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Monitoring Freeze Stress Levels on Winter Wheat from Hyperspectral Reflectance Data Using Principal Component Analysis

文献类型： 外文期刊

作者： Wang Hui-fang 1 ; Wang Ji-hua 2 ; Dong Ying-ying 2 ; Gu Xiao-he 2 ; Huo Zhi-guo 1 ;

作者机构： 1.Chinese Acad Meteorol Sci, Beijing 100081, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Winter wheat freeze injury stress;Hyperspectral;Principal component analysis (PCA)

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2014 年 34 卷 5 期

页码：

收录情况： SCI

摘要： In order to detect the freeze injury stress level of winter wheat growing in natural environment fast and accurately, the present paper takes winter wheat as experimental object. First winter wheat canopy hyperspectral data were treated with resampling smooth. Second hyperspectral data were analyzed based on principal components analysis (PCA), a freeze injury inversion model was established, stems survival rate was dependent, and principal components of spectral data were chosen as independent variables. Third, the precision of the model was testified. The result showed that the freeze injury inversion model based on 6 principal components can estimate the winter wheat freeze injury accurately with the coefficient of determination (R-2) of 0. 697 5, root mean square error (RMSE) of 0. 184 2, and the accuracy of 0. 697 5. And the model was verified. It can be concluded that the PCA technology has been shown to be very promising in detecting winter wheat freeze injury effectively, and provide important reference for detecting other stress on crop.

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