Abstract
This work presents the results of an algorithm for the retrieval of snow surface albedo and grain size by exploiting Sentinel-3 OLCI data. The algorithm is a hybrid approach based on spectral indices and radiative transfer theory and it was tested and intercompared with ground data from 6 field stations and other sensors in the European Alps for the period 2017-2023. The results indicate that for albedo the algorithm agrees well with ground measurements showing an unbiased Root Mean Square Error (ubRMSE) between 0.05 and 0.13 and a correlation coefficient ranging from 0.63 to 0.78 depending on the locations. For grain size, even though a general underestimation is found, the estimates reflect well the typical grain size metamorphosis from winter to spring. To further test the algorithm, these results from Sentinel-3 data were also confronted with ECOSTRESS thermal data specifically useful to show the metamorphosis of the grain size. For albedo, the algorithm was further applied to PRISMA hyperspectral images, showing consistent results with the values obtained by multispectral Sentinel-3 imagery.