Drought Assessment and Monitoring Using Remote Sensing_ Syria’s Ar-Raqqa Governorate as Case Study

Drought remains one of the most critical consequences of climate change, especially in conflict-affected agricultural regions such as Syria. In Ar-Raqqa Governorate, where agriculture is a vital sector, changes in rainfall and vegetation health over recent decades are of particular concern. This study assesses drought dynamics and vegetation response in Ar-Raqqa using satellite-based precipitation and vegetation indices, integrating remote sensing, statistical analysis, and geospatial tools. Monthly precipitation data from 1981 to 2024 were obtained from the CHIRPS dataset, and the Standardized Precipitation Index (SPI-3) was calculated to identify short-term meteorological droughts. Vegetation health was assessed using MODIS-derived NDVI from 2002 to 2024. The Mann-Kendall trend test and Sen’s slope estimator were applied to both NDVI and SPI to detect long-term trends. Results showed no statistically significant trends in either vegetation greenness (NDVI) or precipitation anomalies (SPI), suggesting stable long-term conditions, albeit with annual fluctuations. Correlation analysis revealed a moderate positive relationship between NDVI and SPI values, particularly strong during the winter and early spring months (January–April and December), with correlation coefficients as high as 0.87 in January. This seasonal variation highlights the sensitivity of vegetation to moisture availability during the early growing season and the complexity of drought–vegetation interactions. In contrast, weak or negative correlations during the summer months reflect the decoupling of vegetation response under extreme heat and moisture stress. These findings underline the importance of using SPI-3 as a relevant drought indicator, especially for early-season agricultural planning. Additionally, the study proposes the integration of machine learning techniques as a future direction to model and forecast drought risk in Ar-Raqqa for the next 30 years, based on historical SPI and NDVI data. This predictive approach can enhance early warning systems and inform sustainable water and land management policies in vulnerable arid and semi-arid regions