Abstract This study develops an integrated framework to evaluate soil by combining climatic records, soil analyses, geomorphology, and remote sensing. Forty-three soil profiles were sampled (0–100 cm) and analysed for texture, calcium carbonate (CaCO3), organic matter, pH, electrical conductivity (EC), exchangeable sodium percentage (ESP), cation exchange capacity (CEC), gypsum, and macronutrients nitrogen (N), phosphorus (P), and potassium (K). Long-term meteorological data (1975–2021) were partitioned into three periods (1975–2001, 2002–2011, 2012–2021) to construct time-series of temperature, rainfall, and Normalized Difference Vegetation Index (NDVI), highlighting vegetation dynamics under hyper-arid conditions. Landsat imagery (2001, 2011, 2021) was processed using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and the Landsat Surface Reflectance Code (LaSRC), and spectral indices quantified vegetation vigor, soil brightness, moisture, and salinity. Composite indices soil indices were normalized (0–1) and weighted using the Analytic Hierarchy Process (AHP), then integrated into a Decision Support System (DSS) to generate spatial maps of degradation and sustainability. Results revealed severe salinity in sabkhas, nutrient deficits in sandy plains, and resilient loamy plains as sustainable hotspots. Declining vegetation in degraded units and stability in reclaimed lands were confirmed through NDVI time series analysis. More than 80% agreement with field observations was achieved by the DSS framework, providing reproducible, policy-relevant tools for prioritizing reclamation, conservation, and agricultural expansion.
AbdelRahman et al. (2026) studied this question.