{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T16:34:04Z","timestamp":1774974844836,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,12,24]],"date-time":"2017-12-24T00:00:00Z","timestamp":1514073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS\u2013NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM\u2013based cluster method with known SM (classifying the VIS\u2013NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)\u2013based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI\u2013based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R2), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS\u2013NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS\u2013NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R2cv = 0.69\u20130.77, RPD = 1.79\u20132.08). The estimation of SOM, when using the NSMI\u2013based cluster method with unknown SM (RPD = 1.95\u20132.04), was similar to the use of the SM\u2013based cluster method with known SM (RPD = 1.79\u20132.08). The predictive results (RPD = 1.87\u20132.06) demonstrated that the NSMI-\u2013based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information.<\/jats:p>","DOI":"10.3390\/rs10010028","type":"journal-article","created":{"date-parts":[[2017,12,26]],"date-time":"2017-12-26T03:06:38Z","timestamp":1514257598000},"page":"28","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Prediction of Soil Organic Matter by VIS\u2013NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture"],"prefix":"10.3390","volume":"10","author":[{"given":"Yongsheng","family":"Hong","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Institute of Soil Science, State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"given":"Lei","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"},{"name":"Key Laboratory for Geographical Process Analysis &amp; Simulation of Hubei Province, Central China Normal University, 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 Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Institute of Soil Science, State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"given":"Yanfang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China"}]},{"given":"Yaolin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4443-9440","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Institute of Soil Science, State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"given":"Hang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Institute of Soil Science, State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1365-2389.2010.01338.x","article-title":"Soil organic matters","volume":"62","author":"Powlson","year":"2011","journal-title":"Eur. J. Soil Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geoderma.2010.04.008","article-title":"Effects of soil sample pretreatments and standardised rewetting as interacted with sand classes on VIS\u2013NIR predictions of clay and soil organic carbon","volume":"158","author":"Stenberg","year":"2010","journal-title":"Geoderma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.earscirev.2016.01.012","article-title":"A global spectral library to characterize the world\u2019s soil","volume":"155","author":"Behrens","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.geoderma.2016.10.033","article-title":"Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory","volume":"288","author":"Bao","year":"2017","journal-title":"Geoderma"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.geoderma.2009.12.025","article-title":"Using data mining to model and interpret soil diffuse reflectance spectra","volume":"158","author":"Behrens","year":"2010","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0065-2113(10)07005-7","article-title":"Chapter five-visible and near infrared spectroscopy in soil science","volume":"107","author":"Stenberg","year":"2010","journal-title":"Adv. Agron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.geoderma.2015.07.007","article-title":"Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VIS\u2013NIR) in the field","volume":"261","author":"Cambou","year":"2016","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1255\/jnirs.1184","article-title":"Improving the prediction of soil organic matter using visible and near infrared spectroscopy on moist samples","volume":"24","author":"Wang","year":"2016","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.geoderma.2011.09.008","article-title":"Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon","volume":"167\u2013168","author":"Minasny","year":"2011","journal-title":"Geoderma"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"722","DOI":"10.2136\/sssaj2002.7220","article-title":"Moisture effects on soil reflectance","volume":"66","author":"Lobell","year":"2002","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1111\/ejss.12239","article-title":"Accounting for the effects of water and the environment on proximally sensed VIS\u2013NIR soil spectra and their calibrations","volume":"66","author":"Ji","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1366\/0003702934067694","article-title":"Generalized two-dimensional correlation method applicable to infrared, Raman, and other types of spectroscopy","volume":"47","author":"Noda","year":"1993","journal-title":"Appl. Spectrosc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.geoderma.2014.01.011","article-title":"VIS\u2013NIR spectra of dried ground soils predict properties of soils scanned moist and intact","volume":"221","author":"Ge","year":"2014","journal-title":"Geoderma"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.geoderma.2015.06.002","article-title":"Predicting clay content on field-moist intact tropical soils using a dried, ground VIS\u2013NIR library with external parameter orthogonalization","volume":"259","author":"Ackerson","year":"2015","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.geoderma.2015.12.014","article-title":"Moisture insensitive prediction of soil properties from VNIR reflectance spectra based on external parameter orthogonalization","volume":"267","author":"Wijewardane","year":"2016","journal-title":"Geoderma"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.geoderma.2017.02.014","article-title":"Evaluation of two methods to eliminate the effect of water from soil VIS\u2013NIR spectra for predictions of organic carbon","volume":"296","author":"Roudier","year":"2017","journal-title":"Geoderma"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1111\/ejss.12271","article-title":"Improved estimates of organic carbon using proximally sensed VIS\u2013NIR spectra corrected by piecewise direct standardization","volume":"66","author":"Ji","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_18","first-page":"1705","article-title":"Transferability of hyperspectral model for estimating soil organic matter concerned with soil moisture","volume":"35","author":"Chen","year":"2015","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.geoderma.2009.12.021","article-title":"Spiking of NIR regional models using samples from target sites: Effect of model size on prediction accuracy","volume":"158","author":"Guerrero","year":"2010","journal-title":"Geoderma"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.geoderma.2009.01.025","article-title":"In situ measurements of soil color, mineral composition and clay content by VIS\u2013NIR spectroscopy","volume":"150","author":"Cattle","year":"2009","journal-title":"Geoderma"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1097\/SS.0b013e3181b21491","article-title":"Alleviating moisture content effects on the visible near-infrared diffuse-reflectance sensing of soils","volume":"174","author":"Wu","year":"2009","journal-title":"Soil Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1111\/ejss.12362","article-title":"Prediction of soil organic and inorganic carbon at different moisture contents with dry ground VNIR: A comparative study of different approaches","volume":"67","author":"Wijewardane","year":"2016","journal-title":"Eur. J. Soil Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, Q.H., Chen, Y.Y., Guo, L., Fei, T., and Qi, K. (2016). Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS\u2013NIR spectroscopy. Remote Sens., 8.","DOI":"10.3390\/rs8090755"},{"key":"ref_24","first-page":"267","article-title":"Prediction of total nitrogen in cropland soil at different levels of soil moisture with VIS\/NIR spectroscopy","volume":"64","author":"Liu","year":"2014","journal-title":"Acta Agric. Scand. Sect. B-Soil Plant Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.2136\/sssaj2005.0297","article-title":"Characterization of soil water content using measured visible and near infrared spectra","volume":"70","author":"Mouazen","year":"2006","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.geoderma.2012.07.020","article-title":"Prediction of soil organic carbon for different levels of soil moisture using VIS\u2013NIR spectroscopy","volume":"199","author":"Nocita","year":"2013","journal-title":"Geoderma"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"420","DOI":"10.2136\/sssaj2015.10.0379","article-title":"The prediction of soil texture from visible-near-infrared spectra under varying moisture conditions","volume":"80","author":"Wang","year":"2016","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1007\/s11430-013-4808-x","article-title":"Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations","volume":"57","author":"Shi","year":"2014","journal-title":"Sci. China-Earth Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.geoderma.2015.05.010","article-title":"Fuzzy clustering of VIS\u2013NIR spectra for the objective recognition of soil morphological horizons in soil profiles","volume":"263","author":"Fajardo","year":"2016","journal-title":"Geoderma"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7524","DOI":"10.1021\/acs.jpca.7b06621","article-title":"Investigation on the behavior of noise in asynchronous spectra in generalized two-dimensional (2D) correlation spectroscopy and application of butterworth filter in the improvement of signal-to-noise ratio of 2D asynchronous spectra","volume":"121","author":"He","year":"2017","journal-title":"J. Phys. Chem. A"},{"key":"ref_34","unstructured":"Martens, H., and N\u00e6s, T. (1989). Multivariate Calibration, John Wiley & Sons."},{"key":"ref_35","unstructured":"Minasny, B., and McBratney, A. (2002). Fuzme Version 3.0, Australian Centre for Precision Agriculture, The University of Sydney. Available online: http:\/\/www.usyd.edu.au\/su\/agric\/acpa."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/01431160701294695","article-title":"Surface soil moisture quantification models from reflectance data under field conditions","volume":"29","author":"Haubrock","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chang, C.C., and Lin, C.J. (2011). LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2.","DOI":"10.1145\/1961189.1961199"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.3390\/rs6042699","article-title":"Estimating soil organic carbon using VIS\/NIR spectroscopy with SVMR and SPA methods","volume":"6","author":"Peng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.2136\/sssaj2009.0218","article-title":"Prediction of soil fertility properties from a globally distributed soil mid-infrared spectral library","volume":"74","author":"Vagen","year":"2010","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2136\/sssaj2001.652480x","article-title":"Near-infrared reflectance spectroscopy\u2013principal components regression analyses of soil properties","volume":"65","author":"Chang","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.compag.2015.03.013","article-title":"Soil organic carbon and particle sizes mapping using VIS\u2013NIR, EC and temperature mobile sensor platform","volume":"114","author":"Knadel","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"923","DOI":"10.2134\/jeq2009.0314","article-title":"Spectroscopic models of soil organic carbon in florida, USA","volume":"39","author":"Vasques","year":"2010","journal-title":"J. Environ. Qual."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"949","DOI":"10.2136\/sssaj2013.07.0264","article-title":"Sensing of soil organic carbon using visible and near-infrared spectroscopy at variable moisture and surface roughness","volume":"78","author":"Rodionov","year":"2014","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1097\/SS.0b013e3182986735","article-title":"Prediction of soil organic matter content under moist conditions using VIS\u2013NIR diffuse reflectance spectroscopy","volume":"178","author":"Wang","year":"2013","journal-title":"Soil Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"15561","DOI":"10.3390\/rs71115561","article-title":"Reducing the influence of soil moisture on the estimation of clay from hyperspectral data: A case study using simulated PRISMA data","volume":"7","author":"Castaldi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1021\/es504272x","article-title":"In situ measurements of organic carbon in soil profiles using VIS\u2013NIR spectroscopy on the Qinghai\u2013Tibet plateau","volume":"49","author":"Li","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.geoderma.2017.09.013","article-title":"Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by VIS\u2013NIR spectroscopy","volume":"310","author":"Xu","year":"2018","journal-title":"Geoderma"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/1\/28\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:55:22Z","timestamp":1760208922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/1\/28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,24]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["rs10010028"],"URL":"https:\/\/doi.org\/10.3390\/rs10010028","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,24]]}}}