# Soil Erosion Assessment and Prediction in Urban Landscapes: A New G2 Model Approach
## 2 Materials and Methods
### Data  
The basic factors that affect soil erosion can be divided into static and dynamic input parameters, which are obtained on the basis of different databases and from different sources.  
Climate: For the needs of the first period (2001), the climatic data were obtained empirically from meteorological stations for the observation period from 1971 to 2001. The second period (climate-based) is generated by a climate model from the EURO-CORDEX database, reference, and future periods, as well as scenarios of greenhouse gas emissions [40]. The simulation of climate parameters is based on the EURO-CORDEX database, analyzed using the RCP8.5 climate scenario (scenario of a constant increase in greenhouse gas emissions) expected for the mid-21 st century. The main reason for applying this model is that a period of 20 years (beginning and end of the validity of the Master Plan) is too short for registering changes in the parameters related to climate change.  
Physical-geographical characteristics: The influence of relief and slope of the terrain significantly affects the occurrence of erosion processes. For the needs of this study, a digital terrain model was applied in the form of a raster. The EU-DEM was implemented, which is a pan-European database of elevation available under the Copernicus program with a spatial resolution of 25 m.  
Soil: Soil resistance to erosion processes is primarily conditioned by pedological characteristics which are an almost obligatory element of erosionist models. Soil resistance to erosion is correlated with soil characteristics, such as organic matter content, textural composition, structure, and water permeability. A digitized pedological map with a 1:20,000 scale [38; 39] was used for the purpose of this study, while relevant data on soil characteristics were taken on the basis of relevant literature sources [38; 39], as well as on the basis of soil samples used in projects by the University of Belgrade--Faculty of Forestry [41; 42]. The characteristics required in the model were developed on the basis of soil samples collected from 47 sites in study area. The soil cover in the MPB is dominated by loamy soils, including sandy clay loam, loam, silt loam, clay loam and sandy loam.  
Landsat satellite data: The Landsat is the longest satellite Earth observation program providing data about the Earth's surface since the early 1970s. Different types of data are available through the program, with Landsat 1, 2 and 3 being equipped with the multispectral scanner (MSS) and Landsat 4 and 5 operating thematic mappers (TM). Landsat 7 collects data with enhanced thematic mappers (ETM+) while Landsat 8 uses operational land imager (OLI) [43; 44]. Landsat 9 will be the latest satellite with further improved capabilities and is scheduled for launch in late 2021 [43]. A large number of spectral channels with high spatial resolution (mostly 30 m and up to 15 m for panchromatic channels) make data from the Landsat program useful for a wide range of applications. These include, but are not limited to, mapping land cover and land use [44; 45; 46; 47] and vegetation monitoring [48; 49; 50; 51; 52; 53]. For this study, Landsat data are necessary for creating land cover maps of the study area. For such purposes, Landsat scenes from 2001 and 2019 were acquired. These are Landsat 7 scene LE71860292001228 NSG00 for 2001 and Landsat 8 scene LC81860292019222 LGN00 for 2019. The scenes were chosen so that there is no cloud contamination over the study area. The data have been searched for and downloaded using NASA's EarthExplorer web service ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))) (accessed on 23 March 2021).  
Figure 1: Study area.