{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T08:15:13Z","timestamp":1778832913016,"version":"3.51.4"},"reference-count":65,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,9]],"date-time":"2020-05-09T00:00:00Z","timestamp":1588982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2018YFE0107000"],"award-info":[{"award-number":["2018YFE0107000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Public Welfare Research of Zhejiang Province","award":["LGN18D010003"],"award-info":[{"award-number":["LGN18D010003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs12091512","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T12:26:30Z","timestamp":1589199990000},"page":"1512","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion"],"prefix":"10.3390","volume":"12","author":[{"given":"Hanyi","family":"Xu","sequence":"first","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyun","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1245-0482","authenticated-orcid":false,"given":"Songchao","family":"Chen","sequence":"additional","affiliation":[{"name":"INRAE, Unit\u00e9 InfoSol, 45075 Orl\u00e9ans, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanzhu","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3914-5402","authenticated-orcid":false,"given":"Zhou","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0016-7061(00)00043-4","article-title":"An overview of pedometric techniques for use in soil survey","volume":"97","author":"McBratney","year":"2000","journal-title":"Geoderma"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.geoderma.2014.01.019","article-title":"Soil classification using visible\/near-infrared diffuse reflectance spectra from multiple depths","volume":"223","author":"Vasques","year":"2014","journal-title":"Geoderma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1013656024633","article-title":"The role and function of organic matter in tropical soils","volume":"61","author":"Craswell","year":"2001","journal-title":"Nutr. 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