{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T21:03:53Z","timestamp":1771189433463,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["241930754,32060370"],"award-info":[{"award-number":["241930754,32060370"]}],"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>Visible and near-infrared (Vis\u2013NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R2 = 0.02; P1 correction, validation R2 = 0.56; and P2 correction, validation R2 = 0.57. For MBL, the validation results were as follows: no correction, validation R2 = 0.06; P1 correction, validation R2 = 0.65; and P2 correction, validation R2 = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC.<\/jats:p>","DOI":"10.3390\/rs14061303","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T01:50:53Z","timestamp":1646790653000},"page":"1303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy"],"prefix":"10.3390","volume":"14","author":[{"given":"Wu","family":"Yu","sequence":"first","affiliation":[{"name":"College of Resource and Environment, Tibet Agricultural and Animal Husbandry University, Linzhi 860000, China"},{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Yongsheng","family":"Hong","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":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, 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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7442-3239","authenticated-orcid":false,"given":"Yiyun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China"}]},{"given":"Lianqing","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Resource and Environment, Tibet Agricultural and Animal Husbandry University, Linzhi 860000, 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":[[2022,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.catena.2018.04.040","article-title":"Spatial variation of organic carbon density in topsoils of a typical subtropical forest, southeastern China","volume":"167","author":"Dai","year":"2018","journal-title":"Catena"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"4021","DOI":"10.1073\/pnas.1700291115","article-title":"Carbon pools in China&rsquo\u2019s terrestrial ecosystems: New estimates based on an intensive field survey","volume":"115","author":"Tang","year":"2018","journal-title":"Proc. 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