Drought Monitoring and Vegetation Response Using SPI, SPEI, and EVI in Google Earth Engine for Jalna Tehsil, Maharashtra, India

AbstractDrought monitoring and vegetation response assessment are crucial for understanding climatevariability and its impact on ecosystems. This study utilizes Google Earth Engine (GEE) tocompute and analyze the Standardized Precipitation Index (SPI), Standardized PrecipitationEvapotranspiration Index (SPEI), Enhanced Vegetation Index (EVI), and Normalized DifferenceVegetation Index (NDVI). The objective is to evaluate rainfall variability, drought severity, andvegetation stress trends from 2013 to 2024. The results reveal a declining precipitation trend insemi-arid regions, increasing the frequency of drought events. Temporal analysis indicates a 5.15%annual increase in June rainfall, while SPEI trends highlight prolonged dry spells affectingvegetation resilience. NDVI and EVI assessments confirm that vegetation stress aligns with severedrought years, particularly in 2013, 2016, and 2019. The study underscores the potential ofintegrating climate indices, remote sensing, and machine learning-based approaches for enhanceddrought prediction and climate resilience planning.