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Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.

The concentration of total suspended matter (TSM) is an important parameter for evaluating lake water quality. We determined in situ hyperspectral data and TSM concentration data for Changdang Lake, China, to establish a TSM concentration inversion model. The model was applied using 60 Sentinel-2 images acquired from 2016 to 2021 to determine the temporal and spatial distribution of TSM concentration. Remote sensing images were also utilized to monitor the effect of ecological dredging in Changdang Lake. The following results were obtained: (1) Compared with four existing models, the TSM concentration inversion model established in this study exhibited higher accuracy and was suitable for Changdang Lake. (2) TSM concentrations obtained for the period 2016–2021 were higher in spring and summer, and lower in autumn and winter. (3) The dredging process influenced a small area of the surrounding water body, resulting in higher TSM concentrations. However, a subsequent reduction in TSM concentrations indicated that the ecological dredging project might improve the water quality of Changdang Lake to a considerable extent. Therefore, it was inferred that the use of Sentinel-2 images was effective for the long-term monitoring of water quality changes in small and medium-sized lakes.