Data-driven Drought Monitoring Using Satellite-derived Indices: A Google Earth Engine and Python-based Framework - Sri Sai Tarun, Vemu; Sreenivasulu, Vemu (Journal of Intelligent Data Analysis and Computational Statistics (p-ISSN: 3049-3056 e-ISSN: 3048-7080), 2026)
Droughts are among the most devastating natural hazards, affecting water resources, agriculture, and the livelihoods of millions. This study presents a framework for assessing meteorological drought using satellite data through Google Earth Engine (GEE) and Python. This study focuses on meteorological drought assessment in selected districts of Telangana, India, including Nizamabad, Khammam, Mahbubnagar, Karimnagar, and Jangaon. The standard precipitation index (SPI) and drought severity index (DSI) were employed for drought analysis. SPI values, computed based solely on precipitation data, and the DSI values, computed using GEE, were compared with historical drought records. GEE provided efficient data processing and analysis during 2000–2021. The findings revealed the persistence of drought in the study districts: SPI values indicated drought persistence of 8% for Jangaon, 7.6% for Karimnagar, 8.7% for Mahbubnagar, 8.33% for Nizamabad, and 11.36% for Khammam. DSI values indicated drought persistence of 13% for Jangaon, 14.5% for Karimnagar, 13.27% for Mahbubnagar, 12.88% for Nizamabad, and 11.7% for Khammam. This research highlights the utility of SPI and DSI as effective tools for meteorological drought monitoring and emphasizes the role of GEE in facilitating efficient and scalable drought assessment.