Li Zhe, Guo Xu-dong, Gi Chun, Zhao Jing
Guang Pu Xue Yu Guang Pu Fen Xi. 2017 Mar;37(3):859-64.
Considering the close relationship between spectral absorption features of visible-near infrared and “Fraction of Absorbed Photosynthetically Active Radiation(FAPAR)”, the “automatic recognition method of hyperspectral curve’s characteristic absorption peak” and “quantization method of spectral absorption characteristic parameters” were used to extract the hyperspectral absorption characteristic parameters which are sensitive to FAPAR. Referring to mathematical form of vegetation index, visible-near infrared spectral absorption characteristic parameters were used to replace spectral reflectance and create a new vegetation index to estimate FAPAR of vegetation. The data from 2014 and 2015 on typical natural grassland community canopy in the middle and eastern Inner Mongolia was chosen to build and verify the model of estimating FAPAR. The results showed that new vegetation index “SAI-VI” effectively raised the FAPAR estimating accuracy in the middle and low vegetation coverage areas. Compared with other seven different types of visible-near infrared vegetation index, it has a higher correlation with the value of FAPAR(the largest correlation coefficient is 0.801). The FAPAR prediction index model which takes “SAI-VI” as variable has higher precision and better stability(the determination coefficients of modeling and testing are the highest and both are above 0.75, the “Root Mean Square Error (RMSE)” and “Average Error Coefficient (MEC)” are the minimum). The research also showed that the new vegetation index “SAI-VI” infusing visible-infrared spectral absorption feature highlights the difference between visible spectral and near infrared spectral absorption characteristic parameters. While comparing with single spectral absorption characteristic parameter, “SAI-VI” can depress the influence of soil and enhance the sensitivity to the changes of FAPAR. “SAI-VI” also included the information of hyperspectral absorption characteristic parameters which are sensitive to FAPAR and expressed more detailed information of FAPAR while comparing with original spectral reflectance. “SAI-VI” can be used as a new parameter in inversion of vegetation canopy FAPAR, to some extent it could remedy defect of vegetation index method in estimating FAPAR.
鉴于可见-近红外光谱吸收特征与“光合有效辐射吸收比例（FAPAR）”之间的密切关系，采用“高光谱曲线特征吸收峰自动识别方法”和“光谱吸收特征参数量化方法”，提取对FAPAR敏感的高光谱吸收特征参数。参照植被指数的数学形式，用可见-近红外光谱吸收特征参数代替光谱反射率，构建一种新的植被指数来估算植被的FAPAR。选取内蒙古中东部典型天然草地群落冠层2014年和2015年的数据，建立并验证FAPAR估算模型。结果表明，新植被指数“SAI-VI”有效提高了中低植被覆盖区域FAPAR的估算精度。与其他七种不同类型的可见-近红外植被指数相比，它与FAPAR值具有更高的相关性（最大相关系数为0.801）。以“SAI-VI”为变量的FAPAR预测指数模型具有更高的精度和更好的稳定性（建模和检验的决定系数最高，均在0.75以上，“均方根误差（RMSE）”和“平均误差系数（MEC）”最小）。研究还表明，融入可见-红外光谱吸收特征的新植被指数“SAI-VI”突出了可见光谱与近红外光谱吸收特征参数之间的差异。与单光谱吸收特征参数相比，“SAI-VI”能降低土壤的影响，增强对FAPAR变化的敏感性。“SAI-VI”还包含了对FAPAR敏感的高光谱吸收特征参数信息，与原始光谱反射率相比，能表达更详细的FAPAR信息。“SAI-VI”可作为植被冠层FAPAR反演的新参数，在一定程度上弥补植被指数法估算FAPAR的缺陷。
