中文摘要：

分析不同生长期的马尾松冠层反射光谱特征与相应叶绿素含量的相关关系.利用36个红边参数逐一筛选,最终确定7个与叶绿素含量相关性较高的红边参数作为光谱特征参数,分别应用逐步分析法与BP神经网络构建叶绿素含量的高光谱估算模型;同样,筛选出4个植被指数作为光谱特征参数,同时,将对原始光谱进行主成分分析降维后的前4个主成分作为BP神经网络的输入变量,分别应用逐步分析法与BP神经网络构建叶绿素含量的高光谱估算模型.结果表明：将红边参数作为输入变量建立的逐步回归模型和BP神经网络模型的决定系数（R^2）分别为0.5205、0.7253,均方根误差（RMSE）分别为0.1004、0.0848,相对误差分别为6.3%、5.7%.将植被指数作为输入变量建立的逐步回归模型和BP神经网络模型的R^2分别为0.5392、0.7064,RMSE分别为0.0978、0.0871,相对误差分别为6.2%、6.0%.基于主成分分析的BP神经网络模型的预测效果最好,R^2为0.7475,RMSE为0.0540,相对误差为4.8%.

英文摘要：

The relationships between the leaf chlorophyll content （LCC） of Pinus massoniana at different growth stages and their chlorophyll content were analyzed. 7 of 36 red edge-based parameters were finally selected as the typical spectral response parameters which held the most significant statistical relationship with LCC, and then the hyperspectral assessment model for retrieving the LCC was built based on stepwise regression analysis method and B-P neural network, respectively. In the same way, four different vegetation indices （VIs） were selected as typical spectral parameters, in the meantime, the first four components of the principal component analysis （PCA） trans- formed from original spectral measurements were inputted into the B-P neural network, and then the hyperspectral assessment model for retrieving the LCC was built based on stepwise regression analysis method and B-P neural network, respectively. The results showed that R2 of the red edge-based stepwise regression model and the red edge-based B-P neural network model were 0. 5205 and 0.7253, RMSE were 0.1004 and 0.0848, and relative errors were 6.3% and 5.7%, respectively. R2 of the VIs-based stepwise regression model and the VIs-based B-P neural network model were 0.5392 and 0.7064, RMSE were 0.0978 and 0.0871, and relative errors were at 6.2% and 6.0%, respectively. The prediction effect of PCA-based B-P neural network model was the best, R2 was 0.7475, RMSE was 0.0540, and the relative error was 4.8%.