Title:

Assessing Sentinel-1 Satellite Based Snow Depth Retrieval in Shallow, Wet Snowpack during 2025-2026

Snow plays a critical role in the terrestrial water cycle and land–atmosphere energy exchanges, yet accurate snow depth retrieval in shallow, wet snowpacks remains challenging. This study evaluates the performance of a Sentinel-1 C-band SAR-based snow depth retrieval algorithm (Lievens et al., 2019) under shallow and frequently wet snow conditions in New Hampshire during the 2025–2026 winter season. Snow depth was estimated using a change detection approach based on temporal variations in the cross-polarization ratio (CR), and results were validated against in situ measurements collected along transects spanning multiple Sentinel-1 pixels. The SAR-derived snow depth captured the overall temporal evolution of the snowpack, particularly during cold and dry periods, with the best agreement observed under stable conditions. However, performance degraded during wet snow conditions, where melt–refreeze cycles and potential ice layer formation introduced variability in backscatter, leading to overestimation. Validation results (n = 29) showed moderate agreement with in situ observations (R = 0.659, RMSE = 0.145 m, MAE = 0.098 m), indicating that while the algorithm captures general trends, its applicability is limited in shallow, wet snow environments. These findings highlight the need for improved retrieval approaches that account for wet snow processes.

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