Abstract
The monitoring of the snow cover area (SCA) from optical sensors on board of satellites is affected by the trade-off between spatial and temporal resolution provided by the current operational missions. This limits the possibility to exploit satellite SCA for hydrological purposes. In this paper, we propose a novel downscaling approach driven by the low resolution (LR) information that takes advantage of i) all the high resolution (HR) images acquired in the past over a catchment; and ii) the geomorphometric features that drive the snow redistribution process. Possible applications of the proposed method are time-series gap-filling and snow pattern detection. The downscaled scenes have been validated across existing HR scenes showing an accuracy of about 90%.