Grassland aboveground biomass (AGB) is a crucial component of the global carbon budget in climate change studies. Precise estimation of the AGB of grassland ecosystems is essential to better understand the carbon cycle and to improve grassland conservation as well as to achieve optimal growth. Traditional vegetation indices (VIs) derived from remote sensing often saturate at medium-high biomass levels, limiting estimation accuracy. In this study, we introduced a novel AGB estimation framework by explicitly integrating near-infrared radiance (Lnir) with UAV-based hyperspectral vegetation indices (VIs×Lnir), which effectively alleviated saturation effects commonly observed in conventional VI-based models. Field measurements and hyperspectral imagery were collected in a temperate meadow steppe, and model performance was evaluated using leave-one-out cross-validation (LOOCV). The proposed VIs×Lnir model achieved the highest accuracy (R2 = 0.72, RMSE = 7.52 g/m2), outperforming conventional VIs-based (R2 11.13 g/m2) estimations. The study further investigated the results of fAPARgreen-related VIs×Lnir model, which yielded higher AGB estimation accuracy than that using NDVI×Lnir. Furthermore, we examined the influence of plant diversity using Menhinick’s index (DMn) and found that AGB estimation uncertainty was lowest when DMn ranged from 0.2 to 0.4, likely due to reduced spectral mixing and optimal canopy structural homogeneity. Under both lower (DMn 0.4), AGB could still be estimated, but with increased uncertainty likely caused by insufficient spectral variability at low diversity and stronger spectral mixing at high diversity. This study demonstrates the potential of incorporating Lnir into UAV hyperspectral analysis to enhance grassland AGB estimation and provides insights into the role of biodiversity in remote sensing-based biomass monitoring.
Shan et al. (Mon,) studied this question.
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