Soil erosion and land degradation are global problems and pose major issues in many countries. Both soil erosion and mass movement are two forms of land degradation and humans play important roles in these geomorphological processes. This paper reviews slope processes associated with mass movement and soil erosion and contributory factors, including physical and human agents. Acting together, these cause diverse geomorphological features. Slope processes are illustrated by reference to case studies from Brazil and UK. The causes and impacts of erosion are discussed, along with appropriate remedial bioengineering methods and the potential of the measures to prevent these types of environmental degradation. Although there are several agents of erosion, water is the most important one. Cultivation can promote soil erosion, due to ploughing and harvesting, which moves soil down slopes. Soil erosion and mass movement data would inform the viability of soil conservation practices. Integrated management of drainage basins offers a promising way forward for effective soil conservation and soil remedial bioengineering in Brazil and UK

Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

Food security has become a global concern for humanity with rapid population growth, requiring a sustainable assessment of natural resources. Soil is one of the most important sources that can help to bridge the food demand gap to achieve food security if well assessed and managed. The aim of this study was to determine the soil quality index (SQI) for El Fayoum depression in the Western Egyptian Desert using spatial modeling for soil physical, chemical, and biological properties based on the MEDALUS methodology. For this purpose, a spatial model was developed to evaluate the soil quality of the El Fayoum depression in the Western Egyptian Desert. The integration between Digital Elevation Model (DEM) and Sentinel-2 satellite image was used to produce landforms and digital soil mapping for the study area. Results showed that the study area located under six classes of soil quality, e.g., very high-quality class represents an area of 387.12 km2 (22.7%), high-quality class occupies 441.72 km2 (25.87%), the moderate-quality class represents 208.57 km2 (12.21%), slightly moderate-quality class represents 231.10 km2 (13.5%), as well as, a low-quality class covering an area of 233 km2 (13.60%), and very low-quality class occupies about 206 km2 (12%). The Agricultural Land Evaluation System for arid and semi-arid regions (ALESarid) was used to estimate land capability. Land capability classes were non-agriculture class (C6), poor (C4), fair (C3), and good (C2) with an area 231.87 km2 (13.50%), 291.94 km2 (17%), 767.39 km2 (44.94%), and 416.07 km2 (24.4%), respectively. Land capability along with the normalized difference vegetation index (NDVI) used for validation of the proposed model of soil quality. The spatially-explicit soil quality index (SQI) shows a strong significant positive correlation with the land capability and a positive correlation with NDVI at R2 0.86 (p < 0.001) and 0.18 (p < 0.05), respectively. In arid regions, the strategy outlined here can easily be re-applied in similar environments, allowing decision-makers and regional governments to use the quantitative results achieved to ensure sustainable development.

Heavy metal contamination in the El-Gharbia Governorate (District) of Egypt was identified by using remote sensing, Geographical Information Systems (GIS), and X-ray fluorescence (XRF) spectrometry as the main research tools. Digital Elevation Model (DEM), Landsat 8 and contour map images were used to map the landforms. Different physiographic units in the study area are represented by nine soil profiles. X-ray fluorescence spectrometry (XRF) was used for geochemical analysis of 33 soil samples. Vanadium (V), nickel (Ni), chromium (Cr), copper (Cu) and zinc (Zn) concentrations were measured and they all exceeded the average global concentrations identified by Wedepohl (1995). Ni and Cr concentrations exceeded recommended values in all soil profile horizons (Canadian Soil Quality Guidelines, 2007), while Cu had a variable distribution. Zn concentrations are under recommended concentration limits in most soil samples. Contamination Factor, Pollution Load Index and Degree of Contamination indices were used to assess the environmental risks of heavy metal contamination from the soils. All analysed metals pose some potential hazard and pollution levels were particularly high near industrial and urban areas