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 remote 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 domain can be significant.
Here we provide 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.
The workflow for estimating surface reflectance for commercial VHR multispectral imagery (SRVHR).
| 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 |
| 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) |