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Combination of machine learning and VIRS for predicting soil organic matter

  • Soils, Sec 2 • Global Change, Environ Risk Assess, Sustainable Land Use • Research Article
  • Published:
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Abstract

Purpose

Visible-near-infrared spectroscopy (VIRS) is one of the most promising alternative techniques for soil organic matter (SOM) due to its direct response. In this study, partial least squares regression (PLSR), support vector machine (SVM), artificial neural networks (ANNs), and Cubist combined with VIRS were utilized to develop the calibration model and evaluate the ability of machine learning models to predict soil organic matter content.

Materials and methods

A total of 190 surface soil samples (earth-cumulic-orthic anthrosols) were collected from the Weihe Plain of Shaanxi Province, China. The Kennard–Stone (KS) algorithm was employed to divide them into calibration and validation data. Moreover, the successive projections algorithm (SPA), competitive adaptive weight weighting algorithm (CARS), and their combination (SPA + CARS) were utilized to select characteristic wavelengths and improve the predictive ability of the model. Different evaluation indices, including root mean square error (RMSE), coefficient of determination (R2), the ratio of the performance to deviation (RPD), and the ratio of performance to interquartile range (RPIQ), were adopted to evaluate the accuracy of the model.

Results and discussion

In all cases, the AFS-SPA + CARS-Cubist method outperformed the PLSR, SVM, and ANN. For the Cubist model, the Rv2, RPD, and RPIQ ranged from 0.8629 to 0.9782, 0.8720 to 3.0203, and 2.005 to 4.4164, respectively. According to the results, combining VIRS with Cubist could accurately determine the SOM of earth-cumulic-orthic anthrosol soils of the Weihe Plain, China. Furthermore, SPA + CARS provided more precise calibration–validation models than SPA and CARS.

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Acknowledgements

We thank the anonymous reviewers and editor for their helpful comments.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 51979221), Xi’an University of Technology Doctoral Dissertation Innovation Fund (Grant No. 310-252072018), National Key Research and Development Program of China (Grant No. 2016YFC0401409), and Shaanxi Provincial Land Engineering Construction Group Project (Grant No. DJNY2021-15).

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Authors and Affiliations

  1. State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an, 710048, China

    Zhenyu Dong, Ni Wang, Jinbao Liu & Jiancang Xie

  2. Shaanxi Provincial Land Engineering Construction Group Co, Ltd, Xi’an, Shaanxi, 710075, China

    Jinbao Liu & Jichang Han

  3. Key Laboratory of Degraded and Unused Land Consolidation Engineering, the Ministry of Land and Resources, Xi’an, Shaanxi, 710075, China

    Jinbao Liu & Jichang Han

Authors
  1. Zhenyu Dong
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  2. Ni Wang
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  3. Jinbao Liu
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  4. Jiancang Xie
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  5. Jichang Han
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Contributions

We thank Zhenyu Dong, Ni Wang, and Jinbao Liu for contributing the soil samples and spectroscopic measurements, formal analysis, writing—original draft preparation, and writing—review and editing; Jichang Han and Jiancang Xie for their help with the data curation, article writing; Ni Wang, Jinbao Liu and Jiancang Xie for project administration. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Jinbao Liu.

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Responsible editor: Xiuping Jia

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Dong, Z., Wang, N., Liu, J. et al. Combination of machine learning and VIRS for predicting soil organic matter. J Soils Sediments 21, 2578–2588 (2021). https://doi.org/10.1007/s11368-021-02977-0

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  • DOI: https://doi.org/10.1007/s11368-021-02977-0

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