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Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes.
Salls, Wilson B; Schaeffer, Blake A; Pahlevan, Nima; Coffer, Megan M; Seegers, Bridget N; Werdell, P Jeremy; Ferriby, Hannah; Stumpf, Richard P; Binding, Caren E; Keith, Darryl J.
Afiliação
  • Salls WB; U.S. Environmental Protection Agency Office of Research and Development, Research Triangle Park, NC 27711, USA.
  • Schaeffer BA; U.S. Environmental Protection Agency Office of Research and Development, Research Triangle Park, NC 27711, USA.
  • Pahlevan N; NASA Goddard Space Flight Center, Ocean Ecology Lab, Greenbelt, MD 20771, USA.
  • Coffer MM; Science Systems and Applications, Inc., Lanham, MD 20706, USA.
  • Seegers BN; National Oceanic and Atmospheric Administration, NESDIS Center for Satellite Applications and Research, College Park, MD 20740, USA.
  • Werdell PJ; Global Science & Technology, Inc., Greenbelt, MD 20770, USA.
  • Ferriby H; NASA Goddard Space Flight Center, Ocean Ecology Lab, Greenbelt, MD 20771, USA.
  • Stumpf RP; Morgan State University, Baltimore, MD 21251, USA.
  • Binding CE; NASA Goddard Space Flight Center, Ocean Ecology Lab, Greenbelt, MD 20771, USA.
  • Keith DJ; Tetra Tech, Research Triangle Park, NC 27709, USA.
Remote Sens (Basel) ; 16(11): 1-29, 2024 May 30.
Article em En | MEDLINE | ID: mdl-38994037
ABSTRACT
Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms-the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)-were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article