Soil erosion by water is a widespread environmental problem with significant impacts on soil fertility, crop productivity, and ecosystem sustainability. In Switzerland, up to 10% of arable land is at a higher erosion risk [1], primarily due to unadapted farming methods, and could benefit from control measures. The combination of susceptible terrains with disturbances from reworking the soil or low soil coverage can exacerbate erosion risk. Reliable, spatially explicit information on soil cover dynamics is therefore essential for identifying erosion-prone areas and supporting sustainable land management. A commonly used framework to assess erosion risk in agricultural systems is the Revised Universal Soil Loss Equation (RUSLE), in which the crop cover and management factor (C-factor) represents the protective effect of vegetation and farming practices against soil loss. The C-factor varies over time as a function of crop growth, harvest, residue management, and bare-soil periods [2], making its accurate estimation challenging at large spatial scales. For arable land in Switzerland, the annual average erosion indicator computed within the national agri-environmental monitoring programme [3] is based on generic crop calendars and assumed field management practices, leading to inaccuracies in the representation of crop cover and on-field management. The advent of satellite data provides large-scale access to frequent and high-resolution observations (e.g. 5 days and up to 10 for Sentinel-2) that enable continuous monitoring of land surface conditions. Fractional cover can be retrieved at pixel level using spectral mixture analysis (SMA), which decomposes the mixed satellite signal into proportions of soil, photosynthetic vegetation, and non-photosynthetic vegetation [4]. In this research, we present an automated framework for producing high-resolution, temporally consistent fractional cover maps over Switzerland. We first establish SMA-based regression models by constructing a representative dataset of pure photosynthetic vegetation, non-photosynthetic vegetation, and soil spectra from Sentinel-2 imagery, capturing the diversity of crop types, management practices, and soil conditions across the country. Synthetic spectral mixtures with known proportions of each cover type are created and used as a training dataset for neural network models. The trained models are then applied to Sentinel-2 data to generate nationwide fractional cover time series. We further post-process the outputs to reduce cloud contamination, enforce temporal consistency, and aggregate predictions to regular timestamps and administrative units. The resulting fractional cover product provides updated, spatially explicit inputs for C-factor estimation within the RUSLE framework, enabling up-to-date assessment of erosion risk at national scale. Beyond soil erosion modelling, the proposed approach offers a product for large-scale monitoring of vegetation and soil dynamics in agricultural landscapes.