The spatiotemporal evolution of snowmelt is fundamental for water resources management and risk mitigation in mountain catchments. Synthetic aperture radar (SAR) images acquired by satellite systems such as Sentinel-1 (S1) are promising for monitoring wet snow due to their high sensitivity to liquid water content (LWC) and ability to provide spatially distributed data at a high temporal resolution. While recent studies have linked multitemporal S1 backscatter to snowmelt phases, a correlation with detailed snowpack properties is still missing. To address this, we collected the first dataset of comprehensive wet-snow properties tailored for SAR applications over two consecutive snow seasons at the Weissfluhjoch field site near Davos, Switzerland. First, we tested previous methods which use multitemporal S1 backscatter to characterize melting phases and demonstrated that the observed monotonous increase in backscatter following the local minimum is due to the development of surface roughness. Then, we used the measured snow properties as input to the Snow Microwave Radiative Transfer (SMRT) model to reproduce S1 backscatter signals. Our simulations showed that rather than melting phases, time series of backscatter identify regimes dominated by either LWC, early in the season, or surface roughness, later on. The results also highlight several key challenges for reconciling S1 signals with radiative transfer simulations of wet snow: (i) the discrepancy in spatiotemporal variability of LWC as seen by the satellite and validation measurements, (ii) the lack of fully validated permittivity, microstructure and roughness models for wet snow in the C-band, and (iii) the difficulty of capturing wet-snow features potentially generating stronger scattering effects on a large scale – such as internal snowpack structures, soil features in case of low LWC and surface roughness – which are not necessarily captured by pointwise measurements.