## 3 Results and Discussion
### RUSLE Model
#### 3.1.1 Rainfall Erosivity (R) Factor  
Satellite-Based Rainfall Erosivity (R) Factor  
After applying the validation process, the MLR analysis revealed a significant relationship only between GPM and DEM, with \(p\) values less than 0.05 for all the months; however, there was a weak correlation between GPM and _NDVI_, meaning all MLR models were significant and valid. Using the regression equations, the whole downscaling methodology was executed. An example of the results is shown in the following figure (Figure 3). The _RMSE_ values for the MLR method ranged from 0 to almost 56. Regarding bias, many values were negative, which can be explained by an underestimation of the precipitation. The coefficient of determination (R\({}^{2}\)) values ranged between 0.64 and 0.98. These were higher for March, April, and December and lower for January, October, and November. Once the downscaling process was accomplished, the R-factor was estimated according to Equation (2). The spatial pattern for the annual rainfall Erosivity for Paleochora for 2020 at 10 m spatial resolution is shown in the following figure (Figure 4). The map shows that the southern part of the study area (coastal area) has low R values (from approximately 108 to 500 MJ mm ha\({}^{-1}\) h\({}^{-1}\) y\({}^{-1}\)); however, high R-values (from around 900 to 1200 MJ mm ha\({}^{-1}\) h\({}^{-1}\) y\({}^{-1}\)) are estimated in the north-eastern part of the study area, which correspond to the highest altitude (corresponding to a high precipitation amount). In fact, the rainfall Erosivity is higher in the area with high altitude and its adjoining areas, significantly enhancing the risk of soil erosion; thus, as the topography varies from steep to flat relief, the erosivity declines from 1286.11 to 108.41 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\), with a mean annual value of 501.8 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\). Figure 2: Flowchart of the research methodology. Figure 3: Regression-derived precipitation in the Paleochora region: (**a**) the IMERG precipitation values at 0.1\({}^{\circ}\) resolution; (**b**) the estimated precipitation at 10m resolution; (**c**) the interpolated residuals at 10 m resolution; (**d**) the downscaled precipitation at 10 m resolution. According to [PERSON] et al. [12], the mean annual R-factor for Crete was assessed to be 777 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\) as applied in the G2 erosion model. In the same context, [PERSON] et al. [47] estimated the mean annual R-factor in Crete Island to be 846 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\) and [PERSON] et al. [28] found that the average R-factor for Crete island was estimated at 1818 MJ mm ha\({}^{-1}\) h\({}^{-1}\) year\({}^{-1}\). Compared to the studies by [PERSON] et al. [47; 48] in Europe and Greece, respectively, the spatial distribution pattern for the R-factor was in accordance with the proposed pattern maps; however, the R-factor values corresponding to Paleocha catchments were larger than the estimated R- factor values in this study. Ground-Based Rainfall Erosivity (R) Factor  
The R-factor values estimated from the 5 meteorological stations, which from the west to the east of Chania were denoted as Elos, Paleochora, Kandanos, Sebronas, and Samaria (Figure 5), varied between 131.25 and 958.857 MJ mm ha\({}^{-1}\) h\({}^{-1}\) y\({}^{-1}\) (Table 8), with a mean value of 432.879 MJ mm ha\({}^{-1}\) h\({}^{-1}\) y\({}^{-1}\) and with a relatively high standard deviation of 331.04 MJ mm ha\({}^{-1}\) h\({}^{-1}\) y\({}^{-1}\), which can be explained by the topography variations. Figure 4: R-factor map calculated from downscaled IMERG daily data. The following map (Figure 6) shows the spatial pattern of the annual erosivity resulting from the extrapolation of the R-values. The R-factor values range from 240.248 to 545.536 MJ mm ha\({}^{-1}\) h\({}^{-1}\) y\({}^{-1}\), with a mean value of 347.734 MJ mm ha\({}^{-1}\) h\({}^{-1}\) y\({}^{-1}\).