Influence of snowpack properties and local incidence angle on SAR signal depolarization: a mathematical model for high-resolution snow depth estimation

Recently, Dual-Polarimetric Synthetic Aperture Radar (SAR) has been shown to be effective for large-scale snow cover monitoring, but it faces significant challenges when applied to finer resolutions, which are crucial for applications such as avalanche forecasting. In this study, we propose a novel mathematical model to retrieve snow properties from Sentinel-1 SAR data, leveraging variations in the Dual-Polarimetric Radar Vegetation Index (DpRVIc). We introduce the Snow Index SAR (SIsar), which approximates variations in signal depolarization occurring within the snowpack. Our study, conducted in the Central Italian Alps, reveals a strong correlation between the SIsar index and the snowpack height, enabling accurate snow depth estimation. We also demonstrate the significant impact of the local incidence angle on signal depolarization during the accumulation season. Based on this, we derive a mathematical correction for the incidence angle, whose inclusion in the model reduces snow depth estimation errors by approximately 39 %. The model validation conducted in Tromsø (Norway) and in Davos (Switzerland) confirms its applicability beyond the calibration area, with a root mean squared error (RMSE) of 30.7 cm and a mean absolute error (MAE) of 24.3 cm in Tromsø, and a RMSE of 22.4 cm and a MAE of 18.1 cm in Davos. These findings enhance our understanding of dual-polarimetric Sentinel-1 SAR data sensitivity for high-resolution snow monitoring, providing valuable insights for avalanche forecasting and hydrological applications.