Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes
Citation:
Salls, W., B. Schaeffer, N. Pahlevan, M. Coffer, B. Seegers, P. Werdell, H. Ferriby, C. Binding, R. Stumpf, AND D. Keith. Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes. Remote Sensing. MDPI, Basel, Switzerland, 16(11):1977, (2024). https://doi.org/10.3390/rs16111977
Impact/Purpose:
Eutrophication of waterbodies represents an ongoing environmental and public health concern. Monitoring by satellite remote sensing has proven effective, but most sensors currently in use have a relatively coarse spatial resolution (~300 m); this limits monitoring efforts along shorelines—where most human recreational exposure occurs—and in small waterbodies. The Sentinel-2 satellite constellation delivers finer resolution (20 m), offering strong potential for managers and stakeholders concerned with the coarse nature of most operational satellite water quality products. This work paves the way for use of a Sentinel-2 chlorophyll a product in 99% of the waterbodies recognized in the National Hydrography Dataset.
Description:
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.
URLs/Downloads:
DOI: Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes