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The chlorophyll-a concentration is one of the main indicators to evaluate water quality and measure the degree of eutrophication of the water body. It is of great significance to the environmental protection of rivers and lakes. At present, the commonly used empirical/semi-empirical remote sensing inversion models have low inversion accuracy in rivers with low chlorophyll-a concentration and narrow chlorophyll-a concentration ranges. At the same time, the semianalytical models require specific wavelengths, which makes it more suitable for hyperspectral remote sensing data. But it is difficult to obtain high-resolution hyperspectral satellite data. Taking the Danjiangkou-Fancheng reach of the Han River as the research area, this paper establishes a partial least squares regression model to estimate chlorophyll-a concentration using Sentinel-2 multispectral remote sensing images and synchronously measured chlorophyll-a concentration data. Considering the impact of Wangfuzhou Hydropower Station on the upstream and downstream, a segmented modeling was adopted and verified. The results show that the average relative errors of the verification samples in the Danjiangkou-Wangfuzhou reach and Wangfuzhou-Fancheng reach are 22.53% and 11.75%, respectively and the root mean square errors are 0.65ug/L and 0.17ug/L, respectively. The prediction accuracy is high. It indicates that the partial least squares method can be applied to the inversion of chlorophyll-a in rivers with low chlorophyll-a concentration and narrow chlorophyll-a ranges based on multispectral data.
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