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Estimating leaf area index from remote sensing data: based on data segmentation and principal component analysis

文献类型： 外文期刊

作者： Dong Ying-Ying 1 ; Wang Ji-Hua 1 ; Li Cun-Jun 2 ; Yang Gui-Jun 2 ; Song Xiao-Yu 2 ; Gu Xiao-He 2 ; Huang Wen-Jiang 2 ;

作者机构： 1.Zhejiang Univ, Inst Agr Remote Sensing & Informat Syst Applicat, Hangzhou 310029, Zhejiang, Peoples R China

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

关键词： principal component analysis(PCA); phenology; data segmentation; multi-scale modeling; leaf area index(LAI)

期刊名称：JOURNAL OF INFRARED AND MILLIMETER WAVES （ 影响因子：0.557； 五年影响因子：0.445 ）

ISSN： 1001-9014

年卷期： 2011 年 30 卷 2 期

页码：

收录情况： SCI

摘要： According to the unsatisfactory and lower efficiency of classical statistical models in leaf area index (LAI) estimation, a new inversion method combined with phenology-based data segmentation and principal component analysis was proposed in this paper. In the method, principal components of spectral data and differential (or difference) spectral data were chosen as independent variables, and phenology-based data segmentation was integrated into data processing in order to improve estimation accuracy. In addition, multi-scale was involved in modeling. Winter wheat was selected as experimental object for numerical simulation and comparative analysis. Results not only showed high precision in whole estimation and effectively improved data saturation, but also manifested stability and robustness under full scan.

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