基于不同植被指数的植被-土壤混合像元高光谱偏振信息与模型研究

高光谱遥感被越来越多的应用于确定混合像元的地物组分和比例。将不同面积比例的植被-土壤混合像元作为研究对象，使用偏振装置和 ASD FieldSpec3 光谱仪得到植被-土壤组成的混合像元的偏振反射光谱曲线，计算得到八种植被指数值，讨论不同面积比例，不同偏振角度下植被-土壤混合像元的高光谱偏振特性。研究发现，随着叶片占混合像元面积比例的增大，植被-土壤光谱曲线越来越明显地表现出植被光谱“五谷四峰”的特性，且峰值与谷底的位置与植被光谱基本相同。偏振角越大，混合像元的光谱偏振反射比越大；混合像元条件下，植被所占混合像元的面积比例越大，光谱受偏振角的影响越大。各植被指数与混合像元中植被面积大小呈线性关系，其中植被衰减指数和改进红边归一化植被指数的相关系数最大，可以达到98%左右，适合用于建立植被指数与植被占混合像元面积比例之间的相关模型。在植被面积发生变化时，改进红边比值植被指数的灵敏性更好。在利用光谱吸收特征参数进行植被指数估算时，发现吸收谷深度与光化学植被指数的二次函数模型拟合度最强，决定系数R2为0.963 3；光谱吸收指数与光化学植被指数的二次函数模型拟合度最强，决定系数R2为0.960 5。

Study on Information and Model of High Polarization Hyperspectral about Vegetation-Soil Mixed Pixels Based on Different Vegetation Indices

Hyperspectral remote sensing is increasingly used to determine the feature components of mixed pixels and their proportions. In this paper, the vegetation soil mixed pixels of different area ratio were set as the research object, and polarization means and ASD FieldSpec3 spectrometer was applied to obtain polarized reflectance spectrum curve of vegetation soil mixed pixels to calculated the proportion of 8 different vegetation index and discuss the hyperspectral polarization characteristics of vegetation soil mixed pixels under different area ratio and different polarization angle. The study found that as the increasing of the proportion of leaves, vegetation soil spectral curves increasingly appeared “5 valleys and 4 peaks”, and the positions of the peak and bottom were basically the same. The larger the angle of polarization, the greater the spectral reflectance ratio of mixed pixels was; In mixed pixels, the larger the proportion of the vegetation, the greater the influence of the polarization angle. The vegetation index and the size of vegetation in mixed pixels were in a linear relationship and the correlation coefficient between the vegetation attenuation index and the improved red edge normalized difference vegetation index was the largest, which could reach about 98%, suitable for establishing the correlation model between vegetation index and vegetation proportion of mixed pixel area.. When vegetation area changes, the sensitivity of vegetation index is better by improving the red edge ratio. In the use of spectral absorption characteristic parameters to estimate vegetation index, the two order function model of absorption valley depth and photochemical vegetation index had the strongest fitting degree with the determination coefficient R2 of 0.963 3; The two degree function model of spectral absorption index and photochemical vegetation index had the strongest fitting degree, and the coefficient of determination R2 was 0.960 5.