基于高光谱参数的枫杨叶绿素含量估算模型优化

Optimization of a Model for Estimating Pterocarya stenoptera Chlorophyll Concentration with Hyperspectral Parameters

采用ASD Fieldspec光谱仪测定不同土壤水分条件下枫杨叶片高光谱反射率,并给出对应的枫杨叶绿素含量。研究不同土壤水分条件下枫杨幼苗叶片叶绿素含量的变化规律;综合分析9个常见光谱植被指数与枫杨叶片叶绿素含量的相关性与回归方程,利用主成分分析对光谱数据进行降维,将得到的主成分得分作为BP人工神经网络模型的输入变量进行枫杨叶片叶绿素含量的估算。结果表明:不同水分处理均显著影响枫杨幼苗叶绿素含量。在所列举的9个常用植被指数中,VOG1植被指数与叶绿素含量的关系最密切,相关系数R达到0.865。用主成分分析-BP神经网络模型进行叶绿素含量的估算,预测值与实测值之间的线性回归的相关系数R=0.934,是一种良好的植被叶绿素含量高光谱估算模式。

In this study,hyperspectral reflectance of Pterocarya stenoptera leaves was measured with an ASD portable spectrometer under different soil water condition. The corresponding chlorophyll concentration was a lso measured. The effects of soil water condition on chlorophyll concentration of P. stenoptera are discussed. In order to estimate chlorophyll content and establish the best estimation model,,and the corresponding correlation coefficient and regression equation between 9 hyperspectral indices and chlorophyll concentration were established. The principal component analysis（ PCA） was used to reduce the dimensions of data,while maintaining the data characteristic effectively. The principal component scores were used as the input variable of ANN-BP to estimate chlorophyll concentration. The results showed that different water treatments significantly affected the chlorophyll content of P. stenoptera seedlings. VOG1 had the closest relation to chlorophyll concentration out of the nine hyperspectral indices in this article and the corresponding correlation coefficient was 0. 865. The ANN-BP based on PCA containing more band information was used to estimate chlorophyll content,and the correlation coefficient between the predicted and the measured P. stenoptera chlorophyll content was 0. 934. Thus,the ANN-BP based on PCA in this article is a good method to be applied to hyperspectral data for estimating P. stenoptera chlorophyll concentration.