Drought assessment and monıtorıng usıng remote sensıng: syrıa's ar-raqqa governorate

Drought remains one of the most serious impacts of climate change, especially in conflict-affected agricultural regions such as Ar-Raqqa, Syria. This study analyzes drought dynamics and vegetation response using satellite-based precipitation and vegetation indicators supported by statistical and geospatial methods. CHIRPS monthly rainfall data (1981–2024) were used to compute SPI-3, while MODIS NDVI (2002–2024) assessed vegetation conditions. Mann–Kendall and Sen's slope tests, after applying pre-whitening, showed no significant long-term trends in precipitation or vegetation greenness, despite clear annual variability. Cross-correlation analysis revealed that NDVI responds to rainfall with a one- to three-month delay. VCI and VHI highlighted strong spatial heterogeneity, with notable contrasts between wet conditions in 2019 and severe drought in 2021. Lagged SPI and NDVI values were used in Random Forest and XGBoost models for short-term drought prediction, achieving moderate skill while emphasizing the need for additional climate drivers for improved accuracy. The study contributes region-specific evidence to support early warning systems and sustainable land and water management in arid environments. Keywords: Drought monitoring, Remote sensing, SPI, NDVI, Climate change.