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.

Assessing soil quality is considered one the most important indicators to ensure planned and sustainable use of agricultural lands according to their potential. The current study was carried out to develop a spatial model for the assessment of soil quality, based on four main quality indices, Fertility Index (FI), Physical Index (PI), Chemical Index (CI), and Geomorphologic Index (GI), as well as the Geographic Information System (GIS) and remote sensing data (RS). In addition to the GI, the Normalized Difference Vegetation Index (NDVI) parameter were added to assess soil quality in the study area (western part of Matrouh Governorate, Egypt) as accurately as possible. The study area suffers from a lack of awareness of agriculture practices, and it depends on seasonal rain for cultivation. Thus, it is very important to assess soil quality to deliver valuable data to decision makers and regional governments to find the best ways to improve soil quality and overcome the food security problem. We integrated a Digital Elevation Model (DEM) with Sentinel-2 satellite images to extract landform units of the study area. Forty-eight soil profiles were created to represent identified geomorphic units of the investigated area. We used the model builder function and a geostatistical approach based on ordinary kriging interpolation to map the soil quality index of the study area and categorize it into different classes. The soil quality (SQ) of the study area, classified into four classes (i.e., high quality (SQ2), moderate quality (SQ3), low quality (SQ4), and very low quality (SQ5)), occupied 0.90%, 21.87%, 22.22%, and 49.23% of the total study area, respectively. In addition, 5.74% of the study area was classified as uncultivated area as a reference. The developed soil quality model (DSQM) shows substantial agreement (0.67) with the weighted additive model, according to kappa coefficient statics, and significantly correlated with land capability R² (0.71). Hence, the model provides a full overview of SQ in the study area and can easily be implemented in similar environments to identify soil quality challenges and fight the negative factors that influence SQ, in addition to achieving environmental sustainability.

Soil erosion constitutes one of the main environmental and food security threats, derived from the loss of its productive capacity. With the help of remote sensing (RS), geographic information systems (GIS), and a revised version of the universal soil loss equation (RUSLE), this research has mostly focused on measuring the potential soil erosion hazard and soil water conservation ratio (SWCR) in the El-Minia region of Egypt. Based on the integration of S2A images and the digital elevation model (DEM), geomorphological units of the study area were identified. The RUSLE model includes parameters that allow for mapping soil erosion, such as rain erosivity, soil erodibility, slope length and steepness, soil cover and management, and soil conservation practices. The outcomes revealed that the classes of annual erosion rates of the study area are those of “slight erosion”, “low erosion”, “moderate erosion” and “moderately high erosion”, which represent percentages of 29%, 18%, 33% and 20%, respectively, of the total area. The rate of erosion decreases from east to west. The main erosion factors in the research area are the low vegetation cover and the high slope values. This study highlights the utility of combining the classic RUSLE equation with techniques such as remote sensing (RS) and geographic information systems (GIS) as a basis for assessing current erosion conditions in arid environments and, specifically, for the application of soil management patterns aimed at increasing soil organic matter and any other soil conservation actions. The findings of this study can be used by policymakers to implement soil conservation measures if development projects are to proceed in areas with a high risk of soil erosion. The approach described here is therefore adaptable to similar environments in arid regions.

One of the most significant challenges that global decision-makers are concerned about is soil contamination. It is also related to food security and soil fertility. The quality of the soil and crops in Egypt are being severely impacted by the increased heavy metal content of the soils in the middle Nile Delta. In Egypt’s middle Nile Delta, fifty random soil samples were chosen. Inverse distance weighting (IDW) was used to create the spatial pattern maps for four heavy metals: Cd, Mn, Pb, and Zn. The soil contamination levels in the research area were assessed using principal component analysis (PCA), contamination factors (CF), the geoaccumulation index (I-Geo), and the improved Nemerow pollution index (I_n). The findings demonstrated that using PCA, the soil heavy metal concentrations were divided into two clusters. Moreover, the majority of the study region (44.47%) was assessed to be heavily to extremely polluted by heavy metals. In conclusion, integrating the contamination indices CF, I-Geo, and In with the GIS technique and multivariate model, analysis establishes a practical and helpful strategy for assessing the hazard of heavy metal contamination. The findings could serve as a basis for decision-makers to create effective heavy metal mitigation efforts.

It is essential to assess the soil organic carbon pool (SOCP) in dry environments to apply appropriate management techniques that address sustainable development. A significant opportunity for sustaining agricultural output and reducing climate change is the storage of soil organic carbon in agricultural soil. The goal of this study was to measure the spatial variability of SOCP content, and determine the effects of soil texture, changes in land use, and land cover on SOCP in surface soil samples. The study additionally investigated the relationships between SOCP and other characteristics, including the normalized vegetation index (NDVI) and land surface temperature (LST), as well as the effects of increasing soil organic carbon on the amount of greenhouse gases. To accomplish this goal, 45 soil surface samples were collected to a depth of 30 cm at the Fayoum depression in Egypt, and analyzed. The soil samples were representative of various soil textures and land uses. The average SOCP concentration in cultivated regions is 32.1 and in bare soils it is 6.5 Mg ha⁻¹, with areas of 157,112.94 and 16,073.27 ha, respectively. According to variances in soil textures, sandy soils have the lowest SOCP (1.8 Mg ha⁻¹) and clay loam soils have the highest concentrations (49 Mg ha⁻¹). Additionally, fruit-growing regions have the greatest SOCP values and may therefore be better suited for carbon sequestration. The overall average SOCP showed 32.12 Mg C ha⁻¹ for cultivated areas. A rise in arable land was accompanied by a 112,870.09 Mg C rise in SOCP. With an increase in soil organic carbon, stored carbon dioxide emissions (greenhouse gases) would be reduced by 414,233.24 Mg CO₂. We should consider improving fertilization, irrigation methods, the use of the multiple cropping index, decreasing desertion rates, appropriate crop rotation, and crop variety selection. The research highlights the significance of expanding cultivated areas towards sustainable carbon sequestration and the climate-change-mitigation potential.

For modelling and predicting soil indicators to be fully operational and facilitate decision-making at any spatial level, there is a requirement for precise spatially referenced soil information to be available as input data. This paper focuses on showing the capacity of Sentinal-2A(S2A) multispectral imaging to predict soil properties and provide geostatistical analysis (ordinary kriging) for mapping dry land soil fertility conditions (SOCs). Conditioned Latin hypercube sampling was used to select the representative sampling sites within the study area. To achieve the objectives of this work, 48 surface soil samples were collected from the western part of Matrouh Governorate, Egypt, and pH, soil organic matter (SOM), available nitrogen (N), phosphorus (P), and potassium (K) levels were analyzed. Multilinear regression (MLR) was used to model the relationship between image reflectance and laboratory analysis (of pH, SOM, N, P, and K in the soil), followed by mapping the predicted outputs using ordinary kriging. Model fitting was achieved by removing variables according to the confidence level (95%).Around 30% of the samples were randomly selected to verify the validity of the results. The randomly selected samples helped express the variety of the soil characteristics from the investigated area. The predicted values of pH, SOM, N, P, and K performed well, with R² values of 0.6, 0.7, 0.55, 0.6, and 0.92 achieved for pH, SOM, N, P, and K, respectively. The results from the ArcGIS model builder indicated a descending fertility order within the study area of: 70% low fertility, 22% moderate fertility, 3% very low fertility, and 5% reference terms. This work evidence that which can be predicted from S2A images and provides a reference for soil fertility monitoring in drylands. Additionally, this model can be easily applied to environmental conditions similar to those of the studied area.