Understanding how the sparse distribution of precipitation gauges at higher elevations contributes to uncertainty in snowfall estimation is crucial in mountainous regions. This is particularly important because uncertainties arising in headwater areas can propagate through hydrological modelling, ultimately affecting the estimation of all components of the water balance. However, establishing dense gauge networks in complex mountain terrain remains challenging, highlighting the value of exploring whether remote sensing observations can help reduce uncertainties in snowfall estimates.</p> <p>This study assimilates Sentinel-1 C-band snow-depth observations into the snow module of the GEOframe hydrological model, coupled with a snow-density scheme, to update snow depth, snow water equivalent (SWE), and snowfall estimates. The approach is applied to two key Alpine catchments, the Aosta River catchment and the headwaters of the Piemonte catchment in the upper Po River basin, which are critical sources of snowmelt-driven river discharge for sustaining agricultural activities in the Po Valley and have limited high-elevation gauge coverage. Results show that assimilating satellite-derived snow depth increases snowfall estimates across elevation gradients compared to snowfall partitioned by the hydrological model, and substantially improves simulated river discharge during the snowmelt season. Similar improvements are also observed in years without data assimilation, indicating a sustained positive influence on model performance.