Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification

Journal Article · Mon Sep 24 00:00:00 EDT 2018 · European Journal of Soil Science

DOI:https://doi.org/10.1111/ejss.12715· OSTI ID:1473716

Chen, S.  ^[1]; Li, S.  ^[2]; Ma, W. ^[3]; Ji, W. ^[4]; Xu, D. ^[2]; Shi, Z. ^[5]; Zhang, G. ^[6]

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1. Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University Hangzhou China, INRA, Unité InfoSol Orléans France, SAS, INRA, Agrocampus Ouest Rennes France
2. Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University Hangzhou China
3. Key Laboratory of Information Traceability for Agricultural Products Ministry of Agriculture of China Hangzhou China
4. Department of Soil and Environment Swedish University of Agricultural Sciences Skara Sweden
5. Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University Hangzhou China, State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science, Chinese Academy of Sciences Nanjing China
6. State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science, Chinese Academy of Sciences Nanjing China

Summary Visible‐near infrared (vis–NIR) spectroscopy can reveal various soil properties and facilitate soil classification. However, few studies have attempted to classify vertical soil profiles that contain several genetic horizons. Here, we propose the ‘multiple objectives mixed support vector classification’ (MOM–SVC) method to classify soil profiles. A total of 130 soil profiles were collected from genetic horizons in Zhejiang Province, China. After laboratory analysis, soil profiles were classified according to the Chinese Soil Taxonomy system. Vis–NIR spectra were recorded from each genetic horizon of each soil profile and were then pre‐processed. We performed the MOM–SVC method as follows: (i) created a support vector machine (SVM) model (one‐versus‐one approach) using spectral data from all soil horizons in calibration profiles, (ii) applied the SVM model on each horizon of the profile to be predicted, (iii) extracted ‘votes’ from each horizon and mixed (or summarized) them into the votes of each profile to be predicted and (iv) classified each profile by the majority‐voting method. We also investigated whether the additional input of auxiliary soil information (e.g. moist soil colour, soil organic matter and soil texture), which could be measured easily or be well predicted by vis–NIR spectroscopy, could improve the accuracy of soil classification when combined with it. Independent validation results showed that the MOM–SVC method performed better at the soil order level than at the suborder level. Adding auxiliary soil information to the classification model improved the overall accuracy of classification at the soil order level. The proposed MOM–SVC method provides a fast objective diagnostic of soil classes for use in soil surveys and can help to update soil databases when a more objective soil classification system is developed. Highlights The MOM–SVC method can be used to classify soil profiles objectively with a variety of soil horizons. Stratified random sampling was used to quantify prediction uncertainty in classification MOM–SVC can predict soil orders with greater accuracy than suborders. Adding auxiliary soil information into the classification model improved prediction accuracy.

• Sponsoring Organization: USDOE
• OSTI ID: 1473716
• Journal Information: European Journal of Soil Science, Journal Name: European Journal of Soil Science Vol. 70 Journal Issue: 1; ISSN 1351-0754
• Publisher: Wiley-Blackwell
• Country of Publication: United Kingdom
• Language: English