Spatiotemporal Variability of NDVI Over Indian Region and its Relationship with Rainfall, Temperature, Soil moisture, and Sea Surface Temperature

Abstract:

The Normalized difference Vegetation Index (NDVI) is considered as a proxy for vegetation health. Worldwide it is used as an effective tool to monitor spatiotemporal variations in land use practices and vegetation vigor. The forecast of NDVI can be effectively utilized as an early warning system for the better planning and management, especially in agricultural sector. Here, in this study, we evaluated the spatiotemporal variability of NDVI over Indian region and how it is affected by various factors such as rain fall, temperature, sea surface temperature (SST) and soil moisture. First, we evaluated the seasonal (Pre monsoon, Monsoon, Post Monsoon, Kharif and Rabi) NDVI trend from 1982-2013 and it is found that there is a significant increase in NDVI over the Indian region in land use land cover (LULC) classes. Further the correlation analysis of NDVI with above mention parameters were performed at various lags to evaluate better predictor of NDVI and it is observed that all the parameters exhibit significantly high correlations at various lags. It is well known that over the Indian region vegetation growth/crop growth is largely dependent on climate parameters especially rainfall, and Indian region rainfall is highly correlated with El Nino/Southern Oscillation (ENSO). Thus the relationship of NDVI with SST one of the proxies of ENSO can be utilized to predict vegetation health over Indian region. To evaluate that we performed Maximum Covariance Analysis (MCA) of SST departure field and NDVI time series and found that there exists a decline in vegetation health or NDVI if the equatorial eastern pacific SST is anomalously high.