Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes.
Wilson B Salls, Blake A Schaeffer, Nima Pahlevan, Megan M Coffer, Bridget N Seegers, P Jeremy Werdell, Hannah Ferriby, Richard P Stumpf, Caren E Binding, Darryl J Keith
Author Information
Wilson B Salls: U.S. Environmental Protection Agency Office of Research and Development, Research Triangle Park, NC 27711, USA.
Blake A Schaeffer: U.S. Environmental Protection Agency Office of Research and Development, Research Triangle Park, NC 27711, USA.
Nima Pahlevan: NASA Goddard Space Flight Center, Ocean Ecology Lab, Greenbelt, MD 20771, USA.
Megan M Coffer: National Oceanic and Atmospheric Administration, NESDIS Center for Satellite Applications and Research, College Park, MD 20740, USA. ORCID
Bridget N Seegers: NASA Goddard Space Flight Center, Ocean Ecology Lab, Greenbelt, MD 20771, USA.
P Jeremy Werdell: NASA Goddard Space Flight Center, Ocean Ecology Lab, Greenbelt, MD 20771, USA.
Hannah Ferriby: Tetra Tech, Research Triangle Park, NC 27709, USA. ORCID
Richard P Stumpf: National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, Silver Spring, MD 20910, USA. ORCID
Caren E Binding: Environment and Climate Change Canada, Water Science and Technology Directorate, Burlington, ON L7S 1A1, Canada.
Darryl J Keith: U.S. Environmental Protection Agency Office of Research and Development, Narragansett, RI 02882, USA.
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 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 measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances ( ), Rayleigh-corrected reflectances ( ), and remote sensing reflectances ( ). MCI slightly outperformed NDCI across all reflectance products. MCI using 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 to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-Chlorophyll- 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.
