Monitoring spatio-temporal pattern of drought stress using integrated drought index over Bundelkhand region, India

Monitoring of drought and associated agricultural production deficit using meteorological indices is essential component for drought preparedness. Remote sensing-based NDVI also plays a key role in drought assessment, but alone it fails due to time lag of 3-4 weeks. In view of improving drought monitoring in Bundelkhand region of India, it was proposed to use combination of meteorological and remote sensing-based approach. The study aims to monitor and assess inter-annual variability in spatial drought and related crop loss in Bundelkhand region using time series of daily rainfall of Climate Prediction Centre (NOAA) and SPOT-VGT-based normalized difference vegetation index. Instead of NDVI, vegetation condition index (VCI) was used to normalize geographical differences in vegetation types and physiographical setting. The new approach is linear weighted index called spatial vegetation drought index (SVDI) constructed from VCI derived from SPOT-VGT and meteorological index named rainfall anomaly index (RAI) for monitoring short-term drought stress in Bundelkhand region. The spatial and temporal pattern of drought matches well with RAI. VCI found to be significantly related to drought stress in terms of rainfall anomaly for majority of decades as well as crop yield anomaly of both food grains and pulses. A modified rainfall anomaly (MRAI) was also constructed by assigning weights to RAI of past three decades to normalize the residual moisture status. The newly formulated SVDI obtained by integrating MRAI and VCI improved the spatial prediction of drought and to detect crop loss associated with short-term drought stress. Comparing real-time drought condition from the observation around the concerned area showed that SVDI was able to illustrate drought stress on large-scale efficiently and can give information about the departure in crop productivity when correlated with crop yield anomaly.