Using vegetative indices to quantify agricultural crop characteristics

Abstract

In this study, the winter wheat aboveground biomass (AGB), leaf area index (LAI) and leaf nitrogen concentration (LNC) were estimated using the vegetation indices, derived from a high spatial resolution Pleiades imagery. The AGB, LAI and LNC estimation equations were established between the selected VIs, such as NDVI, EVI and SAVI. Regression models (linear and exponential) were examined to determine the best empirical regression equations for estimating the crop characteristics. The results showed that all three vegetation indices provide the AGB, LAI and LNC estimations. The application of NDVI showed the smallest value of RMSE for the aboveground biomass estimation at stem elongation and heading of winter wheat. EVI gave the best significant estimation of LNC and showed better results to quantify winter wheat vegetation characteristics at stem elongation phase. This study demonstrated that Pleiades high spatial resolution imagery provides in-situ crop monitoring.