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.

Comprehension of the long-term effects of irrigation on basic soil characteristics and quality is essential for sustainable land management and agricultural production, particularly in arid regions where water availability is limited. This study aimed to investigate long-term irrigation effects on soil quality, soil organic carbon (SOC), and nitrogen (N) stocks in the arid lands of Egypt. Seventy soil samples were collected and analyzed to determine various soil properties. A soil quality index (SQI), SOC, and N stocks were computed. ANOVA and PCA analyses were used to identify significant differences between alluvial soils in the southwest part of the investigated area and coastal marine soils in the northeast of the study area. The results demonstrated that most of the studied soil parameters had significantly greater values in alluvial compared to coastal marine soils. Long-term irrigation led to an 8.00% increase in SOC and 7.22% increase in N stocks compared to coastal marine soils production. Furthermore, a 39.53% increase was found in the SQI upon long-term irrigation practice. These results suggest that shifting from rain-fed in coastal marine areas to irrigated production systems in alluvial fields can improve soil quality, SOC, and N stocks. Therefore, further studies are required to investigate the impact of additional factors, such as irrigation method and salinity status of sub-surface soil layers, to enhance agricultural productivity and sustainable land use.

Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO₃), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties.

Rapid population growth accelerates changes in land use and land cover (LULC), straining natural resource availability. Monitoring LULC changes is essential for managing resources and assessing climate change impacts. This study focused on extracting LULC data from 1993 to 2024 using the classification and regression tree (CART) method on the Google Earth Engine (GEE) platform in Qena Governorate, Egypt. Moreover, the cellular automata (CA) Markov model was used to anticipate the future changes in LULC for the research area in 2040 and 2050. Three multispectral satellite images—Landsat thematic mapper (TM), enhanced thematic mapper (ETM+), and operational land imager (OLI)—were analyzed and verified using the GEE code editor. The CART classifier, integrated into GEE, identified four major LULC categories: urban areas, water bodies, cultivated soils, and bare areas. From 1993 to 2008, urban areas expanded by 57 km², while bare and cultivated soils decreased by 12.4 km² and 42.7 km², respectively. Between 2008 and 2024, water bodies increased by 24.4 km², urban areas gained 24.2 km², and cultivated and bare soils declined by 22.2 km² and 26.4 km², respectively. The CA-Markov model’s thematic maps highlighted the spatial distribution of forecasted LULC changes for 2040 and 2050. The results indicated that the urban areas, agricultural land, and water bodies will all increase. However, as anticipated, the areas of bare lands shrank during the years under study. These findings provide valuable insights for decision makers, aiding in improved land-use management, strategic planning for land reclamation, and sustainable agricultural production programs.

Egypt must present a more thorough and accurate picture of the state of the air, as this can contribute to better environmental and public health results. Hence, the goal of the current study is to map and track the spatiotemporal air quality over Egypt’s Qena Governorate using remote sensing data. The current investigation is considered a pioneering study and the first attempt to map the air quality index in the studied area. Multisource remote sensing data sets from the Google Earth Engine (GEE) were used to achieve this. The first is Sentinel-5P’s average annual satellite image data, which were gathered for four important pollutants: carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) over a six year period from 2019 to 2024. The second is the MODIS aerosol optical density (AOD) product satellite image data from the GEE platform, which calculate the average annual particulate matter (PM_2.5 and PM₁₀). All mentioned pollutant images were used to calculate the air quality index (AQI) and aggregated air quality index (AAQI). Lastly, we used Landsat’s average yearly land surface temperature (LST) retrieval (OLI/TIRS). The aggregated air quality index (AAQI) was computed, and the U.S. Environmental Protection Agency’s (USEPA) air quality index (AQI) was created for each pollutant. According to the data, the AQI for CO, PM_2.5, and PM₁₀ in the research region ranged from hazardous to unhealthy; at the same time, the AQI for NO₂ varied between harmful and unhealthy for sensitive groups, with values ranging from 135 to 165. The annual average of the AQI for SO₂ throughout the studied period ranged from 29 to 339, with the categories ranging from good to hazardous. The constant AQI for ozone in the study area indicates that the ozone doses in Qena are surprisingly stable. Lastly, with a minimum value of 265 and a maximum of 489, the AAQI ranged from very unhealthy to dangerous in the current study. According to the data, the area being studied has poor air quality, which impacts the environment and public health. The results of this study have significant implications for environmental sustainability and human health and could be used in other areas.

Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R²), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study’s requirements, with R² > 0.6, RPQ > 2.5, and RIQP > 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R² = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a “quick”, inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions.