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Uncertainties of snowpack models and of their meteorological forcings limittheir use by avalanche hazard forecasters, or for glaciological andhydrological studies. The spatialized simulations currently available foravalanche hazard forecasting are only assimilating sparse meteorologicalobservations. As suggested by recent studies, their forecasting skills could besignificantly improved by assimilating satellite data such as snow reflectancesfrom satellites in the visible and the near-infrared spectra. Indeed, thesedata can help constrain the microstructural properties of surface snow andlight absorbing impurities content, which in turn affect the surface energy andmass budgets. This paper investigates the prerequisites of satellite dataassimilation into a detailed snowpack model. An ensemble version ofMeteo-France operational snowpack forecasting system (named S2M) was built forthis study. This operational system runs on topographic classes instead of gridpoints, so-called semi-distributed approach. Each class corresponds to one ofthe 23 mountain massifs of the French Alps (about 1000km^{2} each), analtitudinal range (by step of 300m) and aspect (by step of 45{\deg}). We assessthe feasability of satellite data assimilation in such a semi-distributedgeometry. Ensemble simulations are compared with satellite observations fromMODIS and Sentinel-2, and with in-situ reflectance observations. The studyfocuses on the 2013-2014 and 2016-2017 winters in the Grandes-Rousses massif.Substantial Pearson R^{2} correlations (0.75-0.90) of MODIS observations withsimulations are found over the domain. This suggests that assimilating it couldhave an impact on the spatialized snowpack forecasting system. However,observations contain significant biases (0.1-0.2 in reflectance) which preventtheir direct assimilation. MODIS spectral band ratios seem to be much lessbiased.

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations