The spatiotemporal evolution of snow melt 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 resolutions. While recent studies have successfully linked S1 backscattering to various phases of snowpack melting, a correlation with detailed snowpack properties is still missing. To address this, we collected the first dataset of detailed wet snow properties tailored for SAR applications over two consecutive snow seasons at the Weissfluhjoch field site in Switzerland. First, our dataset enabled the validation of previous methods relying on multitemporal SAR backscattering to characterize melting snowpacks and physically linked the increase in backscattering following the local minimum to the evolution of surface roughness. Then, the dataset was used as input to the Snow Microwave Radiative Transfer (SMRT) model to reproduce the S1 backscattering signal. Our simulations showed a general negative bias compared to the satellite data, with the most significant drivers being LWC early in the melt season and the 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, (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 point-wise measurements.