Abstract
High-resolution (HR) snow cover maps derived by remotely sensed images are an asset for data assimilation in hydrological models. However, the current satellite missions do not provide daily HR multispectral observations suitable for an accurate snow monitoring in alpine environments. On the contrary, low-resolution (LR) sensors acquire daily information of snow cover fraction (SCF) but at an inappropriate spatial scale. This paper proposes a novel approach that combines multi-source and multi-scale acquisitions to infer the daily HR snow cover area (SCA) for mountainous basins. The approach builds on the assumption that inter-annual snow patterns are both affected by the local geomorphometry and meteorology. We derive these patterns through a hierarchical multi-step approach based on historical statistical analyses on a long and sparse HR time-series. At each step, we obtain HR snow cover maps with higher number of reconstructed pixels but decreasing level of confidence. Historical data are used to estimate the probability that a HR pixel is covered by snow according to two possible multi-scale strategies: i) HR gap-filling, or ii) LR downscaling. These analyses lead to the identification of the patterns that regularly appear given certain conditions. When no systematic patterns are observed, we reinforce the inference of the pixel class by a generalized additive model (GAM) that exploits not only the historical data but also explicit geomorphometric, global snow and multi-temporal properties. The proposed approach has been validated on a catchment in the Sierra Nevada, USA, for three hydrological years (2017-2019) showing an average overall accuracy of 92%.