## 2 Study Areas and Datasets
### Satellite Data Acquisition and Preprocessing  
We included four AHSI images that have match-up in situ measurements with these study regions. Images of Guanting Reservoir and Baiyang Lake were collected on 22 May 2019, while images for the Panjiakou and Daehiting Reservoirs were acquired on 24 September 2019. All images were acquired at approximately 1 p.m. local time. For the in situ experiment, all field data at Guanting and Panjiakou Reservoirs were acquired within 3 h of the overpass time; but 16 of the 29 samples collected from Lake Baiyangdian and all 12 samples from Daehiting Reservoir were measured one day before and after GF-5 overpass, respectively.  
Before \(Z_{sd}\) estimation, the DN values from the original AHSI data should be corrected to \(R_{rs}\), and water areas must be extracted. All scenes were corrected using the FLAASH module in the ENVI5.3 software. Specifically, the VNIR and SWIR images were first merged into one file due to separate storage. Second, DN values were rescaled to top-of-atmosphere radiance using the radiometric calibration coefficients in the metadata file. Third, surface reflectance (\(\rho\)) images were retrieved from the FLAASH module. We used the rural aerosol model in FLAASH for all AHSI images, since aerosols in the study areas were not strongly affected by urban or industrial sources. In addition, we chose the 2-band Kaufman-Tanre aerosol retrieval method (called 2-band (K-T) in the FLAASH interface). The 2-band (K-T) method is based on detecting forested or dense vegetation pixels as the darkest pixels over the land [28]. Since dense vegetation has relatively high reflectance that can be accurately calibrated in AHSI images, it is reasonable to apply the 2-band (K-T) method in AHSI atmospheric correction. A final remaining problem is the retrieval of \(R_{rs}\) from \(\rho\). According to Equation (1), \(\rho\) is related to \(R_{rs}\) as follows:  
\[\begin{split} R_{rs}(\lambda)&=\frac{L_{u}(\lambda )}{\pi L_{d}(\lambda)}-\frac{\rho_{sky}(\lambda)L_{sky}(\lambda)}{\pi L_{d}( \lambda)}\\ &=\frac{\rho(\lambda)}{\pi}-\frac{\rho_{sky}(\lambda)L_{sky}( \lambda)}{\pi L_{d}(\lambda)},\end{split} \tag{2}\]  
Figure 2: Field measured remote sensing reflectance at the sampling sites in the study regions: (a) Guanting Reservoir, (b) Daehiting Reservoir, (c) Lake Baiyangdian and (d) Panjiakou Reservoir.  
where \(L_{d}\) is the downwelling radiance on the water surface. Here, the skylight effects involved in the surface reflectance should be removed for accurate \(R_{rs}\) retrieval. However, as the AHSI radiometric calibration parameters were acquired based on bright land targets [21], the SWIR band data contain a relatively high level of noise and thus cannot be applied for skylight removal of water bodies. Therefore, \(R_{rs}\) is approximately represented as \(\rho/\pi\) in the AHSI images (hereafter referred to as \(R_{rs}^{0}\)), neglecting skylight effects.  
For the water area delineation, the Normalized Difference Water Index (NDWI) was calculated using surface reflectance \(\rho\) followed by the application of the modified histogram bimodal method (MHBM) [29] to NDWI images to automatically segment water areas from other land cover types.