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Quantitative Inversion of Soil Organic Matter Content in Northern Alluvial Soil Based on Binary Wavelet Transform

文献类型： 外文期刊

作者： Wang Yan-cang 1 ; Yang Xiu-feng 1 ; Zhao Qi-chao 1 ; Gu Xiao-he 2 ; Guo Chang 1 ; Liu Yuan-ping 1 ;

作者机构： 1.North China Inst Aerosp Engn, Inst Comp & Remote Sensing Informat Technol, Langfang 065000, Peoples R China

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

3.Aerosp Remote Sensing Informat Proc & Applicat Co, Langfang 065000, Peoples R China

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

关键词： Soil organic matter; Binary wavelet transform; Hyperspectral; Alluvial soil

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： 2019 年 39 卷 9 期

页码：

收录情况： SCI

摘要： In order to separate the information of the content of soil organic matter contained in soil spectra, to extract the spectral response information of the matter, to improve the diagnostic accuracy and reliability of soil organic matter content, this study takes the content of organic matter in tidal soil as the research object, and takes the soil parameters and hyperspectral data of 96 farmlands collected from Beijing area as the data source to research and analyze. First, the binary wavelet technique is used to separate the soil spectral data into 5 scales of high-frequency data and low-frequency data, and then these two kinds of data are respectively used for the correlation analysis with the measured soil organic matter data. Afterwards, the optimal band combination is extracted to build the diagnosis model of organic matter content. Finally, results of the study show that: (1) The binary wavelet technology can restrain the noise interference to high frequency information, and effectively enhance the spectral sensitivity to soil organic matter content so as to improve the diagnostic accuracy and reliability of organic matter content; (2) Under the binary wavelet technique, the diagnostic ability of high frequency information to organic matter content is obviously superior to that of low frequency information. The diagnostic ability of low frequency information to soil organic matter content decreases with the increase of scale, while high frequency information increases with the scale increasing and then decreases; (3) Compared with the mathematical method, the model based on the binary wavelet transform algorithm has higher accuracy and better stability. The prediction accuracy of the optimal model is improved by 31.5% and the reliability is increased by 10.5%.

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