Abstract
Above-ground biomass (AGB) is a critical criterion for assessing the ecological value and productivity of grasslands. Quickly and accurately estimating the AGB of grasslands has become a matter of great concern in grassland ecosystem research. By combining ground ASD hyperspectral data and airborne hyperspectral data, this study successfully extracted the sensitive bands of canopy reflectance and calculated the various vegetation indexes, thus providing information of more dimensions for above-ground biomass estimation. Biomass inversion-based yield modeling results reveal that the 377 nm raw band reflectance of the ASD data and the narrow-band vegetation index of the Resonon data exhibit outstanding performance in yield modeling. Comparative analysis indicates that the Resonon hyperspectral data demonstrate a distinct advantage in assessing meadow steppe yield, and the quadratic polynomial model based on its narrow-band SAVI achieves an R2 of 0.553 and an RMSE of 37.53.
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