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
U.S. Environmental Protection Agency Office of Research and Development, Research Triangle Park, NC 27711, USA.
NASA Goddard Space Flight Center, Ocean Ecology Lab, Greenbelt, MD 20771, USA.
Remote Sens (Basel). 2024 May 30;16(11):1-29. doi: 10.3390/rs16111977.
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.
内陆湖泊的富营养化带来了各种社会和生态威胁,使得水质监测至关重要。卫星为传统的现场采样提供了全面且具成本效益的补充。哨兵 - 2 多光谱仪器(S2 MSI)提供了独特的光谱波段,可用于量化叶绿素(一种水质和营养状态指标),同时具有高空间分辨率,能够监测小型水体。在本研究中,两种算法——最大叶绿素指数(MCI)和归一化差异叶绿素指数(NDCI)——被应用于 S2 MSI 数据。利用美国本土 103 个湖泊的现场叶绿素测量数据对这两种算法进行了校准和验证。两种算法均使用大气顶反射率( )、瑞利校正反射率( )和遥感反射率( )进行了测试。在所有反射率产品中,MCI 的表现略优于 NDCI。使用 的 MCI 总体表现最佳,平均绝对误差因子为 2.08,平均偏差因子为 1.15。将推导得到的叶绿素 转换为营养状态提高了管理应用的潜力,二元分类的准确率达 82%。我们报告了算法到叶绿素的转换,显示出在美国各地应用的潜力,证明了 S2 可作为广泛空间尺度上内陆湖泊的监测工具。