Producing a Science-Ready Commercial Data Archive: A Workflow for Estimating Surface Reflectance for High Resolution Multispectral Imagery
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM（2023）
NASA Goddard Space Flight Ctr
Abstract
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 [1] – [3]. Surface reflectance (SR) 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 [4]. Consistency from 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 [5], [6]. 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 (e.g., Planet Surface Reflectance v.2). Here we describe an open source workflow for the scientific community to improve detection of fine-scale change with commercial VHR imagery.
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Key words
surface,reflectance,imagery,commercial,high-resolution
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