Oasis lands in Egypt are commonly described as salty soils; therefore, waterlogging and higher soil salinity are major obstacles to sustainable agricultural development. This study aims to map and assess soil salinization at El-Farafra Oasis in the Egypt Western Desert based on salinity indices, Imaging Spectroscopy (IS), and statistical techniques. The regression model was developed to test the relationship between the electrical conductivity (EC_e) of 70 surface soil samples and seven salinity indices (SI 1, SI 2, SI 5, SI 6, SI 7, SI 8, and SI 9) to produce soil salinity maps depending on Landsat-8 (OLI) images. The investigations of soil salinization and salinity indices were validated in a studied area based on 30 soil samples; the obtained results represented that all salinity indices have shown satisfactory correlations between EC_e values for each soil sample site and salinity indices, except for the SI 5 index that present non-significant correlations with R² value of 0.2688. The SI 8 index shows a higher negative significant correlation with EC_e and an R² value of 0.6356. There is a significant positive correlation at the (p < 0.01) level between SI 9 and EC_e (r = 0.514), a non-significant correlation at the (p < 0.05) level between soil EC_e and SI 1 index (r = 0.495), and the best-verified salinity index was for SI 7 that has a low estimated RMSE error of 8.58. Finally, the highest standard error (R²) was represented as EC_e (dS m⁻¹) with an R² of 0.881, and the lowest one was SI 9 with an R² of 0.428, according to Tukey’s test analysis. Therefore, observing and investigating soil salinity are essential requirements for appropriate natural resource management plans in the future.

Soil erosion is a major environmental challenge in Mediterranean regions, where climatic variability, steep slopes, and human activities accelerate land degradation. In the north-central region of Algeria, the Mitidja Plain faces increasing erosion pressure, threatening biodiversity, agricultural productivity, and long-term soil sustainability. This study aims to assess soil erosion risk by integrating the Revised Universal Soil Loss Equation (RUSLE), the Analytical Hierarchy Process (AHP), and Geographic Information System (GIS) techniques within a Cloud-Based Geospatial (CBG) framework using the Google Earth Engine (GEE) platform. High-resolution datasets on rainfall, topography, soil properties, and land cover were processed in GEE to derive five RUSLE factors: rainfall runoff erosivity (R^E), soil erodibility (K^S), slope length steepness (L^S), cropping management (C^M), and management practices (P^C). The analysis revealed that 41% of the Mitidja Plain is at severe erosion risk, with an average soil loss of 88.72 t ha⁻¹ yr⁻¹ and a maximum of 161.13 t ha⁻¹ yr⁻¹. Erosion hotspots correspond to areas where slopes exceed 22°, vegetation cover is sparse, and rainfall intensity is high. The AHP-weighted integration achieved strong predictive accuracy (AUC = 0.87), identifying slope characteristics as the most influential factor (weight = 0.292). Forested areas reduced erosion risk in 30% of the region, while unprotected mountainous zones covering 22% of the study area require urgent intervention. These findings demonstrate the effectiveness of CBG-enhanced modeling for mapping priority conservation areas. Recommendations include terracing, check dams, vegetation restoration, and adaptive agricultural practices to reduce soil loss, particularly in agricultural lands with moderate to high vulnerability (48% of the plain). The methodology provides a replicable framework for other Mediterranean regions facing similar erosion pressures, offering robust spatial data to guide soil management and conservation planning.

This study provides a comprehensive assessment of land degradation (LD) in Damietta Governorate, Egypt, by integrating multiple indices, including the Geology Index (GI), Topographic Quality Index (TQI), Physical Quality Index (PQI), Chemical Quality Index (CQI), Wind Erosion Quality Index (WEQI), and Vegetation Quality Index (VQI). The study findings reveal the following: (1) Soil quality shows moderate suitability for agricultural and developmental activities and can support productive land use with proper management (68.14% physical quality, 51.54% chemical quality), with 14.03–37.75% high-quality areas supporting intensive farming and 10.71–17.83% degraded soils requiring intervention; (2) nearly 31.83% of the area faces high degradation risk, particularly from wind erosion (27.41% high-risk areas), emphasizing the need for erosion control measures; and (3) vegetation analysis shows that 51.5% of land has inadequate cover (low/very low quality), highlighting restoration needs. The LD mapping reveals that 32.70% of the area is at low risk, 35.48% at moderate risk, and 31.83% at high to very high risk, underscoring the need for urgent restoration and sustainable land management practices. The study validates the effectiveness of ordinary kriging (OK) models in predicting soil properties, with tailored variogram models (Exponential, Spherical, and Gaussian) enhancing prediction accuracy. Overall, this study identifies statistically significant factors influencing LD in the study area, providing a data-driven foundation for sustainable land management, agricultural development, and environmental conservation.