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Article

Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion

1
Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
2
INRAE, Unité InfoSol, 45075 Orléans, France
3
Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
4
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(9), 1512; https://doi.org/10.3390/rs12091512
Received: 27 March 2020 / Revised: 18 April 2020 / Accepted: 20 April 2020 / Published: 9 May 2020
Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification. View Full-Text
Keywords: support vector machine; vis-NIR; MIR; outer product analysis; soil classification support vector machine; vis-NIR; MIR; outer product analysis; soil classification
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MDPI and ACS Style

Xu, H.; Xu, D.; Chen, S.; Ma, W.; Shi, Z. Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion. Remote Sens. 2020, 12, 1512. https://doi.org/10.3390/rs12091512

AMA Style

Xu H, Xu D, Chen S, Ma W, Shi Z. Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion. Remote Sensing. 2020; 12(9):1512. https://doi.org/10.3390/rs12091512

Chicago/Turabian Style

Xu, Hanyi, Dongyun Xu, Songchao Chen, Wanzhu Ma, and Zhou Shi. 2020. "Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion" Remote Sensing 12, no. 9: 1512. https://doi.org/10.3390/rs12091512

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