Estimation of grassland aboveground biomass combining optimal derivative and raw reflectance vegetation indices at peak productive growth stage

In this paper, field spectroradiometer and aboveground biomass (AGB) data were acquired at the harvest stage at two sites in semiarid grasslands in Inner Mongolia, China. Four forms of commonly used vegetation indices (VIs) using all possible combinations of narrow-band first derivative (FDR) and raw reflectance (RR) were calculated, and the best FDR-VIs and RR-VIs were chosen by a linear regression analysis against AGB. The stepwise multiple linear regression (SMLR) models using the optimal FDR-VIs, RR-VIs, and both FDR-VIs and RR-VIs as input variables were developed for estimating the AGB. Results demonstrated that the estimation performance using the best FDR-VIs were comparable with the best RR-VIs, while the accuracy has been further improved by combining the best FDR-VIs and RR-VIs (maximum decrease in RMSE of 44% and minimum RMAE of 4.7%). The approach was found to be an important step for more accurate and effective grassland AGB estimation.

Automated summary

Field spectroradiometer and aboveground biomass data were collected in semiarid grasslands in Inner Mongolia, China, and four forms of commonly used vegetation indices were calculated. Linear regression analysis was used to select the best vegetation indices for estimating aboveground biomass. The combination of the best vegetation indices improved the accuracy of the estimation significantly. This approach is important for accurate and effective grassland aboveground biomass estimation.