Evaluating the utility of Sentinel-1 in a Data Assimilation System for estimating snow depth in a mountainous basin

Abstract

Seasonal snow plays a critical role in hydrological and energy systems, yet its high spatial and temporal variability makes accurate characterization challenging. Historically, satellite remote sensing has had limited success in mapping snow depth and snow water equivalent (SWE), particularly in global mountain areas. This study evaluates the temporal and spatial accuracy of recently developed snow depth retrievals from the Sentinel-1 (S1) C-band spaceborne radar and their utility within a data assimilation (DA) system for characterizing mountain snowpack. The DA framework integrates the physics-based Flexible Snow Model (FSM2) with a Particle Batch Smoother (PBS) to produce daily snow depth maps at a 500 m resolution using S1 snow depth data. The S1 data were evaluated from 2017 to 2021 in and near the East River Basin, Colorado, using daily data at 12 ground-based stations for temporal evaluation and four LiDAR snow depth surveys from the Airborne Snow Observatory (ASO) for spatial evaluation. The analysis revealed significant inconsistencies in temporal and spatial errors of S1 snow depth, with higher spatial errors. Errors increased with time, especially during ablation periods, with an average temporal RMSE of 0.40 m. In contrast, the spatial RMSE exceeded 0.7 m, and S1 had poor spatial agreement with ASO LiDAR (R2 < 0.3). Experiments with DA window sizes showed minimal performance differences for full-season and early-season windows. Joint assimilation of S1 snow depth with MODIS Snow Disappearance Date (SDD) yielded similar temporal errors but degraded performance in space relative to assimilating S1 alone, while SDD assimilation alone performed best spatially. While S1 may perform better in other regions or snow conditions, our findings are consistent with findings from other error analyses across the Western US and suggest S1 has limited potential to improve snow DA in the East River Basin, specifically. Future work should address retrieval biases, refine algorithms, and consider other snow datasets in the DA system to improve snow depth and SWE mapping in diverse snow environments globally.