra), where conventional monitoring falls short. You can read the paper here for more details
"},{"location":"projects/streamflow_india/#dataset-processing","title":"Dataset processing","text":"The datasets are available for download on Zenodo here the stream shapefile consists of the streamlines and segment information whereas additional datasets were provided without any file extensions. These were converted to csv files and renamed as needed along with making sure that all NaN values were replaced with 9999 since Earth Engine does not support mixed variable types in a column. These were then joined to the stream shapefile and segment id/seg_id was used to then combine variables to the stream network asset. The following contains the dataset and description
Variable Name Description Data Files and Arrangement Mean_flow This folder contains the river segment-wise mean annual and mean monsoon streamflow. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - annual_flow: seg_id, mean_annual_flow (m\u00b3/s)- monsoon_flow: seg_id, mean_monsoon_flow (m\u00b3/s) high&low_flow This folder contains the river segment-wise mean high and mean low streamflow. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - high_flow: seg_id, mean_high_flow (m\u00b3/s)- low_flow: seg_id, mean_low_flow (m\u00b3/s) Coefficient_of_variability This folder contains the river segment-wise coefficient of variability in mean annual and mean monsoon streamflow. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - CV_annual_flow: seg_id, coefficient_of_variability- CV_monsoon_flow: seg_id, coefficient_of_variability Trend_analysis This folder contains the list of stream reaches that exhibit a statistically significant trend in streamflow between 1951 and 2021. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - streamflow_trend: seg_id, hypothesis_test (h=1: significant), sen\u2019s_slope SSI This folder contains the standardized streamflow index (SSI) for the top four dry and wet months during the 1951-2021 period. The top four driest and wettest months were calculated based on the Standardized Precipitation Index (SPI) from the monthly average rainfall data for the Indian Subcontinent. The segment ID can be obtained from the India_streams.shp shapefile in this directory. - dry_years (Sub-folder): - SSI_**monthyear**: seg_id, SSI- wet_years (Sub-folder): - SSI_**monthyear**: seg_id, SSI"},{"location":"projects/streamflow_india/#citation","title":"Citation","text":"Chuphal, D.S., Mishra, V. Hydrological model-based streamflow reconstruction for Indian sub-continental river basins, 1951\u20132021. Sci Data 10, 717\n(2023). https://doi.org/10.1038/s41597-023-02618-w\nChuphal, D. S., & Mishra, V. (2023). Reconstructed streamflow for Indian sub-continental river basins, 1951-2021 [Data set].\nZenodo. https://doi.org/10.5281/zenodo.8004633\nvar cv_annual_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/CV/cv_annual_flow\");\nvar cv_monsoon_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/CV/cv_monsoon_flow\");\nvar high_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/HIGH-LOW-FLOW/high_flow\");\nvar low_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/HIGH-LOW-FLOW/low_flow\");\nvar mean_annual_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/MEAN-FLOW/mean_annual_flow\");\nvar mean_monsoon_flow = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/MEAN-FLOW/mean_monsoon_flow\");\nvar ssi_dry_july_1972 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_dry_july_1972\");\nvar ssi_dry_july_2002 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_dry_july_2002\");\nvar ssi_dry_june_2009 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_dry_june_2009\");\nvar ssi_dry_june_2014 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_dry_june_2014\");\nvar ssi_wet_august_2020 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_wet_august_2020\");\nvar ssi_wet_july_1988 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_wet_july_1988\");\nvar ssi_wet_september_1983 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_wet_september_1983\");\nvar ssi_wet_september_2019 = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/SSI/ssi_wet_september_2019\");\nvar streamflow_trend = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/streamflow_trend\");\nvar streams = ee.FeatureCollection(\"projects/sat-io/open-datasets/indian-streams/streams\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/STREAMFLOW-RECONSTRUCTION-INDIAN-SUBCONTINENT
"},{"location":"projects/streamflow_india/#license","title":"License","text":"These datasets are available under the Creative Commons Attribution 4.0 International license.
Provided by: Chuphal, D. S., & Mishra, V., Indian Institute of Technology (IIT) Gandhinagar
Curated in GEE by: Samapriya Roy
Keywords: H08, mizuRoute, Streamflow,India, Hydrology, Water Management, Climate change adaptation, Hydroclimatic extremes analysis
Last updated in GEE: 2023-10-18
"},{"location":"projects/survey_checkpoints/","title":"USGS Consolidated Survey-Grade Checkpoints 3DEP 2010 to 2017","text":"The USGS Consolidated Standardized Survey-Grade Checkpoints 3DEP dataset contains 41,958 survey-grade checkpoints collected between 2010 and 2017, covering 205 lidar and Interferometric Synthetic Aperture Radar (ifSAR) projects. These checkpoints were collected in the United States as part of the 3D Elevation Program (3DEP). The geospatial data is available in both Esri File-Geodatabase (GDB) and Open Geospatial Consortium\u2019s (OGC) GeoPackage (GPKG) formats. The dataset includes horizontal and vertical coordinates based on the North American Datum of 1983 (2011) in decimal degrees (EPSG:6318) and North American Vertical Datum of 1988 (2011) in meters (EPSG:5703). Updates to vertical datums for the contiguous United States (CONUS) used GEOID18, while updates for Hawaii and Alaska used GEOID12B via NOAA\u2019s VDatum tool.
This dataset is a valuable resource for validating and assessing the accuracy of lidar data collected for the 3DEP program and can be used in geospatial applications that require high-accuracy elevation data. Each checkpoint is characterized by attributes such as its unique identifier, survey type (NVA, VVA, or Unknown), geoid used, collection date, and more.
"},{"location":"projects/survey_checkpoints/#citation","title":"Citation","text":"Miller, B.Y., and Cannici, C., 2024, Consolidated Standardized Survey-Grade Checkpoints 3DEP 2010 to 2017: U.S. Geological Survey data release,\nhttps://doi.org/10.5066/P13NOW9E.\nvar survey_checkpoints = ee.FeatureCollection(\"projects/sat-io/open-datasets/USGS/SURVEY_CHECKPOINTS_3DEP_2010_2017\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/USGS-3DEP-CONSOLIDATED-SURVEY-CHECKPOINTS
"},{"location":"projects/survey_checkpoints/#license","title":"License","text":"This dataset is provided under the Creative Commons Zero v1.0 Universal license (CC0 1.0).
Provided by: U.S. Geological Survey (USGS)
Curated in GEE by: Samapriya Roy
Keywords: 3DEP, Lidar, Survey-Grade Checkpoints, GEOID18, GEOID12B, Elevation, Accuracy, Survey Data, USGS, Vertical Datum, Horizontal Datum
Last updated in GEE: 2024-10-24
"},{"location":"projects/swiss3d/","title":"swissSURFACE3D Raster (DSM)","text":"NoteThis dataset is currently only available to those in the insiders program
swissSURFACE3D Raster is a digital surface model (DSM) which represents the earth\u2019s surface including visible and permanent landscape elements such as soil, natural cover, and all sorts of constructive work with the exception of power lines and masts. swissSURFACE3D Raster is derived from airborne LiDAR data of swissSURFACE3D. To model the surface, the following elements (classified LiDAR points) are used:
To improve the surface representation of large rivers and lakes, watercourse vectors of the topographic landscape model TLM are implemented.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/swiss3d/#data-preprocessing","title":"Data preprocessing","text":"Datasets were preprocessed and distributed in multiple formats. The Cloud Optimized Geotiff files were made available and a program was written to go fetch all and current 31,321 tiles.
"},{"location":"projects/swiss3d/#citation","title":"Citation","text":"swissSURFACE3D Raster digital surface model (DSM). Last accessed **date** original data from https://www.swisstopo.admin.ch/en/geodata/height/\nsurface3d-raster.html\nvar swiss3d = ee.ImageCollection(\"projects/sat-io/open-datasets/swissSURFACE3D\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/swiss3D-RASTER
"},{"location":"projects/swiss3d/#license-terms-of-use","title":"License & Terms of Use","text":"The free geodata and geoservices of swisstopo may be used, distributed and made accessible. Furthermore, they may be enriched and processed and also used commercially. A reference to the source is mandatory. In the case of digital or analogue representations and publications, as well as in the case of dissemination, one of the following source references must be attached in any case:
Provided by: Federal Office of Topography swisstopo
Curated in GEE by : Samapriya Roy
keywords: LIDAR, Digital Surface Model, DSM, Topography
Last updated on GEE: 2023-01-28
"},{"location":"projects/sword/","title":"SWOT River Database (SWORD)","text":"The Surface Water and Ocean Topography (SWOT) satellite mission, successfully launched in December 2022, has revolutionized our ability to observe rivers by providing vast datasets of river water surface elevation, width, and slope measurements. To maximize the utility and flexibility of this data, the SWOT mission delivers a variety of data products, including river vector data in shapefile format for each SWOT overpass. These vector products offer immense potential for multi-temporal analysis of river systems, allowing researchers to track changes in river characteristics over time.
To enable this type of analysis, it was crucial to define consistent river reaches and nodes before the launch of SWOT. This ensures that data from different overpasses can be accurately assigned and compared. The SWOT River Database (SWORD) fulfills this critical role by combining multiple global datasets related to rivers and satellite observations. SWORD provides a standardized framework of high-resolution river nodes (spaced every 200 meters along river centerlines) and reaches (river segments of approximately 10 kilometers) in both shapefile and netCDF formats. These nodes and reaches are accompanied by a range of relevant hydrologic variables such as water surface elevation, width, slope, and information on river obstructions, flow accumulation, and more. This comprehensive dataset, covering global rivers 30 meters wide and greater, empowers researchers to conduct in-depth analysis of river systems and utilize SWOT data to its full potential.
SWORD integrates data from several existing global hydrography datasets, including the Global River Widths from Landsat (GRWL), MERIT Hydro, HydroBASINS, and the Global River Obstruction Database (GROD). It provides a wealth of attributes for each node and reach, such as:
Attribute Description Units x Longitude of each node ranging from 180\u00b0E to 180\u00b0W decimal degrees y Latitude of each node, ranging from 90\u00b0S to 90\u00b0N decimal degrees node_id Unique identifier for each node, formatted as: CBBBBBRRRRNNNT (C: Continent, B: Pfafstetter basin codes, R: Reach ID, N: Node ID within reach, T: Type) none node_length Length of the node measured along the high-resolution centerline points meters reach_id ID of the reach associated with each node, formatted as: CBBBBBRRRRT (C: Continent, B: Pfafstetter basin codes, R: Reach ID, T: Type) none wse Average water surface elevation of the node meters wse_var Variance of water surface elevation along the high-resolution centerline points used to calculate the average water surface elevation for each node meters^2 width Average width of the node meters width_var Variance of width along the high-resolution centerline points used to calculate the average width for each node meters^2 n_chan_max Maximum number of channels observed within the node none n_chan_mod Mode (most frequent) number of channels observed within the node none obstr_type Type of obstruction at the node based on GROD and HydroFALLS databases: 0 - No Dam, 1 - Dam, 2 - Lock, 3 - Low Permeable Dam, 4 - Waterfall none grod_id Unique GROD ID for nodes with obstr_type values 1-3 none hfalls_id Unique HydroFALLS ID for nodes with obstr_type value 4 none dist_out Distance from the river outlet to the node meters type Node type identifier: 1 - River, 3 - Lake on river, 4 - Dam/waterfall, 5 - Unreliable topology, 6 - Ghost node none facc Maximum flow accumulation value for the node kilometers^2 lakeflag GRWL water body identifier: 0 - River, 1 - Lake/reservoir, 2 - Canal, 3 - Tidally influenced river none max_width Maximum width across the channel for the node, including islands and bars meters river_name All river names associated with the node (separated by semicolons if multiple) none sinuosity Ratio of total reach length to the straight-line distance between reach endpoints, indicating the degree of meandering none meand_len Average length of meanders the node belongs to meters manual_add Binary flag indicating if the node was manually added to GRWL centerlines (1) or not (0) none trib_flag Binary flag indicating if a large tributary not in SWORD enters the node (1) or not (0) none Expand to show Reach attribute descriptions
Attribute Description Units x Longitude of the reach center point (ranging from 180\u00b0E to 180\u00b0W) decimal degrees y Latitude of the reach center point (ranging from 90\u00b0S to 90\u00b0N) decimal degrees reach_id Unique identifier for each reach, formatted as: CBBBBBRRRRT (C: Continent, B: Pfafstetter basin codes, R: Reach ID, T: Type) none reach_length Length of the reach measured along the high-resolution centerline points meters wse Average water surface elevation of the reach meters wse_var Variance of water surface elevation along the high-resolution centerline points used to calculate the average water surface elevation for each reach meters^2 width Average width of the reach meters width_var Variance of width along the high-resolution centerline points used to calculate the average width for each reach meters^2 n_nodes Number of nodes associated with the reach none n_chan_max Maximum number of channels observed within the reach none n_chan_mod Mode (most frequent) number of channels observed within the reach none obstr_type Type of obstruction within the reach based on GROD and HydroFALLS databases: 0 - No Dam, 1 - Dam, 2 - Lock, 3 - Low Permeable Dam, 4 - Waterfall none grod_id Unique GROD ID for reaches with obstr_type values 1-3 none hfalls_id Unique HydroFALLS ID for reaches with obstr_type value 4 none slope Average slope of the reach calculated along the high-resolution centerline points m/km dist_out Distance from the river outlet to the reach meters n_rch_up Number of upstream reaches connected to this reach none n_rch_down Number of downstream reaches connected to this reach none rch_id_up IDs of the upstream reaches connected to this reach none rch_id_dn IDs of the downstream reaches connected to this reach none lakeflag GRWL water body identifier: 0 - River, 1 - Lake/reservoir, 2 - Canal, 3 - Tidally influenced river none max_width Maximum width across the channel for the reach, including islands and bars meters type Reach type identifier: 1 - River, 3 - Lake on river, 4 - Dam/waterfall, 5 - Unreliable topology, 6 - Ghost reach none facc Maximum flow accumulation value for the reach kilometers^2 swot_obs Maximum number of SWOT passes intersecting the reach during the 21-day orbit cycle none swot_orbits List of SWOT orbit track numbers that intersect the reach during the 21-day cycle none river_name All river names associated with the reach (separated by semicolons if multiple) none trib_flag Binary flag indicating if a large tributary not in SWORD enters the reach (1) or not (0) none"},{"location":"projects/sword/#dataset-preprocessing","title":"Dataset preprocessing","text":"
Reaches and nodes shapefile datasets were downloaded zipped and uploaded as individual component shapefiles. The folder assets were then merged to create a single nodes and reaches files. Attributes were preserved as is from the shapefiles.
"},{"location":"projects/sword/#citation","title":"Citation","text":"Altenau et al., (2021) The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data\nProducts. Water Resources Research. https://doi.org/10.1029/2021WR030054\nElizabeth H. Altenau, Tamlin M. Pavelsky, Michael T. Durand, Xiao Yang, Renato P. d. M. Frasson, & Liam Bendezu. (2023). SWOT River Database (SWORD)\n(Version v16) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10013982\nvar nodes_merged = ee.FeatureCollection(\"projects/sat-io/open-datasets/SWORD/nodes_merged\");\nvar reaches_merged = ee.FeatureCollection(\"projects/sat-io/open-datasets/SWORD/reaches_merged\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/SWORD-NODES-REACHES-MERGED
Individual nodes and reaches files were ingested for reference and can be access by the users by using
var ee_nodes = ee.data.listAssets(\"projects/sat-io/open-datasets/SWORD/nodes\");\nvar ee_reaches = ee.data.listAssets(\"projects/sat-io/open-datasets/SWORD/reaches\");\n\nprint('Total of '+ee.List(ee_nodes.assets).size().getInfo()+ ' assets in nodes',ee_nodes.assets);\nprint('Total of '+ee.List(ee_reaches.assets).size().getInfo()+ ' reaches in nodes',ee_reaches.assets);\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/SWORD-NODES-REACHES
"},{"location":"projects/sword/#license","title":"License","text":"The datasets are provided under a Creative Commons 4.0 International License.
Provided by: Altenau et al., (2021)
Curated in GEE by: Samapriya Roy
Keywords: SWORD,SWOT,Rivers,Hydrology,Hydrography,River Networks,Global
Last updated in GEE: 2024-04-12
"},{"location":"projects/syn_ntl/","title":"GAN based Synthetic VIIRS (NTL) India","text":"This study utilizes nighttime light (NTL) data from two primary sources: the Defense Meteorological Satellite Program (DMSP) and the Visible and Infrared Imaging Suite (VIIRS) onboard the Suomi National Polar Partnership (SNPP) satellite. DMSP-OLS data provide monthly NTL observations from April 1992 to December 2013 at a 30-arc second spatial resolution, while VIIRS-DNB data offer monthly observations from April 2012 onwards at a finer 15-arc second resolution. Both datasets have been extensively used in research to monitor human activities and natural phenomena, but their different resolutions and temporal coverage present challenges for long-term analysis.
To address these limitations, this study uses preprocessed DMSP data to generate synthetic VIIRS-like imagery for the period 1992-2013. The model was trained using overlapping monthly NTL data for the years 2012 and 2013, and the generated imagery was validated against other VIIRS-like datasets and socio-economic indicators such as GDP and population.
The original DMSP monthly product (from which the current product is derived) have been captured by a series of satellites (F10-F18 ), and have been made available by EOG between 1992 and 2014. The different colours in the image below show its availability over Indian landmass; image source). The green ticks show the 216 monthly images that were used in our paper, for the generation of the improved VIIRS-like product. The red crosses display the files that are available on EOG, however, due to clouds over the Indian region, the products had large spatial gaps, and could not be used for creation of this improved product. The blue cells indicate months for which data was absent on the EOG portal, at the time of creation of this improved product.
Hence there are can be multiple cases where:
The data is not temporally continuous. Users are advised to use the monthly data appropriately.
"},{"location":"projects/syn_ntl/#output-data-characteristics","title":"Output Data Characteristics","text":"The study generates a monthly time series of VIIRS-like nighttime light images for India, spanning the period from April 1992 to December 2013. The images have the following characteristics:
Jindal, M., Gupta, P. K., & Srivastav, S. K. (2024). Generation of monthly VIIRS nighttime lights time-series (1992\u20132013) images using\ndeep learning (cGAN) technique. Remote Sensing Applications: Society and Environment, 35, 101263. https://doi.org/10.1016/j.rsase.2024.101263\nvar syn_ntl_india = ee.ImageCollection(\"projects/sat-io/open-datasets/gan-synthetic-viirs\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/SYNTHETIC-NTL-VIIRS-INDIA
"},{"location":"projects/syn_ntl/#license","title":"License","text":"Creative Commons Attribution 4.0 International
Provided by: Jindal et al
Curated in GEE by: Samapriya Roy
Keywords: VIIRS-DNB, Noise removal, DMSP-OLS, Monthly, Inter-calibration, Conditional GAN
"},{"location":"projects/tallo/","title":"Global tree allometry and crown architecture (Tallo) database","text":"The Tallo database (v1.0.0) is a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. Data were compiled from 61,856 globally distributed sites and include measurements for 5,163 tree species. Tallo includes nearly 500,000 georeferenced and taxonomically standardized records from more than 5000 tree species acquired at over 60,000 sites worldwide, including data from all major terrestrial biomes and some of the world's largest ever recorded trees. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence
You can read the paper here and download the database from
"},{"location":"projects/tallo/#citation","title":"Citation","text":"Jucker, Tommaso, Fabian J\u00f6rg Fischer, J\u00e9r\u00f4me Chave, David A. Coomes, John Caspersen, Arshad Ali, Grace Jopaul Loubota Panzou et al. \"Tallo: A global\ntree allometry and crown architecture database.\" Global change biology 28, no. 17 (2022): 5254-5268.\nJucker, Tommaso, Fischer, Fabian, Chave, J\u00e9r\u00f4me, Coomes, David, Caspersen, John, Ali, Arshad, Loubota Panzou, Grace Jopaul, Feldpausch, Ted,\nFalster, Daniel, Usoltsev, Vladimir, Adu-Bredu, Stephen, Alves, Luciana, Aminpour, Mohammad, Angoboy, Ilondea, Anten, Niels, Antin, C\u00e9cile, Askari,\nYousef, Avil\u00e9s, Rodrigo Mu\u00f1oz, Ayyappan, Narayanan, \u2026 Zavala, Miguel. (2022). Tallo database (1.0.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.6637599\nvar tallo = ee.FeatureCollection(\"projects/sat-io/open-datasets/tallo_database\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/TREE-ALLOMETRY-CROWN-ARCH-DATABASE
"},{"location":"projects/tallo/#license","title":"License","text":"This work is licensed under Creative Commons Attribution 4.0 International.
Created by: Jucker, Tommaso, et al. 2022
Curated in GEE by : Samapriya Roy
Keywords: stem diameter, tree height, crown size, tree allometry, tree architecture, forest biomass, remote sensing
Last updated on GEE: 2022-10-21
"},{"location":"projects/tbdem/","title":"Coastal National Elevation Database (CoNED) Project -Topobathymetric digital elevation models (TBDEMs)","text":"The Coastal National Elevation Database (CoNED) Project - topobathymetric digital elevation models (TBDEMs) are merged renderings of both topography (land elevation) and bathymetry (water depth) to provide seamless elevation products for select coastal regions in the United States (2011-present). This coastal elevation database integrates disparate light detection and ranging (lidar) and bathymetric data sources (such as sonar) into common databases aligned both vertically and horizontally to common reference systems.
This coastal elevation database integrates disparate light detection and ranging (lidar) and bathymetric data sources into common databases aligned both vertically and horizontally to common reference systems. CoNED Project - topobathymetric digital elevation models (TBDEMs) provide a required seamless elevation product for science application studies such as shoreline delineation, coastal inundation mapping, sediment-transport, sea-level rise, storm surge models, tsunami impact assessment, and analysis of the impact of various climate change scenarios on coastal regions.
Dataset description can be found here and the full datasets were downloaded from
Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.
"},{"location":"projects/tbdem/#citation","title":"Citation","text":"Coastal National Elevation Database (CoNED) Project - Topobathymetric Digital Elevation Model (TBDEM)\nDigital Object Identifier (DOI) number: /10.5066/F7Z60MHJ\nThe datasets were collected and made available with 3 meter, 2 meter, or 1 meter and different no data values. While GEE collections will allow for variable values for all of those, the nominal resolution and native CRS was left intact was no data value was reprocessed to -9999 by simply using gdalwarp. Some of the datasets were single mosaics with filesizes upward of 4GB to 200+GB, for efficiency and better handling of these gdal_retile tool was used to retile these into subparts while maintaining the file name for data tracing. I have added a function onto the example script which allows you to add the nominal scale as a property to the collection in case the user would like to split and apply different methods on top.
"},{"location":"projects/tbdem/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var tb_dem = ee.ImageCollection(\"projects/sat-io/open-datasets/NOAA/CoNED_TBDEM\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/NOAA-CoNED-TBDEM
"},{"location":"projects/tbdem/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. There are no restrictions on the use of data received from the U.S. Geological Survey's Earth Resources Observation and Science (EROS) Center or NASA's Land Processes Distributed Active Archive Center (LP DAAC), unless expressly identified prior to or at the time of receipt. Depending on the product source, we request that the following statements be used when citing, copying, or reprinting data: Data available from the U.S. Geological Survey.
Intended use: Development, calibration and validation of coastal open-access EO-derived elevation/topography, vegetation and water quality products.
Provider: USGS, CMGP, NGP, NOAA and NGDC
Curated by: Samapriya Roy
Keywords: Elevation, topography, topobathymetric, bathymetry
Last updated on GEE: 2022-02-27
"},{"location":"projects/terraclim/","title":"Terraclimate Individual years for +2C and +4C climate futures","text":"TerraClimate layers commensurate with global mean temperatures +2C and +4C above preindustrial levels. These data are available for pseudo years 1985-2015. Future climate projections were developed for two different climate futures: (1) when global mean temperatures are 2C warmer than pre-industrial, and (2) when global mean temperatures are 4C above preindustrial. We use a pattern scaling approach that makes use of monthly projections from 23 CMIP5 global climate models as described in Qin et al., 2020 and provide projections for monthly climate by imposing projected changes in means and variance from the modes scalable to the change in global temperature. You can find more information here
This data is at two links
Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch, 2018, Terraclimate, a high-resolution global dataset of monthly climate and climatic water\nbalance from 1958-2015, Scientific Data\nAn automated script was created to fetch all datasets, which contains 31 annual NetCDF files with 12 bands each representing a month for each variable. The netcdf files for each variable was converted into Geotifs with global bounds and with lzw compressions. The collections were made available for both 2C and 4C scenarios and scale and offset were added as metadata for each collection to allow for processing in the appropriate units as mentioned in the variable list below.
"},{"location":"projects/terraclim/#variable-lists","title":"Variable lists","text":"Terraclimate variables and units can be found in the table below. As noted from their webpage these datasets have associated scales and offset value which has to be used to generate a correct representation of the variable data in the intended units. As part of data processing, the variable scale and offset values were included as metadata for each variable which can then be applied in computations directly.
variable units aet (Actual Evapotranspiration, monthly total) mm def (Climate Water Deficit, monthly total) mm pet (Potential evapotranspiration, monthly total) mm ppt (Precipitation, monthly total) mm q (Runoff, monthly total) mm soil (Soil Moisture, total column - at end of month) mm srad (Downward surface shortwave radiation) W/m2 swe (Snow water equivalent - at end of month) mm tmax (Max Temperature, average for month) C tmin (Min Temperature, average for month) C vap (Vapor pressure, average for month)\u00a0 kPa ws (Wind speed, average for month) m/s vpd (Vapor Pressure Deficit, average for month) kpa PDSI (Palmer Drought Severity Index, at end of month) unitless PDSI (Palmer Drought Severity Index, at end of month) unitless
"},{"location":"projects/terraclim/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var aet_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/aet\");\nvar def_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/def\");\nvar pet_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/pet\");\nvar ppt_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/ppt\");\nvar soil_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/soil\");\nvar srad_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/srad\");\nvar swe_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/swe\");\nvar tmax_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/tmax\");\nvar tmin_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/tmin\");\nvar vap_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/vap\");\nvar vpd_2c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/2C/vpd\");\nvar aet_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/aet\");\nvar def_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/def\");\nvar pet_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/pet\");\nvar ppt_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/ppt\");\nvar soil_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/soil\");\nvar swe_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/swe\");\nvar tmax_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/tmax\");\nvar tmin_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/tmin\");\nvar vpd_4c = ee.ImageCollection(\"projects/sat-io/open-datasets/TERRACLIMATE/4C/vpd\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/TERRACLIMATE-CLIMATE-FUTURES
"},{"location":"projects/terraclim/#license","title":"License","text":"This work is licensed under a public domain license.
Created by: Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch
Preprocessed and Curated in GEE by : Samapriya Roy
Keywords: Climate futures, +2C, +4C, TerraClimate
Last updated: 2021-04-15
Last updated on GEE: 2022-04-27
"},{"location":"projects/tile/","title":"Tile Drained Croplands (30m)","text":"Tile drainage is one of the dominant agricultural management practices in the United States and has greatly expanded since the late 1990s. It has proven effects on land surface water balance and quantity and quality of streamflow at the local scale. The effect of tile drainage on crop production, hydrology, and the environment on a regional scale is elusive due to lack of high-resolution, spatially-explicit tile drainage area information for the Contiguous United States (CONUS). We developed a 30-m resolution tile drainage map of the most-likely tile-drained area of the CONUS (AgTile-US) from county-level tile drainage census using a geospatial model that uses soil drainage information and topographic slope as inputs. Validation of AgTile-US with 16000 ground truth points indicated 86.03% accuracy at the CONUS-scale. Over the heavily tile-drained midwestern regions of the U.S., the accuracy ranges from 82.7% to 93.6%. These data can be used to study and model the hydrologic and water quality responses of tile drainage and to enhance streamflow forecasting in tile drainage dominant regions. You can read the full paper here
"},{"location":"projects/tile/#paper-citation","title":"Paper Citation","text":"Valayamkunnath, P., Barlage, M., Chen, F. et al. Mapping of 30-meter resolution tile-drained croplands using a\ngeospatial modeling approach. Sci Data 7, 257 (2020). https://doi.org/10.1038/s41597-020-00596-x\nvar tile30m = ee.Image(\"projects/sat-io/open-datasets/agtile/AgTile-US\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/US-TILE-DRAINED-CROPLANDS
"},{"location":"projects/tile/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Valayamkunnath, P., Barlage, M., Chen, F. et al.
Curated by: Samapriya Roy
Keywords: Agriculture,Tile Drainage,Subsurface,USA,CONUS,GIS,30 meter,data,gridded,raster
Last updated: 2021-08-16
"},{"location":"projects/tillage/","title":"Global crop production tillage practices","text":"No tillage (NT) is often presented as a means to grow crops with positive environmental externalities, such as enhanced carbon sequestration, improved soil quality, reduced soil erosion, and increased biodiversity. However, whether NT systems are as productive as those relying on conventional tillage (CT) is a controversial issue, fraught by a high variability over time and space. Here, we expand existing datasets to include the results of the most recent field experiments, and we produce a global dataset comparing the crop yields obtained under CT and NT systems. In addition to crop yield, our dataset also reports information on crop growing season, management practices, soil characteristics and key climate parameters throughout the experimental year. The final dataset contains 4403 paired yield observations between 1980 and 2017 for eight major staple crops in 50 countries. This dataset can help to gain insight into the main drivers explaining the variability of the productivity of NT and the consequence of its adoption on crop yields.
"},{"location":"projects/tillage/#data-citation","title":"Data Citation","text":"Su, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage systems. figshare https://doi.org/10.6084/m9.figshare.12155553 (2020). v14\nSu, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage systems. Scientific Data 8, 33 (2021).\nvar tillage = ee.FeatureCollection(\"projects/sat-io/open-datasets/global_tillage_production\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-CROP-PRODUCTION-TILLAGE-PRACTICES
"},{"location":"projects/tillage/#property-mapping","title":"Property Mapping","text":"Property GEE property Author author Journal journal Year year Site country site_country Location location Latitude latitude Longitude longitude Soil information recorded in the paper soil_from_paper pH (surface layer) pH_surface_layer Replications in experiment replications_exp Crop crop Initial year of NT practice ( or first year of experiment if missing) init_yr_nt Sowing year sowing_year Harvest year harvest_year Years since NT started (yrs) yrs_from_nt Crop growing season recorded in the paper cgsp Crop rotation with at least 3 crops involved in CT crit Crop rotation with at least 3 crops involved in NT crint Crop sequence (details) c_seq Cover crop before sowing cc_bf_sowing Soil cover in CT soil_cover_ct Soil cover in NT soil_cover_nt Residue management of previous crop in CT (details) rm_ct Residue management of previous crop in NT (details) rm_nt Weed and pest control CT wp_ct Weed and pest control NT wp_nt Weed and pest control CT (details) wpc_ct Weed and pest control NT (details) wpc_nt Fertilization CT ft_ct Fertilization NT ft_nt N input n_inp N input rates with the unit kg N ha-1 (details) n_inp_unit Field fertilization (details) fft Irrigation CT i_ct Irrigation NT i_nt Water applied in CT w_ct Water applied in NT w_nt Other information other Yield of CT yield_ct Yield of NT yield_nt Relative yield change rel_yl_chg Yield increase with NT yl_inc_nt Outlier of CT outlier_ct Outlier of NT outlier_nt Sowing month sw_month Harvesting month hv_month P P E E PB PB Tave Tave Tmax Tmax Tmin Tmin ST ST"},{"location":"projects/tillage/#license","title":"License","text":"This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Created by: Yang Su et al.
Curated by: Samapriya Roy
Keywords: Conservation agriculture, Conventional tillage, crop yield, No tillage, No-till
Last updated: 2021-08-30
"},{"location":"projects/tzero/","title":"TransitionZero Solar Asset Mapper","text":"TransitionZero's Solar Asset Mapper is a global, satellite-derived dataset of utility-scale solar farms, created through a combination of machine learning and human annotation. The Q1 2024 dataset includes the location and shape of 63,616 assets, along with their estimated capacities. Construction dates are estimated for over 80% of these assets. This dataset covers over 19,100 square kilometers of solar farms across 183 countries, with a total estimated capacity of 711 GW.
This dataset represents the most comprehensive view of global asset-level solar installations, combining TransitionZero's detections with known solar farm geometries from other datasets. By integrating data from various sources, it provides a detailed and reliable picture of the current state of utility-scale solar farms worldwide.The data can be downloaded here
"},{"location":"projects/tzero/#dataset-preprocessing","title":"Dataset Preprocessing","text":"The dataset fields constructed_before and constructed_after was converted to system:time_start and system:time_end for easy filtering. Nulls are kept as nulls for both of those columns.
Please refer to the suggested citation formats
\"TransitionZero Solar Asset Mapper, TransitionZero, May 2024 release.\"\n\"TZ-SAM, TransitionZero, May 2024 release.\"\n\"TransitionZero (2024) Solar Asset Mapper.\"\nPhillpott, M., O'Connor, J., Ferreira, A., Max, S., Kruitwagen, L., & Guzzardi, M. (2024). Solar Asset Mapper: A continuously-updated global\ninventory of solar energy facilities built with satellite data and machine learning (1.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.11368204\nvar tzero_solar = ee.FeatureCollection(\"projects/sat-io/open-datasets/TZERO/TZ-SOLAR-2024Q1\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/TZERO-GLOBAL-SOLAR-MAPPER
"},{"location":"projects/tzero/#license","title":"License","text":"TZ-SAM is made available under a Creative Commons Attribution Non-Commercial 4.0 International License (CC-BY-NC-4.0). Attribution to TransitionZero is required. You must also clearly indicate if you have made any changes to the TZ-SAM dataset and what these are.
Keywords: solar energy, energy transition, open data
Provided by; Transition Zero
Curated in GEE by: Samapriya Roy
Last updated in GEE: 2024-06-01
"},{"location":"projects/uhii/","title":"Urban Heat Island Intensity (UHII)","text":"The Urban Heat Island (UHI) effect, characterized by localized warming over urban areas, is a critical consequence of urbanization on climate. Traditional methods of estimating UHI intensity (UHII) have been constrained by limitations, such as focusing solely on clear-sky surface UHII and neglecting all-sky surface and canopy (air temperature) UHII. These approaches often overlook anthropogenic disturbances, leading to uncertainties in the estimates. To overcome these challenges, this study introduces a new dynamic equal-area (DEA) method designed to reduce the impact of confounding factors through a dynamic cyclic process. By applying the DEA method and integrating gridded temperature data, a comprehensive global-scale UHII dataset has been developed, covering over 10,000 cities and spanning more than 20 years with monthly temporal resolution. This dataset offers multi-faceted UHII estimates, including clear-sky surface, all-sky surface, and canopy UHII, providing a robust foundation for analyzing UHI trends in urban environments.
The dataset reveals that UHII is greater than zero in more than 80% of the studied cities, with global annual average magnitudes around 1.0\u00b0C (day) and 0.8\u00b0C (night) for surface UHII, and approximately 0.5\u00b0C for canopy UHII. Moreover, an interannual upward trend in UHII is observed in over 60% of cities, with global average trends exceeding 0.1\u00b0C per decade (day) and 0.06\u00b0C per decade (night) for surface UHII, and slightly over 0.03\u00b0C per decade for canopy UHII. A positive correlation is also identified between the magnitude and trend of UHII, indicating that cities with stronger UHII tend to experience faster growth in UHII over time. The dataset further highlights discrepancies in UHII estimates based on differences in data types (surface or air temperature), data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies. This comprehensive dataset and the accompanying analysis offer valuable insights for future urban climate studies and are publicly accessible at https://doi.org/10.6084/m9.figshare.24821538. A global dataset of urban heat island using multiple methods and including estimates for both air temperature and land surface temperature. It is available monthly from 2003 to 2020 (from 2001 for the dataset from MODIS Terra satellite). You can read more information in the paper here
NoteThe \"Diurnal\" field can be either \"Day\" or \"Nig\", signifying daytime and nighttime UHII, respectively. \"Year\u201d denotes the UHII year, and \"Month\u201d indicates the specific month. It\u2019s important to note that, besides monthly UHII results, we also provide quarterly and annual averages. When \u201cMonth\u201d takes values from 1 to 12, it signifies the monthly average. If \u201cMonth\u201d is between 21 and 24, it indicates the quarterly average (21 for March-May, 22 for June-August, 23 for September-November, and 24 for December-February). When \u201cMonth\u201d is 30, it denotes the annual average. The UHII value can be converted to degrees Celsius by multiplying a scaling factor of 0.01.
"},{"location":"projects/uhii/#dataset-details","title":"Dataset Details","text":"Indicator Data Source Period Description Surface UHI intensity estimated by the clear-sky LST data IMod1 (MOD11A1) 2001-2021 Clear-sky surface UHI from the MODIS Terra daily LST (A1) and 8-day LST (A2) products; both corresponding to an equatorial overpass time of 10:30 am local time during daytime and 10:30 pm at night IMod2 (MOD11A2) IMyd1 (MYD11A1) 2003-2021 Clear-sky surface UHI from the MODIS Aqua daily LST (A1) and 8-day LST (A2) products; both corresponding to an equatorial overpass time of 1:30 pm local time during daytime and 1:30 am at night IMyd2 (MYD11A2) Surface UHI intensity estimated by the seamless clear-sky LST data ISMod2 (Seamless MOD11A2) 2001-2020 Clear-sky surface UHI based on the seamless LST product DOI ISMyd1 (Seamless MYD11A1) 2003-2020 Clear-sky surface UHI based on a second seamless LST product DOI Surface UHI intensity estimated by the seamless all-sky LST data IAMod2 (All-sky MOD11A2) 2001-2020 All-sky surface UHI based on the seamless all-sky LST product DOI Canopy UHI intensity estimated by the surface air temperature data ISAT (Surface air temperature) 2001-2020 Air temperature or canopy UHI based on the global surface air temperature product DOI"},{"location":"projects/uhii/#citation","title":"Citation","text":"Yang, Qiquan, Yi Xu, T. C. Chakraborty, Meng Du, Ting Hu, Ling Zhang, Yue Liu et al. \"A global urban heat island intensity dataset: Generation,\ncomparison, and analysis.\" Remote Sensing of Environment 312 (2024): 114343.\nQiquan Yang.Global Urban Heat Island Intensity Dataset. Figshare. https://doi.org/10.6084/m9.figshare.24821538, 2024.\nvar AMOD2 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/AMOD2');\nvar MOD1 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/MOD1');\nvar MOD2 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/MOD2');\nvar MYD1 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/MYD1');\nvar MYD2 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/MYD2');\nvar SAT = ee.ImageCollection('projects/sat-io/open-datasets/UHII/SAT');\nvar SMOD2 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/SMOD2');\nvar SMYD1 = ee.ImageCollection('projects/sat-io/open-datasets/UHII/SMYD1');\nThe datasets are provided under a Attribution 4.0 International (CC BY 4.0) license.
Provided by: Yang et al 2024
Curated in GEE by : Samapriya Roy
Keywords: urban, heat, climate, city
Last updated: 2024-09-06
"},{"location":"projects/uhii/#changelog","title":"Changelog","text":"Umbra satellites generate the highest resolution SAR imagery ever offered commercially from space (better than 25 cm / 10 inches). SAR satellites can capture images at night, through cloud cover, smoke and rain. SAR is unique in its abilities to monitor changes. The Open Data Program (ODP) monitors ten diverse locations around the world. Updated frequently with new images. ODP enables users to analyze the time-series data to detect changes in each location.
"},{"location":"projects/umbra_opendata/#ciation","title":"Ciation","text":"Umbra Synthetic Aperture Radar (SAR) Open Data was accessed on DATE from https://registry.opendata.aws/umbra-open-data.\nOnly GEC data was selected from the available collections since they were already encoded as geotiffs. SAR GEC data is a geocoded product flattened to a single elevation to reduce visual distortions. It is analysis-ready, though it also contains layover and foreshortening.SAR GEC data is often taken as a post product processed from slant or ground range radar images. It contains amplitude information only.
"},{"location":"projects/umbra_opendata/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var umbra_open = ee.ImageCollection('projects/sat-io/open-datasets/UMBRA/open-data');\nvar notoPeninsula = ee.ImageCollection('projects/sat-io/open-datasets/disaster/japan-earthquake-2024_UMBRA')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/UMBRA-OPENDATA
"},{"location":"projects/umbra_opendata/#license","title":"License","text":"All Umbra data is provided to customers under a modified version of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Creative Commons, the nonprofit that created and maintains CC BY 4.0. While Umbra will always remain the underlying copyright holder for data provided to customers, our licensing strategy allows customers to i) Freely publish the data, with attribution, under the CC BY 4.0 license ii) Resell our data, at a profit, without paying a royalty to Umbra iii) Create derivative works, like analytics, and sell them at a profit (again, without paying a royalty to Umbra)
Provided by: Umbra
Curated in GEE by: Samapriya Roy
Keywords: UMBRA, SAR, GED, Open data
Last updated in GEE: 23/03/2023
"},{"location":"projects/urban-watch/","title":"UrbanWatch 1m Land Cover & Land Use","text":"Very-high-resolution (VHR) land cover and land use (LCLU) is an essential baseline data for understanding fine-scale interactions between humans and the heterogeneous landscapes of urban environments. In this study, we developed a Fine-resolution, Large-area Urban Thematic information Extraction (FLUTE) framework to address multiple challenges facing large-area, high-resolution urban mapping, including the view angle effect, high intraclass and low interclass variation, and multiscale land cover types. FLUTE builds upon a teacher-student deep learning architecture and includes two new feature extraction modules \u2013 Scale-aware Parsing Module (SPM) and View-aware Embedding Module (VEM).
Our model was trained with a new benchmark database containing 52.43 million labeled pixels (from 2014 to 2017 NAIP airborne Imagery) to capture diverse LCLU types and spatial patterns. We assessed the credibility of FLUTE by producing a 1-meter resolution database named UrbanWatch for 22 major cities across the conterminous United States. UrbanWatch contains nine LCLU classes \u2013 building, road, parking lot, tree canopy, grass/shrub, water, agriculture, barren, and others, with an overall accuracy of 91.52%. You can read the entire paper UrbanWatch: A 1-meter resolution land cover and land use database for 22 major cities in the United States here
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/urban-watch/#citation","title":"Citation","text":"Zhang, Yindan, Gang Chen, Soe W. Myint, Yuyu Zhou, Geoffrey J. Hay, Jelena Vukomanovic, and Ross K. Meentemeyer. \"UrbanWatch: A 1-\nmeter resolution land cover and land use database for 22 major cities in the United States.\"\nRemote Sensing of Environment 278 (2022): 113106.\nI added additional metadata to the images, including city names and abbreviations. While the uncompressed size for these datasets is 211 GB as per the paper, the total GEE collection size is only 4.54 GB. The city list is included in the sample code for easy filtering between the 23 cities.
A nine-class urban classification scheme with diverse geographic patches
Building A human-made structure with a roof (various sizes, shapes, colors, and materials) and walls across commercial, industrial, institutional, and residential areas,such as office buildings, stores, single family houses, townhouses, and condos. Road A long, narrow stretch with a leveled or paved surface that has specific orientation, length, and width. It differs from building and parking lot with its unique feature of connectivity, such as highway, bridge, sidewalk, driveway, railway, rural pathway, and airport runway. Parking Lot A cleared area intended for parking vehicles such as an on-the-ground or a surface parking lot. It differs from building and road with its unique feature of vehicle presence and/or surface markings. Tree Canopy Individual trees or tree patches representing woody vegetation typically taller than 2 m, such as trees in yards, along streets and utility corridors, and in parks and nature reserves. Grass/Shrub Small-sized perennial woody plants or herbaceous plants with height lower than 2 m, such as bushes, lawns, roadway medians, and grasslands. Agriculture Land for cultivating crops, such as corn, wheat, and soy, as well as fallow plots. Water Areas where water is predominantly present throughout the year, such as rivers, ponds, lakes, oceans, flooded plains, canals, streams, bays, estuaries, and swimming pools. Barren Areas of rock, sand or soil with very sparse to no vegetation all year round, such as exposed rock or soil, desert, dunes, dry salt flats, dried lake beds, clay, mud, quarries, golf course sand traps, mine lands, and construction site, etc. Others All other land cover/use not assigned to the above eight classes, such as outdoor tennis/basketball courts with artificial turf or acrylic surface, transmission towers, and areas covered by disturbed soils/sands without uniformed structures.
"},{"location":"projects/urban-watch/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var urban_watch = ee.ImageCollection(\"projects/sat-io/open-datasets/HRLC/urban-watch-cities\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/URBAN-WATCH-CITIES
"},{"location":"projects/urban-watch/#license","title":"License","text":"As per the authors, the urban watch data is freely accessible to support urban-related research, urban planning and management, and community outreach efforts. Therefore, the 1-m maps can be freely used for noncommercial purposes and cited; the assumed license is CC-BY-NC-4.0.
Produced by: Laboratory for Remote Sensing and Environmental Change (LRSEC) at the University of North Carolina
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, FLUTE
Last updated on GEE: 2022-06-12
"},{"location":"projects/urban_projection/","title":"Global urban projections under SSPs (2020-2100)","text":"These datasets include two separate global projections of future urban land under shared socioeconomic pathways (SSPs), one from Chen et al. (2020) and the other from Gao & O'Neill (2020). The Chen et al. dataset provides a binary classification of urban and non-urban land (pixel value of 2 for urban; 1 otherwise) at 1 km resolution for every 10th year from 2020 to 2100 (inclusive). On the other hand, the Gao & O'Neill (2020) dataset provides continuous values representing the probability of full urbanization of each \u215b degree grid for the same years.
When using these future projections, it is important to recognize that they are based on different methodologies, different training data, and different assumptions about future scenarios. For instance, the Gao & O'Neill dataset considers broad urbanization patterns across 375 sub-regions, while the Chen et al. data uses 32 regions. While both of these datasets are trained using the Global Human Settlement Layer (GHSL), the Chen et al. data are further calibrated against the European Space Agency's Climate Change Initiative (ESA CCI) data for 2015. There are many other differences and users should ideally go through the assumptions and methodology described in the respective papers before using the data.
As an example of these differences, below is a plot of projected urban percentage over time for Asia for different SSP scenarios from these datasets and the Li et al. (2021) urban extent data, which is also in the community catalog. Note that the Li et al. data are not based on GHSL, but on historical urban extent dataset from nighttime lights
"},{"location":"projects/urban_projection/#dataset-notes","title":"Dataset notes","text":"(Chen et al 2020): This dataset provides future estimates of urban expansion for all Shared Socioeconomic Pathways (SSPs) every 10 years from 2020 to 2100 (inclusive). The data are at 1 km resolution. Pixels have a value of 2 (for urban) or 1 (for non-urban). Each image corresponds to a date and there are separate bands for each SSP scenario.
(Gao et al 2020): This dataset provides future estimates of urban expansion for all Shared Socioeconomic Pathways (SSPs) every 10 years from 2020 to 2100 (inclusive). The data are at \u215b degree resolution. Probabilities of conversion of entire grid to urban is provided instead of a binary classification. Each image corresponds to a date and there are separate bands for each SSP scenario.
Also noting that these projections are all over the place. See the figure below (also comparing with the Li et al. data you have already ingested). Always good to have a word of caution about these datasets and encourage users to go back to the paper and understand the various assumptions, methodological differences, and what they might mean for use cases.
"},{"location":"projects/urban_projection/#citation","title":"Citation","text":"Chen, G., Li, X., Liu, X. et al. Global projections of future urban land expansion under shared socioeconomic pathways. Nat Commun 11, 537 (2020).\nhttps://doi.org/10.1038/s41467-020-14386-x\nGao, J., O\u2019Neill, B.C. Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. Nat Commun 11,\n2302 (2020). https://doi.org/10.1038/s41467-020-15788-7\nvar chenSSP = ee.ImageCollection(\"projects/sat-io/open-datasets/FUTURE-URBAN-LAND/CHEN_2020_2100\");\nvar gaoSSP = ee.ImageCollection(\"projects/sat-io/open-datasets/FUTURE-URBAN-LAND/GAO_2020_2100\");\nhttps://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-URBAN-SCENARIO-PROJECTIONS
"},{"location":"projects/urban_projection/#license","title":"License","text":"This work is licensed under Creative Commons Attribution 4.0 International for Gao et al 2022 and under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International for Chen et al 2020.
Created by: Gao, et al. 2022 and Chen, et al. 2022
Curated in GEE by : TC Chakraborty and Samapriya Roy
Keywords: urban, SSPs, urban projection, temporal models
Last updated on GEE: 2022-10-23
"},{"location":"projects/us_ftype_fgroup/","title":"US National Forest Type and Groups","text":""},{"location":"projects/us_ftype_fgroup/#forest-type","title":"Forest Type","text":"This dataset portrays 141 forest types across CONUS and Alaska. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, and ecoregions. The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Geospatial Technology and Applications Center. The purpose of this dataset is to portray broad distribution patterns of forest cover in the United States and provide input to national scale modeling projects.
"},{"location":"projects/us_ftype_fgroup/#forest-groups","title":"Forest Groups","text":"This dataset portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data. The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Geospatial Technology and Applications Center. Forest Type Groups are aggregations of forest types (Eyre 1980) into logical ecological groupings. There are 28 national forest type groups. Class accuracy was assessed using a randomly selected independent hold out of 6552 plots. Overall CONUS-wide accuracy for the forest type groups was 65%.
You can get detailed sample forest group metadata here
"},{"location":"projects/us_ftype_fgroup/#earth-engine-snippet-forest-type","title":"Earth Engine snippet: Forest Type","text":"var forest_type = ee.ImageCollection(\"projects/sat-io/open-datasets/USFS/national-forest-type\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/US-NATIONAL-FOREST-TYPE
"},{"location":"projects/us_ftype_fgroup/#earth-engine-snippet-forest-group","title":"Earth Engine snippet: Forest Group","text":"var forest_group = ee.ImageCollection(\"projects/sat-io/open-datasets/USFS/national-forest-group\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/US-NATIONAL-FOREST-GROUP
"},{"location":"projects/us_ftype_fgroup/#license","title":"License","text":"Although these data have been used by the USDA Forest Service, the USDA Forest Service shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data are not legal documents and are not intended to be used as such.
Created by: USDA Forest Service-Forest Inventory and Analysis (FIA) Program & Geospatial Technology and Applications Center (GTAC)
Curated in GEE by : Samapriya Roy
Keywords: forest type, forest group, forest, remote sensing
Last updated on GEE: 2022-10-25
"},{"location":"projects/usa_structures/","title":"USA Structures","text":"DHS, FIMA, FEMA\u2019s Response Geospatial Office, Oak Ridge National Laboratory, and the U.S. Geological Survey collaborated to build and maintain the nation\u2019s first comprehensive inventory of all structures larger than 450 square feet for use in Flood Insurance Mitigation, Emergency Preparedness and Response. To create the building outline inventory, FEMA, in conjunction with DHS Science and Technology, partnered with the Oak Ridge National Laboratory (ORNL) to extract the outlines via commercially available satellite imagery. You can download the datasets here or explore them using this link
"},{"location":"projects/usa_structures/#dataset-attributes","title":"Dataset Attributes","text":""},{"location":"projects/usa_structures/#building-occupancy-types","title":"Building Occupancy Types","text":"As of December 2021, the USA Structures dataset includes occupancy type (e.g., Residential, Commercial, Industrial) and primary occupancy type (e.g., Single Family Residential, Restaurant, Hospital) classifications for all structures. The team developed the data using a variety of sources including Census Housing Unit data, HIFLD, LightBox parcel data, and a modeled approach.
"},{"location":"projects/usa_structures/#universal-unique-identifier-uuid","title":"Universal Unique Identifier (UUID)","text":"In addition to the occupancy type and geometry, each polygon includes an Universally Unique Identifier (UUID) which is a unique ID for each structure across the entire dataset. This allows for connections to individual structures to unique data sources. The data schema is flexible enough to add new data fields and attributes.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or providers of the dataset and their works.
"},{"location":"projects/usa_structures/#citation","title":"Citation","text":"Oak Ridge National Laboratory (ORNL); Federal Emergency Management Agency (FEMA) Geospatial Response Office USA Structures : Last accessed date\nAll datasets are in the format
var state = ee.FeatureCollection('projects/sat-io/open-datasets/ORNL/USA-STRUCTURES/US_ST_{Two letter abbreviation for US state or territory}');\nfor a list of all US states and territories use this
var ee_folder = ee.data.listAssets(\"projects/sat-io/open-datasets/ORNL/USA-STRUCTURES\");\nHere are some example setups for two states/territories
var dc = ee.FeatureCollection('projects/sat-io/open-datasets/ORNL/USA-STRUCTURES/USA_ST_DC')\nvar arizona = ee.FeatureCollection('projects/sat-io/open-datasets/ORNL/USA-STRUCTURES/USA_ST_AZ')\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/ORNL-US-STRUCTURES
"},{"location":"projects/usa_structures/#license","title":"License","text":"This work is licensed under a Creative Commons by Attribution (CC BY 4.0) license.
Created by: DHS, FEMA, ORNL
Curated in GEE by: Samapriya Roy
keywords: homeland security, homeland defense, emergency response, structures, building outlines, USA structures, buildings, FEMA, Federal Emergency Management Agency, ORNL, Oak Ridge National Laboratory, federal, industrial, education, assembly, residential, commercial
Last updated in GEE: 2022-12-01
"},{"location":"projects/usbuild_raster/","title":"Rasterized building footprint dataset for the US","text":"The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with vector layers is highly challenging in practice. Although vector layers provide accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost.
High Performance Computing (HPC) were used by the authors to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state 1. total footprint coverage 2. number of unique buildings intersecting each cell 3. number of building centroids falling inside each cell, and 4. Minimum area of the buildings that intersect each cell 5. Maximum area of the buildings that intersect each cell 6. Average area of the buildings that intersect each cell
These values are represented as raster layers with 30m cell size covering the 48 conterminous states, to better support incorporation of building footprint data into large-extent modelling.
This Project is funded by NASA\u2019s Biological Diversity and Ecological Forecasting program
Award : 80NSSC18k0341
You can download the datasets here
"},{"location":"projects/usbuild_raster/#data-citation","title":"Data Citation","text":"Heris, M.P., Foks, N., Bagstad, K., and Troy, A., 2020, A national dataset of rasterized building\nfootprints for the U.S.: U.S. Geological Survey data release, https://doi.org/10.5066/P9J2Y1WG.\nHeris, M.P., Foks, N.L., Bagstad, K.J. et al. A rasterized building footprint dataset for the\nUnited States. Sci Data 7, 207 (2020). https://doi.org/10.1038/s41597-020-0542-3\nvar avg_area = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_avg_area\");\nvar max_area = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_max_area\");\nvar min_area = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_min_area\");\nvar total_area = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_total_area\");\nvar building_count = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_count\");\nvar centroid_count = ee.Image(\"projects/sat-io/open-datasets/us_building_raster/building_centroid_count\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/RASTERIZED-BUILDING-FOOTPRINT-US
"},{"location":"projects/usbuild_raster/#license","title":"License","text":"Datasets are freely available to the public under the Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/.
Created by: Heris et al, NASA, USGS
Curated by: Samapriya Roy
Keywords: : Building Footprint, Built Environment Density, Land cover, Land use
Last updated: 2021-11-26
"},{"location":"projects/usdm/","title":"United States Drought Monitor","text":"The U.S. Drought Monitor is a map released every Thursday, showing parts of the U.S. that are in drought. The map uses five classifications: abnormally dry (D0), showing areas that may be going into or are coming out of drought, and four levels of drought: moderate (D1), severe (D2), extreme (D3) and exceptional (D4).
The Drought Monitor has been a team effort since its inception in 1999, produced jointly by the National Drought Mitigation Center (NDMC) at the University of Nebraska-Lincoln, the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Department of Agriculture (USDA). The NDMC hosts the web site of the drought monitor and the associated data, and provides the map and data to NOAA, USDA and other agencies. It is freely available at droughtmonitor.unl.edu.
Unlike most of the weather maps people see in the news, the U.S. Drought Monitor is not a forecast. In fact, it looks backward. It\u2019s a weekly assessment of drought conditions, based on how much precipitation did or didn\u2019t fall, up to the Tuesday morning before the map comes out. That gives authors about two working days to review the latest data. If a lot of rain falls in a drought area on a Wednesday, the soonest drought would be removed from the map is the following week. Drought is a slow-moving hazard, so you can be certain that an area will still be in drought if it doesn\u2019t get rain. But it also may take more than one good rainfall to end a drought, especially if an area has been in drought for a long time.
"},{"location":"projects/usdm/#preprocessing","title":"Preprocessing","text":"Drought Monitor GIS Data is available as shapefiles. To create a consistent data structure, the shapefiles are ingested for all years starting from 2000 and with a weekly cadence. These have 5 different drought classes/categories and are converted into a raster with the DM(Drought Monitor class/category values) as raster property. This makes using it as collection and analysis of the data much easier. Start and end dates are added with the release week date as the end date and a week ago as a start date. For now the goal is to keep this collection updated so that this dataset is consistently synced with the source dataset.
"},{"location":"projects/usdm/#earth-engine-snippet","title":"Earth Engine Snippet","text":"var usdm = ee.ImageCollection(\"projects/sat-io/open-datasets/us-drought-monitor\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/UNITED-STATES-DROUGHT-MONITOR
Earth Engine App: https://sat-io.earthengine.app/view/usdm-explorer
"},{"location":"projects/usdm/#license","title":"License","text":"The work is licensed under an Open data license for use.
The U.S. Drought Monitor is jointly produced by the National Drought Mitigation Center\nat the University of Nebraska-Lincoln, the United States Department of Agriculture\nand the National Oceanic and Atmospheric Administration. Map courtesy of NDMC.\nProduced by : National Drought Mitigation Center at the University of Nebraska-Lincoln, the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. Map courtesy of NDMC
Processed secondary/formatted & Curated by: Samapriya Roy
Keywords: \"National Drought Mitigation Center, NDMC, Drought, University of Nebraska-Lincoln, United States Department of Agriculture, USDA, National Oceanic and Atmospheric Administration, NOAA, USDM\"
Last updated: 2021-04-24
"},{"location":"projects/usgs_modis_et/","title":"USGS MODIS Evapotranspiration","text":"The evapotranspiration (ET) dataset presented here is the result of remote sensing techniques, primarily harnessing MODIS-thermal imagery alongside global weather datasets. This dataset corresponds to version 5 of the global ET product employed by Climate Engine. It provides valuable insights into the spatiotemporal dynamics of ET, covering the period from 2003 to 2023'. The dataset's cornerstone is the operational Simplified Surface Energy Balance (SSEBop) model, meticulously detailed by Senay et al. (2013). Built upon the foundations of the Simplified Surface Energy Balance (SSEB) approach, initially proposed by Senay et al. in 2007 and further refined in subsequent publications (Senay et al., 2011), the SSEBop model features unique parameterization specifically tailored for operational applications, akin to principles associated with psychrometry. Its robustness is underscored by a comprehensive model evaluation conducted by Velpuri et al. in 2013.
The global ET estimates are meticulously derived by integrating MODIS-based land surface temperature data, acquired from the Aqua satellite, with maximum air temperature data sourced from WorldClim. Additionally, reference ET values are obtained through global data assimilation systems (GDAS) for calibration and validation purposes, further enhancing the accuracy of this dataset. This comprehensive approach not only enriches our understanding of the intricate processes of evapotranspiration on a global scale but also offers invaluable temporal and spatial insights into these dynamics. Additional information regarding this dataset can be found here. You can find a link to this dataset within climate engine org here
"},{"location":"projects/usgs_modis_et/#dataset-details","title":"Dataset details","text":"Spatial extent Global Spatial resolution 1-km grid (1/96-deg) Temporal resolution Dekadal, monthly, and yearly Time span 2003 to Present Update frequency Updated every 10-12 days
Variables
Variable Evapotranspiration (ETa) ('et') Units Millimeters Scale factor 1.0
"},{"location":"projects/usgs_modis_et/#citation","title":"Citation","text":"Senay, G.B., Kagone S., Velpuri N.M., 2020, Operational Global Actual Evapotranspiration using the SSEBop model: U.S. Geological Survey data release, [https://doi.org/10.5066/P9OUVUUI.](https://doi.org/10.5066/P9OUVUUI) Publication: https://www.mdpi.com/1424-8220/20/7/1915\n\nSenay, G. B., Budde, M. E., & Verdin, J. P. (2011). Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Agricultural Water Management, 98(4), 606-618.\n\nSenay, G. B., Budde, M., Verdin, J. P., & Melesse, A. M. (2007). A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors, 7(6), 979-1000.\n\nVelpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P. (2013). A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET, Remote Sensing of Environment, 139, 35-49, [(Abstract and Article)](http://dx.doi.org/10.1016/j.rse.2013.07.013)\n// Read in dekadal, monthly, and annual Image Collections and get single image from each\nvar modis_et_d_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/usgs-ssebop/modis_et_v5_dekadal')\nvar modis_et_d_i = modis_et_d_ic.first()\nvar modis_et_m_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/usgs-ssebop/modis_et_v5_monthly')\nvar modis_et_m_i = modis_et_m_ic.first()\nvar modis_et_a_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/usgs-ssebop/modis_et_v5_annual')\nvar modis_et_a_i = modis_et_a_ic.first()\n\n// Print first image to see bands\nprint(modis_et_d_i)\nprint(modis_et_m_i)\nprint(modis_et_a_i)\n\n// Visualize select bands from first image\nvar et_palette = ['#f5e4a9', '#fff4ad', '#c3e683', '#6bcc5c', '#3bb369', '#20998f', '#1c8691']\nMap.addLayer(modis_et_d_i.select('et'), {min: 0, max: 10, palette: et_palette}, 'et, dekadal')\nMap.addLayer(modis_et_m_i.select('et'), {min: 0, max: 30, palette: et_palette}, 'et, monthly')\nMap.addLayer(modis_et_a_i.select('et'), {min: 0, max: 1000, palette: et_palette}, 'et, annual')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/USGS-MODIS-ET
"},{"location":"projects/usgs_modis_et/#license","title":"License","text":"USGS-authored or produced data and information are considered to be in the U.S. Public Domain.
Keywords: evapotranspiration, MODIS, ETa, SSEBop, global, near real-time, monthly, annual, dekadal
Created & provided by: USGS
Curated by: USGS & Climate Engine Org
"},{"location":"projects/usgs_topo/","title":"USGS Historical Topo Maps","text":"The history of USGS Topo Maps traces back to the late 19th century when the USGS embarked on a mission to map the entire United States in intricate detail. The 1:24,000 scale, also known as 7.5-minute quadrangle maps, emerged as one of the most widely used scales. Each map sheet covers an area of 7.5 minutes of longitude and latitude, resulting in a detailed representation of approximately 64 square miles (166 square kilometers).
A subset of these are ingested into the overall collection about 81,000+ and improvements and additions will be made in the future. Metadata including state name, place name and scale. States like Texas, California and others were not added directly but might be added over time.
You can read about the preprocessing steps here
"},{"location":"projects/usgs_topo/#citation","title":"Citation","text":"United States Geological Survey. (2019). Yosemite National Park [Topographic map, Map No. 12345]. 1:24,000. U.S. Geological Survey.\nvar usgs_topo = ee.ImageCollection(\"projects/sat-io/open-datasets/USGS/historical_topo\");\nvar map_index = ee.FeatureCollection(\"projects/sat-io/open-datasets/USGS/TOPO_24K_MAPINDEX\");\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/USGS-TOPO-RENDER
"},{"location":"projects/usgs_topo/#license-terms-of-use","title":"License & Terms of Use","text":"USGS topographic maps are typically in the public domain, which means they are not protected by copyright and can be freely used, reproduced, and distributed. The USGS allows the public to access and use its maps for various purposes without the need for a formal license or permission.
Provided by: USGS
Curated in GEE by : Samapriya Roy
keywords: USGS, Historical Topographical Maps, Orthophoto mosaics, Topography,Cartography
Last updated on GEE: 2023-11-25
"},{"location":"projects/usgs_viirs/","title":"USGS VIIRS Evapotranspiration","text":"The VIIRS Evapotranspiration (ET) dataset, based on Version 6 of the global ET product, is derived from remote sensing utilizing VIIRS thermal imagery and global weather datasets. It employs the SSEBop (Simplified Surface Energy Balance operational version) methodology, initially proposed by Senay et al. in 2007, with specialized parameterization tailored for operational applications using satellite psychrometry principles, as introduced by Senay in 2018. In SSEBop Version 6, the novel Forcing And Normalizing Operation (FANO) algorithm, as outlined by Senay et al. in 2023, is employed to establish the wet-bulb boundary condition, enabling robust modeling of spatiotemporal dynamics of ETa (actual evapotranspiration) across various landscapes and seasons, irrespective of vegetation cover density.
Notably, recent assessments of the global ETa product indicate its promising performance for drought monitoring through ETa Anomaly analysis. However, for studies involving water budget analysis necessitating absolute magnitudes, a local or region-specific bias correction procedure, as detailed by Senay et al. in 2020, may be required. The dataset's creation involves the integration of VIIRS-based land surface temperature, maximum air temperature from WorldClim, and reference ET obtained from gridded weather datasets such as TerraClimate by Abatzoglou et al. (2018) for global coverage and Chiew et al. (2002) for Australia.
"},{"location":"projects/usgs_viirs/#dataset-details","title":"Dataset details","text":"Spatial extent Global Spatial resolution 1-km grid (1/96-deg) Temporal resolution Dekadal, monthly, and yearly Time span 2012 to Present Update frequency Updated every 10-12 days
Variables
Variable Evapotranspiration (ETa) ('et') Units Millimeters Scale factor 1.0
"},{"location":"projects/usgs_viirs/#additional-information","title":"Additional information","text":"You can find additional information on these datasets in the links below - https://earlywarning.usgs.gov/fews/search/Global - https://earlywarning.usgs.gov/fews/product/461 - https://earlywarning.usgs.gov/fews/product/460 - https://earlywarning.usgs.gov/fews/product/458
"},{"location":"projects/usgs_viirs/#citation","title":"Citation","text":"Senay, G.B., Parrish, G.E., Schauer, M., Friedrichs, M., Khand, K., Boiko, O., Kagone, S., Dittmeier, R., Arab, S. and Ji, L., 2023. Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation. Remote Sensing,15(1), p.260. https://doi.org/10.3390/rs15010260\n\nSenay, G.B., Kagone S., Velpuri N.M., 2020, Operational Global Actual Evapotranspiration using the SSEBop model: U.S. Geological Survey data release, [https://doi.org/10.5066/P9OUVUUI.](https://doi.org/10.5066/P9OUVUUI) Publication: https://www.mdpi.com/1424-8220/20/7/1915\n\nAbatzoglou, J., Dobrowski, S., Parks, S. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u20132015. Sci Data 5, 170191 (2018). https://doi.org/10.1038/sdata.2017.191\n\nSenay, G. B. (2018). Satellite psychrometric formulation of the Operational Simplified Surface Energy Balance (SSEBop) model for quantifying and mapping evapotranspiration. Applied Engineering in Agriculture, 34(3), 555-566. https://doi.org/10.13031/aea.12614\n\nVelpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P. (2013). A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET, Remote Sensing of Environment, 139, 35-49, [(Abstract and Article)](http://dx.doi.org/10.1016/j.rse.2013.07.013)\n\nSenay, G. B., Budde, M., Verdin, J. P., & Melesse, A. M. (2007). A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors, 7(6), 979-1000.\n\nChiew, F, Q.J. Wang, F. McConachy, R. James, W. Wright, and G. deHoedt, (2002). Evapotranspiration maps for Australia. Hydrology and Water Resources Symposium, Melbourne, 20-23, 2002, Institution of Engineers, Australia.\n// Read in dekadal, monthly, and annual Image Collections and get single image from each\nvar viirs_et_d_ic = ee.ImageCollection('projects/usgs-ssebop/viirs_et_v6_dekadal')\nvar viirs_et_d_i = viirs_et_d_ic.first()\nvar viirs_et_m_ic = ee.ImageCollection('projects/usgs-ssebop/viirs_et_v6_monthly')\nvar viirs_et_m_i = viirs_et_m_ic.first()\nvar viirs_et_a_ic = ee.ImageCollection('projects/usgs-ssebop/viirs_et_v6_annual')\nvar viirs_et_a_i = viirs_et_a_ic.first()\n\n// Print first image to see bands\nprint(viirs_et_d_i)\nprint(viirs_et_m_i)\nprint(viirs_et_a_i)\n\n// Visualize select bands from first image\nvar et_palette = ['#f5e4a9', '#fff4ad', '#c3e683', '#6bcc5c', '#3bb369', '#20998f', '#1c8691']\nMap.addLayer(viirs_et_d_i.select('et'), {min: 0, max: 10, palette: et_palette}, 'et, dekadal')\nMap.addLayer(viirs_et_m_i.select('et'), {min: 0, max: 30, palette: et_palette}, 'et, monthly')\nMap.addLayer(viirs_et_a_i.select('et'), {min: 0, max: 1000, palette: et_palette}, 'et, annual')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/USGS-VIIRS-ET
"},{"location":"projects/usgs_viirs/#license","title":"License","text":"USGS-authored or produced data and information are considered to be in the U.S. Public Domain.
"},{"location":"projects/usgs_viirs/#keywords","title":"Keywords","text":"VIIRS, remote sensing, satellite, evapotranspiration, monthly, yearly, dekadal, USGS, global
Created & provided by: USGS
Curated by: USGS & Climate Engine Org
"},{"location":"projects/usgwd/","title":"United States Groundwater Well Database (USGWD)","text":"Groundwater wells are critical infrastructure that enable the monitoring, extraction, and use of groundwater, which has important implications for the environment, water security, and economic development. Despite the importance of wells, a unified database collecting and standardizing information on the characteristics and locations of these wells across the United States has been lacking. To bridge this gap, we have created a comprehensive database of groundwater well records collected from state and federal agencies, which we call the United States Groundwater Well Database (USGWD). Presented in both tabular form and as vector points, USGWD comprises over 14.2 million well records with attributes, such as well purpose, location, depth, and capacity, for wells constructed as far back as 1763 to 2023. Rigorous cross-verification steps have been applied to ensure the accuracy of the data. The USGWD stands as a valuable tool for improving our understanding of how groundwater is accessed and managed across various regions and sectors within the United States. You can read the paper here and download the dataset here.
"},{"location":"projects/usgwd/#dataset-preprocessing","title":"Dataset preprocessing","text":"The datasets were provided as state wide extracts and while the 50 state wide extracts were uploaded they were finally merged into a single feature collection for ease of use. While datasets were provided in both geospatial and tabular formats shapefiles are notorious for the property length truncation and size limit of 2GB, so tabular CSV datasets were selected which contained spatial information. However the tabular datasets themselves were has a lot of rows without location information which meant those rows had to be dropped and as such the files were reprocessed to allow us to select only those rows with location information for wells.
"},{"location":"projects/usgwd/#citation","title":"Citation","text":"Lin, CY., Miller, A., Waqar, M. et al. A database of groundwater wells in the United States. Sci Data 11, 335 (2024).\nhttps://doi.org/10.1038/s41597-024-03186-3\nLin, C., A. Miller, M. Waqar, L. Marston (2024). A Database of Groundwater Wells in the United States, HydroShare,\nhttps://doi.org/10.4211/hs.8b02895f02c14dd1a749bcc5584a5c55\nvar usgwd = ee.FeatureCollection(\"projects/sat-io/open-datasets/USGWD-TABULAR-MERGED\")\nIndividual states were also ingested for reference and can be access by the users by using
var usgwd_states = ee.data.listAssets(\"projects/sat-io/open-datasets/USGWD-TABULAR\");\n\nprint('Total of '+ee.List(usgwd_states.assets).size().getInfo()+ ' assets in nodes',usgwd_states.assets);\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/US-GROUNDWATER-WELL-DATABASE
"},{"location":"projects/usgwd/#license","title":"License","text":"The datasets are provided under a Creative Commons 4.0 International License.
Provided by: Lin, CY., Miller, A., Waqar, M. et al, (2024)
Curated in GEE by: Samapriya Roy
Keywords: USGWD, Groundwater well, Point of diversion, United States, Water infrastructure
Last updated in GEE: 2024-04-17
"},{"location":"projects/ussdo/","title":"United States Seasonal Drought Outlook","text":"The United States Drought Outlook raster dataset is produced by the National Weather Service Climate Prediction Center. It is released on the last day of each month and provides information on the drought outlook for the following month. The \"US Seasonal Drought Outlook\" dataset is released on a monthly basis, specifically on the third Thursday of each month. This dataset provides a qualitative assessment of the likelihood of drought conditions across different regions of the United States. The assessment is conducted using a four-category scale to characterize the anticipated drought conditions:
You can find additional information here and on the climate engine org website. You can download the datesets here
Categorical Values
Value Interpretation -9999 NoData Value 0 No drought 1 Drought removal likely 2 Drought remains but improves 3 Drought development likely 4 Drought persistsSpatial Information
Parameter Value Spatial extent United States Spatial resolution 500 m (1/48-deg) Temporal resolution Monthly Time span 2013-08-01 to present Update frequency Updated last day of each monthVariables
Variable Details Drought category ('drought_outlook_class') - Units: Drought outlook classification - Scale factor: 1.0 "},{"location":"projects/ussdo/#earth-engine-snippet","title":"Earth Engine Snippet","text":"// Read in Image Collection and get single image\nvar usdo_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-cpc-usdo-monthly')\nvar usdo_i = usdo_ic.first()\n\n// Print image to see bands\nprint(usdo_i)\n\n// Visualize a single image\n\nvar usdo_palette = [\"#ffffff\", \"#ABA362\", \"#DACBB5\", \"#FFD861\", \"#935743\"]\nMap.addLayer(usdo_i, {min:0, max:4, palette: usdo_palette}, 'usdo_i')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/US-DROUGHT-OUTLOOK
"},{"location":"projects/ussdo/#license","title":"License","text":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.
Keywords: drought, United Stated, outlook, forecast, NOAA, NWS, CPC, monthly
Provided by: NOAA
Curated in GEE by: Climate Engine Org
"},{"location":"projects/veg_dri/","title":"Vegetation Drought Response Index (VegDRI)","text":"The Vegetation Drought Response Index (VegDRI) is a weekly geospatial model that depicts drought stress on vegetation within the conterminous United States. The development of the VegDRI drought-monitoring tool was a collaborative effort by scientists at the USGS EROS Center, the National Drought Mitigation Center (NDMC) at the University of Nebraska, and the High Plains Regional Climate Center (HPRCC).
VegDRI methodology integrates remote sensing data from NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra platform with climate and biophysical data to create a seamless product with a 1 km spatial resolution. The satellite components related to general vegetation conditions are Percent Annual Seasonal Greenness (PASG) and Start of Season Anomaly (SOSA) data. PASG is calculated weekly from eMODIS Normalized Difference Vegetation Index (NDVI) composites.
The climate-based drought data include the Palmer Drought Severity Index (PDSI) and weekly Standardized Precipitation Index (SPI) data from the HPRCC. Climate data identify areas that are experiencing dryness to help distinguish vegetation stress due to drought. The biophysical characteristics of the environment are derived from land use/land cover, soil available water capacity, ecological setting, irrigation status, and elevation data. Environmental stressors such as land use change, soil conditions, pest infestations, disease, hail, flooding, or fire can also influence vegetation conditions.
This integrated approach considers climate and biophysical conditions to determine the cause of vegetation stress. This information is incorporated into the calculation of VegDRI to create an easy to interpret, color-coded map of drought stress on vegetation. Drought-monitoring maps are produced every week using the latest information and are usually posted each Monday by 10:30 a.m. CT. You can get access to Climate Engine ORG's website here. Additional information about DRI can be found here and from USGS here
"},{"location":"projects/veg_dri/#dataset-description","title":"Dataset Description","text":"Spatial Information
Attribute Details Spatial extent Conterminous United States Spatial resolution 1000m Temporal resolution Weekly Time span 2009-04-22 to present Update frequency Weekly on Monday by 10:30 a.m. CTVariables
Variable Units Offset Scale factor Description VegDRI (\u2018vegdri\u2019) Unitless -128 0.0625 Values provided as 8-bit integers that can be scaled to range consistent with Palmer Drought Severity Index. Water (\u2018water\u2019) Unitless N/A 1.0 Binary mask of water. Out-of-Season (\u2018out_of_season\u2019) Unitless N/A 1.0 Binary mask of out-of-season (see documentation for more information)."},{"location":"projects/veg_dri/#citation","title":"Citation","text":"Brown, J. F., Wardlow, B. D., Tadesse, T., Hayes, M. J., & Reed, B. C. (2008). The Vegetation Drought Response Index (VegDRI): A New Integrated\nApproach for Monitoring Drought Stress in Vegetation. GIScience & Remote Sensing, 45(1), 16\u201346. https://doi.org/10.2747/1548-1603.45.1.16\n// Import VegDRI\nvar vegdri_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-veg-dri')\nvar vegdri_i = vegdri_ic.first()\nprint(vegdri_i)\n\n// Link to methods webpage: https://www.usgs.gov/special-topics/monitoring-vegetation-drought-stress/science/methods-vegdri\n// Link to EROS page: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-vegetation-monitoring-vegetation-drought-response-index\n\n// VegDRI data are stored as 8-bit integer data and can be scaled into the values below\n// Drought categories from EROS page\n// Category Bitmap PDSI vals\n// Extreme drought: 001-064 -7.9375 - -4.0000\n// Severe drought: 065-080 -3.9375 - -3.0000\n// Moderate drought: 081-096 -2.9375 - -2.0000\n// Abnormally dry: 097-112 -1.9375 - -1.0000\n// Near normal: 113-160 -0.9375 - 2.0000\n// Abnormally wet: 161-176 2.0625 - 3.0000\n// Moderately wet: 177-192 3.0625 - 4.0000\n// Extremely wet: 193-255 4.0625 - 7.7500\n// Water: 253\n// Out of season: 254\n// Other landcover: 255\n\n// Function to apply stretch to make consistent with values above\nfunction scale_vegdri(img){\n\n // Select vegdri band and scale to PDSI range.\n var vegdri_scale = img.select('vegdri')\n .subtract(128) // convert to signed 8-bit integer\n .divide(16) // scale to PDSI range\n .rename('vegdri_scale') // rename image\n return img.addBands(vegdri_scale)\n}\nvegdri_ic = vegdri_ic.map(scale_vegdri)\nprint(vegdri_ic)\n\n// VegDRI color palette\nvar vegdri_palette = [\"#720206\", \"#cb3121\", \"#e36b09\", \"#fee301\", \"#ffffff\", \"#ffffff\", \"#ffffff\", \"#88f9c7\", \"#53c285\", \"#2b8032\"]\n\n// Select individual image and apply to map\nvar vegdri_i = vegdri_ic.first()\nMap.addLayer(vegdri_i.select('vegdri_scale'), {min: -5, max: 5, palette: vegdri_palette}, 'VegDRI')\nMap.addLayer(vegdri_i.select('out_of_season'), {min:254, max:254, palette: ['878787']}, 'VegDRI Out-of-Season')\nMap.addLayer(vegdri_i.select('water'), {min:253, max:253, palette: ['0000FF']}, 'Water')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/VEGETATION-DROUGHT-RESPONSE-INDEX
"},{"location":"projects/veg_dri/#license","title":"License","text":"USGS-authored or produced data and information are considered to be in the U.S. Public Domain.
Dataset provider: USGS
Keywords : Drought, Climate, Remote sensing, MODIS, PDSI, CONUS, United States
Curated in GEE by: Climate Engine Org
"},{"location":"projects/veg_dry/","title":"Vegetation dryness for western USA","text":"This dataset shows how dry or wet the vegetation is in western US. The dataset is available at 15-day temporal resolution and 250 m spatial resolution. It spans April 2016 to present.
The variable contained in the maps is live fuel moisture content. It is defined as the mass of water per unit mass of live biomass (expressed as a percentage quantity). For e.g., if a pixel value = 150, it means that the vegetation in that pixel has 1.5 Kg of water for every 1 Kg of live biomass. Live fuel moisture content was estimated from a deep learning model trained using Sentinel-1 C-band backscatter, Landsat-8 optical reflectance, and various other land surface characteristics like canopy height, soil texture, etc.
"},{"location":"projects/veg_dry/#citation","title":"Citation","text":"Rao, K., Williams, A.P., Flefil, J.F. & Konings, A.G. (2020). SAR-enhanced mapping\nof live fuel moisture content. Remote Sensing of Environment, 245, 111797.\nDOI: 10.1016/j.rse.2020.111797\nRead the paper here
"},{"location":"projects/veg_dry/#earth-engine-snippet","title":"Earth Engine Snippet","text":"
Asset Link
var asset_name = ee.ImageCollection(\"users/kkraoj/lfm-mapper/lfmc_col_25_may_2021\")\nSample Code
This script imports and visualizes average vegetation dryness for 2019, go to script
var start_date = '2019-01-01';\nvar end_date = '2019-12-31';\n\n// Import LFMC collection\nvar collection = ee.ImageCollection('users/kkraoj/lfm-mapper/lfmc_col_25_may_2021')\n .filterDate(start_date,end_date)\n\nvar image = collection.mean(); //calculate mean for the selected date range\nvar palette_lfmc = ['#703103','#945629','#ce7e45', '#df923d', '#f1b555', '#fcd163', '#99b718',\n '#74a901', '#66a000', '#529400', '#3e8601', '#207401', '#056201',\n '#004c00', '#023b01', '#012e01'\n , '#011d01', '#011301'];\n\nMap.addLayer(image, {min: [50], max: [200], palette: palette_lfmc, opacity: 0.95}, 'LFMC mean');\nMap.setCenter(-113.03, 38, 5);\nEarth Engine App https://kkraoj.users.earthengine.app/view/live-fuel-moisture
Earth Engine App Code https://code.earthengine.google.com/e6b336fa58124f4f8cda2b3be76d156f
The scripts supporting the analysis can be found at https://github.com/kkraoj/lfmc_from_sar
"},{"location":"projects/veg_dry/#license-information","title":"License Information","text":"CC BY-NC-ND 4.0 Under which you can copy and redistribute the material in any medium or format.
Created and Curated by: KrishnaRao, A. ParkWilliams, Jacqueline Fortin Flefil, Alexandra G.Konings
Keywords: vegetation, dryness, drought, wildfire, USA, live fuel moisture content
Last updated: 2021-06-29
"},{"location":"projects/vodca/","title":"Global Long-term Microwave Vegetation Optical Depth Climate Archive (VODCA)","text":"Vegetation optical depth (VOD) describes the attenuation of radiation by plants. VOD a function of frequency as well as vegetation water content, and by extension biomass. VOD has many possible applications in studies of the biosphere, such as biomass monitoring, drought monitoring, phenology analyzes or fire risk management. We merged VOD observations from various spaceborne sensors (SSM/I, TMI, AMSR-E, AMSR2, WindSat) to create global long-term vod time series. Prior to aggregation the data has been rescaled to AMSR-E, removing systematic differences between them.
There is a product for C-band (~6.9 GHz, 2002 - 2018), X-band (10.7 GHz, 1997 - 2018) and Ku-band (~19 GHz, 1987 - 2017). The data is global sampled on a regular 0.25 degrees grid.
Variables of data in VODCA files:
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
"},{"location":"projects/vodca/#dataset-preprocessing","title":"Dataset preprocessing","text":"The dataset were provided as zipped NetCDF files with subdatasets for VOD, Sensor flags and processing flags. The datasets including the subdatasets were exported as individual tif files and then stacked with the band order VOD, Sensor Flag and Processing Flag.
Bands Band Name Unit b1 VOD Unitless b2 Sensor Flag Refer to variable description b3 Processing Flag Refer to variable description
"},{"location":"projects/vodca/#dataset-citation","title":"Dataset citation","text":"Moesinger, Leander, Dorigo, Wouter, De Jeu, Richard, Van der Schalie, Robin, Scanlon, Tracy, Teubner, Irene, & Forkel, Matthias. (2019).\nThe Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA (1.0) [Data set].\nZenodo. https://doi.org/10.5281/zenodo.2575599\nMoesinger, Leander, Wouter Dorigo, Richard de Jeu, Robin van der Schalie, Tracy Scanlon, Irene Teubner, and Matthias Forkel.\n\"The global long-term microwave vegetation optical depth climate archive (VODCA).\" Earth System Science Data 12, no. 1 (2020): 177-196.\nvar cband = ee.ImageCollection(\"projects/sat-io/open-datasets/VODCA/C-BAND\");\nvar kband = ee.ImageCollection(\"projects/sat-io/open-datasets/VODCA/K-BAND\");\nvar xband = ee.ImageCollection(\"projects/sat-io/open-datasets/VODCA/X-BAND\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/VODCA
"},{"location":"projects/vodca/#license","title":"License","text":"This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Created by : Moesinger, Leander, Wouter Dorigo, Richard de Jeu, Robin van der Schalie, Tracy Scanlon, Irene Teubner, and Matthias Forkel
Curated in GEE by: Samapriya Roy
Keywords: VOD, Vegetation Optical Depth, Biomass, Vegetation water content, Microwave Remote Sensing, Biosphere, Time Series, global, vegetation
Last updated : 2023-01-19
"},{"location":"projects/vt_lc/","title":"Vermont High Resolution Land Cover 2016","text":"High resolution land cover dataset for Vermont. The primary sources used to derive this land cover layer were 2013-2017 LiDAR data and 2016 NAIP imagery. Ancillary data sources included GIS data provided by the State of Vermont or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
This dataset was developed as part of the Vermont High-Resolution Land Cover. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. Eight land cover classes were mapped:
This assessment and development of methods necessary for its conduct were completed by the University of Vermont's Spatial Analysis Laboratory with funding from the State of Vermont Clean Water Fund, Vermont Agency of Natural Resources, Vermont Agency of Transportation, Lake Champlain Basin Program, and the Vermont Center for Geographic Information (VCGI). Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establishing tree canopy goals.
Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.
University of Vermont Spatial Analysis Laboratory and VT Center for Geographic Information. Vermont High Resolution Land Cover. Accessed [Month\nYear] at https://geodata.vermont.gov/pages/land-cover\nvar VT_baseLC2016 = ee.Image(\"projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/VT_BaseLC_2016\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/VT-BASE-LC-2016
"},{"location":"projects/vt_lc/#license","title":"License","text":"The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. VCGI and the State of Vermont make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.
Produced by: University of Vermont Spatial Analysis Laboratory, VT Center for Geographic Information
Curated in GEE by: Samapriya Roy
Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, OBIA
Last updated on GEE: 2022-06-12
"},{"location":"projects/wa_lulc/","title":"West Africa Land Use Land Cover","text":"Started in 1999, the West Africa Land Use Dynamics project represents an effort to map land use and land cover, characterize the trends in time and space, and understand their effects on the environment across West Africa. The outcome of the West Africa Land Use Dynamics project is the production of a three-time period (1975, 2000, and 2013) land use and land cover dataset for the Sub-Saharan region of West Africa, including the Cabo Verde archipelago. The West Africa Land Use Land Cover Time Series dataset offers a unique basis for characterizing and analyzing land changes across the region, systematically and at an unprecedented level of detail.
Tappan, G. G., Cushing, W.M., Cotillon, S.E., Mathis, M.L., Hutchinson, J.A., Herrmann, S.M., and\nDalsted, K.J., 2016, West Africa Land Use Land Cover Time Series:\nU.S. Geological Survey data release, http://dx.doi.org/10.5066/F73N21JF\nvar wa1975 = ee.Image(\"projects/sat-io/open-datasets/wa-datasets/wa_lc_usgs_1975\");\nvar wa2000 = ee.Image(\"projects/sat-io/open-datasets/wa-datasets/wa_lc_usgs_2000\");\nvar wa2013 = ee.Image(\"projects/sat-io/open-datasets/wa-datasets/wa_lc_usgs_2013\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/WEST-AFRICA-LULC
"},{"location":"projects/wa_lulc/#license","title":"License","text":"Creative Commons Attribution-Share Alike 4.0 International License
Curated by: Samapriya Roy
Keywords: United States Geological Survey, USGS, Land Use, Land Cover, West Africa
Last updated: 2021-04-17
"},{"location":"projects/wacvm/","title":"West Africa Coastal Vulnerability Mapping","text":"The West Africa Coastal Vulnerability Mapping: Social Vulnerability Indices data set includes three indices: Social Vulnerability, Population Exposure, and Poverty and Adaptive Capacity. The Social Vulnerability Index (SVI) was developed using six indicators: population density (2010), population growth (2000-2010), subnational poverty and extreme poverty (2005), maternal education levels circa 2008, market accessibility (travel time to markets) circa 2000, and conflict data for political violence (1997-2013). Because areas of high population density and growth (high vulnerability) are generally associated with urban areas that have lower levels of poverty and higher degrees of adaptive capacity (low vulnerability), to some degree, the population factors cancel out the poverty and adaptive capacity indicators. To account for this, the data set includes two sub-indices, a Population Exposure Index (PEI), which only includes population density and population growth; and a Poverty and Adaptive Capacity Index (PACI), composed of subnational poverty, maternal education levels, market accessibility, and conflict. These sub-indices are able to isolate the population indicators from the poverty and conflict metrics. The indices represent Social Vulnerability in the West Africa region within 200 kilometers of the coast.
Purpose: To provide a measure of social vulnerability and \"defenselessness\" in the face of climate stressors in the coastal zone of West Africa.
The documentation for this dataset is available here
Use the following citation
Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. West Africa Coastal Vulnerability Mapping: Social Vulnerability Indices. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4H41PCK. Accessed DAY MONTH YEAR.\nvar wacvm_paci = ee.FeatureCollection(\"projects/sat-io/open-datasets/sedac/wacvm-social-vulnerability-indices-paci\");\nvar wacvm_pei = ee.FeatureCollection(\"projects/sat-io/open-datasets/sedac/wacvm-social-vulnerability-indices-pei\");\nvar wacvm_svi = ee.FeatureCollection(\"projects/sat-io/open-datasets/sedac/wacvm-social-vulnerability-indices-svi\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/WEST_AFRICA-COASTAL-VULN
Shared License: This work is licensed under the Creative Commons Attribution 4.0 International License. Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.
Curated by: Samapriya Roy
Keywords: census geography, GPWv4, gridded population, uniform distribution
Last updated: 2021-04-11
"},{"location":"projects/wpschool/","title":"Gridded Sex-Disaggregated School-Age Population (2020)","text":"Following the IIEP-UNESCO methodology for reconstructing georeferenced school-age populations (ISCED 1 to 3) by year and sex, these datasets were produced by WorldPop (University of Southampton) by applying the Sprague Multipliers to 30-arcsecond (approximately 1km at the equator) gridded datasets depicting the estimated spatial distribution of sex-disaggregated 5-year age groups. These datasets include the sex disaggregated school age population for countries and Dependent territories in Africa only.
"},{"location":"projects/wpschool/#inputs","title":"Inputs","text":"Bondarenko, M., Sorichetta, A., Vargas Mesa, G., Gagnon, A.A., Tatem, A.J. (2022). Gridded Sex Disaggregated School-Age Population Datasets for Countries\nand Dependent Territories in Africa in 2020, doi:10.5258/SOTON/WP00732\nvar f_primary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_F_PRIMARY\");\nvar f_secondary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_F_secondary\");\nvar m_primary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_M_PRIMARY\");\nvar m_secondary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_M_secondary\");\nvar fm_primary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_F_M_PRIMARY\");\nvar fm_secondary = ee.ImageCollection(\"projects/sat-io/open-datasets/worldpop/africa_F_M_SECONDARY\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/WORLDPOP-GRIDDED-SCHOOL-AGE
"},{"location":"projects/wpschool/#license","title":"License","text":"WorldPop datasets are available under the Creative Commons Attribution 4.0 International License. This means that you are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially, provided attribution is included (appropriate credit and a link to the licence).
Created by: WorldPop (University of Southampton)
Curated in GEE by: Samapriya Roy
Keywords: gridded model, population data, school age, disaggregated, worldpop
"},{"location":"projects/wrc/","title":"Wildfire Risk to Communities (WRC)","text":"The Wildfire Risk to Communities dataset was created by the USDA Forest Service to provide a nationwide view of wildfire risk potential. The dataset includes spatial data on the following components of wildfire risk: wildfire likelihood, wildfire intensity, susceptibility, and exposure. The data is available at a 270-meter resolution and covers the entire United States. It is based on a variety of sources, including vegetation data, fuel models, historical fire data, and population data.
The Wildfire Risk to Communities dataset can be used to assess wildfire risk at the community level and to develop wildfire mitigation plans. It can also be used to identify communities that are most in need of assistance. The Wildfire Risk to Communities dataset was created by USDA Forest Service to help assess risk to homes, businesses, and other valued resources. The dataset contains nationally-consistent information for the purpose of comparing relative wildfire risk among communities nationally or within a state or county. In situ risk (risk at the location where the adverse effects take place on the landscape) are modeled using the large fire simulation system (FSim) and LANDFIRE fuel loading datasets from 2014. The original data at 250m has been upsampled to 30m for this dataset on Climate Engine. You can find additional information about the dataset here and read more about this on climate engine org page here. Here is a link to the report documentation
Spatial Extent United States Spatial Resolution 30 m Temporal Resolution Single point in time Time Span 2014 Update Frequency Static
Variables Burn probability ('BP') - Units: Fractional probability - Scale Factor: 1.0 Conditional flame length ('CFL') - Units: Feet - Scale Factor: 1.0 Conditional risk to potential structures ('CRPS') - Units: Percentile - Scale Factor: 1.0 Exposure type ('Exposure') - Units: Exposure type - Scale Factor: 1.0 Flame length exceedance probability - 4 ft ('FLEP4') - Units: Fractional probability - Scale Factor: 1.0 Flame length exceedance probability - 8 ft ('FLEP8') - Units: Fractional probability - Scale Factor: 1.0 Risk to potential structures ('RPS') - Units: Percentiles - Scale Factor: 1.0 Wildfire hazard potential index ('WHP') - Units: Unitless - Scale Factor: 1.0
"},{"location":"projects/wrc/#citation","title":"Citation","text":"Scott, Joe H.; Gilbertson-Day, Julie W.; Moran, Christopher; Dillon, Gregory K.; Short, Karen C.; Vogler, Kevin C. 2020. Wildfire Risk to\nCommunities: Spatial datasets of landscape-wide wildfire risk components for the United States. Fort Collins, CO: Forest Service Research Data\nArchive. Updated 25 November 2020.\n// Read in Image Collection and mosaic to single image\nvar wrc_i = ee.ImageCollection('projects/climate-engine-pro/assets/ce-wrc-static').mosaic()\n// Print image to see bands\nprint(wrc_i)\n// Visualize select bands from first image \u2014 additional bands are present in the Image Collection\nvar bp_palette = [\"#d53e4f\", \"#fc8d59\", \"#fee08b\", \"#ffffbf\", \"#e6f598\", \"#99d594\", \"#3288bd\"].reverse()\nvar exposure_palette = [\"#f6eff7\", \"#d0d1e6\", \"#a6bddb\", \"#67a9cf\", \"#3690c0\", \"#02818a\", \"#016450\"].reverse()\nvar crps_palette = [\"#ffffd4\", \"#fee391\", \"#fec44f\", \"#fe9929\", \"#ec7014\", \"#cc4c02\", \"#8c2d04\"]\nvar flep_palette = [\"#8c510a\", \"#d8b365\", \"#f6e8c3\", \"#f5f5f5\", \"#c7eae5\", \"#5ab4ac\", \"#01665e\"].reverse()\nvar rps_palette = [\"#ffffb2\", \"#fed976\", \"#feb24c\", \"#fd8d3c\", \"#fc4e2a\", \"#e31a1c\", \"#b10026\"]\nvar whp_palette = [\"#d73027\", \"#fc8d59\", \"#fee08b\", \"#ffffbf\", \"#d9ef8b\", \"#91cf60\", \"#1a9850\"].reverse()\nMap.addLayer(wrc_i.select('BP'), {min: 0, max: 0.025, palette: bp_palette}, 'BP')\nMap.addLayer(wrc_i.select('CFL').selfMask(), {min: 0, max: 15, palette: flep_palette}, 'CFL')\nMap.addLayer(wrc_i.select('CRPS'), {min: 30, max: 80, palette: crps_palette}, 'CRPS')\nMap.addLayer(wrc_i.select('Exposure'), {min: 0, max: 1, palette: exposure_palette}, 'Exposure')\nMap.addLayer(wrc_i.select('FLEP4'), {min: 0.1, max: 0.9, palette: flep_palette}, 'FLEP4')\nMap.addLayer(wrc_i.select('FLEP8'), {min: 0.1, max: 0.9, palette: flep_palette}, 'FLEP8')\nMap.addLayer(wrc_i.select('RPS'), {min: 0, max: 1, palette: rps_palette}, 'RPS')\nMap.addLayer(wrc_i.select('WHP'), {min: 0, max: 2000, palette: whp_palette}, 'WHP')\nSample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/WILDFIRE-RISK-COMMUNITIES
"},{"location":"projects/wrc/#license","title":"License","text":"CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Keywords: Wildfire, CONUS, United States, USDA, Forest Service, LANDFIRE
Provided by: USDA Forest Service
Curated in GEE by: Climate Engine Org
"},{"location":"projects/wsf/","title":"World Settlement Footprint & Evolution","text":""},{"location":"projects/wsf/#world-settlement-footprint-2015","title":"World Settlement Footprint 2015","text":"The World Settlement Footprint (WSF) 2015 is a 10m (0.32 arc sec) resolution binary mask outlining the 2015 global settlement extent derived by jointly exploiting multitemporal Sentinel-1 radar and Landsat-8 optical satellite imagery.
The world settlement footprint 2015 is now available in the official GEE catalog and you can find it here
The entire catalog consists of 306 GeoTIFF files (EPSG4326 projection, deflate compression) each one referring to a portion of 10x10 degree size (~1110x1110km) whose upper-left and lower-right corner coordinates are specified in the file name [e.g., the tile WSF2015_v1_EPSG4326_e010_n60_e020_n50.tif covers the area between (10E;60N) and (20E;50N)].
Settlements are associated with value 255; all other pixels are associated with value 0.
"},{"location":"projects/wsf/#world-settlement-footprint-wsf-2019","title":"World Settlement Footprint (WSF) 2019","text":"The World Settlement Footprint (WSF 2019) is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery.
The dataset is organized in 5138 GeoTIFF files (EPSG4326 projection) each one referring to a portion of 2x2 degree size (~222x222km on the ground) with an extra buffer of 0.1 degree to avoid any discontinuity between neighbour tiles. Each tile is identified by the lower-left corner coordinates specified in the file name [e.g., the tile WSF2019_v1_12_18.tif covers the area between (12E;18N) and (14E;20N)]. You can download the files here
Settlements are associated with value 255; all other pixels are associated with value 0.
"},{"location":"projects/wsf/#world-settlement-footprint-evolution-1985-2015","title":"World Settlement Footprint Evolution (1985-2015)","text":"This repository contains the World Settlement Footprint (WSF) Evolution, a 30m resolution layer outlining the global settlement extent on a yearly basis from 1985 to 2015 derived by means of multitemporal Landsat-5 and Landsat-7 imagery.
The dataset is organized in 5138 GeoTIFF files (EPSG4326 projection) each one referring to a portion of 2x2 degree size (~222x222km on the ground) with an extra buffer of 0.1 degree to avoid any discontinuity between neighbour tiles. Each tile is identified by the lower-left corner coordinates specified in the file name [e.g., the tile WSFevolution_v1_12_18.tif covers the area between (12E;18N) and (14E;20N)]. You can download the files here
Values range between 1985 and 2015 corresponding to the estimated year of settlement detection, whereas 0 is no data.
"},{"location":"projects/wsf/#world-settlement-footprint-evolution-input-data-consistency-score","title":"World Settlement Footprint Evolution: Input Data Consistency Score","text":"This repository contains the Input Data Consistency (IDC) score, which provides a suitable and intuitive measure that accounts for the goodness of the Landsat imagery used for generating the WSF evolution and supports a proper interpretation of the product.
The dataset is organized in 5138 GeoTIFF files (EPSG4326 projection) each one referring to a portion of 2x2 degree size (~222x222km on the ground) with an extra buffer of 0.1 degree to avoid any discontinuity between neighbour tiles. Each tile is identified by the lower-left corner coordinates specified in the file name [e.g., the tile IDC_Score_12_18.tif covers the area between (12E;18N) and (14E;20N)]. Values range from 6 to 1 with: 6) very good; 5) good; 4) fair; 3) moderate; 2) low; 1) very low. You can download the files here
"},{"location":"projects/wsf/#data-citation","title":"Data Citation","text":"Marconcini, Mattia; Metz-Marconcini, Annekatrin; \u00dcreyen, Soner; Palacios-Lopez,\nDaniela; Hanke, Wiebke; Bachofer, Felix; et al. (2020): World Settlement Footprint (WSF) 2015.\nfigshare. Dataset. https://doi.org/10.6084/m9.figshare.10048412.v1\nYou can read the Outlining where humans live, the World Settlement Footprint 2015 here and Understanding Current Trends in Global Urbanisation \u2013 The World Settlement Footprint suite here
"},{"location":"projects/wsf/#paper-citation","title":"Paper Citation","text":"Marconcini, Mattia, Annekatrin Metz-Marconcini, Soner \u00dcreyen, Daniela Palacios-Lopez,\nWiebke Hanke, Felix Bachofer, Julian Zeidler et al. \"Outlining where humans live,\nthe World Settlement Footprint 2015.\" Scientific Data 7, no. 1 (2020): 1-14.\n\nMarconcini, M., Metz-Marconcini, A., Esch, T., Gorelick, N. (2021). Understanding Current Trends\nin Global Urbanisation \u2013 The World Settlement Footprint suite. GI_Forum, 1, 33-38.\nhttps://doi.org/10.1553/giscience2021_01_s33.\nThe dataset is single value only with a value of 255 for WSF 2015 and 2019 and pixel values are 1985 to 2015 for the WSF evolution dataset.
var wsf2015 = ee.ImageCollection(\"projects/sat-io/open-datasets/WSF/WSF_2015\");\nvar wsf2019 = ee.ImageCollection(\"projects/sat-io/open-datasets/WSF/WSF_2019\");\nvar wsf_evo = ee.ImageCollection(\"projects/sat-io/open-datasets/WSF/WSF_EVO\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/WORLD-SETTLEMENT-FOOTPRINT
the IDC Score is a measure of goodness of imagery used for evolution layers
var wsf_evo_idc = ee.ImageCollection(\"projects/sat-io/open-datasets/WSF/WSF_EVO_IDC\");\nSample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/WORLD-SETTLEMENT-FOOTPRINT-IDC
"},{"location":"projects/wsf/#license","title":"License","text":"The World Settlement Footprint 2015 is released under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
The World Settlement Footprint 2019 is licensed under CC-BY-4.0.
The World Settlement Footprint evolution 1985-2015 is licensed under CC-BY-4.0.
Created by : Marconcini, et al
Curated in GEE by: Samapriya Roy
Keywords: World Settlement Footprint, Settlement Extent, Urbanization, Earth Observation, Remote Sensing, Sentinel-1, Landsat-8
Last updated : 2021-12-12
"},{"location":"reference/","title":"Reference & Citation","text":"While most datasets in our community catalog are citable themselves, it's important to note that the catalog as a whole should also be cited by users. Our citation information and release details are sourced from Zenodo and you can always find the latest DOI & Citation here.
We encourage participation in our releases by contributing code examples, tutorials, edits through pull requests, documentation, or by being involved in planning and developing the community catalog further. Your contributions ensure that you are recognized as part of the citation for each release.
"},{"location":"reference/#citation","title":"Citation","text":"Samapriya Roy, Swetnam, T., & Saah, A. (2024). samapriya/awesome-gee-community-datasets: Community Catalog (3.1.0).\nZenodo. https://doi.org/10.5281/zenodo.14042069\nSubmit a tutorial or Example
Create and share examples demonstrating how you've leveraged the catalog's data in your projects.
Submit a tutorial
Collaborate with Pull requests
Creating a new pull request means you fixed something that I missed & I and the community apppreciate it.
Create a pull request
The Advanced Search feature of the Community Catalog leverages Vertex AI to enhance documentation search. This search is powered by both the catalog pages and Data JSON tabular structures. As more people use the feature, the indexed results improve over time. The feature benefits from grounded search, meaning that its summaries are always linked to the source material, ensuring transparency and trustworthiness.
This is made possible by RAG (Retrieval Augmented Generation), which not only improves search relevancy but also minimizes misinformation by grounding the output in reliable sources. Learn more about RAGs here in an earlier blog post to understand why this is so powerful and why grounded search can be effective. Currently, this search is in Beta, allowing us to test and refine it further with your participation. You can still use the embedded search within the Community Catalog for basic keyword or text-match searches.
Find the Advanced Search here.
NoteIf the search doesn't work as expected, try disabling any widget blocker extensions or using an incognito window to troubleshoot.
"},{"location":"search_features/#salient-features","title":"Salient Features","text":"It is feasible to sometimes use a machine readable list of catalog assets. While we are going to introduce a STAC catalog again at some point the assets are also available in two specific formats with the total running count above.
\u00a0 Download latest JSON version here
"},{"location":"startup/catalog-assets/#json-format","title":"JSON format","text":"This holds information about the datasets in this structure as a JSON list. If the license is custom for a dataset license text is included to clarify the details. The structure is the following
Field Description Title The name of the dataset. Sample Code A link to a sample script demonstrating how to use the dataset in Google Earth Engine. Type The type of data (e.g., table). ID The unique identifier for the dataset in the Earth Engine catalog. Provider The organization or entity that provides the dataset. Tags Keywords associated with the dataset to help with search and categorization. License The licensing terms under which the dataset is provided. License Text Additional text explaining the license (if applicable). Docs A link to documentation or more information about the dataset. Thematic Group The category or group under which the dataset falls (e.g., Oceans and Shorelines, Hydrology).[\n {\n \"title\": \"Global Shoreline Dataset\",\n \"sample_code\": \"https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL_SHORELINES\",\n \"type\": \"table\",\n \"id\": \"projects/sat-io/open-datasets/shoreline/mainlands\",\n \"provider\": \"United States Geological Survey, USGS\",\n \"tags\": \"Global Shoreline, Shoreline, mainlands, Oceans\",\n \"license\": \"Creative Commons Attribution Share Alike 4.0 International\",\n \"docs\": \"https://gee-community-catalog.org/projects/shoreline/\",\n \"thematic_group\": \"Oceans and Shorelines\"\n },\n {\n \"title\": \"NWI_CO_Riparian_Project_Metadata\",\n \"sample_code\": \"https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-WETLANDS-INVENTORY\",\n \"type\": \"table\",\n \"id\": \"projects/sat-io/open-datasets/NWI/rpm/CO_Riparian_Project_Metadata\",\n \"provider\": \"U.S. Fish and Wildlife Service\",\n \"tags\": \"wetlands, conservation areas, habitats, fish, wildlife, drinking water, recreation, U.S. Fish and Wildlife Service,CO_Riparian_Project_Metadata\",\n \"license\": \"custom\",\n \"license_text\": \"The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US\",\n \"docs\": \"https://gee-community-catalog.org/projects/nwi/\",\n \"thematic_group\": \"Hydrology\"\n }\n]\nThe CSV file is created using a Github action within the repository and contains all fields in the JSON representation. Fields like license_text if empty for a specific license are left empty.
\u00a0 Download latest CSV version here
id provider title type tags sample_code license license_text docs_page thematic_group projects/sat-io/open-datasets/shoreline/mainlands United States Geological Survey, USGS Global Shoreline Dataset table Global Shoreline, Shoreline, mainlands, Oceans https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL_SHORELINES Creative Commons Attribution Share Alike 4.0 International NA https://gee-community-catalog.org/projects/shoreline/ Oceans and Shorelines projects/sat-io/open-datasets/NWI/rpm/CO_Riparian_Project_Metadata U.S. Fish and Wildlife Service NWI_CO_Riparian_Project_Metadata table wetlands, conservation areas, habitats, fish, wildlife, drinking water, recreation, U.S. Fish and Wildlife Service,CO_Riparian_Project_Metadata https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-WETLANDS-INVENTORY custom The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US https://gee-community-catalog.org/projects/nwi/ Hydrology"},{"location":"startup/catalog-examples/","title":"Access awesome-gee-catalog-examples repo","text":"The awesome GEE catalog dataset examples are now part of a repo. Add this to your code editor space for easy access and updates to datasets and examples.
\u00a0 Add examples repo to your GEE reader repository list
OR
\u00a0 Download GEE Community Catalog Examples Folder
"},{"location":"startup/navigation/","title":"Navigating the Catalog","text":"The awesome-gee-community catalog is designed to be completely compatible with the existing GEE master catalog. It is also designed to be as open and exploratory as possible. The catalog is grouped by domains for example datasets that belong to themes about Population and Socioeconomics will be within that group and you should be able to expand and look at the datasets.
You can also search by dataset name or keywords or tags in the search bar.
Each page also includes the assets for each datasets as well some example code for you to explore and load the dataset quickly. You can also add the entire catalog examples as a repo now so you can search for and use the dataset. But don't stop there bring new datasets to the catalog, suggest things we can add and share back examples you make with the data. Explore how to do this and more in the Getting started section.
If you use geeadd: Google Earth Engine Batch Asset Manager with Addons you can also search both the main catalog and the community catalog using that tool.
"},{"location":"tutorials/","title":"Community Catalog Tutorials","text":"In this section of the community catalog, we'll explore user contributed Tutorials featuring catalog datasets or a mix of community catalog and the main catalog.
"},{"location":"tutorials/#the-importance-of-tutorials","title":"The Importance of Tutorials","text":"Tutorials are an essential component of any learning platform, as they provide a clear and concise guide on how to achieve specific goals or solve particular problems. In the context of Earth Engine, tutorials are particularly valuable because they help users:
The Community Catalog tutorials are designed to be a comprehensive resource for learning about Earth Engine. These tutorials cover a range of topics, including:
These tutorials are not only helpful for beginners but also serve as a valuable reference for experienced users looking to expand their skills or explore new applications.
"},{"location":"tutorials/#encouraging-participation-contributing-code-examples-tutorials-edits-and-more","title":"Encouraging Participation: Contributing Code Examples, Tutorials, Edits, and More!","text":"We encourage participation in our releases by contributing code examples, tutorials, edits through pull requests, documentation, or by being involved in planning and developing the community catalog further. Your contributions ensure that you are recognized as part of the citation for each release.
"},{"location":"tutorials/#benefits-of-contributing","title":"Benefits of Contributing","text":"By participating in the development of the Community Catalog, you can:
The Community Catalog is a vital resource for learning about Earth Engine, and its tutorials are an essential component. By contributing code examples, tutorials, edits, or other forms of participation, you can help shape the catalog and benefit from the collective knowledge and expertise of the community.
"},{"location":"tutorials/examples/glc_fcs30d_lulc/","title":"Exploring the Global 30m Land Cover Change Dataset (1985-2022) GLC_FCS30D","text":"by Ujaval Gandhi from Spatial Thoughts
A temporally consistent global multi-class time-series classification dataset is critical to understand and quantify long-term changes. Previously, options were limited to lower resolution datasets such as MODIS Landcover (2000-present) at 500m resolution or ESA CCI (1992-present) at 300m resolution. The new GLC_FCS30D dataset provides a high-resolution landcover time-series derived from the Landsat archive (1984-2022) at 30m resolution with 35 classes. This dataset is valuable for studying landscape dynamics at high resolution and is available in the public domain. It can be downloaded from Zenodo as GeoTIFF files or accessed directly in the Google Earth Engine (GEE) Community catalog. In this tutorial, we will:
The original dataset was produced in 5\u00b0 x 5\u00b0 tiles with each image having bands for each year of classification. This structure was uploaded to the Earth Engine Community Catalog, split into datasets for five-yearly classifications (1985-90, 1990-95, and 1995-2000) and yearly classifications (2000-2022). GEE workflows are structured around ImageCollections rather than multiband images, so we need to transform the original data into an ImageCollection.
"},{"location":"tutorials/examples/glc_fcs30d_lulc/#steps","title":"Steps:","text":"Here's the Earth Engine code for the preprocessing step and a link to the code
// Example script showing how to pre-process the GLC_FCS30D landcover dataset\n\n// Yearly data from 2000-2022\nvar annual = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/annual');\n// Five-Yearly data for 1985-90, 1990-95 and 1995-2000\nvar fiveyear = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/five-years-map');\n\n// Classification scheme has 36 classes (35 landcover classes and 1 fill value)\nvar classValues = [10, 11, 12, 20, 51, 52, 61, 62, 71, 72, 81, 82, 91, 92, 120, 121, 122, 130, 140, 150, 152, 153, 181, 182, 183, 184, 185, 186, 187, 190, 200, 201, 202, 210, 220, 0];\nvar classNames = ['Rainfed_cropland', 'Herbaceous_cover_cropland', 'Tree_or_shrub_cover_cropland', 'Irrigated_cropland', 'Open_evergreen_broadleaved_forest', 'Closed_evergreen_broadleaved_forest', 'Open_deciduous_broadleaved_forest', 'Closed_deciduous_broadleaved_forest', 'Open_evergreen_needle_leaved_forest', 'Closed_evergreen_needle_leaved_forest', 'Open_deciduous_needle_leaved_forest', 'Closed_deciduous_needle_leaved_forest', 'Open_mixed_leaf_forest', 'Closed_mixed_leaf_forest', 'Shrubland', 'Evergreen_shrubland', 'Deciduous_shrubland', 'Grassland', 'Lichens_and_mosses', 'Sparse_vegetation', 'Sparse_shrubland', 'Sparse_herbaceous', 'Swamp', 'Marsh', 'Flooded_flat', 'Saline', 'Mangrove', 'Salt_marsh', 'Tidal_flat', 'Impervious_surfaces', 'Bare_areas', 'Consolidated_bare_areas', 'Unconsolidated_bare_areas', 'Water_body', 'Permanent_ice_and_snow', 'Filled_value'];\nvar classColors = ['#ffff64', '#ffff64', '#ffff00', '#aaf0f0', '#4c7300', '#006400', '#a8c800', '#00a000', '#005000', '#003c00', '#286400', '#285000', '#a0b432', '#788200', '#966400', '#964b00', '#966400', '#ffb432', '#ffdcd2', '#ffebaf', '#ffd278', '#ffebaf', '#00a884', '#73ffdf', '#9ebb3b', '#828282', '#f57ab6', '#66cdab', '#444f89', '#c31400', '#fff5d7', '#dcdcdc', '#fff5d7', '#0046c8', '#ffffff', '#ffffff'];\n\n// Mosaic the data into a single image\nvar annualMosaic = annual.mosaic();\nvar fiveYearMosaic = fiveyear.mosaic();\n\n// Rename bands from b1, b2, etc. to 2000, 2001, etc.\nvar fiveYearsList = ee.List.sequence(1985, 1995, 5).map(function(year) { return ee.Number(year).format('%04d'); });\nvar fiveyearMosaicRenamed = fiveYearMosaic.rename(fiveYearsList);\nvar yearsList = ee.List.sequence(2000, 2022).map(function(year) { return ee.Number(year).format('%04d'); });\nvar annualMosaicRenamed = annualMosaic.rename(yearsList);\nvar years = fiveYearsList.cat(yearsList);\n\n// Convert the multiband image to an ImageCollection\nvar fiveYearlyMosaics = fiveYearsList.map(function(year) {\n var date = ee.Date.fromYMD(ee.Number.parse(year), 1, 1);\n return fiveyearMosaicRenamed.select([year]).set({'system:time_start': date.millis(), 'system:index': year, 'year': ee.Number.parse(year)});\n});\nvar yearlyMosaics = yearsList.map(function(year) {\n var date = ee.Date.fromYMD(ee.Number.parse(year), 1, 1);\n return annualMosaicRenamed.select([year]).set({'system:time_start': date.millis(), 'system:index': year, 'year': ee.Number.parse(year)});\n});\nvar allMosaics = fiveYearlyMosaics.cat(yearlyMosaics);\nvar mosaicsCol = ee.ImageCollection.fromImages(allMosaics);\n\n// Recode the class values into sequential values\nvar newClassValues = ee.List.sequence(1, ee.List(classValues).length());\nvar renameClasses = function(image) {\n var reclassified = image.remap(classValues, newClassValues).rename('classification');\n return reclassified;\n};\nvar landcoverCol = mosaicsCol.map(renameClasses);\n\nprint('Pre-processed Collection', landcoverCol);\n\n// Visualize the data\nvar year = 2022;\nvar selectedLandcover = landcoverCol.filter(ee.Filter.eq('year', year)).first();\nvar palette = ['#ffff64', '#ffff64', '#ffff00', '#aaf0f0', '#4c7300', '#006400', '#a8c800', '#00a000', '#005000', '#003c00', '#286400', '#285000', '#a0b432', '#788200', '#966400', '#964b00', '#966400', '#ffb432', '#ffdcd2', '#ffebaf', '#ffd278', '#ffebaf', '#00a884', '#73ffdf', '#9ebb3b', '#828282', '#f57ab6', '#66cdab', '#444f89', '#c31400', '#fff5d7', '#dcdcdc', '#fff5d7', '#0046c8', '#ffffff', '#ffffff'];\nvar classVisParams = {min:1, max:36, palette: palette};\nMap.addLayer(selectedLandcover, classVisParams, 'Landcover ' + year);\nA useful way to visualize a landcover time-series is through a user interface that allows us to compare and contrast data for multiple years. Using a split-panel, we can load classifications for two different years and swipe to see changes between them. We will create a split panel interface with a dropdown selector allowing you to change the year and visualize the changes. To make the map interpretation easier, we will also construct a legend.
You can explore the app at Global Landcover Change Explorer.
Here's the source code for the app:
// Example script for an App to explore GLC_FCS30D landcover dataset using a split-panel\n\n// Pre-process the Collection\nvar annual = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/annual');\nvar fiveyear = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/five-years-map');\nvar classValues = [10, 11, 12, 20, 51, 52, 61, 62, 71, 72, 81,\n\n 82, 91, 92, 120, 121, 122, 130, 140, 150, 152, 153, 181, 182, 183, 184, 185, 186, 187, 190, 200, 201, 202, 210, 220, 0];\nvar newClassValues = ee.List.sequence(1, ee.List(classValues).length());\nvar allImages = annual.merge(fiveyear);\nvar renameClasses = function(image) {\n var reclassified = image.remap(classValues, newClassValues).rename('classification');\n return reclassified;\n};\nvar landcoverCol = allImages.map(renameClasses);\nvar years = ee.List.sequence(1985, 2022).map(function(year) { return ee.Number(year).format('%04d'); });\n\nvar visParams = {min:1, max:36, palette: ['#ffff64', '#ffff64', '#ffff00', '#aaf0f0', '#4c7300', '#006400', '#a8c800', '#00a000', '#005000', '#003c00', '#286400', '#285000', '#a0b432', '#788200', '#966400', '#964b00', '#966400', '#ffb432', '#ffdcd2', '#ffebaf', '#ffd278', '#ffebaf', '#00a884', '#73ffdf', '#9ebb3b', '#828282', '#f57ab6', '#66cdab', '#444f89', '#c31400', '#fff5d7', '#dcdcdc', '#fff5d7', '#0046c8', '#ffffff', '#ffffff']};\n\n// Create a Split-panel Map\nvar leftMap = ui.Map();\nvar rightMap = ui.Map();\nvar splitPanel = ui.SplitPanel({\n firstPanel: leftMap,\n secondPanel: rightMap,\n wipe: true,\n style: {stretch: 'both'}\n});\nui.root.clear();\nui.root.add(splitPanel);\n\nvar createLegend = function() {\n var legend = ui.Panel({\n style: {\n position: 'bottom-left',\n padding: '8px 15px'\n }\n });\n var legendTitle = ui.Label({\n value: 'Landcover Legend',\n style: {fontWeight: 'bold', fontSize: '14px', margin: '0 0 4px 0', padding: '0'}\n });\n legend.add(legendTitle);\n\n var makeRow = function(color, name) {\n var colorBox = ui.Label({\n style: {\n backgroundColor: color,\n padding: '8px',\n margin: '0 0 4px 0'\n }\n });\n var description = ui.Label({\n value: name,\n style: {margin: '0 0 4px 6px'}\n });\n return ui.Panel({\n widgets: [colorBox, description],\n layout: ui.Panel.Layout.Flow('horizontal')\n });\n };\n\n for (var i = 0; i < classNames.length; i++) {\n legend.add(makeRow(classColors[i], classNames[i]));\n }\n return legend;\n};\n\nvar legend = createLegend();\nleftMap.add(legend);\nvar intro = ui.Panel([\n ui.Label('Global Landcover Change Explorer', {fontWeight: 'bold', fontSize: '20px'}),\n ui.Label('Explore landcover change over time by selecting different years and comparing side-by-side. Zoom in and out to explore regions of interest.')\n]);\nleftMap.add(intro);\n\nvar createDropdown = function(map, labelText, defaultValue) {\n var yearLabel = ui.Label(labelText);\n var yearSelect = ui.Select({\n items: years.getInfo(),\n value: defaultValue,\n onChange: function(year) {\n var selectedImage = landcoverCol.filter(ee.Filter.eq('year', parseInt(year))).first();\n map.layers().set(0, ui.Map.Layer(selectedImage, visParams, 'Landcover ' + year));\n }\n });\n var panel = ui.Panel([yearLabel, yearSelect]);\n map.add(panel);\n return yearSelect;\n};\n\nvar leftYearSelect = createDropdown(leftMap, 'Select Left Year:', '1985');\nvar rightYearSelect = createDropdown(rightMap, 'Select Right Year:', '2022');\n\nleftMap.centerObject(landcoverCol.first().geometry(), 3);\nA crucial step for any analysis is to calculate the area covered by each landcover class over time and export the data for further analysis. Here, we will use the zonal statistics approach to compute the area of each class for the entire time series. We will create a table with landcover class and year-wise area statistics and export it as a CSV file.
Here's the Earth Engine code to achieve this. You can find the full code here
// Function to calculate area of each class\nvar calculateArea = function(image) {\n var areaImage = ee.Image.pixelArea().divide(10000).addBands(image);\n var areas = areaImage.reduceRegion({\n reducer: ee.Reducer.sum().group({\n groupField: 1,\n groupName: 'class'\n }),\n geometry: geometry,\n scale: 30,\n maxPixels: 1e10\n });\n return ee.Feature(null, areas);\n};\n\n// Apply the function on the ImageCollection\nvar areasCol = landcoverCol.map(calculateArea);\n\n// Flatten the collection to create a single FeatureCollection\nvar features = areasCol.map(function(feature) {\n var dict = ee.Dictionary(feature.get('groups')).map(function(key, value) {\n return ee.Number(value).get(0);\n });\n return ee.Feature(null, dict);\n});\n\n// Export the data\nExport.table.toDrive({\n collection: features,\n description: 'LandcoverAreaStatistics',\n fileFormat: 'CSV'\n});\nThe GLC_FCS30D dataset opens up new avenues for detailed and high-resolution landcover analysis. This tutorial demonstrated how to preprocess, visualize, and analyze the dataset within Google Earth Engine, providing a robust starting point for further exploration and research.
Keywords: GLC_FCS30D, Preprocessing, Mosaicking, Reclassification, Visualization, Split-panel App, Interactive Map, Legend, Land Cover Statistics, Zonal Statistics, Area Calculation, CSV Export
"},{"location":"tutorials/examples/global_shorelines/","title":"Using the Global Shoreline Dataset to Create Land and Ocean Masks with Google Earth Engine (GEE)","text":"by Ujaval Gandhi from Spatial Thoughts
"},{"location":"tutorials/examples/global_shorelines/#introduction","title":"Introduction","text":"The Global Shoreline dataset, hosted on the Gee-Community Catalog, is a valuable resource for creating land and ocean masks in Google Earth Engine (GEE). This tutorial provides an overview of how to use this dataset to generate these masks, which can be useful for various geospatial analyses and applications. The complete code can be found here
"},{"location":"tutorials/examples/global_shorelines/#dataset-description","title":"Dataset Description","text":"The Global Shoreline dataset comprises three sets of polygon features:
To begin working with this dataset on Google Earth Engine, first import the necessary collections:
// Importing Global Shoreline Dataset Collections\nvar mainlands ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/mainlands');\nvar big_islands ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/big_islands');\nvar small_islands ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/small_islands');\nThe next step is to merge the individual collections into a single collection, which can then be rasterized:
// Merge all collections\nvar merged mainlands.merge(big_islands).merge(small_islands);\n\n// Rasterize polygons using 'ee.Reducer.count()' to get land pixel counts\nvar mask merged.reduceToImage({\n reducer: ee.Reducer.count(),\n});\nThe rasterized image can now be visualized on the map using different color palettes - brown for land (with higher pixel counts) and blue for ocean:
// Add mask as a layer to display land areas in brown\nMap.addLayer(mask, {min: 0, max: 1}, 'Land Mask');\n\n// Create an inverted version of the mask to visualize ocean areas in blue\nvar invertMask mask.multiply(-1);\nMap.addLayer(invertMask, {min: -1, max: 0}, 'Ocean Mask');\nThe rasterized image can be used to create separate land and ocean masks for further processing within GEE:
// Create an ocean mask using '.selfMask()' on the inverted version of 'mask'\nvar oceanMask invertMask.updateMask(invertMask);\n\n// Use 'image.updateMask(oceanMask)' to remove ocean pixels from another image\n\n// Create a land mask by inverting 'oceanMask' and using '.selfMask()'\nvar landMask oceanMask.not().updateMask(oceanMask);\n\n// Use 'image.updateMask(landMask)' to remove land pixels from another image\nThis tutorial demonstrates how to use the Global Shoreline dataset in Google Earth Engine to create rasterized masks representing land and ocean areas, which can be visualized on a map or used for further processing. This approach provides a valuable resource for various geospatial analyses and applications.
Keywords: GEE, Global Shoreline Dataset, Land Mask, Ocean Mask, Rasterization, Shoreline, Landcover, Image Processing
"},{"location":"tutorials/examples/landscan_extracts/","title":"Comparing Global Population Trends with GeoBoundaries and Landscan","text":"by Ujaval Gandhi from Spatial Thoughts
"},{"location":"tutorials/examples/landscan_extracts/#introduction","title":"Introduction","text":"This tutorial demonstrates how to use GeoBoundaries and the Landscan Population Dataset to compare population data for different Admin1 regions using Earth Engine. You will learn how to load admin boundaries, filter a population dataset by date range, extract resolution information, and create a time-series chart comparing population data.
To view the complete code for this tutorial, click here.
"},{"location":"tutorials/examples/landscan_extracts/#section-1-load-admin-boundaries-geoboundaries-and-select-regions","title":"Section 1: Load Admin Boundaries (GeoBoundaries) and Select Regions","text":"Use the ee.FeatureCollection method to load the admin boundaries dataset from GeoBoundaries.
var admin0 = ee.FeatureCollection(\"projects/sat-io/open-datasets/geoboundaries/CGAZ_ADM0\");\nSelect two Admin1 regions to compare: Japan and Mexico.
var region1 = 'Japan';\nvar region2 = 'Mexico';\nUse the filter method to select the desired regions from the admin boundaries dataset.
var selectedRegions = admin0.filter(ee.Filter.inList('shapeName', [region1, region2]));\nprint('Filtered Admin1 collection', selectedRegions);\nUse the ee.ImageCollection method to load the Landscan population dataset.
var landscan = ee.ImageCollection(\"projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL\");\nvar band = 'b1';\nSet the date range for the population data using ee.Date.fromYMD.
var startYear = 2000;\nvar endYear = 2020;\n\nvar startDate = ee.Date.fromYMD(startYear, 1, 1);\nvar endDate = ee.Date.fromYMD(endYear + 1, 1, 1);\nUse the filter method to filter the population dataset by date range.
var populationFiltered = landscan.filter(ee.Filter.date(startDate, endDate)).select(band);\nprint('Filtered Population Collection', populationFiltered);\nGet the resolution of the population dataset using projection.nominalScale.
var projection = populationFiltered.first().projection();\nvar resolution = projection.nominalScale();\nprint('Landscan Global Resolution', resolution);\nCreate a time-series chart comparing the population data for the selected regions.
var chartOptions = {\n title: 'Population Time Series',\n vAxis: {\n title: 'Population',\n },\n hAxis: {\n title: '',\n format: 'YYYY',\n gridlines: {color: 'transparent'}\n\n },\n }\n\nvar chart = ui.Chart.image.seriesByRegion({\n imageCollection: populationFiltered,\n regions: selectedRegions,\n reducer: ee.Reducer.sum(),\n scale: resolution,\n seriesProperty: 'shapeName'\n}).setChartType('ColumnChart')\n .setOptions(chartOptions);\nprint(chart);\nKeywords: GeoBoundaries, Landscan Population Dataset, Earth Engine, Admin1 regions, Population data
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