Abstract: Grassland biomass is an important parameter of grassland ecosystem, and the complexity of grassland canopy vegetation spectrum makes it a challenge to evaluate grassland growth status in a long term. However, at present, few studies have deeply explored the original spectral information of typical grasslands in Inner Mongolia, and discussed the impact of spectral information of surface objects on the estimation of existing above ground biomass. In this study, ASD Field Spec3 portable spectrometer was used to collect the canopy height spectral data of grassland in Lemaudeng Pasture, Xilin Gul, Inner Mongolia from July 2017 to August 2018, and the reflectance spectral curve of grassland was analyzed to characterize the trend of vegetation change. At the same time, the optimal prediction model is selected by spectral preprocessing method combined with various hyperspectral models. The results showed that: (1) From the perspective of comparing the prediction accuracy of different GLM generalize linear models, the best hyperspectral modeling method is select $\frac{S D_r}{S D_b}$, the best model for the variable is y=-3.795 3x²+60.065x-78.455(x is $\frac{S D_r}{S D_b}$, y is the estimated fresh weight of aboveground biomass), fitted R² =0.662, Predicted R²=0.302. (2) In the analysis between hyperspectral variables and above-ground biomass dry weight, select $\frac{S D_r-S D_y}{S D_r+S D_y}$. The best model as a variable is y=7.744e^3.4349x (x is $\frac{S D_r-S D_b}{S D_r+S D_b}$, y is the estimated dry weight of above-ground biomass), fitted R²=0.559, Predicted R²=0.304. The results of this study have scientific value for hyperspectral prediction modeling of grassland biomass.

Key words: Above-ground biomass, Vegetation index, Hyperspectral, Typical grassland, Inversion model

摘要： 草地生物量是草地生态系统的重要参数，草原冠层植被光谱的复杂性使得长期评估草场生长状况成为一种挑战。目前少有研究对内蒙典型草原原始光谱信息进行深度探索，探讨地物光谱信息对地上生物量估算的影响。本研究于2017年7月至2018年8月使用ASD Field Spec3 野外便携式高光谱仪采集内蒙古锡林郭勒毛登牧场的草地冠层高光谱数据，分析草地的反射光谱曲线来表征植被变化的趋势。同时采用光谱预处理方法结合多种高光谱模型选出最优预测模型。结果表明：(1)从对比不同的广义线性拟合模型(Generalize linear model, GLM)的预测精度来看，最佳的高光谱建模方法为，选取$\frac{S D_r}{S D_b}$为变量的最佳模型为y=-3.795 3x²+60.065x-78.455(x为$\frac{S D_r}{S D_b}$，y是估算的地上生物量鲜重)，拟合R²=0.662，预测R²=0.302。(2)高光谱变量与地上生物量干重之间分析中，选择$\frac{S D_r-S D_y}{S D_r+S D_y}$作为变量的最佳模型为y=7.744e^{3.434 9x}(x为$\frac{S D_r-S D_b}{S D_r+S D_b}$，y是估算的地上生物量干重)，拟合R²=0.559；预测R²=0.304。该研究结果对草地生物量高光谱预测建模具有科学价值。

关键词: 地上生物量, 植被指数, 高光谱, 典型草原, 反演模型