Surface reflectance from commercial very high resolution multispectral imagery estimated empirically with synthetic Landsat

Scientific analysis of changes of the Earth's land surface benefit from well-characterized, science quality remotely sensed data. This data quality is the result of models that estimate and remove atmospheric constituents and account for sun-sensor geometry. Top-of-atmosphere (TOA) reflectance in commercial very high resolution (< 5 m; VHR) spaceborne imagery routinely varies for unchanged surface features because of signal variation from the combined effects of atmospheric haze and a range of sun-sensor geometric scenarios of acquisitions. Consistency from surface reflectance (SR) versions of this imagery must be sufficient to identify and track the change or stability of fine-scale features that, though small, may be widely distributed across geographic domains, and serve as key indicators of critical broad-scale environmental change. Currently commercial SR products are available, but typically the model employed is proprietary and the costs for using these products over a large spatial domain can be significant. We presented an open-source workflow for the scientific community for fine-scaled empirical estimation of surface reflectance from multispectral VHR imagery using reference from synthetically-derived coincident Landsat-based surface reflectance in Montesano et al. (2024) [1]. The $SR_{VHR}$ tool that sits at the end of this workflow [2], as well as the tools that precede it in this workflow, continue to evolve.

The most recent version of the $SR_{VHR}$ tool can be found at this repository, but development is underway on a software package that combines and presents these tools together as a toolkit.

SR_VHR: empirical estimation of VHR surface reflectance

The workflow for estimating surface reflectance for commercial VHR multispectral imagery (SR_VHR).

References:

1. Surface Reflectance From Commercial Very High Resolution Multispectral Imagery Estimated Empirically With Synthetic Landsat (2023): Montesano et al. 2024 https://ieeexplore.ieee.org/document/10670299
2. Preliminary User Guide (October 2024)

Workflow Contributors	Role	Affiliation
Paul M. Montesano	Author ; Evaluator	NASA Goddard Space Flight Center Data Science Group
Matthew J. Macander	Author ; Evaluator	Alaska Biological Research, Inc.
Jordan A. Caraballo-Vega	Developer	NASA Goddard Space Flight Center Data Science Group
Melanie J. Frost	Author ; Evaluator	NASA Goddard Space Flight Center Data Science Group
Jian Li	Developer	NASA Goddard Space Flight Center Data Science Group
Glenn S. Tamkin	Developer	NASA Goddard Space Flight Center Data Science Group
Mark L. Carroll	PI	NASA Goddard Space Flight Center Data Science Group (Lead)