中文摘要：

基于中国农业科学院在呼伦贝尔草原实测的120组草地冠层光谱反射率及相应的叶面积指数（LAI）数据,在进行主成分分析（PCA）实现降维处理的基础上,利用径向基函数（radial basis function,RBF）神经网络方法对草地LAI进行了高光谱反演研究。PCA结果表明,前9个主成分的累积贡献率达到了99.782%,能包含原光谱数据的绝大部分信息。将120组LAI及相应的9个主成分样本数据随机分为校正集数据（90组）和预测集数据（30组）,分别用于神经网络模型的建立和LAI的预测。所构建的神经网络模型的模拟结果表明,RBF神经网络模型对校正集样本的模拟准确率达到100%（RMSE=0.009 6,R2=0.999）;预测集样本的实测LAI和模拟LAI之间的均方误差和决定系数分别为0.218 6和0.839,取得了较好的模拟效果,有效提高了传统的多元线性回归方程（RMSE=0.416 5,R2=0.570）的计算精度。

英文摘要：

In accordance with the 120 sites of grassland canopy spectral reflectance and the leaf area index（LAI） data collected by Chinese Academy of Agricultural Science,the method of Radial Basis Function（RBF） neural network was developed for the prediction of LAI after the compression of spectral reflectance using principal component analysis（PCA）.The PCA results show that the cumulative reliability of the first 9 PCs is up to 99.782%,covering the majority of original spectral information.The 120 sites of LAI and 9 PC samples were classified randomly for training dataset（90 sites） and predicting dataset（30 sites）,and were used to establish the neural network and predict the LAI,respectively.The results show that the accuracy rate of training data is up to 100%（RMSE=0.009 6,R2=0.999）.The root mean square error（RMSE） and correlation coefficient（R2） for the prediction dataset are 0.839 and 0.218 6 respectivdg,thus achieving more preferable results and improved the accuracy（RMSE=0.416 5,R2=0.570）of the traditional multiple linear regression method.