## 2 Materials and Methods
### RUSLE Model  
#### 2.3.1 Model Description  
The RUSLE model predicts average annual erosion by water as a product of six parameters or factors (rainfall erosivity factor, soil erodibility factor, slope length and slope steepness factors, cover management factor, support practice factor) and is expressed as:  
\[A=R\;K\;LS\;C\;P \tag{1}\]  
where \(A\) is the soil loss (t/ha/year), \(R\) is the rainfall-runoff erosivity factor (MJ mm/(ha h yr)), \(K\) is the soil erodibility factor (t ha h/(ha MJ mm)), \(LS\) is the slope length and steepness factor (dimensionless), \(C\) is the cover management factor (dimensionless), and \(P\) is the conservation support practice factor (dimensionless). The R-factor is one of the key factors that influences water-prone erosion. It is expressed as a function of the rainfall amount and intensity. Basically, rainfall data are extracted from in situ measurements (meteorological gauge stations). Such data should be interpolated to gain a continuous spatial distribution and variability of rainfall. Despite their good accuracy, it is sometimes difficult to acquire data from inaccessible locations; thus, satellite data could be a potential alternative to derive rainfall data. In fact, many space-based mission were launched to monitor precipitation, such as the Tropical Rainfall Measurement Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. The K-factor is the erodibility factor, which indicates the intrinsic susceptibility of soil to water and runoff erosion. It depends mainly on the texture and structure of the soil. In fact, the RUSLE model takes into account the physical properties, such as the primary particle size distribution, organic matter, soil structure, and permeability, as the key factors affecting the erodibility of a soil. Initially, these properties were determined using laboratory-based methods [6]. At the European level, [PERSON] et al. [20] assessed soil erodibility based on properties (such as texture, organic carbon) that were available from the Land Use-Cover Area Frame Survey (LUCAS) topsoil data. The LS-factor refers to the slope length (L) and the slope steepness (S). L reflects the distance from the point of origin of overland flow to the point where deposition begins. S represents the slope gradient to designate flat, moderate, and steep slopes. Both slope length and steepness are proportional to erosion. Consequently, increases in L and S lead to higher runoff velocities and higher soil loss rates [1]. These topographic parameters are so sensitive to soil loss that a minor miscalculation could lead to inaccurate assessment of the overall soil erosion rates. Although this topographic factor is one of the principal factors for the RUSLE model, ground-based measurement data are usually unavailable. As an alternative to ground-based measurement, the LS-factor can be estimated using digital elevation models (DEMs) using geographic information system software [1; 21]. The C-factor estimation distinguishes between arable lands and non-arable lands (all other land uses). Artificial areas, wetlands, water bodies, bare rocks, beaches, and glaciers are not included in the C-factor estimation [22]. The C-factor is defined as the ratio of soil loss under specific cover and management conditions to soil loss affecting an identical bare soil area [23]. It reflects the impacts of cropping and other management practices on the intensity of erosion. Traditionally, this factor was retrieved from field surveys; however,remote-sensing-based methods, such as land use/land cover (LULC) classifications; 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 [6]. The P-factor is the ratio of soil loss following a specific support practice to the corresponding soil loss after up and down cultivation [24].