Using LIDAR and SNOTEL Data for Evaluating the Performance of Snow Water Equivalent Retrieval Using Sentinel-1 Repeat-Pass Interferometry

Abstract. Accurate estimation of snow water equivalent (SWE) at high spatial and temporal resolution remains a critical challenge for hydrologic prediction and climate monitoring. Interferometric Synthetic Aperture Radar (InSAR) provides a promising approach for retrieving SWE by exploiting phase changes induced by snow accumulation. In this study, we evaluate the performance of Sentinel-1 repeat-pass interferometry for SWE retrieval using airborne LIDAR snow depth data and in situ SNOTEL SWE observations across diverse snow climates in the western United States. Using six-day Sentinel-1 acquisitions collected during the NASA SnowEx campaigns of 2020 and 2021, we compare retrieved SWE against independent datasets to quantify retrieval accuracy and assess the influence of environmental factors. Results show that retrievals using six-day repeat pass data yield strong agreement with LIDAR measurements, with Pearson correlation coefficients ranging from 0.42 to 0.66, while 12-day repeat pass data exhibit poor performance due to temporal decorrelation and phase ambiguity. Comparisons with SNOTEL SWE change indicate correlations up to 0.81 and RMSE as low as 0.78 cm. Analysis of retrieval drivers reveals that temporal coherence is the dominant control on performance, followed by temperature, snow wetness, and vegetation cover. Coherence declines with increasing snow depth, slope, and temperature, but improves under dry, cold conditions and gentle terrain. These findings demonstrate that C-band Sentinel-1 InSAR can successfully retrieve SWE change under dry-snow, high-coherence conditions, and highlight the potential of currently in-orbit missions such as NASA-ISRO NISAR to enable global SWE monitoring with improved temporal sampling and wavelength sensitivity.