Monitoring Drought in Guntur with MODIS Data and the Google Earth Engine Platform

This evaluation explores the utility of specific methodologies, particularly Geographic Information System (GIS) and Remote Sensing (RS), in the near real-time assessment of drought conditions across regions.The investigation focuses on comparing Remote Sensing Derived Drought Indices (RSDIs) with the Standardized Precipitation Evapotranspiration Index (SPEI) for the period 2001-2023.The RSDIs, including Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), are computed utilized MODIS data through Google Earth Engine (GEE) platform.To assess the efficacy of these indices, correlation analyses are conducted between VCI, TCI, VHI, and the Standardized Precipitation Index (SPI).Pearson correlation coefficients (CC) are employed to quantify the agreement between SPEI and RSDIs.Results indicate varying levels of agreement, with higher correlation observed between VCI and SPEI across different time scales (12, 9, 6, 3, and 1 month).Conversely, TCI demonstrates comparatively lower agreement with SPEI.Moderate correlation is noted between VCI and SPEI across different time scales.These findings suggest that VHI and SPEI exhibit stronger correlation, making them preferable for drought monitoring in regions with limited meteorological data Using the VCI, TCI, and SPEI, one can determine the drought in the Guntur district.The VCI geographical distribution maps show that the year of extreme drought conditions was observed in 2001, 2003, 2005, 2007, 2011, 2016, and 2019.However, conditions recovered in the subsequent years, with a relatively mild drought.Furthermore, the study reveals a persistent presence of drought across the study area throughout the analysed period.This research contributes to enhancing our comprehension of the interrelationships among Weather and remote sensing-based drought indices.