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| Quantitative Inversion of Soil Organic Matter Content in Northern Alluvial Soil Based on Binary Wavelet Transform |
| WANG Yan-cang1, 3, YANG Xiu-feng1, 3, ZHAO Qi-chao1, 3, GU Xiao-he2, 4*, GUO Chang1, 3, LIU Yuan-ping1,3 |
1. Institute of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering，Langfang 065000, China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province，Langfang 065000, China
4. Key Laboratory of Information Technology in Agriculture, Ministry of Agriculture, Beijing 100097, China |
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Abstract 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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Received: 2018-08-08
Accepted: 2018-12-25
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Corresponding Authors:
GU Xiao-he
E-mail: guxh@nercita.org.cn
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