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Lixin Sun and Richard Fernandes, Canada Centre for Remote Sensing, Government of Canada
The LEAF-Toolbox is a Google Earth Engine (GEE) application that produces biophysical parameter maps with associated uncertainty estimates from analysis ready surface reflectance satellite imagery. In order to efficiently produce biophysical parameter maps for a large area (e.g., continental or national scale), a production version of LEAF-Toolbox was developed with Python language. This LEAF production code is highly automated and flexible for various production requirements. The execution environment of the code is the Jupyter Notebook. If the “geemap” Python package (https://geemap.org/) is also installed and Chrome browser (rather than FireFox browser) is utilized to open the notebook file, then the resultant parameter maps can be visualized and explored as well within the notebook.
The basic spatial output unit of the LEAF production code is a tile (900km x 900km), which is defined by the Canadian geospatial tile grinding system (there are 26 tiles covering the Canadian landmass). Currently, the biophysical parameter dataset associated with one tile consists of 11 image files. Specifically, for each of four biophysical parameters (LAI, fCOVER, Albedo and fAPAR), there are two associated images, a parameter estimation and its corresponding uncertainty map (8 image files in total). Additionally, there are three ancillary image files (quality control, acquisition date and land cover partition). With spatial resolution set to 20m, the size of one tile’s parameter dataset (in GeoTiff format) ranges from 3.1GB to 44GB depending on the location of the tile. It must be noted that a smaller dataset size unnecessarily means less computing time is needed. The reason for this is that the size of the image files is determined by the homogeneity of the covered land surface. In northern Canada, the land cover is relatively homogeneous, however the number of satellite images involved in the calculation is larger due to the geometry of satellite paths. This leads to a longer computing time.
LEAF production code is released under the Government of Canada's Open Government License
A GEE application (in either JavaScript or Python) must be executed on a client-server architecture with majority of the computations are carried out on GEE servers. This means that the efficiency of the LEAF production code is not determined by a client-side computer.
Currently, 2 hours on average is required to produce a 20m resolution biophysical vegetation parameter data set for one tile with the satellite images acquired by Sentinel-2, meaning that the generation of a Canadian national (26 tiles) one month 20m resolution biophysical parameter data set requires at least 52 hours. Of course, this time requirement varies depending on the location of a tile, network connection status and time zone (day, night or weekend). So it can be estimated that three or 4 days are required to operationally finish a monthly 20m resolution national biophysical parameter map.
This repository does not include all the Python code of LEAF production tool. The main purpose of this repository is to keep a communication channel for investigating the efficiency of running LEAF production code on a Google Cloud VM.
We are: Lixin Sun, Richard Fernandes, Gang Hong and Hugo Drouin