Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy

dc.contributor.authorMouazen, Abdul M
dc.contributor.buuauthorTümsavaş, Zeynal
dc.contributor.buuauthorTekin, Yücel
dc.contributor.buuauthorUlusoy, Yahya
dc.contributor.departmentBursa Uludağ Üniversitesi/Ziraat Fakültesi/Toprak Bilimi ve Bitki Besleme Bölümü.tr_TR
dc.contributor.orcid0000-0003-2658-3905tr_TR
dc.contributor.researcheridAAG-6056-2021tr_TR
dc.contributor.scopusid6507710594tr_TR
dc.contributor.scopusid15064756600tr_TR
dc.contributor.scopusid6508189419tr_TR
dc.date.accessioned2022-12-13T10:30:41Z
dc.date.available2022-12-13T10:30:41Z
dc.date.issued2018-06-28
dc.description.abstractThe aim of this research was to examine the potential of visible and near infrared (Vis-NIR) spectroscopy for the prediction and mapping of sand and clay fractions of soils in one irrigated field having clay texture in Karacabey district of Bursa Province, Turkey. Eighty six soil samples, collected from the study area, were divided into calibration (80%) and validation (20%) sets. A partial least squares regression (PLSR) with leave-one-out cross-validation analysis was carried out using the calibration set, and the resulting model prediction ability was tested using the prediction set. Models developed were used to predict sand and clay content using laboratory spectra and spectra collected on-line from the field. Results showed an "excellent" laboratory prediction performance for both sand (regression coefficient (R-2) = 0.90, root mean square error of prediction (RMSEP) = 2.91% and ratio of prediction deviation (RPD) = 3.25 in cross-validation; R-2 = 0.81, RMSEP = 3.84% and RPD = 2.33 in the prediction set) and clay (R-2 = 0.91, RMSEP = 2.67% and RPD = 3.51 in cross validation; R-2 = 0.85, RMSEP = 3.40% and RPD = 2.66 in the prediction set). On-line predictions were less accurate than the laboratory results, although the online predictions were still very good (RPD = 2.25-2.31). Kappa statistics showed reasonable similarities between measured and predicted maps, particularly for those obtained with laboratory scanning. This study demonstrated that soil sand and clay can be successfully measured and mapped using Vis-NIR spectroscopy under both laboratory and on-line scanning conditions.en_US
dc.description.sponsorshipICT-AGRI (The European Commission's ERA-NET scheme under the 7th Framework Programme)en_US
dc.description.sponsorshipDepartment for Environment, Food & Rural Affairs (DEFRA)en_US
dc.description.sponsorshipFWOen_US
dc.identifier.citationTümsavaş, Z. vd. (2019). ''Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy''. Biosystems Engineering, 177, 90-100.en_US
dc.identifier.endpage100tr_TR
dc.identifier.issn1537-5110
dc.identifier.issn1537-5129
dc.identifier.scopus2-s2.0-85049025061tr_TR
dc.identifier.startpage90tr_TR
dc.identifier.urihttps://doi.org/10.1016/j.biosystemseng.2018.06.008
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1537511017311480
dc.identifier.urihttp://hdl.handle.net/11452/29844
dc.identifier.volume177tr_TR
dc.identifier.wos000456352500009tr_TR
dc.indexed.scopusScopusen_US
dc.indexed.wosSCIEen_US
dc.language.isoenen_US
dc.publisherAcademic Press Inc Elsevier Scienceen_US
dc.relation.collaborationYurt dışıtr_TR
dc.relation.journalBiosystems Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.relation.tubitak1120471tr_TR
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPls regression analysisen_US
dc.subjectSanden_US
dc.subjectClayen_US
dc.subjectVis-nir spectroscopyen_US
dc.subjectReflectance spectroscopyen_US
dc.subjectMoisture-contenten_US
dc.subjectOrganic-carbonen_US
dc.subjectTextureen_US
dc.subjectQaulityen_US
dc.subjectColoren_US
dc.subjectCalibrationen_US
dc.subjectForecastingen_US
dc.subjectInfrared devicesen_US
dc.subjectLaboratoriesen_US
dc.subjectLeast squares approximationsen_US
dc.subjectMappingen_US
dc.subjectMean square erroren_US
dc.subjectNear infrared spectroscopyen_US
dc.subjectRegression analysisen_US
dc.subjectSoilsen_US
dc.subjectSpectrum analysisen_US
dc.subjectTexturesen_US
dc.subjectLeave-one-out cross validationsen_US
dc.subjectNir spectroscopyen_US
dc.subjectPartial least squares regressions (PLSR)en_US
dc.subjectPrediction performanceen_US
dc.subjectRegression coefficienten_US
dc.subjectRoot-mean-square error of predictionsen_US
dc.subjectVisible and near infrareden_US
dc.subjectVisible and near-infrared spectroscopyen_US
dc.subjectPredictive analyticsen_US
dc.subjectAgricultureen_US
dc.subject.scopusSoil Color; Near-Infrared Spectroscopy; Hyperspectralen_US
dc.subject.wosAgricultural engineeringen_US
dc.subject.wosAgriculture, multidisciplinaryen_US
dc.titlePrediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopyen_US
dc.typeArticle
dc.wos.quartileQ1 (Agriculture, multidisciplinary)en_US
dc.wos.quartileQ2en_US

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