Soil water erosion is a creeping natural phenomenon, mostly related to weather and climate, and one of the main hydrogeological risk in Europe. It causes nutrients loss and exposes the environment to landslides, with negative impacts on agriculture, ecosystem services and infrastructures. Conversely, several human activities induce environmental modifications which intensify pressure on soils, thus increasing their predisposition to erosion. This study describes the integration of satellite observations with a modified version of the well-known Revised Universal Soil Loss Equation (RUSLE) model for estimating soil erosion in an Italian Alpine river basin. Compared to traditional RUSLE formulation, in this study we assigned the cover management factor using a combination of DUSAF land cover classification and NDVI values computed from Landsat time series. Rainfall erosivity was estimated separating liquid precipitation (erosive) and solid precipitation (non-erosive) from hourly data. Soil erodibility for the study area was tuned combining soil maps with total organic carbon (TOC), acidity (pH) and texture (granulometry) from soil samples collected on site. Finally, the slope length and steepness factor was derived using a 30-meter spatial resolution digital elevation model. Integrating the RUSLE-like model with spectral indices derived from satellite data allows highlighting spatial patterns useful for understanding soil erosion dynamic and forcing. Thus, satellite-derived spectral information, that include both seasonal and long-term land cover changes, opens new ways for modelling the dynamics of soil erosion.