SWE retrieval in the European Alps based on Sentinel-1 snow depth observations and modeled snow density

Seasonal snow is an essential source of water, especially in mountainous regions. However, accurate satellite observations of the snow water equivalent (SWE), i.e., snow depth multiplied by the snow density, are still lacking. Therefore, new and robust remote sensing techniques are urgently needed. This study presents a novel method for SWE retrieval in mountainous regions at sub-weekly temporal and 500-m spatial resolution, based on snow depth observations from the ESA and Copernicus Sentinel-1 (S1) satellite mission and model simulations of snow density. The snow depth observations rely on a change detection algorithm which translates the temporal changes in the S1 radar backscatter measurements into the accumulation or ablation of snow. The snow density estimates are obtained from different modeling approaches, including empirical methods (e.g., based on the day of the year, the snow depth, snow climate class, etc.) and a physics-based mass and energy balance model. The performance of the different snow density modeling approaches is here compared, both with respect to their ability to accurately simulate in situ measurements of snow density, as well as their ability to accurately simulate in situ measurements of SWE after combination with the S1 snow depth observations. The performance is evaluated over the European Alps, using a large dataset of in situ time series measurements for the period 2015-2022. The results show that the physics-based snow density modeling approach outperforms the empirical approaches, yielding high spatio-temporal correlation between S1 SWE retrievals and in situ measurements. Therefore, the study demonstrates the capability of the Sentinel-1 satellite mission, in combination with a physics-based snow model, to accurately represent the spatio-temporal distribution of SWE in mountainous regions, which can benefit a large range of applications, including hydropower generation, water management, flood forecasting, and numerical weather prediction.