The erosion processes are causing significant damage to both the environment and the economy. Therefore, it is important to be able to predict the erosion processes, assess their distribution and forecast development. All of this can be done using models, such as USLE (RUSLE). The accuracy of these models depends on their parameters. One of the most important factors is the Ñ-factor (influence of vegetation cover). The vegetation cover protects the soil by dissipating the energy of raindrops before they reach the surface. The definition of this factor may in several ways. One such is the estimation of vegetation cover by interpreting the Earth’s remote sensing images, which includes the definition of NDVI – indicator, which reflects vegetation cover. The purpose of the study is to determine the advantages of using NDVI in forecasting erosion risks compared to traditional ones. The traditional method is to assign values that were evaluated by experimental means to plant groups or to a specific type of land use. This method is outdated and inaccurate. However, it has the right to exist. Vegetation cover can be estimated using vegetation indices based on satellite imagery. The vegetation indices make it possible to determine the distribution of vegetation and soils based on the characteristic patterns of the display of green vegetation. The most common index is the Normalized Difference Vegetation Index (NDVI). NDVI reflects the current state of vegetation cover. For applications on regional or national scale the C-factor can be estimated from mid-resolution satellite images by applying the NDVI. NDVI is derived from satellite image of Sentinel-2A in period 2016. The final C-factor map was generated using the regression equation in Spatial Analyst tool of ArcGIS V.10.1 software. The results show that large parts of areas in central part of study area with bare soil has higher C-factor. In contrary vegetated areas, especially the forests, shrub and grasslands has lower C-factor. However, this study shows that NDVI is an optimal method to estimate C-factor values of land cover classes of large areas in a short time. 
Keywords: Erosion processes, vegetation cover, NDVI, USLE, C-factor.