###### Abstract  
Snow plays a crucial role in the global water and energy cycles, and its melting process can have a series of impacts on hydrological or climatic systems. Accurately capturing the timing of snowmelt runoff is essential for the utilization of snow resources and the early warning of snow-related disasters. A synthetic aperture radar (SAR) offers an effective means for capturing snowmelt runoff onset dates (RODs) over large areas, but its accuracy under different land cover types remains unclear. This study focuses on the Sierra Nevada Mountains and surrounding areas in the western United States. Using a total of 3117 Sentinel-1 images from 2017 to 2023, we extracted the annual ROD based on the Google Earth Engine (GEE) platform. The satellite extraction results were validated using the ROD derived from the snow water equivalent (SWE) data from 125 stations within the study area. The mean absolute errors (MAEs) for the four land cover types--tree cover, shrubland, grassland, and bare land--are 24, 18, 18, and 16 d, respectively. It indicates that vegetation significantly influences the accuracy of the ROD captured from Sentinel-1 data. Furthermore, we analyze the variation trends in the ROD from 2017 to 2023. The average ROD captured by the stations shows an advancing trend under different land cover types, while that derived from Sentinel-1 data only exhibits an advancing trend in bare land areas. It indicates that vegetation leads to a delayed trend in the ROD captured by using Sentinel-1 data, opposite to the results from the stations. Meanwhile, the variation trends of the average ROD captured by both methods are not significant (\(p>0.05\)) due to the impact of the extreme snowfall in 2023. Finally, we analyze the influence of the SWE on RODs under different land cover types. A significant correlation (\(p<0.05\)) is observed between the SWE and ROD captured from both stations and Sentinel-1 data. An increase in the SWE causes a delay in the ROD, with a greater delay rate in vegetated areas. These findings will provide vital reference for the accurate acquisition of the ROD and water resources management in the study area. S 2024 1 4684  
maximum water-holding capacity, leading to the release of meltwater and the formation of runoff. The date when meltwater begins to flow is defined as the runoff onset date (ROD). The ROD can be determined based on the variation in the snow water equivalent (SWE) during the snowmelt period. During the pre-runoff phase of snowmelt, the SWE typically exhibits a continuous increase. Once the snowmelt runoff is formed, the SWE rapidly decreases [18,19]. Therefore, the date of maximum SWE can be used to estimate the initial date of the runoff [20]. However, obtaining high-precision SWE data over large areas is challenging. Remote sensing technology provides a solution for large-scale monitoring of snowmelt [21,22]. Synthetic aperture radar (SAR) data with strong penetration capabilities, can be used to capture changes in the internal parameters of snow. Microwave signals are highly sensitive to wetness, which is closely related to the ROD, making it possible to quickly and conveniently obtain the ROD using SAR data [23,24]. SAR data have high spatial resolution, reaching meter-level or even sub-meter-level, which is suitable for fine-scale monitoring of snowmelt in watersheds. An increase in wetness leads to an increase in the snow's dielectric loss factor, thereby enhancing the absorption of SAR backscatter signals [25, 26, 27]. When the wetness reaches its maximum value, the SAR backscatter diminishes to its minimum. When the meltwater flows out, which marks the beginning of the runoff phase, scattering at the air-snow interface becomes dominant in the backscatter. Moreover, the backscatter will gradually increase due to the increased roughness of the snow surface. Hence, the date when the backscatter reaches its minimum can be designated as the ROD.