Genetic Algorithm for Atmospheric Correction (GAAC) of water bodies impacted by adjacency effects
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 opt...
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Published in | Remote sensing of environment Vol. 317; p. 114508 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier Inc
01.02.2025
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Subjects | |
Online Access | Get full text |
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Summary: | 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 (Rrs) collected in ∼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 Rrs 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 (∼10 km of width) surfaces.
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•A novel genetic-based algorithm for atmospheric correction (AC) for medium spatial resolution multispectral instruments (Landsat OLI and Sentinel MSI).•The algorithm corrects the adjacency effects affecting small inland waters surrounded by diverse land cover.•The algorithm is tested on 200+ Canadian lakes, and its performance is compared to other state-of-the-art AC algorithms (ICOR and ACOLITE). |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0034-4257 |
DOI: | 10.1016/j.rse.2024.114508 |