Prediction of vegetation anomalies to improve food security and water management in India

Abstract

Prediction of vegetation anomalies at regional scales is essential for management of food and water resources. Forecast of vegetation anomalies at 1–3 months lead time can help in decision making. Here we show that normalized difference vegetation index (NDVI) along with other hydroclimatic variables (soil moisture and sea surface temperature) can be effectively used to predict vegetation anomalies in India. The spatiotemporal analysis of NDVI showed significant greening over the region during the period of 1982–2013. The root-zone soil moisture showed a positive correlation with NDVI, whereas the El Niño–Southern Oscillation index (Nino 3.4) is negatively correlated in most of the regions. We extended this relationship to develop a model to predict NDVI in 1 to 3 months lead time. The predicted vegetation anomalies compare well with observations, which can be effectively utilized in early warning and better planning in water resources and agricultural sectors in India.