{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:24:04Z","timestamp":1778801044540,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T00:00:00Z","timestamp":1559260800000},"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>The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400\u20131000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000\u20132500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711\u2013715, 727, 986\u2013998, and 433\u2013435 nm regions (VNIR); and 2365\u20132373, 2481\u20132500, and 2198\u20132206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors.<\/jats:p>","DOI":"10.3390\/rs11111298","type":"journal-article","created":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T11:59:56Z","timestamp":1559303996000},"page":"1298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada"],"prefix":"10.3390","volume":"11","author":[{"given":"Ahmed","family":"Laamrani","sequence":"first","affiliation":[{"name":"Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada"},{"name":"Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8438-5662","authenticated-orcid":false,"given":"Aaron A.","family":"Berg","sequence":"additional","affiliation":[{"name":"Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Voroney","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hannes","family":"Feilhauer","sequence":"additional","affiliation":[{"name":"Institute of Geography, University of Erlangen-Nuremberg, 91058 Erlangen, Germany"},{"name":"Institute of Geographical Sciences, Free University Berlin, 12249 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Line","family":"Blackburn","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"March","sequence":"additional","affiliation":[{"name":"Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3712-9022","authenticated-orcid":false,"given":"Phuong D.","family":"Dao","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhong","family":"He","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ralph C.","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,31]]},"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":"341","DOI":"10.1016\/j.geoderma.2007.06.013","article-title":"Regional assessment of soil organic carbon changes under agriculture in Southern Belgium (1955\u20132005)","volume":"141","author":"Goidts","year":"2007","journal-title":"Geoderma"},{"key":"ref_3","unstructured":"Weng, L. (2019, May 13). 2016 & 2011 Census of Agriculture and Strategic Policy Branch, OMAFRA, Available online: http:\/\/www.omafra.gov.on.ca\/english\/stats\/county\/southern_ontario.htm."},{"key":"ref_4","first-page":"364","article-title":"Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties","volume":"44","author":"Banin","year":"1995","journal-title":"Soil. Sci. Soc. Am. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0065-2113(10)07005-7","article-title":"Visible and near infrared spectroscopy in soil science","volume":"107","author":"Stenberg","year":"2010","journal-title":"Adv. Agron."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., and Bochtis, D. (2019). Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sens., 11.","DOI":"10.3390\/rs11060676"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/bs.agron.2015.02.002","article-title":"Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring","volume":"132","author":"Nocita","year":"2015","journal-title":"Adv. Agron."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Crucil, G., Castaldi, F., Aldana-Jague, E., van Wesemael, B., Macdonald, A., and Van Oost, K. (2019). Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors for Soil Organic Carbon Prediction. Sustainability, 11.","DOI":"10.3390\/su11071889"},{"key":"ref_9","first-page":"554","article-title":"Spectral band selection for vegetation properties retrieval using Gaussian processes regression","volume":"52","author":"Verrelst","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.engappai.2013.07.010","article-title":"A novel approach to hyperspectral band selection based on spectral shape similarity analysis and fast branch and bound search","volume":"27","author":"Li","year":"2014","journal-title":"Eng. Appl. Artif. Intel."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.patrec.2016.05.013","article-title":"A new hyperspectral band selection and classification framework based on combining multiple classifiers","volume":"83","author":"Li","year":"2016","journal-title":"Pattern Recogn. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1016\/j.rse.2008.09.019","article-title":"Using Imaging Spectroscopy to study soil properties","volume":"113","author":"Chabrillat","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.geoderma.2009.11.032","article-title":"Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy","volume":"158","author":"Stevens","year":"2010","journal-title":"Geoderma"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.geoderma.2008.06.011","article-title":"Soil organic carbon prediction by hyperspectral remote sensing and fi eld vis-NIR spectroscopy: An Australian case study","volume":"146","author":"Gomez","year":"2008","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.chemolab.2019.03.011","article-title":"Using interpretable fuzzy rule-based models for the estimation of soil organic carbon from VNIR\/SWIR spectra and soil texture","volume":"189","author":"Tsakiridis","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1097\/SS.0000000000000002","article-title":"Using vis-NIR spectroscopy for monitoring temporal changes in soil organic carbon","volume":"178","author":"Deng","year":"2013","journal-title":"Soil Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.geoderma.2016.10.019","article-title":"Modelling the topsoil carbon stock of agricultural lands with the Stochastic Gradient Treeboost in a semi-arid Mediterranean region","volume":"286","author":"Schillaci","year":"2017","journal-title":"Geoderma"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.03.033","article-title":"Multi-method ensemble selection of spectral bands related to leaf biochemistry","volume":"164","author":"Feilhauer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recogn. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"53","DOI":"10.4141\/cjss10029","article-title":"Predicting soil organic carbon and total nitrogen using mid-and near-infrared spectra for Brookston clay loam soil in Southwestern Ontario, Canada","volume":"91","author":"Xie","year":"2011","journal-title":"Can. J. Soil Sci."},{"key":"ref_21","first-page":"77","article-title":"Infrared spectroscopy prediction of organic carbon and total nitrogen in soil and particulate organic matter from diverse Canadian agricultural regions","volume":"98","author":"Zhang","year":"2018","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1093\/jxb\/erl123","article-title":"Hyperspectral remote sensing of plant pigments","volume":"58","author":"Blackburn","year":"2006","journal-title":"J. Exp. Bot."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"S67","DOI":"10.1016\/j.rse.2008.10.019","article-title":"Retrieval of foliar information about plant pigment systems from high resolution spectroscopy","volume":"113","author":"Ustin","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4305","DOI":"10.3390\/rs6054305","article-title":"Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes","volume":"6","author":"Liu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_25","unstructured":"Environment Canada (2019, May 13). Canadian Climate Normals 1981\u20132010: Fergus Shand Dam Weather Station, Available online: http:\/\/climate.weather.gc.ca\/climate_normals\/index_e.html."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/00103629809369925","article-title":"Direct measurement of organic carbon content in soils by the Leco CR-12 carbon analyzer","volume":"29","author":"Wang","year":"1998","journal-title":"Commun. Soil Sci Plan."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1139\/cjss-2016-0116","article-title":"Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy","volume":"97","author":"Sorenson","year":"2017","journal-title":"Can. J. Soil Sci."},{"key":"ref_28","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_29","first-page":"17","article-title":"Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy","volume":"12","author":"Schlerf","year":"2010","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_30","unstructured":"Constantine, W., and Percival, D. (2019, May 29). Wavelet Methods for Time Series Analysis. Available online: https:\/\/cran.r-project.org\/package=wmtsa."},{"key":"ref_31","unstructured":"(2019). R Core Team R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v011.i09","article-title":"kernlab-an S4 package for kernel methods in R","volume":"11","author":"Karatzoglou","year":"2004","journal-title":"J. Stat. Softw."},{"key":"ref_34","unstructured":"Mevik, B.-H., and Wehrens, R. (2015). Introduction to the pls Package. Help Section of The \u201cPls\u201d Package of R Studio Software, R Foundation for Statistical Computing."},{"key":"ref_35","first-page":"18","article-title":"Classification and regression by random Forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_36","unstructured":"Mevik, B.-H., Wehrens, R., and Liland, K.H. (2018). Pls: Partial Least Squares and Principal Component Regression, R Foundation for Statistical Computing. R Package Version 2.4-3."},{"key":"ref_37","unstructured":"Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C.-C., and Lin, C.-C. (2019). e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. R package version 1.7-0.1."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.catena.2014.09.004","article-title":"Laboratory-based Vis-NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content","volume":"124","author":"Conforti","year":"2015","journal-title":"Catena"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2136\/sssaj2001.652480x","article-title":"Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties","volume":"65","author":"Chang","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.geoderma.2011.05.006","article-title":"Comparison and detection of total and available soil carbon fractions using visible\/near infrared diffuse reflectance spectroscopy","volume":"164","author":"Sarkhot","year":"2011","journal-title":"Geoderma"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4764","DOI":"10.3390\/s120404764","article-title":"Multiple classifier system for remote sensing image classification: A review","volume":"12","author":"Du","year":"2012","journal-title":"Sensors"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Chen, Y., Guo, L., Fei, T., and Qi, K. (2016). Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy. Remote Sens., 8.","DOI":"10.3390\/rs8090755"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"958","DOI":"10.2136\/sssaj2013.09.0408","article-title":"Prediction of soil organic carbon under varying moisture levels using reflectance spectroscopy","volume":"78","author":"Rienzi","year":"2014","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/02757259509532297","article-title":"Spectral reflectance of carbonate mineral mixtures and bidirectional reflectance theory: Quantitative analysis techniques for application in remote sensing","volume":"13","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1366\/0003702904086821","article-title":"Near-infrared reflectance analysis of carbonate concentrations in soils","volume":"44","author":"Banin","year":"1990","journal-title":"Appl. Spectrosc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:55Z","timestamp":1760187295000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,31]]},"references-count":45,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11111298"],"URL":"https:\/\/doi.org\/10.3390\/rs11111298","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,31]]}}}