## 3 Results
### Main Outcomes of the Top 20 Highly Cited Articles from Each Thematic Area
#### 3.2.4 Land  
Land degradation due to soil erosion is one of the major problems which the EMMENA region is facing as highlighted by most of the studies in the land thematic area [93; 94; 95; 96; 97; 98; 99]. [PERSON] et al. [99] integrated the revised universal soil loss equation (RUSLE) in a GIS environment to quantify soil loss and to map erosion risk of the Oum Er Rba River basin, located in the Middle Atlas (Morocco), for 2013, 2013, and 2017. For their analysis, they used satellite images of Sentinel-2A, Landsat Oli-8, and ETM and applied cellular automata Markov to forecast the land use/land cover map and to detect the associated changes. Their results showed an estimated annual soil loss of 58 t ha\({}^{-1}\) yr\({}^{-1}\) in 2003, while the predicted annual soil loss for 2030 increased to 142 t ha\({}^{-1}\) yr\({}^{-1}\). Different techniques can be found in the literature for creation of soil erosion susceptibility maps. [PERSON] et al. [98] used four different multi-criteria decision-making models (SAW, VIKOR, TOPSIS, and CF) and concluded that the VIKOR method has higher predictive accuracy than TOPSIS, SAW, and CF models for the Ghaemshahr Basin in Iran. They used the Advanced Space Thermal Emission Radiometer (ASTER) digital elevation model (DEM) with spatial resolution of 30 m for the extraction and analysis of 23 morphometric parameters. [PERSON] et al. [94] evaluated the performance of three data-driven models (frequency ratio, weights of evidence, index of entropy) and the AHP knowledge-based technique for gully erosion susceptibility mapping in the Toroud watershed (Iran). Morphological and land use parameters were derived from ALOS PALSAR and Landsat 8 satellite images. All four models showed excellent accuracy in prediction of areas prone to gully erosion. [PERSON] et al. [97] tested the same models in the Valasht watershed (Iran) and found that the frequency ratio model had better performance (80.4%) than the weight of evidence (79.5%) and index of entropy (79%) for erosion mapping. In another study, [PERSON] et al. [95] studied the performance of three algorithms (random forest, boosted regression tree, and multi-variate adaptive regression spline) for gully erosion susceptibility mapping in the Shahroud watershed, Iran. Landsat 8 and ASTER GDEM data were used in their analysis. According to the authors, the random forest model had the highest prediction accuracy. Similar high performance for the random forest algorithm was reported by [PERSON] et al. [96]. Finally, [PERSON] et al. [93] proposed a new model which combines the geographically weighted regression technique with the certainty factor and random forest models to produce gully erosion zonation mapping. The proper selection of the data in these studies depends on their availability and site-specific characteristics. Information about the location, extent, and type of land use/land cover (LULC) is essential for improving land management practices [100]. [PERSON] et al. [101] applied advanced GIS techniques, spatial modeling, and utilization of satellite images (Landsat 5/8) to simulate LULC changes and to predict the urban expansion in the mountainous cities of Oman, for the years 2028 and 2038. Their predictions showed a 50% increase in the built-up area in 2038; on the contrary, vegetation will decrease by 36.5%. [PERSON] et al. [100] developed an object-based land cover classification methodology using the Google Earth Engine in order to improve the land cover map of Iran. They used Sentinel-1 and Sentinel-2 imagery data for the fusion of SAR and multi-spectral data to increase the land cover classification accuracy. The land cover map for Iran (2017) had an overall accuracy of 95.6%. [PERSON] et al. [102] studied the vegetation change in Azrou Forest (Morocco) between 1987 and 2017. They combined Landsat images (MSS, ETM+, TM, OLI) and used the NDVI and ground-based survey to improve the discrimination between LULC categories. Land cover maps for each year were produced with the maximum likelihood (ML) classification method.