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| Hyperspectral Inversion of Soil Organic Matter Content Using a Discrete Wavelet Coupling Algorithm |
| LI Xiao-fang1, WANG Jin-gao1, HUO Jian-hong1, LI Zi-tong1, HAO Hong-chun1, HAN Rui-xin1, GU Xiao-he2*, ZHU Yu-chen4, WANG Yan-cang2, 3 |
1. Langfang Normal University, Langfang 065000, China
2. Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
3. North China Institute of Aerospace Engineering，Langfang 065000, China
4. Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijizhuang 050061, China
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Abstract Soil organic matter content in the plow layer is a key indicator for evaluating soil quality. It not only provides crops with abundant nutrients but also improves the soil environment in the plow layer, making it an essential component of the plow layer. This study proposes a spectral data mining algorithm to enhance the sensitivity of spectral data to soil organic matter content and improve its estimation capability. The study first employed discrete wavelet algorithms to sequentially perform separation, correlation analysis, and model construction on soil spectral data, thereby establishing a model for estimating soil organic matter content. Subsequently, coupled algorithms were used to sequentially perform data mining, correlation analysis, and model construction on soil spectral data, with evaluation metrics used to assess the accuracy of the resulting model. Finally, the sensitivity and estimation capability of spectral data toward soil organic matter content were compared before and after coupling. The research results indicate: (1) The spectral information mining algorithm proposed in this study can integrate the advantages of various wavelet bases, significantly enhancing the sensitivity of the spectra to soil organic matter content, with the correlation coefficient increasing by an average of 15.33%. (2) A comparison of model accuracy before and after coupling indicates that the spectral information mining algorithm proposed in this study can significantly enhance the estimation capability of spectra for soil organic matter content and reduce estimation errors. The conclusions of this study can support the mining and analysis of spectral data across different locations and serve as a reference for the development of related algorithms.
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Received: 2025-01-27
Accepted: 2025-10-11
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Corresponding Authors:
GU Xiao-he
E-mail: guxh@nercita.org.cn
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