## 3 Methods
### Development of Different Water Masks  
The defects of this approach are the confusion between the sand dunes, snow, terrain shadows, and waters. Therefore, we build three kinds of water masks, i.e., Water Mask-slope, Water Mask-OLI, and Max Water-GSWE, to refine the preliminary water body results produced in the first step.  
Some studies have shown that synthetic ascending and descending SAR scenes reduce some errors caused by radar shadows or layover, but do not completely eliminate them [17]. Using optical sensors to detect surface water will also encounter the problem of terrain shadows, and there has been a lot of research performed using slope dataset to solve this problem [6; 11; 27; 28]. We obtain the slope dataset from SRTM DEM by calculating the maximum elevation change rate of each grid cell to its eight neighboring cells. Finally, we use a threshold of to 3 degrees to exclude steep locations where water is unlikely to exist. We extract the pixels that have slopes less than 3 degrees from the slope dataset as Water Mask-slope.  
Second, the limitation of the threshold method itself will confuse the extracted water body based on Sentinel-1 with the sand dunes dominated by dry sand. The normalized differential water body index (NDWI) can be used to distinguish between sand dunes and water; therefore, we use NDWI calculated based on Landsat OLI images to generate Water Mask-OLI. Every Landsat OLI scene during one year excludes cloud pixels by using its own quality control band, and then uses a mean composite method to obtain an annual cloudless Landsat OLI image. Each pixel value of the annual cloudless Landsat OLI image is derived from the average value of the original clean pixels. [PERSON] et al. propose different NDWI calculation methods [29; 30; 31]. We compare these calculation methods through experiments. The results show that the calculation method developed by [PERSON] et al. can best reflect the water body based on the Landsat OLI image. Since it is not the main purpose of this study, no more detailed experimental results are given in the article. With all this, the NDWI mentioned in this article is calculated using the near-infrared spectrum (0.85-0.88 \(\upmu\)m) and the green spectrum (0.53-0.59 \(\upmu\)m) of the annual cloudless Landsat OLI image. We use the water sample in Section 3.1 to determine that the NDWI threshold is \(-\)0.06, that is, pixels with an NDWI value greater than \(-\)0.06 are identified as a water body, and pixels with an NDWI value not greater than \(-\)0.06 are identified as a non-water body.  
The water mask obtained in this way contains snow pixels. It has been proven in a previous study that the reflectivity of snow in the near infrared band is higher than that of water bodies [32]. This feature can help us exclude snow pixels in the above water mask; therefore, based on the near-infrared spectrum of the annual cloudless Landsat OLI image, the snow cover threshold (0.17) is obtained using the snow cover sample in Section 3.1. Those pixels with an NDWI value of greater than \(-\)0.06 and a near-infrared surface reflectance value of greater than 0.17 are removed from the above  
Figure 3: The backscatter coefficients of 11 land-cover types on (**a**) VH polarized images and (**b**) VV polarized images.  
water mask. In order to avoid the restriction of a static mask, the annual water mask is buffered 10 m outwards to obtain Water Mask-OLI, using a morphological dilation operation [33; 34].  
In a study by [PERSON] et al. [11], the GSWE included global surface water dynamics from 1984 to 2015 in 2016, and the GSWE was later updated to 2018. The max extent water surface mask of GSWE contains any region where water has ever been detected during 1984-2018, and the max extent water surface is much larger the primary water body extraction in the first step. We use this max extent water surface as Max Water-GSWE. We resample all of these auxiliary masks to 10 m.