Modelling the relationship between groundwater depth and NDVI using time series regression with Distributed Lag M

Abstract

Groundwater plays a key role in hydrological processes, including in determining aboveground vegetal growth characteristics and species distribution. This study aimed at estimating time-series data of Normalized Difference Vegetation Index (NDVI) using groundwater depth as a predictor in two land cover types: grassland and shrubland. The study also investigated the significance of past (lagged) groundwater and NDVI in estimating the current NDVI. Results showed that lagged groundwater depth and vegetation conditions influence the amount of current NDVI. It was also observed that first lags of groundwater depth and NDVI were significant predictors of NDVI in grassland. In addition, first and second lags of NDVI were consistently significant predictors of NDVI in shrubland. This shows the importance of vegetation type when modelling the relationship between groundwater depth and NDVI.