叶面积指数（leaf area index, LAI）是反映作物生长状况和进行产量预测预报的主要指标之一, 对诊断作物生长状况具有重要意义。 遥感技术为大面积、 快速监测植被LAI提供了有效途径。 利用高光谱遥感影像, 结合田间同步实验数据, 探讨不同方法对冬小麦叶面积指数遥感反演的能力。 介绍了支持向量机、 离散小波变换、 连续小波变换和主成分分析四种LAI反演方法。 分别利用上述四种方法构建冬小麦LAI反演模型, 并对不同算法反演的LAI模型进行了真实性检验。 结果显示, 支持向量机非线性回归模型精度最高, 对冬小麦LAI估算能力最强, 反演值与实测值拟合的决定系数为0.823 4、 均方根误差为0.419 5。 离散小波变换法和主成分分析法都是基于特征提取和数据降维, 其多元变量回归分析对LAI估算能力相近, 决定系数分别为0.697 1和0.692 4, 均方根误差分别为0.605 8和0.554 1。 连续小波变换法回归模型精度最低, 不适宜直接用其小波系数来反演LAI。 结果表明, 非线性支持向量机模型最适宜用于研究区域的冬小麦LAI反演。

The present study aims to explore capability of different methods for winter wheat leaf area index inversion by integrating remote sensing image and synchronization field experiment. There were four kinds of LAI inversion methods discussed, specifically, support vector machines (SVM), discrete wavelet transform (DWT), continuous wavelet transform (CWT) and principal component analysis (PCA). Winter wheat LAI inversion models were established with the above four methods respectively, then estimation precision for each model was analyzed. Both discrete wavelet transform method and principal component analysis method are based on feature extraction and data dimension reduction, and multivariate regression models of the two methods showed comparable accuracy (R2 of DWT and PCA model was 0.697 1 and 0.692 4 respectively; RMSE was 0.605 8 and 0.554 1 respectively). While the model based on continuous wavelet transform suffered the lowest accuracy and didn’t seem to be qualified to inverse LAI. It was indicated that the nonlinear regression model with support vector machines method is the most eligible model for estimating winter wheat LAI in the study area.