Spatiotemporal assessment of agricultural drought using satellite-derived indices

AbstractSoil moisture plays a critical role in agricultural productivity, and its deficiency signals the onset of agricultural drought, having significant implications for food security, particularly in agrarian regions like Ranchi, Jharkhand (India), an already moisture-deficit region. The study assesses the Soil Moisture Index (SMI) and its spatiotemporal variations using remote sensing and GIS techniques, with a particular focus on its relationship with drought parameters such as Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). Data from Landsat 8, MODIS, and SMAP were utilized for the years 2015-2020 to generate seasonal maps and analyze variations in LST, NDVI, and SMI. Results indicated that SMI had a positive correlation with NDVI (R=0.73) and a negative correlation with LST (R=-0.49); this highlights the influence of vegetation on soil moisture retention and temperature moderation. NDVI showed widespread vegetation stress, particularly in pre-monsoon months. The study highlights the effectiveness of remote sensing in agricultural drought monitoring and provides valuable insights for developing early warning systems. These findings can aid in implementing timely interventions to mitigate adverse effects of drought, contributing to improved food security and sustainable agricultural practices in drought-prone areas.