以高光谱遥感技术实现了小麦叶面积指数(leaf area index, LAI)的反演。 对18种高光谱指数进行了比较分析, 筛选出了可敏感反映小麦LAI的高光谱指数OSAVI, 并以地面光谱数据为样本建立了小麦LAI的反演模型。 分析表明, 指数OSAVI所建立的反演模型校正集与预测集R2分别达0.823与0.818, 在各指数中反演精度最高。 利用反演模型逐象元对OMIS影像进行解算, 实现小麦LAI的空间量化表达, 并将反演结果与地面实测值进行回归拟合, 发现两组数据的拟合模型R2达0.756, RMSE为0.500, 具有较高的相似度。 结果表明: 以高光谱指数进行小麦LAI的反演是可行的, 且OSAVI为优选指数。

The wheat leaf area index (LAI) was inverted using hyperspectral remote sensing technology in the present paper. Eighteen kinds of hyperspectral indices were comparatively analyzed, and the index OSAVI, which could reflect wheat LAI most sensitively, was screened out. The models for wheat LAI inversion were built using the field spectra as the training samples. The study showed that the calibration R-square and prediction R-square of the inversion model which were built by hyperspectral index OSAVI were 0.823 and 0.818, respectively, higher than that of other indices, indicating that the accuracy was highest. The inversion model was spatially quantitatively expressed in OMIS image, and then the inversion value and measured value was compared by the method of regression fitting. The R-square and RMSE of the fitting model were 0.756 and 0.500, respectively, indicating that the similarity between the inversion value and measured value was high. The result showed that it was feasible to invert the wheat LAI by hyperspectral indices, and OSVAI was an optimal one.