## 3 Method  
Based on the snowmelt dynamics theory, [17] noted that during the snowmelt period, the maximum snow wetness corresponds to the minimum backscatter and maximum SWE. The ROD was extracted using changes in the SAR backscatter, and validated it with measured SWE data. This study employs the same method to extract the ROD for the study area, encompassing both Sentinel-1 data extraction and in-situ data extraction (Figure 4). The ROD extraction process for Sentinel-1 data was conducted on the GEE platform. Within the GEE platform, existing resources for Sentinel-1 GRD products are available, which are the results of previous processing steps such as thermal noise removal, radiometric calibration, and terrain correction using the Sentinel-1 Toolbox software (Version 3). However, further processing steps are still necessary, and the processing is performed in the Google Earth Engine. First, the radiometric slope correction as well as the removal of the radar shadow and layover areas should be conducted. The authors of [38] have provided the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{5}{c}{**Number of Stations**} & \multicolumn{2}{c}{**Number of the Minimum**} & \multicolumn{2}{c}{**Number of the Maximum**} \\  & **Tree Cover** & **Shrubland** & **Grassland** & **Bare Land** & **Images** & **the Stations** & **Images Corresponding to** \\ \hline
2017 & 90 & 2 & 12 & 6 & 332 & 26 & 60 \\ \hline
2018 & 92 & 2 & 14 & 6 & 309 & 21 & 54 \\ \hline
2019 & 95 & 2 & 16 & 4 & 505 & 40 & 86 \\ \hline
2020 & 90 & 2  
method which is used in this study. The equation of the radiometric slope correction can be expressed:  
\[\gamma_{f}^{0}=\frac{\sigma^{0}\text{tan}(90-\theta_{i})}{\text{cos}(\theta_{i}) \text{tan}(90-\theta_{i}+\alpha_{r})} \tag{1}\]  
where \(\gamma_{f}^{0}\) is the backscatter after correction; \(\sigma^{0}\) is the backscatter before correction; \(\theta_{i}\) is the incidence angle, which is available in the Sentinel-1 metadata; \(\alpha_{r}\) is the slope steepness in range, which can be written as:  
\[\alpha_{r}=\arctan(\tan(\alpha_{s})\text{cos}(\phi_{i}-\phi_{s})) \tag{2}\]  
where \(\alpha_{s}\) and \(\phi_{s}\) are terrain geometry factors, and \(\phi_{i}\) is the radar geometry factor. For their calculation methods, please refer to [38]. The shadow and layover areas can be obtained using:  
\[\text{shadow}:\ \alpha_{r}<-(90-\theta_{i}) \tag{3}\]  
\[\text{layover}:\ \alpha_{r}>\theta_{i} \tag{4}\]  
Subsequently, temporal-spatial filtering is also necessary to further reduce the SAR image noise and mitigate the effects of short-term snowmelt on microwave signals. Considering the large dataset, a highly efficient mean filtering is employed for the temporal-spatial filtering. Spatial filtering is performed using a mean filter with a window size of \(3\times 3\) pixels. Temporal filtering is performed using a mean filter with a window size of 5 observations. Following these steps, the resulting image spatial resolution is 20 m.  
The processed SAR data include observations from both the Sentinel-1A and Sentinel-1B satellites, covering both ascending and descending orbit modes. This results in variations in both the data acquisition time and the observational geometry for the same geographical area. However, we analyze all available data to improve the revisit frequency, given the rapid changes in snow during the melting period. Additionally, wetness is considered the primary factor affecting the SAR backscatter during the melting period. Hence, differences in data acquisition time and observational geometry for the same area were ignored in this study. Combining these considerations, the date of the minimum occurrence of VV polarization backscatter within the snowmelt period from 1 February to 31 July is identified as the ROD. Figure 4: Extraction process and result analysis of ROD under different land cover types. The blue-filled boxes represent the steps processed by the GEE platform.