Land degradation in Iraq’s Mesopotamian plain threatens food security and rural livelihoods, yet the relative roles of climatic water deficits versus anthropogenic pressures remain poorly attributed in space. We test the hypothesis that multi-timescale climatic water deficits (SPEI-03/-06/-12) exert a stronger effect on vegetation degradation risk than anthropogenic pressures, conditional on hydrological connectivity and irrigation. Using Babil and Al-Qadisiyah (2000–2023) as a case, we implement a four-part pipeline: (i) Fractional Vegetation Cover with Mann–Kendall/Sen’s slope to quantify greening/browning trends; (ii) LandTrendr to extract disturbance timing and magnitude; (iii) annual LULC maps from a Random Forest classifier to resolve transitions; and (iv) an XGBoost classifier to map degradation risk and attribute climate vs. anthropogenic influence via drop-group permutation (ΔAUC), grouped SHAP shares, and leave-group-out ablation, all under spatial block cross-validation. Driver attribution shows mid-term and short-term drought (SPEI-06, SPEI-03) as the strongest predictors, and conditional permutation yields a larger average AUC loss for the climate block than for the anthropogenic block, while grouped SHAP shares are comparable between the two, and ablation suggests a neutral to weak anthropogenic edge. The XGBoost model attains AUC = 0.884 (test) and maps 9.7% of the area as high risk (>0.70), concentrated away from perennial water bodies. Over 2000–2023, LULC change indicates CA +515 km², HO +129 km², UL +70 km², BL −697 km², WB −16.7 km². Trend analysis shows recovery across 51.5% of the landscape (+29.6% dec⁻¹ median) and severe decline over 2.5% (−22.0% dec⁻¹). The integrated design couples trend mapping with driver attribution, clarifying how compounded climatic stress and intensive land use shape contemporary desertification risk and providing spatial priorities for restoration and adaptive water management.

Desertification poses a significant environmental threat to arid and semi-arid regions, with Iraq experiencing escalating land degradation due to climate variability and human-induced pressures. This study refines desertification risk assessment by integrating an enhanced MEDALUS model with entropy-based weighting, machine learning, and uncertainty quantification. Unlike conventional fixed-weight indices, the entropy-weighted approach objectively determines the influence of key environmental and anthropogenic factors, improving classification accuracy and spatial differentiation. The analysis incorporates satellite-derived vegetation indices (NDVI, SAVI, FVC), field-measured soil properties, climatic parameters, and human activity indicators. A hybrid Desertification Risk Index (DRI) is computed using a combined geometric mean and entropy-based summation approach, ensuring a more adaptive and regionally specific risk classification. Results reveal that nearly half of Iraq's land area falls within moderate to extreme desertification risk zones, with climatic aridity and vegetation health emerging as the strongest predictors of land degradation. The machine learning-based classification using XGBoost refines risk categorization, achieving an overall accuracy of 94.46 % (κ = 0.9261), highlighting the limitations of traditional approaches. SHAP analysis provides a transparent interpretation of model predictions, confirming that climatic variability and vegetation stability exert the greatest influence on desertification processes. Monte Carlo simulations quantify classification uncertainty, particularly in transition zones where mixed land-use and environmental heterogeneity contribute to higher prediction variability. The entropy-weighted approach enhances spatial precision in desertification risk mapping, addressing methodological gaps of previous studies. This research integrates statistical weighting, remote sensing, and machine learning, providing a robust, data-driven framework for land management and policy to mitigate land degradation.