Impact of Forest Canopy Parameterization on Space-Borne Snow on Ground Detection

Snow on ground time series in forested regions are essential for climate and hydrological modelling. The SCAmod algorithm corrects for forest canopy using predetermined values of three reflectance constituents (opaque forest, wet snow, and snow-free ground) and a static transmissivity map. Thus, snow cover extent retrievals depend largely on the representativeness of the static model parameters. In this study, we assess the impact of forest canopy reflectance and its changes on AVHRR retrieved snow cover extent time series for the European Alps. Our initial results explain variations caused by different SCAmod parameterizations and land cover types and suggest the need for improvement using dynamic, time-dependent, and sensor-specific (non-)forest reflectance constituents.