Enhancing Drought Monitoring Using Integrated Remote Sensing and Ground-Based Indices

Accurate and timely drought monitoring is essential for sustainable water resource management, particularly in semi-arid regions where meteorological observations are sparse and spatially heterogeneous. This study presents an integrated evaluation of drought conditions by applying established individual and composite drought indices within a unified multi-source framework that combines ground-based meteorological data with satellite-derived datasets, including MODIS land surface products and GPM/TRMM precipitation estimates, and is implemented on the Google Earth Engine platform. A set of drought indicators representing vegetation condition, thermal stress, precipitation deficit, and soil moisture variability was analyzed alongside five composite indices to provide a multi-dimensional assessment of drought dynamics. The analysis was conducted across monthly, seasonal, and annual timescales for Tehran, Iran, over the period 2000–2022. In addition, trend significance was assessed using the Mann–Kendall test and Sen’s slope estimator to quantify long-term changes in drought behavior. The results indicate that composite indices provide a more coherent representation of drought evolution and are more effective in capturing the interaction among key climatic drivers. In particular, the analysis reveals a transition from precipitation-driven droughts (e.g., 2008 and 2016) to increasingly temperature-driven drought conditions (e.g., 2022), associated with elevated potential evapotranspiration and thermal stress. This highlights the growing importance of atmospheric evaporative demand in intensifying drought severity under warming conditions. From an applied perspective, the proposed framework supports drought early warning, spatial risk assessment, and informed decision-making in water resource management and agricultural planning. However, the reliability of the results depends on data quality, spatial resolution, and the representation of key drought drivers and should therefore be interpreted with these limitations in mind. The study demonstrates that the integrated application of multi-source data and multi-scale drought indices provides a robust basis for understanding and monitoring drought dynamics in semi-arid urban environments.