Estimating Snow Water Equivalent Using Sentinel-1 Repeat-Pass Interferometry over Idaho

The Snow Water Equivalent (SWE) is identified as the key element of a snowpack that impacts rivers’ streamflow andwater cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE. Active sensors providehigher-resolution observations. Interferometric Synthetic Aperture Radar (InSAR) has been shown to have the potential toestimate SWE change. In this study, we apply this technique to a large time series of Sentinel-1 data from winter 2021. Theretrieved SWE change observations align really well with in situ stations with 0.82 correlation and 0.76cm RSME. The total5retrieved SWE also align really well with 16 in situ values in the scene with less than 20cm SWE error. On the other hand,the retrieved SWE using Sentinel-1 data is highly correlated with LIDAR snow depth data with correlation of more than 0.5.Low temporal correlation is the main reason for degrading the performance of SWE retrieval using InSAR data. Low temporalcorrelation also causes the degradation of phase unwrapping algorithms.