Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and physically-based model simulations

Abstract. Seasonal mountain snow is an indispensable resource, providing drinking water to more than a billion people worldwide, supporting agriculture, industry and hydropower generation, and sustaining river discharge, soil moisture and groundwater recharge. However, accurate estimates of this seasonal water storage remain limited, even in the European Alps, where there is a dense network of in situ monitoring stations. In this study, we address this issue by estimating Alpine snow depth at a 100 m spatial and sub-weekly temporal resolution with an extreme gradient boosting model (XGBoost) for the time period 2015–2024. We explore the potential for using Sentinel-1 C-band dual-polarized synthetic aperture radar polarimetry (PolSAR) observations to improve upon backscatter-based approaches, and include regionally downscaled meteorological forcing data and modeled snow depth inputs to further explain interannual and spatial variability. To account for the spatio-temporal dependencies present in the snow depth data, we conduct a threefold nested cross-validation, and incorporate spatial training data to better represent topographical patterns in snow depth variability. Finally, we utilize XGBoost's booster and Shapley additive explanation values to understand the relationship between the input features and predicted snow depths during both dry and wet snow conditions. Our results demonstrate that incorporating Sentinel-1 PolSAR observations leads to more accurate snow depth retrievals compared to using backscatter alone. In addition, our analyses indicate that including either meteorological forcing data or modeled snow depth estimates substantially improves the XGBoost snow depth estimates, both of which yield comparable accuracy. Finally, we demonstrate that the inclusion of spatial training data is essential for capturing the topographic influence on snow depth estimates, and to obtain good spatial prediction accuracy. Overall, this work contributes to an improved large-scale monitoring of water stored in seasonal mountain snow.