The VENµS mission launched in 2017 provides multispectral optical images of the land surface with a 2-day revisit time at 5 m resolution for over 100 selected sites. A few sites are subject to seasonal snow accumulation, which gives the opportunity to monitor the variations of the snow cover area at unprecedented spatial and temporal resolution. However, the 12 spectral bands of VENµS only cover the visible and near-infrared region of the spectra while existing snow detection algorithms typically make use of a shortwave infrared band to determine the presence of snow. Here, we evaluate two alternative snow detection algorithms. The first one is based on a normalized difference index between the near-infrared and the visible bands, and the second one is based on a machine learning approach using the Theia Sentinel-2 snow products as training data. Both approaches are tested using Sentinel-2 data (as surrogate of VENµS data) as well as actual VENµS in the Pyrenees and the High Atlas. The results confirm the possibility of retrieving snow cover without SWIR with a slight loss in performance. As expected, the results confirm that the machine learning method provides better results than the index-based approach (e.g., an RMSE equal to the learning method 1.35% and for the index-based method 10.80% in the High Atlas.). The improvement is more evident in the Pyrenees probably due to the presence of vegetation which complicates the spectral signature of the snow cover area in VENµS images.