## 2 Materials and Methods
### Data  
Different datasets were collected and analyzed to produce the factors of the RUSLE model. These datasets contained rainfall data, soil and topographical data, satellite imagery, and land use/land cover data. The Integrated Multisatellite Retrievals (IMERG) for Global Precipitation Measurement (GPM) dataset for 2020 was downloaded from the National Aeronautics and Space Administration (NASA). In this study, the GPM_3 IMERGDL v06 (D: daily, L: late run) products were employed, as they are widely used for research purposes and have enhanced accuracy. In fact, IMERG for GPM (IMERG) is the level 3 GPM algorithm, which integrates precipitation estimates from all constellation microwave (MW) sensors, IR-based sensors  
Figure 1: Shaded relief map of the study area. onboard geosynchronous satellites, and a monthly precipitation gauge product [14]. The spatial resolution of IMERG is 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\). In total, 4 grids covered the study area. The LUCAS database includes particle size distributions expressed as percentages (%) of clay (0.0002-0.002 mm), silt (0.002-0.063 mm), and sand (0.063-0.2 mm), as well as percentages of organic carbon (%) and coarse material (>2 mm) [15]. It was used to estimate the soil erodibility factor. Since terrain morphology is an important factor in erosion, a digital elevation model (DEM) is essential for estimating various geomorphological parameters such as slope. The RUSLE requires a DEM as an input in order to quantitatively represent the continuous variation of topographic features across the landscape. The DEM generated from Radiometrically Terrain Corrected (RTC) Advanced Land Observing Satellite-Phased Array-type l-band Synthetic Aperture Radar (ALOS-PALSAR) data has a spatial resolution of 12.5 m. The Radiometric Terrain Correction (RTC) corrects the geometric distortions using the best DEM available for the region of interest (SRTM DEM in this case) and also adjusts the radiometry in the affected foreshortening and layover regions using the pixel-area integration approach for radiometric normalization. ALOS-PALSAR DEM products have been used in the past to provide accurate results for soil erosion [16]. ALOS-PALSAR DEM data were downloaded from the Alaska Satellite Facility (ASF) and were used to derive the slope parameters (the slope length-steepness factor (LS-factor) and support practice factor (P-factor)). Prior to deriving the LS-factor from any space-borne DEM products, its accuracy must be evaluated [6]. The high accuracy of the ALOS-PALSAR DEM comes from applying high-quality DEMs (such as SRTM and the National Elevation Dataset (NED)) in radiometric and geometric corrections of PALSAR imagery [17]. The Sentinel-2 level 2A data were accessed on Google Earth Engine (GEE) provided by the European Space Agency (ESA). Level 2A is an ortho-image bottom-of-atmosphere (BOA)-corrected reflectance product according to the Sentinel-2 User Handbook [18]. The \"ee.ImageCollection\" GEE function was used to filter the time series data for the summer period of 2020 in order to avoid cloud cover. Good Agricultural and Environmental Conditions (GAEC) promote the prevention of erosive farming practices (ploughing and planting up and down the slope) in mountainous areas and insist on the maintenance of landscape features, such as stone walls and terraces. In Greece, the farming practice for areas exceeding 10% slope is the cultivation along or diagonal to the contour (cross slope contour farming) in order to conserve rainwater and to reduce soil losses [19]. In Crete, the main conservation support practices are contour farming, stone walls, and grass margins [1]; thus, these three types of support practices were considered to estimate the P-factor. The following table (Table 1) summarizes the input dataset required for RUSLE model.