Comparison of snowmelt timing estimates from Sentinel-1 SAR and surface observations in British Columbia, Canada

Snowmelt provides critical water resources that impact ecosystem health and hazard frequency; however, the timing of melt is difficult to infer across large spatial scales. While Synthetic Aperture Radar (SAR) has been used to detect snowmelt onset, the accuracy of different methodological approaches requires evaluation. We use Sentinel-1 SAR observations to estimate snowmelt timing at automated snow water equivalent (SWE) stations (n = 52) across British Columbia between 2018 and 2021. The timing of backscatter minima are compared to snowmelt onset estimates derived from continuous SWE and surface air temperature records. First, we develop a manual selection method, which requires SWE and air temperature records, to determine the feasibility of SAR for estimating snowmelt onset. Using this approach, we demonstrate that images with identical viewing geometries (i.e., those from the same orbital track) should be utilized for snowmelt onset estimation with SAR, and that including the timing of local minima, a minimum value over an interval, as potential dates of snowmelt initiation increases accuracy compared to the use of absolute minima alone. Accuracy further increases when snowmelt analysis is constrained to specific timeframes informed by region and topography, with error reduced by greater than 50 % in some study years. After demonstrating the potential accuracy of SAR with manually selected estimates (RMSE = 5.3 d), we develop an automated method that can produce SAR estimates of melt onset that are independent of surface observations. While accuracy was reduced from automated approaches, the mean date of absolute backscatter minima in track-separated SAR time series (both cross and co-polarized images) provided the most accurate automated estimates of snowmelt (RMSE = 11 d). We observed no spatially coherent bias in SAR error which suggests that SAR observations can be used to detect snowmelt onset across large regions.