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Different variable selection methods have been used to deal with the high redundancy, heavy computation, and model complexity of using full spectra in spectral modelling. However, most previous studies used a linear algorithm in the variable selection, and the application of a non-linear algorithm remains poorly explored. To address the current knowledge gap, based on a regional soil Vis-NIR spectral library (1430 soil samples), we evaluated seven variable selection algorithms together with three predictive algorithms in predicting seven soil properties. Our results showed that Cubist overperformed partial least squares regression (PLSR) and random forests (RF) in most soil properties (R2 &gt; 0.75 for soil organic matter, total nitrogen and pH) when using the full spectra. Most of variable selection can greatly reduce the number of spectral bands and therefore simplified predictive models without losing accuracy. The results also showed that there was no silver bullet for the optimal variable selection algorithm among different predictive algorithms: (1) competitive adaptive reweighted sampling (CARS) always performed best for the PLSR algorithm, followed by forward recursive feature selection (FRFS); (2) recursive feature elimination (RFE) and genetic algorithm (GA) generally had better accuracy than others for the Cubist algorithm; and (3) FRFS had the best model performance for the RF algorithm. In addition, the performance was generally better when the algorithm used in the variable selection matched the predictive algorithm. The outcome of this study provides a valuable reference for predicting soil information using spectroscopic techniques together with variable selection algorithms.<\/jats:p>","DOI":"10.3390\/rs15020465","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T02:29:57Z","timestamp":1673576997000},"page":"465","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0792-4700","authenticated-orcid":false,"given":"Xianglin","family":"Zhang","sequence":"first","affiliation":[{"name":"ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China"},{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0340-5594","authenticated-orcid":false,"given":"Jie","family":"Xue","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Yi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1245-0482","authenticated-orcid":false,"given":"Songchao","family":"Chen","sequence":"additional","affiliation":[{"name":"ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China"},{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.5194\/soil-2-79-2016","article-title":"World\u2019s soils are under threat","volume":"2","author":"Montanarella","year":"2016","journal-title":"Soil"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1261071","DOI":"10.1126\/science.1261071","article-title":"Soil and human security in the 21st century","volume":"348","author":"Amundson","year":"2015","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9575","DOI":"10.1073\/pnas.1706103114","article-title":"Soil carbon debt of 12,000 years of human land use","volume":"114","author":"Sanderman","year":"2017","journal-title":"Proc. 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