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1、PublishedonlineMay17,2018SpecialSection:NoninvasiveImagingofProcessesinNaturalPorousMediaCombiningX-rayComputedTomographyandVisibleNear-InfraredSpectroscopyforPredictionofSoilStructuralPropertiesSheelaKatuwal,*CecilieHermansen,MariaKnadel,PerMoldrup,MogensH.Greve,a
2、ndL.W.deJongeSoilstructureisakeysoilpropertyaffectingasoil’sflowandtransportbehavior.X-raycomputedtomography(CT)isincreasinglyusedtoquan-tifysoilstructure.However,theavailability,cost,time,andskillsrequiredforprocessingarestilllimitingthenumberofsoilsstudied.Visibl
3、enear-infrared(vis-NIR)spectroscopyisarapidanalyticaltechniqueusedsuccessfullytoCoreIdeaspredictvarioussoilproperties.Inthisstudy,thepotentialofusingvis-NIR?Vis-NIRcanbeusedforestima-spectroscopytopredictX-rayCTderivedsoilstructuralpropertieswastionofsoilphysicalan
4、dstructuralinvestigated.Inthisstudy,127soilsamplesfromsixagriculturalfieldswithinproperties.DenmarkwithawiderangeoftexturalpropertiesandorganicC(OC)con-?Structuralparametersarebettertentswerestudied.Macroporosity(>1.2mmindiameter)andCTpredictedusingvis-NIRthanpedo-
5、matrix(thetransferfunctions.densityofthefield-moistsoilmatrixdevoidoflargemacroporesandstones)?Vis-NIRcanbeafastandreliableweredeterminedfromX-rayCTscansofundisturbedsoilcores(19by20cm).methodforpredictingsoils’transportBothmacroporosityandCTmatixaresoilstructuralp
6、ropertiesthataffectthebehavior.degreeofpreferentialtransport.Bulksoilsfromthe127samplinglocationswerescannedwithavis-NIRspectrometer(400–2500nm).MacroporosityandCTmatrixwerestatisticallypredictedwithpartialleastsquaresregres-sion(PLSR)usingthevis-NIRdata(vis-NIR-PL
7、SR)andmultiplelinearregression(MLR)basedonsoiltextureandOC.Thestatisticalpredictionofmacroporos-itywaspoor,withbothvis-NIR-PLSRandMLR(R2<0.45,ratioofperformancetodeviation[RPD]<1.4,andratioofperformancetointerquartiledistance[RPIQ]<1.8).TheCT2>0.65,RPD>1.5,andRPIQm
8、atrixwaspredictedbetter(R>2.0)combiningthemethods.Theresultsillustratethepotentialapplicabilityofvis-NIRspectroscopyforrapidassessment/prediction
