The snow cover exhibits tremendous spatial and temporal variability, especially in complex topography. Fractional snow-covered area (fSCA) is a parameter used to describe how much ground is covered by snow. As such, fSCA is a relevant parameter in large-scale model applications, for instance to compute the surface radiation balance or for scaling snow melt runoff. Many studies therefore focused on formulating parameterizations for fSCA, requiring mean snow depth estimates and knowledge about the underlying topography. With the emergence of high-resolution satellite products, remotely-sensed fSCA estimates are becoming more readily available, opening new avenues to assimilate fSCA in models. Nevertheless, parameterizations are still required to fill the gap when satellite data are unavailable (e.g. between satellite revisits, when clouds obscure the ground and for forecasting). We therefore evaluated fSCA estimates from a recently proposed fSCA parameterization developed for peak of winter snow data with satellite fSCA from Sentinel-2 images generated by Theia over an entire winter season for Switzerland. Modelled fSCA maps were obtained from a 1 km gridded operational hydrological model that assimilated several hundred snow depth measurements and ran with data from the numerical weather forecast model COSMO. Furthermore, snow depth maxima were tracked over the season. While both fSCA estimates correlated significantly, intra-season differences were present. We therefore investigated fSCA maps derived from parameterized mean snow depth using flat field snow depth measurements whilst similarly tracking snow depth maxima. Overall, our results show that complementing satellite snow maps with a fSCA parameterization has great potential for large-scale models.