## 5 Discussion
### Uncertainty of This Study  
This paper proposes a threshold method based on Sentinel-1 SAR imagery to develop an unprecedented high spatiotemporal resolution water body dataset. We use 14,070 samples to verify the accuracy of HSWDC. These points are mainly derived from stratified random sampling. They may not have enough points to fall on the edge of the water body and small water body, so the actual error may be higher than the error value we obtain. The error mainly comes from two aspects: the limitation of the Sentinel-1 image itself and the error of auxiliary data. The C-band electromagnetic wave of the Sentinel-1 sensor makes the detection of open water relatively simple, with almost no signal returning to the antenna. When the water level is high or the transmission is low, the radar signal is usually attenuated, while when the water level is low relative to the vegetation, double rebound scattering may occur [16]. Some studies have tried to detect water with obvious vegetation canopy on its surface [26; 39; 40], but most of these studies require the relative heights of vegetation and water surface and the distribution characteristics of vegetation leaves as input parameters of the model. These parameters need to be measured in the wild, so these existing studies are basically limited to small scales. The influence of the complex scattering mechanism between vegetation and water is difficult to unify in a large region, and the water with significant vegetation canopy over its surface contributes very little to the total amount of water in China, so we ignore it in the method we design. In addition, during the radar-scanning process, the amplitude of sub-spherical waves between pixels may be repeated or attenuated, and finally the addition, subtraction, and difference generate random variables. This phenomenon is reflected as speckle noise in SAR images. For speckle noise, previous researches provide two schemes: one is to filter the Sentinel-1 image before water extraction [41], and the other is to perform a morphological operation after water extraction to remove speckle noise [33; 34]. We have conducted experiments on  
Figure 10: The map of Selinco Lake and small water body in January 2016 in (**a**) inland surface water dataset in China (ISWDC) and (**b**) high spatial-temporal water body dataset in China (HSWDC). these two methods, and the results show that while these methods reduce noise, they also eliminate some small waters in the HSWDC, such as narrow rivers (Figure 11). Therefore, we retain the original results in our study, but the post-processing could be carried on according to their specific needs. In checking the classification results by visual inspection, we find the confusion of the sand dunes and waters mainly occurs in the west region of China. The 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. 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. We overlap Water Mask-OLI and the water body we produce to exclude the impact of the dunes in the west. This method is based on the assumption that the dynamic change of the surface of the western water body is less than 10 m, which could bring some uncertainty to the west region. In addition, the use of STRM DEM and GSWE maximum water extent map may limit the water surface area of HSWDC or introduce their errors to HSWDC. In the future, a new classification method that does not use these auxiliary data can be developed. Sentinel-1 data in GEE is only processed through primary processing such as noise removal, calibration and geocoding. As we all know, layover and shadow will bring some errors to the extraction of water based on radar images [17]. In this study, compositing ascending and descending SAR scenes have reduced this error but do not fully eliminate it.