C-Band Radar-Based Improved Snow Depth Estimation (C-RISE) in the Indian Western Himalayas and Colorado Rocky Mountains

Abstract

Monitoring snow depth (SD) in mountainous regions is essential for water resource management, climate studies, and disaster predictions. Synthetic aperture radar (SAR)-based remote sensing, with its high spatial resolution and penetration capability, is more suited for such areas. Sentinel-1 backscatter-based SD estimation currently provides the only SAR-derived global SD product. However, this method is still in an early stage of development, with limited understanding of the underlying mechanisms, minimal region-specific evaluations, and lack of consideration of diurnal, seasonal, and regional effects on the backscatter-SD relationship. This study introduces C-band radar-based improved snow depth estimation (C-RISE) to improve Sentinel-1-based SD estimation by integrating diurnal, seasonal, and regional factors, along with auxiliary variables like snow cover duration (SCD) and elevation. Implemented on Google Earth Engine (GEE), the approach is applied to two contrasting regions: the Indian Western Himalayas (IWH), characterized by deep snowpacks, and the Colorado Rocky Mountains (CRM), with shallower snowpacks. The results demonstrate enhanced model accuracy when incorporating these factors. For IWH, the model's performance improved by 9%, achieving an MAE of 77.3 cm and R of 0.7. In CRM, the model's performance primarily benefited from regional zoning, leading to an 8% improvement with MAE of 19.6 cm and R of 0.8. Compared to the Sentinel-1 C-Snow product, the refined models reduced MAE by 17% in IWH and 51% in CRM. These findings advance the understanding of C-band backscatter-based SD estimation, particularly for less-studied regions such as IWH, and demonstrate its potential for improving SD monitoring globally.