## 2 Materials and Methods
### RUSLE Model
#### Cover Management (C) Factor  
Remote-sensing-based methods such as land use/land cover (LULC) classification and spectral indices such as the Normalized Difference Vegetation Index (_NDVI_), Soil-Adjusted Vegetation Index (SAVI), and others, have been proven to be informative in deriving the C-factor. LULC classification is one of the most used methods to derive the C-factor. In fact, after determining the LULC classes from the remotely sensed data, corresponding C-factor values were assigned from the literature. Generally, the C-factor ranges between 0 and 1; areas with sparse vegetation tend to have higher C values due to the effect that low vegetation cover has on erosion, while areas with dense vegetation have values close to 0 due to limited soil erosion [34]. Machine learning algorithms, such as Classification and Regression Trees (CART), Support Vector Machine (SVM), Minimum Distance (MD), and Random Forest (RF), have been proven to be relevant classifiers for land cover [35]. Spectral indices and their derivations (such as Fractional Vegetation Cover (FVC) derived from _NDVI_) were also used to evaluate the fraction of vegetation and its influence on C-factor values. In fact, they have the ability to enhance the spectral contribution of different vegetation cover types. Consequently, several empirical relations were established to correlate _NDVI_ values to C-factor values. Commonly, _NDVI_ is directly correlated to the C-factor by an exponential regression [36] or a linear regression [37]. The main aim is to derive the most relevant C-factor map from these methods. To do so, the above-mentioned classifiers were run followed by an accuracy assessment based on Google Earth free data to choose the best classification algorithm. Moreover, spectral indices were used to derive C-factor values. In order to define the most reasonable values, the resulting C-factor values obtained from different methods were compared between each other. The satellite data (Sentinel-2 level 2A assets) were accessed on Google Earth Engine (GEE) provided by the European Space Agency (ESA). Images were filtered by a cloud cover percentage of less than 10%. Corine was used to create a label dataset for training. GEE trains this dataset to recognize patterns in the image by selecting pixels that represent patterns or land cover features that it recognizes. Bands 4, 3, and 2 of Sentinel-2 images were combined to display false color composite images for better visualization of land cover. The land cover classes were compared to the Corine Land Cover (CLC) map. In order to overcome the problem of isolated pixels on the classified images, a post-classification process is recommended [38]. In this study, statistical filtering (majority filter process) was conducted, whereby isolated pixels were grouped into the closest value of the majority of their contiguous neighboring pixels. Concerning the classification accuracy assessment, Google Earth is a powerful source of high spatial resolution data that can be used for investigation and preliminary studies with suitable accuracy and low cost. In this study, the accuracy assessment for the classified image was based on Google Earth imagery. A set of 150 points were randomly generated and identified in Google earth, then the overall accuracy and Kappa statistics were estimated. In fact, the overall accuracy represents the probability that a pixel will be correctly classified, while the Kappa coefficient expresses the proportionate reduction in error generated by a classification process compared with the error of a completely random classification [39]. Once the classification was validated, the following C-factor values were assigned to each specific land cover type according to [PERSON] et al. [22] (Table 4).