CAJ | 학술논문

综合分析了玉米叶面积指数与几种常见光谱植被指数相关性，确定主成分分析方法在反演叶面积指数中的作用。首先，借助MATLAB编程软件，以植被指数与玉米叶面积指数相关性最高为原则，选出遥感影像上各种植被指数，其波段组合为NDVI（752．4／701．5），RVI（752．4／701．5），MSR（752．4／701．5），SAVI（823．77／01．5）， MSAVI（823．7／701．5），然后，对这5种植被指数进行主成分分析，建立LAI－VI多元逐步回归模型，并对模型精度进行验证，总体估测精度为96．237％。经实验验证，利用主成分分析方法在反演植被叶面积指数时能够起到较好的效果，具有广泛的应用前景。
종합분석료옥미협면적지수여궤충상견광보식피지수상관성，학정주성분분석방법재반연협면적지수중적작용。수선，차조MATLAB편정연건，이식피지수여옥미협면적지수상관성최고위원칙，선출요감영상상각충식피지수，기파단조합위NDVI（752．4／701．5），RVI（752．4／701．5），MSR（752．4／701．5），SAVI（823．77／01．5）， MSAVI（823．7／701．5），연후，대저5충식피지수진행주성분분석，건립LAI－VI다원축보회귀모형，병대모형정도진행험증，총체고측정도위96．237％。경실험험증，이용주성분분석방법재반연식피협면적지수시능구기도교호적효과，구유엄범적응용전경。
This article gave a comprehensive analysis of correlation between leaf area index ( LAI) of corn and several common spectral vegetation indices, determined functions of principle component analysis ( PCA) in LAI inversion.Firstly, the correlation between LAI of corn and spectral vegetation index was set up as highest principle, a variety of vegetation indices was selected on remote sensing by using MATLAB programming software, and the band combination are:NDVI(752.4/701.5),RVI(752.4/701.5),MSR(752.4/701.5),SAV I(823.7/701.5), and MSAVI(823.7/701.5).Then PCA was used to analyze these five vegetation indices, LAI-VI multiple regression model was established.Meanwhile, the accuracy of the model was also verified, which was 96.237%.With the experiment, it’ s confirmed that the PCA in the inversion of vegetation LAI worked effectively, which could be widely used in the fu-ture.