{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:59:07Z","timestamp":1773622747747,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,10]],"date-time":"2018-11-10T00:00:00Z","timestamp":1541808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In situ, diffuse reflectance spectroscopy (DRS) profile soil sensors have the potential to provide both rapid and high-resolution prediction of multiple soil properties for precision agriculture, soil health assessment, and other applications related to environmental protection and agronomic sustainability. However, the effects of soil moisture, other environmental factors, and artefacts of the in-field spectral data collection process often hamper the utility of in situ DRS data. Various processing and modeling techniques have been developed to overcome these challenges, including external parameter orthogonalization (EPO) transformation of the spectra. In addition, Bayesian modeling approaches may improve prediction over traditional partial least squares (PLS) regression. The objectives of this study were to predict soil organic carbon (SOC), total nitrogen (TN), and texture fractions using a large, regional dataset of in situ profile DRS spectra and compare the performance of (1) traditional PLS analysis, (2) PLS on EPO-transformed spectra (PLS-EPO), (3) PLS-EPO with the Bayesian Lasso (PLS-EPO-BL), and (4) covariate-assisted PLS-EPO-BL models. In this study, soil cores and in situ profile DRS spectrometer scans were obtained to ~1 m depth from 22 fields across Missouri and Indiana, USA. In the laboratory, soil cores were split by horizon, air-dried, and sieved (&lt;2 mm) for a total of 708 samples. Soil properties were measured and DRS spectra were collected on these air-dried soil samples. The data were randomly split into training (n = 308), testing (n = 200), and EPO calibration (n = 200) sets, and soil textural class was used as the categorical covariate in the Bayesian models. Model performance was evaluated using the root mean square error of prediction (RMSEP). For the prediction of soil properties using a model trained on dry spectra and tested on field moist spectra, the PLS-EPO transformation dramatically improved model performance relative to PLS alone, reducing RMSEP by 66% and 53% for SOC and TN, respectively, and by 76%, 91%, and 87% for clay, silt, and sand, respectively. The addition of the Bayesian Lasso further reduced RMSEP by 4\u201311% across soil properties, and the categorical covariate reduced RMSEP by another 2\u20139%. Overall, this study illustrates the strength of the combination of EPO spectral transformation paired with Bayesian modeling techniques to overcome environmental factors and in-field data collection artefacts when using in situ DRS data, and highlights the potential for in-field DRS spectroscopy as a tool for rapid, high-resolution prediction of soil properties.<\/jats:p>","DOI":"10.3390\/s18113869","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T02:42:41Z","timestamp":1542163361000},"page":"3869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Predicting Profile Soil Properties with Reflectance Spectra via Bayesian Covariate-Assisted External Parameter Orthogonalization"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6492-913X","authenticated-orcid":false,"given":"Kristen","family":"S. Veum","sequence":"first","affiliation":[{"name":"USDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"A. Parker","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Missouri, Columbia, MO 65211, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth","family":"A. Sudduth","sequence":"additional","affiliation":[{"name":"USDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott","family":"H. Holan","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Missouri, Columbia, MO 65211, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"637","DOI":"10.2136\/sssaj2014.09.0390","article-title":"Estimating a soil quality index with VNIR reflectance spectroscopy","volume":"79","author":"Veum","year":"2015","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2136\/sssaj2001.652480x","article-title":"Near-infrared reflectance spectroscopy-principal components regression analysis of soil properties","volume":"65","author":"Chang","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.13031\/2013.28498","article-title":"Soil organic matter, CEC, and moisture sensing with a prototype NIR spectrometer","volume":"36","author":"Sudduth","year":"1993","journal-title":"Trans. ASAE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"683","DOI":"10.13031\/2013.27385","article-title":"Wavelength identification and diffuse reflectance estimation for surface and profile soil properties","volume":"52","author":"Lee","year":"2009","journal-title":"Trans. ASAE"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.geoderma.2009.04.010","article-title":"Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy","volume":"151","author":"Morgan","year":"2009","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"844","DOI":"10.2136\/sssaj2005.0025","article-title":"Detection of carbon stock change in agricultural soils using spectroscopic techniques","volume":"70","author":"Stevens","year":"2006","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_7","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-NIR spectroscopy","volume":"199","author":"Nocita","year":"2013","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1020612319014","article-title":"The prediction of C and N content and their potential mineralization in heterogeneous soil samples using VIS-NIR spectroscopy and comparative methods","volume":"246","author":"Fystro","year":"2002","journal-title":"Plant Soil"},{"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","author":"Minasny","year":"2011","journal-title":"Geoderma"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.geoderma.2009.04.005","article-title":"Near-versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis: Where are we and what needs to be done?","volume":"158","author":"Reeves","year":"2010","journal-title":"Geoderma"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.still.2006.03.009","article-title":"On-line measurement of some selected soil properties using a VIS\u2013NIR sensor","volume":"93","author":"Mouazen","year":"2007","journal-title":"Soil Till. Res."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0169-7439(03)00051-0","article-title":"EPO-PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits","volume":"66","author":"Roger","year":"2003","journal-title":"Chemometrics Intellig. Lab. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.geoderma.2014.01.011","article-title":"VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact","volume":"221","author":"Ge","year":"2014","journal-title":"Geoderma"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.geoderma.2016.10.018","article-title":"Penetrometer-mounted VisNIR spectroscopy: Application of EPO-PLS to in situ VisNIR spectra","volume":"286","author":"Ackerson","year":"2017","journal-title":"Geoderma"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1255\/jnirs.71","article-title":"Development of a calibration equation with temperature compensation for determining the Brix value in intact peaches","volume":"3","author":"Kawano","year":"1995","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1111\/j.1365-2389.2010.01283.x","article-title":"Near-infrared spectroscopy for within-field soil characterization: Small local calibrations compared with national libraries spiked with local samples","volume":"61","author":"Wetterlind","year":"2010","journal-title":"Eur. J. Soil Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.geoderma.2007.04.021","article-title":"Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed","volume":"140","author":"Brown","year":"2007","journal-title":"Geoderma"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2005.03.007","article-title":"Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties","volume":"131","author":"Walvoort","year":"2006","journal-title":"Geoderma"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1111\/j.1365-2389.2009.01178.x","article-title":"Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS)","volume":"60","author":"Gomez","year":"2009","journal-title":"Eur. J. Soil Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.13031\/2013.31816","article-title":"Evaluation of reflectance methods for soil organic matter sensing","volume":"34","author":"Sudduth","year":"1991","journal-title":"Trans. ASAE"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1097\/00010694-200202000-00003","article-title":"Near-infrared reflectance spectroscopic analysis of soil C and N","volume":"167","author":"Chang","year":"2002","journal-title":"Soil Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1111\/j.1365-2389.2011.01372.x","article-title":"On the soil information content of visible\u2013near infrared reflectance spectra","volume":"62","author":"Chappell","year":"2011","journal-title":"Eur. J. Soil Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0065-2113(10)07005-7","article-title":"Chapter Five\u2014Visible and Near Infrared Spectroscopy in Soil Science","volume":"107","author":"Stenberg","year":"2010","journal-title":"Adv. Agron."},{"key":"ref_26","unstructured":"Adamchuk, V.I., Allred, B., Doolittle, J., Grote, K., and Viscarra Rossel, R. (2015). Tools for proximal soil sensing. Soil Survey Manual, USDA."},{"key":"ref_27","unstructured":"Kweon, G., Lund, E., Maxton, C., Drummond, P., and Jensen, K. (2008). Situ Measurement of Soil Properties Using a Probe Based VIS-NIR Spectrophotometer, American Society of Agricultural and Biological Engineers."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1071\/SR08118","article-title":"The use of diffuse reflectance spectroscopy for in situ carbon and nitrogen analysis of pastoral soils","volume":"46","author":"Kusumo","year":"2008","journal-title":"Aust. J. Soil Res."},{"key":"ref_29","unstructured":"Christy, C., Drummond, P., Kweon, G., Maxton, C., Drelling, K., Jensen, K., and Lund, E. (2016). Multiple Sensor System and Method for Mapping Soil in Three Dimensions. (9285501B2), U.S. Patent."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.13031\/trans.12299","article-title":"Comparison of field and laboratory VNIR spectroscopy for profile soil property estimation","volume":"60","author":"Cho","year":"2017","journal-title":"Trans. ASABE"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1111\/ejss.12228","article-title":"Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool","volume":"66","author":"Wetterlind","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"683","DOI":"10.13031\/trans.12049","article-title":"Profile soil property estimation using a VIS-NIR-EC-force probe","volume":"60","author":"Cho","year":"2017","journal-title":"Trans. ASABE"},{"key":"ref_33","unstructured":"(2018, November 02). USDA-NRCS Land Resource Regions and Major Land Resource Areas of the United States, Available online: https:\/\/naldc.nal.usda.gov\/download\/CAT82777198\/PDF."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nelson, D.W., and Sommers, L.E. (2018, November 02). Total Carbon, Organic Carbon and Organic Matter. Available online: https:\/\/dl.sciencesocieties.org\/publications\/books\/abstracts\/sssabookseries\/methodsofsoilan3\/961.","DOI":"10.2136\/sssabookser5.3.c34"},{"key":"ref_35","unstructured":"Gee, G.W., and Or, D. (2018, November 02). Particle-Size Analysis. Available online: https:\/\/s3.amazonaws.com\/academia.edu.documents\/42835761\/2_4_Particle_Size_Analysis_2002.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1541150374&Signature=mvBgnQEiCff9TECuQCXyr2sg78Q%3D&response-content-disposition=inline%3B%20filename%3D2_4_Particle_Size_Analysis_2002.pdf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v018.i02","article-title":"The pls package: Principal component and partial least squares regression in R","volume":"18","author":"Mevik","year":"2007","journal-title":"J. Stat. Software"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1198\/016214508000000337","article-title":"The Bayesian Lasso","volume":"103","author":"Park","year":"2008","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_39","first-page":"382","article-title":"Bayesian model averaging: A tutorial","volume":"14","author":"Hoeting","year":"1999","journal-title":"Stat. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1080\/01621459.2000.10474335","article-title":"Gibbs sampling","volume":"95","author":"Gelfand","year":"2000","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_41","unstructured":"Casella, G., and Berger, R.L. (2002). Statistical Inference, Duxbury."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3869\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:29:03Z","timestamp":1760196543000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3869"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,10]]},"references-count":41,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113869"],"URL":"https:\/\/doi.org\/10.3390\/s18113869","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,10]]}}}