{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:19:06Z","timestamp":1775593146068,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41061031"],"award-info":[{"award-number":["41061031"]}]},{"name":"National Natural Science Foundation of China","award":["20212BAB205022"],"award-info":[{"award-number":["20212BAB205022"]}]},{"name":"National Natural Science Foundation of China","award":["GJJ181150"],"award-info":[{"award-number":["GJJ181150"]}]},{"name":"Natural Science Foundation of Jiangxi Province","award":["41061031"],"award-info":[{"award-number":["41061031"]}]},{"name":"Natural Science Foundation of Jiangxi Province","award":["20212BAB205022"],"award-info":[{"award-number":["20212BAB205022"]}]},{"name":"Natural Science Foundation of Jiangxi Province","award":["GJJ181150"],"award-info":[{"award-number":["GJJ181150"]}]},{"name":"Science and Technology Research Project of Jiangxi Provincial Department of Education","award":["41061031"],"award-info":[{"award-number":["41061031"]}]},{"name":"Science and Technology Research Project of Jiangxi Provincial Department of Education","award":["20212BAB205022"],"award-info":[{"award-number":["20212BAB205022"]}]},{"name":"Science and Technology Research Project of Jiangxi Provincial Department of Education","award":["GJJ181150"],"award-info":[{"award-number":["GJJ181150"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Robust soil organic matter (SOM) mapping is required by farms, but their generation requires a large number of samples to be chemically analyzed, which is cost prohibitive. Recently, research has shown that visible and near-infrared (vis-NIR) reflectance spectroscopy is a fast and accurate technique for estimating SOM in a cost-effective manner. However, few studies have focused on using vis-NIR spectroscopy as a covariate to improve the accuracy of spatial modeling. In this study, our objective was to compare the mapping accuracy from a spatial model using kriging methods with and without the covariate of vis-NIR spectroscopy. We split the 261 samples into a calibration set (104) for building the spectral predictive model, a test set for generating the vis-NIR augmented set from the prediction of the fitted spectral predictive model (131), and a validation set (26) for evaluating map accuracy. We used two datasets (235 samples) for Kriging: a laboratory-based dataset (Ld, observations from calibration and test datasets) and a laboratory-based dataset with vis-NIR augmented predictions (Au.p, observations from calibration and predictions from test dataset), a laboratory-based dataset with vis-NIR spectra as the covariance (Ld.co) and augmented dataset with predictions using vis-NIR with vis-NIR spectra for the covariance (Au.p.co). The first one to seven accumulated principal components of vis-NIR spectra were used as the covariates when we used the measurement of Ld.co and Au.p.co. The map accuracy was evaluated by the validation set for the four datasets using Kriging. The results indicated that adding vis-NIR spectra as covariates had great potential in improving the map accuracy using kriging, and much higher accuracies were observed for Ld.p.co (RMSE of 5.51 g kg\u22121) and Au.p.co (RMSE of 5.66 g kg\u22121) than without using vis-NIR spectra as covariates for Ld (RMSE of 7.12 g kg\u22121) and Au.p (RMSE of 7.69 g kg\u22121). With a similar model performance to Ld.p.co, Au.p.co can reduce the cost of laboratory analysis for 60% of soil samples, demonstrating its advantage in cost-efficiency for spatial modeling of soil information. Therefore, we conclude that vis-NIR spectra can be used as a cost-effective technique to obtain augmented data to improve fine-resolution spatial mapping of soil information.<\/jats:p>","DOI":"10.3390\/rs15061617","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T02:29:59Z","timestamp":1679020199000},"page":"1617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Exploring the Potential of vis-NIR Spectroscopy as a Covariate in Soil Organic Matter Mapping"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7315-5826","authenticated-orcid":false,"given":"Meihua","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Environmental Management, Yuzhang Normal University, Nanchang 330103, 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"}]},{"given":"Xi","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, 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"}]},{"given":"Xiaomin","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.geoderma.2015.03.027","article-title":"Mapping of clay, iron oxide and adsorbed phosphate in Oxisols using diffuse reflectance spectroscopy","volume":"251","author":"Camargo","year":"2015","journal-title":"Geoderma"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.2136\/sssaj2006.0059","article-title":"A Novel Method of Classifying Soil Profiles in the Field using Optical Means","volume":"72","author":"Heller","year":"2008","journal-title":"Soil Sci. 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