Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices

Hyperspectral remote sensing data with a greater number of bands and narrower bandwidths can be effectively exploited for the study of ecosystem patterns and processes. Hyperspectral remote sensing of semiarid mixed grassland faces the following two challenges, however: (i) providing a good understanding of the performance of different vegetation indices (VIs) in estimating biophysical properties of grassland with a small amount of green vegetation, a large amount of dead material on the ground, and variable soil-ground conditions; and (ii) examining the spatial characterization of hyperspectral remotely sensed data to optimize sampling procedures and address scaling issues. Using ground-based hyperspectral and biophysical data, this study has compared the predictive capability of VIs for estimation of grassland leaf area index (LAI) (this paper) and examined the spatial variation of grassland LAI (the companion paper). The results in this paper indicate that the relationships between grassland LAI and VIs are significant. The performance of the renormalized difference vegetation index (RDVI), adjusted transformed soil-adjusted vegetation index (ATSAVI), and modified chlorophyll absorption ratio index 2 (MCARI2) was slightly better than that of the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating the cellulose absorption index (CAI) as a litter factor in ATSAVI, a new VI was computed (L-ATSAVI), and it improved the LAI estimation capability in our study area by about 10%.