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Research on Universality of Least Squares Support Vector Machine Method for Estimating Leaf Area Index of Winter Wheat

文献类型： 外文期刊

作者： Xie Qiao-yun 1 ; Huang Wen-jiang 1 ; Liang Dong 2 ; Peng Dai-liang 1 ; Huang Lin-sheng 2 ; Song Xiao-yu 4 ; Zhang Dong 1 ;

作者机构： 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China

2.Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Peoples R China

3.Anhui Univ, Sch Elect & Informat Engn, Hefei 230039, Peoples R China

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

关键词： Least squares support vector machine;Leaf area index;Hyperspectral;Universality;Winter wheat

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 2 期

页码：

收录情况： SCI

摘要： Leaf area index (LAI) is one of the most important parameters for evaluating winter wheat growth status and forecasting its yield. Hyperspectral remote sensing is a new technical approach that can be used to acquire the instant information of vegetation LAI at large scale. This study aims to explore the capability of least squares support vector machines (LS-SVM) method to winter wheat LAI estimation with hyperspectral data. After the compression of PHI airborne data with principal component analysis (PCA), the sample set based on the measured LAI data and hyperspectral reflectance data was established. Then the method of LS-SVM was developed respectively to estimate winter wheat LAI under four different conditions, to be specific, different plant type cultivars, different periods, different nitrogenous fertilizer and water conditions. Compared with traditional NDVI model estimation results, each experiment of LS-SVM model yielded higher determination coefficient as well as lower RMSE value, which meant that the LS-SVM method performed better than the NDVI method. In addition, NDVI model was unstable for winter wheat under the condition of different plant type cultivars, different nitrogenous fertilizer and different water, while the LS-SVM model showed good stability. Therefore, LS-SVM has high accuracy for learning and considerable universality for estimation of LAI of winter wheat under different conditions using hyperspectral data.

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