Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes

Author:

Salls Wilson B.1,Schaeffer Blake A.1,Pahlevan Nima23,Coffer Megan M.45ORCID,Seegers Bridget N.26,Werdell P. Jeremy2,Ferriby Hannah7ORCID,Stumpf Richard P.8ORCID,Binding Caren E.9,Keith Darryl J.10

Affiliation:

1. U.S. Environmental Protection Agency Office of Research and Development, Research Triangle Park, NC 27711, USA

2. NASA Goddard Space Flight Center, Ocean Ecology Lab, Greenbelt, MD 20771, USA

3. Science Systems and Applications, Inc., Lanham, MD 20706, USA

4. National Oceanic and Atmospheric Administration, NESDIS Center for Satellite Applications and Research, College Park, MD 20740, USA

5. Global Science & Technology, Inc., Greenbelt, MD 20770, USA

6. Morgan State University, Baltimore, MD 21251, USA

7. Tetra Tech, Research Triangle Park, NC 27709, USA

8. National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, Silver Spring, MD 20910, USA

9. Environment and Climate Change Canada, Water Science and Technology Directorate, Burlington, ON L7S 1A1, Canada

10. U.S. Environmental Protection Agency Office of Research and Development, Narragansett, RI 02882, USA

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 (ρt), Rayleigh-corrected reflectances (ρs), and remote sensing reflectances (Rrs). MCI slightly outperformed NDCI across all reflectance products. MCI using ρt 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.

Funder

National Aeronautics and Space Administration (NASA) Ocean Biology and Biogeochemistry / Applied Sciences Program

Publisher

MDPI AG

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