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Band Depth Analysis and Partial Least Square Regression Based Winter Wheat Biomass Estimation Using Hyperspectral Measurements

文献类型： 外文期刊

作者： Fu Yuan-yuan 1 ; Wang Ji-hua 1 ; Yang Gui-jun 2 ; Song Xiao-yu 2 ; Xu Xin-gang 2 ; Feng Hai-kuan 2 ;

作者机构： 1.Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310029, Zhejiang, Peoples R China

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

3.Minist Agr, Key Lab Agriinformat, Beijing 100097, Peoples R China

4.Minist Agr, Key Lab Agriinformat, Beijing 100097, Peopl

关键词： Hyperspectral remote sensing;Winter wheat;biomass;Band depth analysis;Partial least square regression

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： 2013 年 33 卷 5 期

页码：

收录情况： SCI

摘要： The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently. Band depth analysis was conducted in the visible spectral domain (550 similar to 750 nm). Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area were utilized to represent band depth information. Among the calibrated estimation models, the models based on the combination of band depth analysis and PLSR reached higher accuracy than those based on the vegetation indices. Among them, the combination of BDR and PLSR got the highest accuracy (R-2=0.792, RMSE=0.164 kg . m(-2)). The results indicated that the combination of band depth analysis and PLSR could well overcome the saturation problem and improve the biomass estimation accuracy when winter wheat biomass is large.

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