{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:07:31Z","timestamp":1773706051464,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,21]],"date-time":"2019-02-21T00:00:00Z","timestamp":1550707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFD1100801"],"award-info":[{"award-number":["2018YFD1100801"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771440"],"award-info":[{"award-number":["41771440"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771432"],"award-info":[{"award-number":["41771432"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In constructing models for predicting soil organic matter (SOM) by using visible and near-infrared (vis\u2013NIR) spectroscopy, the selection of representative calibration samples is decisive. Few researchers have studied the inclusion of spectral pretreatments in the sample selection strategy. We collected 108 soil samples and applied six commonly used spectral pretreatments to preprocess soil spectra, namely, Savitzky\u2013Golay (SG) smoothing, first derivative (FD), logarithmic function log(1\/R), mean centering (MC), standard normal variate (SNV), and multiplicative scatter correction (MSC). Then, the Kennard\u2013Stone (KS) strategy was used to select calibration samples based on the pretreated spectra, and the size of the calibration set varied from 10 samples to 86 samples (80% of the total samples). These calibration sets were employed to construct partial least squares regression models (PLSR) to predict SOM, and the built models were validated by a set of 21 samples (20% of the total samples). The results showed that 64\u221278% of the calibration sets selected by the inclusion of pretreatment demonstrated significantly better performance of SOM estimation. The average improved residual predictive deviations (\u0394RPD) were 0.06, 0.13, 0.19, and 0.13 for FD, log(1\/R), MSC, and SNV, respectively. Thus, we concluded that spectral pretreatment improves the sample selection strategy, and the degree of its influence varies with the size of the calibration set and the type of pretreatment.<\/jats:p>","DOI":"10.3390\/rs11040450","type":"journal-article","created":{"date-parts":[[2019,2,22]],"date-time":"2019-02-22T03:49:44Z","timestamp":1550807384000},"page":"450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4443-9440","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Yaolin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7442-3239","authenticated-orcid":false,"given":"Yiyun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"},{"name":"State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3909-7789","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing 100070, China"}]},{"given":"Tiezhu","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation &amp; Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4839-7724","authenticated-orcid":false,"given":"Junjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation &amp; Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Yongsheng","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3415-1654","authenticated-orcid":false,"given":"Teng","family":"Fei","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1126\/science.1097396","article-title":"Soil carbon sequestration impacts on global climate change and food security","volume":"304","author":"Lal","year":"2004","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1038\/nature10386","article-title":"Persistence of soil organic matter as an ecosystem property","volume":"478","author":"Schmidt","year":"2011","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/nature16069","article-title":"The contentious nature of soil organic matter","volume":"528","author":"Lehmann","year":"2015","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1097\/00010694-193401000-00003","article-title":"An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method","volume":"37","author":"Walkley","year":"1934","journal-title":"Soil Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nelson, D., and Sommers, L.E. 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