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

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 posit...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-05, Vol.16 (11), p.1
Main Authors: 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
Format: Article
Language:English
Subjects:
Algorithms
Artificial satellites in remote sensing
Chlorophyll
chlorophyll a
Comparative analysis
Cyanobacteria
Datasets
Drinking water
Environmental aspects
Eutrophication
Lakes
Measurement
Monitoring
Plankton
Remote sensing
Satellites
Sensors
Sentinel-2
Spatial discrimination
Spatial resolution
Spectral bands
Testing
Water monitoring
Water quality
Water quality management
water quality monitoring
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16111977