A Remote Sensing Approach to Identifying Drought Onset and Progression in Central India

Abstract. Climate change is intensifying the frequency and severity of droughts, making it important to understand their impacts on ecosystems and society. However, to analyse the relationship between the main sources of agriculture drought is always challenging. The development of remote sensing technology, provides diverse datasets at varying spatial and temporal resolutions, making drought assessment and monitoring comparatively easier than previous time. This study aims to evaluate the effectiveness of multisource remote sensing data in monitoring agricultural drought conditions in the Bundelkhand region. The datasets used are CHIRPS, ERA5 Land, and PKU GIMMS NDVI to derive four drought indices: the Standardized Precipitation Index (SPI), the Rainfall Anomaly Index (RAI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). In addition, we also performed pixel wise Pearson correlation analysis to examine relationships among these indices, resulting in a correlation from 0.25 to 0.7 between SMCI and VCI. The rainfall and soil moisture relation was found to be 0.294 between RAI and SMCI, but vegetation response is well observed with SPI 3 and VCI at 0.492, indicating their lagged relationship. Monthly variation analysis indicated that June experienced the lowest vegetation activity, while September exhibited both soil moisture and vegetation stress, indicating drought development across the monsoon season. The analysis of 2015 showed the rainfall anomalies and vegetation response are highly interrelated, validating drought in the year. The paper highlights the importance of remote sensing for drought assessment and supplementing its importance for disaster management and mitigation implementations.