Abstract
In this work, we tested two algorithms for the retrieval of snow surface albedo and grain size by exploiting Sentinel-3 OLCI data. The first algorithm is semi-empirical based on spectral indices and radiative transfer theory adapted from Painter et al. (2009, 2012) and the second is based on an approximation of the radiative transfer theory proposed by Kokhanovsky et al. (2019). Being interested mainly in mountain areas, we introduced adaptations to account for topography and heterogeneity of the area of interest. The algorithms were tested and intercompared in the European Alps for the period 2018-2021. The results indicate that for albedo both algorithms can follow the snow dynamics from winter to springtime. Both algorithms agree well with ground measurements showing a Root Mean Square Error (RMSE) between 0.05 and 0.15 and a correlation coefficient ranging from 0.71 to 0.81. As for grain size, a general underestimation is found for both methods, even though the semiempirical method follows better the typical grain size metamorphosis from winter to spring.