The decrease in vegetation cover is one of the main triggering factors for soil erosion of grasslands. Within the Revised Universal Soil Loss Equation (RUSLE), a model commonly used to describe soil erosion, the vegetation cover for grassland is expressed in the cover and management factor (C-factor). The site-specific C-factor is a combination of the relative erosion susceptibility of a particular plant development stage (here expressed as soil loss ratio SLR) and the corresponding rainfall pattern (here expressed as R-factor ratio). Thus, for grasslands the fraction of green vegetation cover (FGVC) determines the SLRs. Although Switzerland is a country dominated by grassland with high percentages of mountainous regions and evidence for high erosion rates of grassland exists, soil erosion risk modeling of grasslands and especially of mountainous grasslands in Switzerland is restricted to a few studies. Here, we present a spatio-temporal approach to assess the dynamics of the C-factor for Swiss grasslands and to identify erosion prone regions and seasons simultaneously. We combine different satellite data, aerial data, and derivative products like Climate Change Initiative (CCI) Land Cover, Swissimage false-color infrared (Swissimage FCIR), PROBA-V Fraction of green Vegetation Cover (FCover300m), and MODIS Vegetation Indices 16-Day L3 Global (MOD13Q1) for the FGVC mapping of grasslands. In the spatial mapping, the FGVC is extracted from Swissimage FCIR (spat. res. 2 m) by linear spectral unmixing (LSU). The spatially derived results are then fused with the 10-day deviations of temporal FGVC derived by FCover300m. Following the original RUSLE approach, the combined FGVC are transformed to SLRs and weighted with high spatio-temporal resolved ratios of R-factors to result in spatio-temporal C-factors for Swiss grasslands. The annual average C-factor of all Swiss grasslands is 0.012. Seasonal and regional patterns (low C in winter, high C in summer, dependency on elevation) are recognizable in the spatio-temporal mapping approach. They are mainly explicable by the R-factor distribution within a year. Knowledge about the spatio-temporal dynamic of erosion triggering factors is of high interest for agronomists who can introduce areal and time specific selective erosion control measures and thereby reduce the direct costs of mitigation as well as erosion measures.