Multisource Remote Sensing and Machine Learning for Spatio-Temporal Drought Assessment in Northeast Syria

Increasing aridity across the Middle East Region has intensified concerns about the impacts of drought in conflict-affected Northeast Syria (NES). In this study, drought dynamics and their drivers from 2000 to 2023 were analyzed by integrating ERA5-Land meteorological data, MODIS land-surface indicators, FLDAS soil moisture, and ISRIC soil properties at 250 m resolution. The integration of these multisource datasets contributes to a more comprehensive understanding of drought dynamics by combining information on weather conditions, vegetation status, and soil characteristics. The proposed drought analysis framework clarifies independent controls on meteorological, agricultural, and hydrological drought, underscoring the role of land-atmosphere feedback through soil temperature. This workflow provides a transferable approach for drought monitoring and hypothesis generation in arid regions. For this purpose, different XGBoost models were trained for the vegetation health index (VHI), the standardized precipitation-evapotranspiration index (SPEI), and surface soil-moisture anomalies, excluding target-related variables to prevent data leakage. Model interpretability was achieved using SHAP, complemented by time-series, trend, clustering, and spatial autocorrelation analyses. The models performed well (R² = 0.86–0.90), identifying soil temperature, SPEI, relative humidity, precipitation, and soil-moisture anomalies as key predictors. Regionally, soil temperature rose (+0.069 °C yr⁻¹), while rainfall (−1.203 mm yr⁻¹) and relative humidity (−0.075% yr⁻¹) declined. Spatial analyses demonstrated expanding heat hotspots and persistent soil moisture deficits. Although 2018–2019 were anomalously wet, recent years (2021–2023) exhibited severe drought.