冬小麦叶面积指数（leaf area index, LAI）是进行作物长势判断和产量估测的重要农学指标之一， 高光谱遥感技术为大面积、 快速监测植被LAI提供了有效途径。 在探讨利用最小二乘支持向量机（least squares support vector machines, LS-SVM）方法和高光谱数据对不同条件下冬小麦LAI的估算能力。 在用主成分分析法（principal component analysis, PCA）对PHI航空数据降维的基础上， 利用实测LAI数据和高光谱反射率数据， 构建LS-SVM模型， 采用独立变量法， 分别估算不同株型品种、 不同生育时期、 不同氮素和水分处理条件下的冬小麦LAI， 并与传统NDVI模型反演结果对比。 结果显示， 每种条件下的LS-SVM 模型都具有比NDVI模型更高的决定系数和更低的均方根误差值, 即反演精度高于相应的NDVI模型。 NDVI模型对不同株型品种、 不同氮素和水分条件下冬小麦LAI估算精度不稳定，　LS-SVM则表现出较好的稳定性。 表明LS-SVM 方法利用高光谱反射率数据对于不同条件下的冬小麦LAI反演具有良好的学习能力和普适性。

Leaf area index (LAI) is one of the most important parameters for evaluating winter wheat growth status and forecasting its yield. Hyperspectral remote sensing is a new technical approach that can be used to acquire the instant information of vegetation LAI at large scale. This study aims to explore the capability of least squares support vector machines (LS-SVM) method to winter wheat LAI estimation with hyperspectral data. After the compression of PHI airborne data with principal component analysis (PCA), the sample set based on the measured LAI data and hyperspectral reflectance data was established. Then the method of LS-SVM was developed respectively to estimate winter wheat LAI under four different conditions, to be specific, different plant type cultivars， different periods， different nitrogenous fertilizer and water conditions. Compared with traditional NDVI model estimation results, each experiment of LS-SVM model yielded higher determination coefficient as well as lower RMSE value, which meant that the LS-SVM method performed better than the NDVI method. In addition, NDVI model was unstable for winter wheat under the condition of different plant type cultivars， different nitrogenous fertilizer and different water, while the LS-SVM model showed good stability. Therefore, LS-SVM has high accuracy for learning and considerable universality for estimation of LAI of winter wheat under different conditions using hyperspectral data.