Genetic Algorithm for Atmospheric Correction (GAAC) of water bodies impacted by adjacency effects

Abstract

Adjacency effect (AE) corrections over inland water surfaces has been a known issue in space-borne optical remote sensing over more than four decades. Here we present a novel algorithm able to simultaneously retrieve the aerosol optical depth, sun glint, AE, water reflectance, and water inherent optical properties (IOPs). The method was evaluated against an in situ data set of remote sensing reflectance ($R_{rs}$$R_{rs}$) collected in $\sim$$\sim$100 lakes across Canada. The new algorithm is based on a genetic optimization scheme (GAAC: Genetic Algorithm for Atmospheric Correction), and was here compared to the most popular atmospheric correction algorithms available (ACOLITE, iCOR+SIMEC). The statistical metrics of the $R_{rs}$$R_{rs}$ retrieval were improved by a factor of almost 2 in all wavelengths, and for all metrics (Bias, Error, Similarity Angle) relative to other algorithms. Demonstrations of GAAC on scenes of Lansdat-8 OLI, and Sentinel-2 MSI sensors demonstrate the algorithm’s robustness when applied to spatially complex small lake ($\sim$$\sim$10 km of width) surfaces.