PDF(Pubmed)
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 (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t 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.
摘要：
内陆湖泊的富营养化构成了各种社会和生态威胁，水质监测至关重要。卫星为传统的现场采样提供了全面和具有成本效益的补充。Sentinel-2多光谱仪器（S2MSI）提供独特的光谱带，用于量化叶绿素a，水质和营养状态指标，以及精细的空间分辨率，能够监测小水体。在这项研究中,将两种算法-最大叶绿素指数（MCI）和归一化差异叶绿素指数（NDCI）-应用于S2MSI数据。使用连续美国103个湖泊的原位叶绿素a测量对它们进行了校准和验证。两种算法都使用大气顶部反射率（ρt）进行了测试，瑞利校正反射率(ρs)，和遥感反射率(Rrs)。在所有反射率产品中，MCI的表现都略高于NDCI。使用ρt的MCI显示出最佳的整体性能，平均绝对误差因子为2.08，平均偏差因子为1.15。将叶绿素a转化为营养状态提高了管理应用的潜力，使用二元分类法，准确率为82%。我们报告了算法到叶绿素a的转换，显示出在美国各地应用的潜力，证明S2可以在广泛的空间尺度上作为内陆湖泊的监测工具。
