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Estimation of Organic Matter Content of North Fluvo-Aquic Soil Based on the Coupling Model of Wavelet Transform and Partial Least Squares

文献类型： 外文期刊

作者： Wang Yan-cang 1 ; Yang Gui-jun 2 ; Zhu Jin-shan 1 ; Gu Xiao-he 2 ; Xu Peng 2 ; Liao Qin-hong 2 ;

作者机构： 1.Shandong Univ Sci & Technol, Coll Geometr, Qingdao 266590, Peoples R China

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

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

关键词： Organic matter; Discrete wavelet; Hyperspectral; Partial least squares 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： 2014 年 34 卷 7 期

页码：

收录情况： SCI

摘要： For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R-2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0. 722 and 0. 221, respectively. The R-2 and RMSE of the best model for detail coefficients are 0. 670 and 0. 255, respectively.

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