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 inRemote sensing of environment Vol. 317; p. 114508
Main Authors Pan, Yanqun, Bélanger, Simon
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.02.2025
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ISSN0034-4257
DOI10.1016/j.rse.2024.114508

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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 (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. [Display omitted] •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).
AbstractList 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.
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. [Display omitted] •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).
ArticleNumber 114508
Author Pan, Yanqun
Bélanger, Simon
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Keywords Adjacency effect
Optically complex waters
Landsat
Sentinel-2
Keyword atmospheric correction
Lakes
Inherent optical properties
Inland water
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Snippet 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...
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StartPage 114508
SubjectTerms Adjacency effect
aerosols
algorithms
Canada
data collection
environment
Inherent optical properties
Inland water
Keyword atmospheric correction
Lakes
Landsat
Optically complex waters
reflectance
Sentinel-2
Title Genetic Algorithm for Atmospheric Correction (GAAC) of water bodies impacted by adjacency effects
URI https://dx.doi.org/10.1016/j.rse.2024.114508
https://www.proquest.com/docview/3154178301
Volume 317
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