## 3 Results and Discussion
### RUSLE Model
#### 3.1.4 Cover Management (C) Factor  
This factor can be assessed in different ways depending on the availability of the data. Remote-sensing-based methods such as land use/land cover (LULC) classification and spectral indices such as the Normalized Difference Vegetation Index (_NDVI_) and its derivation the Fractional Vegetation Cover (FVC) have been proven to be informative in deriving the C-factor.  
Regardless of the classification algorithm, the resulting classified images showed spectral confusion in some thematic classes, such as for young olive groves (Corine class no. 223) with Sclerophyllous vegetation (Corine class no. 323) and for Sclerophyllous vegetation (Corine class no. 323) with broadleaf forest areas (Corine class no. 311).  
The RF classifier showed the highest overall accuracy (-86%) and Kappa statistics (-78%) among the classifiers. Consequently, all of the selected Sentinel-2 images were classified separately, since every image was acquired on a different date using the RF classifier. The final land cover map (Figure 9) showed an overall accuracy of about 95% and Kappa statistics of about 93%. The Kappa coefficient values ranged between 81% and 100%, means there was an almost perfect agreement between the classification and the reference data.  
Figure 8: LS-factor map.  
After determining the LULC classes from the remotely sensed data, corresponding C-factor values were assigned from literature. The C-factor map derived from the land cover classification (Figure 10) showed that forested areas had the lowest C-factor values, while agriculture areas had higher values.  
Figure 10: C(literature)-factor map.  
Figure 9: Classified LULC map for Paleochora (2020).  
Regarding the _NDVI_, 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]. C(vk)-factor and C(r)-factor maps (Figures 11 and 12, respectively) derived from the _NDVI_ index seemed to have approximately the same trend for the value distribution. These maps showed almost similar values for forested areas (almost zero) and agricultural areas (ranging between \(\sim\)0.15 and \(\sim\)0.2) in comparison to the C-factor map derived from supervised classification; however, C(vk)-factor and C(r)-factor values were incredibly high in some areas, such as the \"natural grassland\" areas. As such, C(vk)-values and C(r)-values for those areas were replaced by C\({}_{\text{LIT}}\)-values from the literature, as recommended by [PERSON] et al. [25].  
Figure 11: C(vk)-factor map.  
Figure 12: C(r)-factor map.  
Similar to the C-factor map derived from the _NDVI_, the C(FVC)-values assigned to the \"natural grassland\" were overestimated; thus, these values were substituted for C-values from the literature (Figure 13).  
The comparison of the different methods (classification and _NDVI_) used to obtain C-factor showed that C-values derived from _NDVI_ were overestimated in comparison to C-values from the literature; however, C-values derived from FVC seemed to be more reliable compared to C-values from the literature.  
Another problem with _NDVI_ and its sub-product (FVC) is the high values assigned to the \"natural grassland\". This can be explained by the fact that they are considered as moderate vegetation so their _NDVI_ is quite low (\(\sim\)0.2 to \(\sim\)0.3). Consequently, they will have high C-values. This type of land cover has a significant impact on the C-factor as well. The effect of grassland on soil conservation is much better than that of agricultural land, as confirmed by C-values from the literature.  
On the other hand, the rescaled C-factor equation was originally proposed for tropical climatic conditions and FVC equations were originally suggested for Chinese catchments. Since these zone have different climatic conditions and land covers to the Mediterranean study area, this could also influence the results.