ContentslistsavailableatScienceDirect
International
Journal
of
Applied
Earth
Observation
and
Geoinformation
j ou rn a l h o m epa g e :w w w . e l s e v i e r . c o m / l o c a t e /j a g
Estimating
leaf
functional
traits
by
inversion
of
PROSPECT:
Assessing
leaf
dry
matter
content
and
specific
leaf
area
in
mixed
mountainous
forest
Abebe
Mohammed
Ali
a,b,∗,
Roshanak
Darvishzadeh
a,
Andrew
K.
Skidmore
a,
Iris
van
Duren
a,
Uta
Heiden
c,
Marco
Heurich
daFacultyofGeo-InformationScienceandEarthObservation(ITC),UniversityofTwente,P.O.Box217,7500AEEnschede,TheNetherlands bDepartmentofGeographyandEnvironmentalStudies,WolloUniversity,P.O.Box1145,Dessie,Ethiopia
cGermanAerospaceCenter(DLR),GermanRemoteSensingDataCenter(DFD)Oberpfaffenhofen,82234Wessling,Germany dBavarianForestNationalPark,94481,Grafenau,Germany
a
r
t
i
c
l
e
i
n
f
o
Articlehistory: Received30April2015
Receivedinrevisedform16October2015 Accepted3November2015
Availableonline16November2015 Keywords:
Functionalleaftraits Radiativetransfermodel PROSPECT
LDMC SLA
a
b
s
t
r
a
c
t
Assessmentsofecosystemfunctioningrelyheavilyonquantificationofvegetationproperties.Thesearch isonformethodsthatproducereliableandaccuratebaselineinformationonplantfunctionaltraits.In thisstudy,theinversionofthePROSPECTradiativetransfermodelwasusedtoestimatetwofunctional leaftraits:leafdrymattercontent(LDMC)andspecificleafarea(SLA).InversionofPROSPECTusually aimsatquantifyingitsdirectinputparameters.Thisisthefirsttimethetechniquehasbeenusedto indirectlymodelLDMCandSLA.Biophysicalparametersof137leafsamplesweremeasuredinJuly2013 intheBavarianForestNationalPark,Germany.Spectraoftheleafsamplesweremeasuredusingan ASDFieldSpec3equippedwithanintegratingsphere.PROSPECTwasinvertedusingalook-uptable(LUT) approach.TheLUTsweregeneratedwithandwithoutusingpriorinformation.Theeffectofincorporating priorinformationontheretrievalaccuracywasstudiedbeforeandafterstratifyingthesamplesinto broadleafandconifercategories.TheestimatedvalueswereevaluatedusingR2andnormalizedroot
meansquareerror(nRMSE).
AmongtheretrievedvariablesthelowestnRMSE(0.0899)wasobservedforLDMC.Forbothtraits higherR2values(0.83forLDMCand0.89forSLA)werediscoveredinthepooledsamples.Theuseofprior
informationimprovedaccuracyoftheretrievedtraits.Thestrongcorrelationbetweentheestimated traitsandtheNIR/SWIRregionoftheelectromagneticspectrumsuggeststhattheseleaftraitscouldbe assessedatcanopylevelbyusingremotelysenseddata.
©2015ElsevierB.V.Allrightsreserved.
1. Introduction
Componentsofbiodiversitythatinfluenceecosystem dynam-ics,stability,productivity,nutrientbalanceandotheraspectsof ecosystemfunctioningarecollectivelyreferredasfunctional diver-sity(e.g.,Tilmanetal.,1997;Tilman,2001).Mostecologistsnow agreethatamajordeterminantofecosystemfunctioningis func-tionaldiversity,ratherthannumberof speciesperse(Díaz and Cabido,2001).Byquantifyingfunctionaldiversityinnatural com-munities,researchersgainadditionalunderstandingofthespatial andtemporaldistributionofbiodiversity,ecosystemservicesand
∗ Correspondingauthorat:FacultyofGeo-InformationScienceandEarth Observa-tion(ITC),UniversityofTwente,P.O.Box217,Enschede,7500AE,TheNetherlands.
E-mailaddress:abebemohammed@yahoo.co.uk(A.M.Ali).
plantcommunityproductivity(Cadotteetal.,2009;Lavoreletal., 2011).Itisbelievedthatbetterconservationandrestoration deci-sions canbe madeby measuringand understandingfunctional diversity(Cadotteetal.,2011).Thisrealizationhasunderpinned theshiftinfocusofbiodiversityresearchfromspeciesdiversityto functionaldiversity(Tilman,2001).
Likespeciesdiversity,functionaldiversityisquantifiedonthe basisoftraitvaluesoforganisms(PetcheyandGaston,2006;Zhang etal.,2012).Atraitisanymeasurablemorphological, physiologi-calorphenologicalfeatureofanorganism(Violleetal.,2007).In plants,atraitiscalledafunctionaltrait(e.g.,specificleafarea)when itaffectsplantfitnessindirectlyviaitsimpactsonplantgrowth, reproduction,andsurvival(Violleetal.,2007).Itisthe combina-tionofplantfunctionaltraitsthatdetermineshowplantsrespond toenvironmentalfactors,affectothertrophiclevels,andinfluence
http://dx.doi.org/10.1016/j.jag.2015.11.004
ecosystemprocessesandservices(Zhangetal.,2012).Forinstance, plantsgrowingina resource-richenvironmentwillhavea rela-tivelyhighspecificleafareaandlowdrymattercontent,whereas forplantsgrowinginaresource-poorenvironmenttheoppositeis true(Wilsonetal.,1999).Traitsalsoprovidealinkbetween ecosys-temfunctionaldiversityandspeciesrichness(Carlsonetal.,2007; Gregory,2008).Thefunctionaltraitsareincreasinglyusedto inves-tigatecommunitystructureandecosystemfunctioning,aswellas toclassifyspeciesintofunctionaltypes(Smithetal.,1997)orforto validateglobalvegetationmodels(Albertetal.,2010).
In general, plant traits can be categorized into four groups (Cornelissenetal.,2003):whole-planttraits(e.g.,growthformand height),stemandbelowgroundtraits(e.g.,stemspecificdensity andspecificrootlength),regenerativetraits(e.g.,seedmassand dispersalmode)andleaffunctionaltraits.Twofundamentalleaf functionaltraitsthatareofcentralinterestforresearchersareLeaf DryMatterContent(LDMC)andSpecificLeafArea(SLA)(Wilson etal.,1999;Asneretal.,2011).TheLDMC,sometimesreferredto astissuedensity,isthedrymassofaleafdividedbyitsfreshmass, commonlyexpressedinmg/g(Cornelissenetal.,2003).Itreflects plantgrowthrateandcarbonassimilationandisabetterpredictor oflocationonanaxisofresourcecapture,usageandavailability (Wilsonetal.,1999).TheSLAisdefinedastheleafareaperunitof dryleafmassusuallyexpressedinm2/kg(Cornelissenetal.,2003).
Itisreferredtoasleafmassperunitarea,asspecificleafmass,as wellasleafspecificmass.SLAlinksplantcarbonandwatercycles, andprovidesinformationonthespatialvariationofphotosynthetic capacity andleafnitrogencontent(Pierceet al.,1994). Accord-ingtothelatter,“SLAisindicativeofplantphysiologicalprocesses suchaslightcapture,growthratesandlifestrategiesofplants”.A worldwidefoliardatasetindicatesthat82%ofallvariationin pho-tosyntheticcapacitycanbeexplainedbySLAandnitrogen(Wright etal.,2004).SLAisspecies-specific,butsignificantplasticityexists withinandbetweenindividualplantsofthesamespecies(Pierce etal.,1994;Asneretal.,2011).
Besidestheirindependentroleasimportantecological indica-tors,LDMCandSLAcouldbeusedtoestimateleafthickness(LT). TheestimationofLTfromthetwotraitshasbeeninvestigatedin detailbyVileetal.(2005).ThisimpliesSLAisacompoundtrait whichisinverselyproportionaltotheproductofLDMCandLT.A studybyHodgsonetal.(2011)foundthatLDMC×LTaccountedfor nearlythreequartersoftheobservedvariationinSLAandthatvery differentcombinationsofLTandLDMCregularlygeneratesimilar valuesofSLA.However,therearemisconceptionsinthedefinition ofthestatedtraits.Inmanypublications,leafmassperarea(LMA orCm),whichistheinverseofSLA,isdefinedasLDMC.
Several trait data bases have been established worldwide throughfieldmeasurements(e.g.,Kleyeretal.,2008;Kattgeetal., 2011).However,acquiringinformation onsuchtraitspurely on the basis of field measurements is labor-intensive and time-consuming,andthusexpensive.Remotelysenseddatacanplaya criticalroleinacquiringsuchdataatbroadspatialscales. Hyper-spectralremotesensinghastheadvantageofprovidingdetailed andcontinuousspectralinformation,whichcanpotentiallybeused formeasuringthesetraits.Previousstudieshavefocusedonusing hyperspectraldatatoquantifybiochemicalandbiophysical vari-ablesofvegetation,suchaschlorophyllcontent,nitrogenandleaf areaindex(Darvishzadehetal.,2008a;VohlandandJarmer,2008; AsnerandMartin,2009;Knoxetal.,2010;Skidmoreetal.,2010; Asneretal.,2011;Laurentetal.,2011;Ramoeloetal.,2011;Asner and Martin, 2012;Ramoelo et al., 2012). Hyperspectralremote sensinghasalsobeenusedtomapcanopyfunctionalandspecies diversity(Carlsonetal.,2007;Papes¸etal.,2010)andtoestimate biodiversity(evensimplyasthenumberofspecies)(Lauver,1997; Gould,2000;Saatchietal.,2008;Papes¸etal.,2010;FéretandAsner, 2011;Ruiliang,2011;FéretandAsner,2014).However,directly
mappingindividualspeciesfromremotesensingbecomesdifficult atlargerscalesandinecosystemswithveryhighspecies variabil-ity.Analternativeapproachtomappingspeciesistoestimateplant functionaltraits,particularlythosefoundintreecrownleaves,and tousethese forassessing and monitoringbiodiversity (Carlson etal.,2007;Gregory,2008).
Themethodsappliedtoretrieveplanttraitsfromremote sens-ingdatacanbegroupedintostatisticalandphysical(Darvishzadeh etal.,2008b;leMaireetal.,2008):statisticaltechniquesareused tofindarelationbetweentheplanttraitmeasuredinsituandits spectralreflectanceorsometransformationofreflectance. Vegeta-tionindicesarewidelyusedinthisapproach.Whenhyperspectral dataareutilized,itispossibletoselectthemostinformative nar-rowspectrumfeaturesfromtheentireelectromagneticspectrum domainandusethemforsimpleandfastassessmentof vegeta-tionproperties(BrogeandMortensen,2002).However,statistical methods are knownto besite-specific and lack generalization. Analternativeistouseadeductiveorphysicalmodelapproach (Radiative Transfer Model (RTM)) inversion, which is based on physicallaws..RunninganRTMenablesthecreationofasimulated trainingdatabasecoveringawiderangeofsituationsand configu-rations.SuchforwardRTMsimulationsallowforsensitivitystudies ofparametersanddevelopmentofvegetationindices.Thismakes RTMinversionapproaches morepowerfulthanstatistical meth-ods.However,theretrievalofvariablesthroughRTMsinversion isill-posed,sincedifferentcombinationoftheinputparameters mayproducethesamespectralsignature.Toovercometheeffect oftheill-posedproblem,Combaletal.(2003)recommendedthe useofpriorinformation.Severalstudieshavereportedsignificant improvement totheaccuracy ofparameterretrievalafterusing priorinformation (e.g.,Malenovskyetal.,2006;Dasguptaetal., 2009);others(Feretetal.,2011;Romeroetal.,2012)havetried toexcludeunrealisticcombinationsofinputparametersby apply-ingalinearregressionequationderivedfromcorrelatingtheinput parameters.
LeafRTMssimulateleafreflectanceandtransmittancebyusing certaininputparametersderived fromleaves.Therearea num-ber of leaf RTMs and each one requires a different number of inputparameters. Onesuchleafradiativetransfer modelis the LIBERTY(LeafIncorporatingBiochemistryExhibitingReflectance andTransmittanceYields)model(Dawsonetal.,1998)forconifer needles.However,itrequiresmanyinputparameterswhichneed to be obtained by intensive fieldwork and laboratory analysis (Malenovskyetal.,2006;Morsdorfetal.,2009).Anotherwidely appliedleafradiativetransfermodelisPROSPECT(Jacquemoudand Baret,1990).PROSPECT,whichstandsforPROpriétésSPECTrales (FrenchforSpectralProperties).Itsimulatesleafreflectanceand transmittanceandisthemostpopularleafopticalpropertiesmodel ofallthosepublishedsince1990(Jacquemoudetal.,2009).
Althoughmuchworkhasbeendoneonestimatingplanttraits fromremotesensing,theestimationofLDMCandSLAatallscales (i.e.,leaf,canopyandlandscape)israre.Toourknowledge,theuse ofremotesensingtechniquestoestimateLDMChasnotyetbeen testedatanyscale.Comparedtootherbiophysicalvariables,studies conductedonSLAarealsolimitedandhavemainlybeenconducted usingstatisticalmethodsatacanopyscale.Lymburneretal.(2000) testedseveralexistingvegetationindicesinordertoestimateSLA fromLandsatTMimageryandfoundastrongcorrelationbetween averagecanopySLAandgreen,red,NIRandMIRreflectanceof Land-satTMdata.Astrongcorrelationbetweenleafmassperareaand reflectanceinthe750–2500nmwavelengthrangehasbeenalso reportedfor tropicalrainforestleaf samples(Asner andMartin, 2008;Asneretal.,2011).Normalizedindicesforleafmassperarea atleafandcanopyscaleshavebeendevelopedonlyrecently,byle Maireetal.(2008)andFeretetal.(2011).However,theseindices needtobetestedonotherimages,sitesandcanopies(leMaire
Table1
Distributionofcollectedsamples,byspecies.
Category Species No.ofsamples
Broadleaftrees 1.Europeanbeech 44
2.Sycamoremaple 4
3.Mountainash 3
4.Goatwillow 2
Subtotal 53
aConifers 1.Norwayspruce 63
2.Fir 21
Subtotal 84
TotalNo.ofsamples 137
aEqualnumberofsamplesweretakenfromthecurrentgrowingperiod(C),
sec-ondgrowingperiod(C+)andthreeandmoregrowingperiods(C++)needleageclass (i.e.,thenumbersofsamplesperageclasswere21forNorwayspruceand7forfir species).
etal.,2008).Physicalmodels,whicharesupposedtobemuchmore robustthanstatisticalapproaches,havenotbeentestedforLDMC andSLAestimations.Ourstudythereforeaimedtoinvestigatehow accuratelyandpreciselytheLDMCandSLAcanbeestimatedin het-erogeneousforestsatleaflevelbyusingradiativetransfermodels, sothattheapplicationcanbeextendedtocanopyandlandscape scales.
2. Dataandmethods
2.1. Studyareaandfielddatacollection
Theareachosenforthisstudywasthemixedmountainforestof theBavarianForestNationalPark,whichismoreheterogeneousin treespeciesthansimilarareasintheregion.Itislocatedin south-easternGermanyalongtheborderwiththeCzechRepublic(490 319N,130129E).Elevationvariesfrom600mto1473mabove sealevel.Theclimateoftheregionistemperate,withhighannual precipitation(1200mmto1800mm)andlowaverageannual tem-perature(3◦to6◦Celsius).Heavysnowcoverischaracteristicofthe areainwinter.Brownsoilsarethepredominantsoiltypeatlower altitude(below900ma.s.l)whereasathighaltitude(above900m a.s.l)brownsoilsandbrownpodzolicsoilpredominate.Thesoilsin theareaarenaturallyacidicandlowinnutrientcontent(Heurich etal.,2010).
ThenaturalforestecosystemsoftheBavarianForestNational Parkvarywithaltitude:therearealluvialspruceforestsinthe val-leys,mixedmountainforestsonthehillsidesandmountainspruce forestsinthehighareas.Thedominanttreespeciesinclude Euro-peanbeech(Fagussylvatica),Norwayspruce(Piceaabies)andFir (Abiesalba).InthemixedmountainforestsSycamoremaple(Acer pseudoplatanusL.),Mountain ash(Sorbus aucupariaL.)andGoat willow(Salixcaprea)arealsofound(HeurichandNeufanger,2005). Afieldcampaignwasconductedbetween11Julyand23August 2013.Thestudyareawasstratifiedinto broadleaf,conifersand mixedforestcategories.Consideringthenatureoftheforest het-erogeneity,timeandcostconstraints,26plots(8inbroadleaf,7 inconiferand12inmixedstands)wererandomlyselectedwithin eachforestcategory.Eachplotwas30by30m,sothatthesamples couldsubsequently beusedtoestimatevariablesfrommedium resolutionremotesensingdata.Leafsampleswerethencollected fromeachtreespeciesfoundintheplot(Table1).Asthetwotraits ofinterest(SLAandLDMC)tendtovaryasonemovesdownward fromthetopofthetree,allthesamplesweretakenfrommature sunlitleavesatthetopofthecanopy.Acrossbowwasusedtoshoot anarrowattachedtoafishinglineatabranchwithsunlitleaves. Oncethefishinglinehadpassedaroundthetargetedbranch,the fishinglinewasusedtofeedaropeoverthebranchandthenthe branchwaspulleddowngentlyuntilitbrokeoff.Leaves/shoots
wereimmediatelyremovedfromthebranchandSPADchlorophyll measurementsweremadeforthebroadleafsamples.Theshootsof theconiferneedleswereclassifiedintothreeneedleageclasses: currentgrowingperiod(C),secondgrowingperiod(C+)andthree andmoregrowingperiods(C++).Thesampleswerethenplacedin azip-lockedplasticbagtogetherwithwetpulppaperand trans-portedtothelaboratoryinaportablecoolerwithfrozenicepacks. Inthelaboratory,theleafsampleswerestoredinacolddarkroom andprocessedwithinthedayofcollection.
2.2. Laboratorymeasurements 2.2.1. Physicalvariablemeasurements
Thebiophysicalcharacteristicsofthesamplessuchasfreshand dryweightandhemisphericalsurfaceareawereacquired simulta-neouslywiththespectrameasurements.Thefreshweightofeach samplewas determined(beforethespectral measurements)by usingadigitalscaleofhighprecision.Leafareaofbroadleafsamples wasmeasuredusingtheLI-3000Cportableleafareameter(Li-Cor, Inc,Lincoln,NE,USA).Inthecaseoftheconiferneedles,the sur-faceofthesampleneedleswasscannedusinganHPdoublelamp desktopscannerataresolutionof1200dpi;theneedleprojections werecomputedfromthegreyscaleimagesusingImageJimage pro-cessingsoftware(whichisfreelyavailableonline).Norwayspruce needlesarecylindricalandthereforetheirtotalsurfacewasfirst computedasaprojectedareamultipliedbyauniversalconversion factorof2.57derivedexperimentallyforNorwayspruceneedles (Waring, 1983).Then, thetotal needlesurfaceareawasdivided bytwotoacquirethehemispherical-surfaceprojectionofsampled spruceneedles.Finally,thesampleswereoven-driedat80◦Cfor 48handtheirdrybiomasswasweighed.Theleaftraitswerethen computedusingthesampleleavesorneedlesarea,freshweightand dryweight.Thesummarystatisticsofallthemeasuredvariablesare presentedinTable2.
2.2.2. Spectralmeasurements
Hemispherical reflectance and transmittance from 350 to 2500nmwith 1nm spectral resolution were measuredusing a FieldSpec®3portablespectroradiometerequippedwithan
inte-gratingspheremanufacturedbyAnalytical SpectralDevices,Inc (ASD), USA. The spectral measurement for each sample was obtainedbyaveragingthespectraon10randomlyselectedleaves inthecaseofbroadleafspeciesandon12–16needlesforconifers. Carewastakentoavoidlargeprimaryandsecondaryveinsduring thespectralmeasurement.Inordertominimizetheeffectof sig-nalvariance,twohundredscanswereaveragedineveryspectra measurementtoasinglespectrum.A calibratedreference stan-dard(withapproximately99%reflectance)wasusedtoconvertraw radiancetoreflectance.
Whereas thespectral measurement of broadleaf material is straightforward,thespectral measurementof coniferneedlesis not. Thisis because theconifer needles are very small and do notcoverthesampleportoftheintegratingsphere,whichhasa portdiameterof15mmforreflectanceand13.5mmfor transmit-tance.Therefore,thetechniquefirstdevelopedbyDaughtryetal. (1989)andlaterrevisedby(Mesarchetal.,1999)wasappliedto measurethespectral propertyoftheconifer needles.A univer-salsampleholderthatcouldaccommodatealllengthsofconifer needleswasdesigned,followingMalenovskyet al.(2006). Nee-dlesweredetachedfromeachsampleshoot,placedonthesample holder,securedwithscotchtapeandleavingaspaceof approx-imately one needle’s widthbetween needles toavoid multiple reflectancefromadjacentneedles(Daughtryetal.,1989).The sam-pleholderwascarefullyplacedatthesampleportoftheintegrating sphere,andreflectanceandtransmittancespectrawereacquired
Table2
Summarystatisticsofthemeasuredvariablesin137leafsamples.Thecalculatedvariableswereleafdrymattercontent(LDMC),leafmassperarea(Cm),Specificleafarea
(SLA)andleafwatercontent(Cw).
Category Basicstatistics Cm(g/cm2) Cw(g/cm2) LDMC(mg/g) SLA(cm2/g)
Broadleaf Minimumvalue 0.0034 0.0063 337.3 73.48
Maximumvalue 0.0136 0.0156 541.3 294.09
Meanvalue 0.0079 0.0092 460.6 135.38
St.deviation 0.0021 0.0021 42.70 38.25
Conifers Minimumvalue 0.0110 0.0116 361.5 34.36
Maximumvalue 0.0291 0.0337 598.4 90.74
Meanvalue 0.0202 0.0247 449.7 51.52
St.deviation 0.0039 0.0043 43.20 10.92
Fig.1.Theholescutintoabroadleafsampletomimicconiferneedlesamplesset upontheintegratingspheresampleport.
followingtheportconfigurationproceduresoftheASDintegrating
sphere.
Ablackpaintedpapermask witha15mmdiametercircular
aperture wasprecisely superimposed onthesamplesand
pho-tographsweretakenusinga16.1megapixelPanasonicDCM-TZ35
camera.Thenthegapfraction(GF)betweenilluminatedneedles
wascalculatedbasedontheilluminatedareaofthesampleport,
whichwas9mmdiameterforbothreflectanceandtransmittance.
Theilluminatedareasofthesampleswereclippedbydrawinga
circleof9mmdiameteratthecenterofeachpicture.The
propor-tionofpixelswithgapsbetweenneedleswasthendeterminedby
dividingthenumberofpixelswithgapsintothetotalnumberof
pixelsfoundinthe9mmcircularapertureareausingImageJ
soft-ware.Then,thefollowingequations(Eqs.(1)and(2))wereadapted
fromMesarchetal.(1999)fortheFieldspecASDspectrometer,to computethehemisphericalreflectanceandtransmittanceofthe sampledneedles. Reflectance= −Rd 1−Rd×Rr 1−GF (1) Transmittance=
1−−RdRd− (PwGF)1−1GFRr (2) whereandaremeasuredsamplereflectanceandtransmittance, Rdisstraylight(measuredinreflectancemode),Rrisreferenceof samplereflectance,GFisthegapfractionofthesample,andPwis thereflectanceoftheintegratingspherewall.Straylight(ambient light)insidetheintegratingspherewasmeasuredasaradiation fluxof theempty illuminatedsample portin reflectancemode. Thereflectanceoftheintegratingspherewallwasdeterminedby: PW=1−FnFtn RrhereFtnandFnareradiancemeasurementsin trans-mittanceandreflectancemodeswithnosample(Daughtryetal., 1989).Tocheckthereliabilityofthemethod,weconductedsome pre-liminaryexperimentsbeforemeasuringtheconiferneedles.The testentailedusingabroadleafsamplethatcoveredtheentireport oftheintegratingsphere.Firstwemeasuredthereflectanceand transmittancewhiletheentireportoftheintegratingspherewas coveredbytheselectedleafsample.Intheexactcircularareaof theleafthatcoveredtheportwethencutholes(Fig.1)tomimic thearrangementoftheconiferneedlesonthesampleport;we henceforthrefertothesampletreatedthiswayasthestriped sam-ple.We repositionedthecutcircular areaofthestripedsample
overtheportandrepeatedthespectralmeasurements.Wethen adjustedthestripedsample’sreflectanceandtransmittancesothat wecouldevaluatethegapfractioneffectsonthemeasured spec-traofconiferneedles.ThisreliabilitytestyieldedRMSE=0.08for transmittanceandRMSE=0.023forreflectance.Largererrors, par-ticularlyfortransmittance,wereobservedinthewavelengthranges 750–1300nmand1900–2500nm(Fig.2).
Throughvisualinspection,spectralmeasurementsintheranges of 350–400 and 2351–2500 were found tobe noisy and were removedfromallspectraldatasets.TheSavitzky–Golaysmoothing filter(SavitzkyandGolay,1964)withasecondorderpolynomial functionandbandwidthof15nmwasapplied,toeliminaterandom noisewithinthereflectanceandtransmittancespectralsignatures. 2.3. PROSPECTmodelsimulation
Testingtheaccuracyandrobustnessofphysical(i.e.,deductive) models,demanda largevolume ofdata.Obtaining thisdataby measuringrealleaveswouldbeverylaborious. Asolutionis to useradiativetransfermodels(RTM)togeneratealargespectral datasetincorporatingawiderangeofparametersandvariability. LeafRTMssimulateleafreflectanceandtransmittanceproperties byusingspecificinputparametersderivedfromleaves.Themost popularRTMforleafparametersisthePROSPECTleafoptical prop-ertiesmodel(Jacquemoudetal.,2009).Itidealizesaleafasastack ofplatescomposedofabsorbinganddiffusingconstituents.It sim-ulatesleafopticalproperties(i.e.,reflectanceandtransmittance) parameterizedbythefollowinginputs:chlorophyllcontent(Cab) ing/cm2,leafdrymassperunitarea(C
m)inmg/cm2,leafwater
massperunitarea(Cw)inmg/cm2,andeffectivenumberofleaf
lay-ers(N)(JacquemoudandBaret,1990).Themodelhasbeenwidely appliedtobroadleaf vegetationtoestimatechlorophyllcontent (Zhanget al., 2008;Ma etal., 2012;Rivera et al.,2013).It has alsobeensuccessfullyrecalibratedandusedtosimulatetheoptical propertiesofconiferousneedles(Malenovsk ´yetal.,2008;Morsdorf etal.,2009).ThemodelwasrevisedbyFeretetal.(2008)toimprove itsperformanceandapplicability.
BothPROSPECT-4andPROSPECT-5werereleasedatthesame time.ThedifferencebetweenthemisthatinPROSPECT-5leaf pig-mentsareseparatedintototalchlorophyllsandtotalcarotenoids, which improves the chlorophyll retrieval (Feret et al., 2008). Sincewewerenotinterestedinpigmentretrieval,inthisstudy, PROSPECT-4wasusedtosimulateleafreflectanceand transmit-tance.SLA(cm2/mg) wascomputed as1/C
m.SinceLDMCis the
amountofleafdryweightperunitoffreshleafmass,this parame-terwasderivedfromCmandCw.Eqs.(3)–(6)showthederivation
ofLDMCfromCmandCw: LDMC=Wd Wf (3) Cw= Wf−Wd A (4)
Fig.2.Comparisonofreflectance(left)andTransmittance(right)spectraofawholeleafsampleandstripedleafsampletakenfromabroadleaftree.
Fig.3. Comparisonofminimum,meanandmaximumleafreflectance(left)andtransmittance(right)ofmeasured(Mes)andPROSPECTsimulated(Sim)spectraforall137 samplescollectedinthemixedmountainforestsoftheBavarianForestNationalPark.
Cm=Wd
A (5)
ByreformulatingEqs.(4)and(5)forWdandWf
LDMC= Cm
Cm+Cw
(6) whereLDMCisleafdrymattercontentinmg/g,Cwisleafwater
massperareainmg/cm2,C
misleafdrymassperareaincm2/mg,
WdandWfareleafdryandfreshweightsinmgandgrespectively
andAisleafareaincm2.
Manystudieshaveconfirmedthatwavelengthsinthevisible andnearinfraredregion(400–800nm)arehighlysensitivetoleaf pigmentssuchaschlorophyllsandcarotenoids,whilethe short-waveinfrared regionisthemostsensitiveregionfor retrieving parametersrelatedtodrymatter(Jacquemoudetal.,1996;Asner etal., 2009,2011;Romero etal., 2012).Therefore,the spectral regionfrom800to2350nmwasusedtoretrieveLDMCandSLA. Thisrangeavoidstheneedtomeasureleafpigmentsformodel cal-ibrationandvalidation,sincetheyhavenoimpactontheselected rangespectralsignature(Romeroetal.,2012).Thelatterwasalso confirmedbysensitivityanalysisforthechlorophyll,byrunning themodelinforwardmode.
2.3.1. Generationoflook-uptables
Variousinversion algorithmscanbeusedtoretrievea given parameterthroughRTMs.Oneofthemostpopularandefficient isthe Look-UpTable(LUT) approach (Dasgupta etal., 2009).It involvesperformingrepeatedsimulationsofspectrabyusingthe
modelwithallcombinationsoftheinputparametersconstrained byreasonablerangesoftheinputvariables.TheLUTistheninverted duringretrievals.
WefollowedthegeneralproceduressetbyFeretetal.(2008)in theLUTgenerationandinversion.Thefirststepwastodetermine thestructuralinputparameterNofthe137leafsamples,which couldneitherbecollectedinthefieldnormeasuredinthe labora-tory.Thewavelength-independentparameterNwasretrievedfor eachsamplebyinvertingthemodelbyusingsimulationatthree wavelengthscorrespondingtomaximumreflectance,maximum transmittanceandminimumabsorptionofthemeasuredspectra (Jacquemoud andBaret, 1990;Feret etal., 2008).Althoughleaf structureparameterNcorrespondstothenumberofleaflayers andismostplausibleasawholenumber,totakeaccountofthe subtlevariationsinleafstructure,Ncanbeconsideredasareal numberwithcontinuousvalues(Jacquemoudetal.,1996).Tostart thesimulationthemaximumandminimumvaluesfoundinthe lit-eraturewereusedfortherangeofN.Therefore,Nwassetbetween 0.5and3.0(Combaletal.,2003;Malenovskyetal.,2006;Féretand Asner,2011).AminiLUT(251N×137=34387)wasgeneratedfrom allpossiblecombinationsofthefieldmeasuredvaluesofCmand
Cw(137pairs)withthatofNvaluessetatastepof0.01withinthe
statedrange(251Nvalues).ThestructuralparameterNwasthen retrieved(hereafterconsideredasmeasuredvalueofN)usingthe simulatedspectrawhichbestfitthemeasuredspectraofeach sam-ple.Thesearchforbestsimulationwasdeterminedbycalculating
Table3
LeafvariablesusedtobuildthespectraLUTwithandwithoutpriorinformationforthe137leafandconiferneedlesamplesbyusingthemaximumandminimumlimitsfrom theliteratureandfieldobservation.
Method Category Inputvariables
Cab(g/cm2) Cm(g/cm2) Cw(g/cm2) N
Unconstrained approach(without priorinformation)
Allthethreestrata (broadleaf,coniferand pooledsamples Min 40 0.0030 0.0060 0.50 Max 40 0.0295 0.0340 3.00 Step – 0.0005 0.0005 0.05 Constrainedapproach (withprior information)
Broadleafsamples Min 40 0.0034 0.0063 1.0
Max 40 0.0136 0.0156 1.85
Step – 0.0005 0.0005 0.05
Coniferneedles Min 40 0.0110 0.0116 1.0
Max 40 0.0291 0.0337 2.25
Step – 0.0005 0.0005 0.05
Pooledsamples Min 40 0.0034 0.0063 1.0
Max 40 0.0291 0.0337 2.25
Step – 0.0005 0.0005 0.05
Table4
DescriptivestatisticsofthestructuralparameterNvaluesforsampledtreesas retrievedbyinversionofthePROSPECT-4modelatthreeselectedwavelengthsin theinfraredregion.
Beech Maple Mountainash Fir Norwayspruce
C C+ C++ C C+ C++
Mean 1.42 1.43 1.70 1.63 1.69 1.89 1.47 1.50 1.56 Minimum 1.00 1.20 1.60 1.35 1.45 1.50 1.00 1.15 1.20 Maximum 1.80 1.60 1.85 2.05 2.00 2.25 2.20 2.10 2.20 St.deviation 0.16 0.17 0.13 0.23 0.20 0.25 0.28 0.23 0.27
andfindingthelowestrootmeansquareerrorofanunconstrained
non-linearmultivariatefunction(ColemanandLi,1996):
MN=
(mes−sim)2+ (mes−sim)2
n (7)
where n is the number of wavelengths selected (three in this case), mesand mesare measured values of reflectance and transmittance,andsimandsimaresimulatedvaluesofthethree wavelengths ().
Beforeusing thePROSPECTmodel for simulation, it maybe necessary to calibrate physical and optical constants such as therefractiveindex andabsorption coefficientsof leafmaterial withexperimentaldata (Feret et al.,2008).Thus, forward sim-ulations werefirst conductedusing theretrieved N values and inputparameters correspondingto38broadleaf and56 conifer needle samples randomly selected from the total sample. The suitabilityoftheoriginalPROSPECTmodel hadbeenverifiedby calculating the RMSE between measured and simulated spec-traof theselectedsamples.TheRMSEwaswithintherange of RMSEs documented in the literature (Feret et al., 2008). As a result,PROSPECT-4wasapplieddirectly,withoutcalibration,to simulatethespectraofthedifferenttypesofsampleleavesand needles.
To generate theLUTs, the PROSPECTmodel was run in the forward mode under two scenarios. In the first scenario, the model input parameters (Cm, Cw and N) were generated using
a uniformdistributionbasedona maximum range availablein theliterature(Table3)(Combalet al.,2003;Malenovsky etal., 2006;Féretand Asner,2011).Inthesecondscenario,the max-imum and minimum values of the input parameters were set based onsamples statistics as prior information. The values of CmandCw weresetbasedonthemaximumandminimum
val-uesofthesamplescollectedinthefield(Table2).Forstructural parameterN,weusedtheretrieved rangepresented inTable4 in the Results section. During all simulations, chlorophyll con-tentwasarbitrarilysetto40g/cm2.Allpossiblecombinations
oftheinputvariables weresystematicallyusedtogenerateLUT records.
Therewashighvariation inthe spectralproperties between broadleaf and conifer needle samples. In order to observe the effect of sample heterogeneity onaccuracy (i.e.,R2 and RMSE)
we ran the model with and without stratifying the samples intobroadleavesandconiferneedles.Runningthemodelin for-ward mode generated a LUT for conifer needle and broadleaf samples separately, as well as for the samples pooled when constrained approach was applied. The unconstrained method yielded 156,978 LUT records (51N×54Cm×57Cw). When the
maximum and minimum limits of the input parameters were constrainedbypriorinformation,therewasalargereductionin the total number of LUTrecords: to 7182(18N×21Cm×19Cw)
forbroadleaf,43,290(26N×37Cm×45Cw)forconiferneedlesand
74,360(26N×52Cm×55Cw)forthepooledsamples.The
computa-tiontimeforgeneratingtheLUTvariedaccordingtothevolumeof theLUT.
2.3.2. Modelinversionandvalidation
ThePROSPECTinputparameters(N,CmandCw)andthetwoleaf
functionaltraits(LDMCandSLA)weresimultaneouslyretrievedby searchingthebestmatchestothemeasuredspectrainthe gener-atedLUT.Duringinversion,LDMCwasrepresentedbyCm/(Cm+Cw)
asshownonEq.(6)andSLAasinverseofCm.Thisistominimize
effects ofmeasurement errors ofCmand Cw onLDMC andSLA
estimateswheninversionwithpriorinformationwasused. Oth-erwisethetwoleaftraitsaretotallydependentonCmandCwand
canbecomputedfromtheestimatedvaluesofthesemodelinput parameters.
Twosearchingalgorithmswereusedintheinversionprocess. For the LUT generated without prior information we used an unconstrainedapproachwhichdependedsolelyoncomparingthe similaritybetweensimulatedandmeasuredspectra.Thiswasdone bycomputingtheRMSEandfindingitslowestvalue,usingthemerit function(MN)inEq.(7).However,differentsetsofinputvariables andsimulatedspectracouldresultintothesameRMSEwhen com-paredwiththemeasureddata.Therefore,when(spectral)RMSEis thecriteriontosearchthebestmatch,multiplesolutionsmayexist intheLUT.Toovercomethislimitation,theLUTsgeneratedwith priorinformationwereinvertedbyusingmeanandvarianceofthe measuredinputparametersasaconstraintinadditionto measur-ingthesimilaritiesbetweentheobservedandsimulatedspectra (Eq.(8))(Combaletal.,2003;Malenovskyetal.,2006;Lauvernet etal.,2008;Jacquemoudetal.,2009).Theestimationsofallthe variablesweremadeusingallavailablewavelengthsintheNIRand SWIR(801–2350nm).
Fig.4.RootMeanSquareErrors(RMSE)computedbetweenthemeasuredand PROSPECTsimulatedreflectanceandtransmittanceforthe137leafandneedle samples. (8)MN=
(mes−sim)2 2 mes + (mes−sim)2 2 mes + j (Vj−Vjprior) 2 2 Vjmeswhere Vj is the estimated value of the simulta-neouslyretrievedvariablessuchasCm,Cw,LDMC,SLAandN,Vjprior
isthemeasuredpriorvalueofthevariablej(i.e.,meansofCm,Cw,
LDMCandSLAinTable2andmeanofNinTable4), 2 mes and
2
mesarevariancesofthemeasuredreflectanceandtransmittance
respectively and 2
Vjmes is the variance of the measured input variablej.The estimatedparameters werethen plotted against themeasuredleaftraitconcentrationsandevaluatedbymeansof rootmeansquareerror(RMSE); normalizedRMSE(nRMSE)was calculatedasRMSEdividedbythemeanofthegivenvariableand coefficientofdetermination(R2).
3. Results
3.1. DeterminationofthestructuralparameterNandmodel suitability
TheretrievedvaluesofNrangefrom1to2.25.ThemaximumN valuewasrecordedfortheC++ageclassoffirtree,whilethe min-imumvalueswereobservedinNorwayspruceCageclassneedles andEuropeanbeechleaves.TheaverageNvalueswere1.74forFir and1.5forNorwayspruce.Amongthebroadleafspecies,ahigher meanvalueofN(1.7)wasobservedinMountainash(Table4).
Inordertoevaluatethesuitabilityoftheleafmodel,we cal-culated R2 and the RMSE between measured spectra and the
correspondingsimulatedspectra. Thehemispherical reflectance andtransmittancemeasuredinlaboratoryandsimulatedusingthe PROSPECT-4leafmodelareillustratedinFig.3.Boththereflectance andtransmittancesignaturesshowedgoodmatchingthroughout theNIR-SWIRregion(R2valueof0.99and0.97forreflectanceand
transmittancerespectively).Greaterdisagreement(RMSEcloseto 3.5%)wasobservedinthewavelengthrangefrom1900to2350nm (Fig.4)forboth reflectanceand transmittance.ThemeanRMSE forbothreflectanceandtransmittancewasnear2%.Meanspectral valuesalsoshowedtheresemblanceofthesimulatedspectrato themeasuredspectralinformation.Nevertheless,moredeviations betweenthemeasuredandsimulatedmeanvalueswereobserved forreflectancethanfortransmittance.
3.2. Retrievaloftraitsbyinversionandevaluation
TheLUTswereusedtosearchforthematchesofalltheinput variables andthetwo traits(i.e.,LDMC and SLA)using Eqs.(7) and(8).Thevariationsbetweenthemeasuredvalueswithvalues retrievedwithorwithoutconstraininginformationarepresented inTable5.Theretrievalabilityoftheunconstrainedsearch algo-rithmrangesfromannRMSEvalueof0.8291(forSLAofbroadleaf samples)to0.2046(fortheLDMCofpooledsamples).Inmostcases, thelowestnRMSEwasobservedforLDMC.
The retrieval of SLA showed poor performance for most of theunconstrained inversion cases, ranging fornRMSE between 0.6903–0.8291.Inallcases,theapplicationofprior information enormously improvedthe accuracy of the retrieved values, as expected.Forinstance,inbroadleafsamples,whenprior informa-tionwasusedthenRMSEbetweenthemeasuredandestimated valuesdroppedfrom0.4683to0.2278forCm,from0.2909to0.2065
forCwandfrom0.2186to0.0925forLDMC.Despiteover
estima-tionoflowerandunderestimationofhighervaluesinsomecases, theretrieved valueswithprior informationwerealsoshoweda betterrelationshipwiththemeasuredvaluesinthescatterplots (Fig.5).ThenRMSEvaluesformanyoftheretrievedvariableswith priorinformationarelowerwhenthevariableswereretrievedfor broadleafandconifersamplesseparatelythanwhenretrievingthe variablesforthepooledsamples.ThelowestnRMSEwasobserved inallsamplesforLDMC.HighR2 valuesforthePROSPECTinput
parametersandthetwoleaftraitsweredetectedinthepooled sam-ples.Inmostcases,thecorrelationcoefficientbetweenretrieved andmeasuresSLAwashigherthanLDMC,buttheforecastprecision ofLDMCwasbetterthanthatofSLA.
4. Discussionandconclusions
Thisstudyquantifiesandestimatestwoimportantleaf func-tionaltraits: SLAand LDMC.These traits,whichare notwidely addressedinthefieldofremotesensing,canbeaccuratelyderived from the input parameters of the PROSPECT radiative transfer model.Themodel’sperformancewasevaluatedforsamplesfrom mixedforest.TheresultsindicatethatthePROSPECT4leafmodel accuratelysimulatesspectralinformationofsamplesfrommixed mountain forests and can be used to retrieve the biochemical contentof leaves/needlesdirectlyand indirectlythrough inver-sion.Insomecases,wefoundhigheraccuraciesfortheindirect estimatedvariable(LDMC) thanfor thedirectinputvariablesof themodel,whichfurthersupportsthereliability oftheindirect retrievalapproach.
ThevaluesofthestructuralparameterNinfirtreeneedlesof threeseasonsorolderwerehighcomparedtotheNvaluesofthe youngerneedles.Thiscanbeattributedtothelowerwatercontent intheolderleavesandconfirmsearlierfindingsbyJacquemoud etal.(1996),whostatedthatforthesamespecies,Nestimatedon dryleavesishigherthantheNestimatedforfreshleaves,duetoan increaseofmultiplescatteringresultingfromthelossofwater.Our estimatedvaluesofthestructuralparameterNfitwellwithinthe knownrange(1.0–2.5)forawidevarietyofspecies(Jacquemoud andBaret,1990).However,Malenovskyetal.(2006)foundhigher values(1.72–2.63)ofNforNorwayspruce;thismightbebecause ofsite-specificnatureoftheparameterorthebiasinspruceneedle samplesspectrameasurement(particularlytransmittance)(Fig.2) couldhavenegativelyaffectedtheretrievedNvalues.Themean valueof1.42forbeechtreesagreeswiththeresultsofDemarez etal.(1999),whostudiedtheseasonalvariationofNforselected broadleaftreespecies.
PROSPECTinversionwithaprioriinformationforbroadleaf sam-plesyieldedanRMSE=0.0018g/cm2 forC
Table5
Rootmeansquareerror(RMSE),normalizedRMSE(nRMSE)andregressionequationsbetweenobservedandestimatedvaluesofleafdrymassperarea(Cm),leafwater
contentperarea(Cw),SLAandLDMCwithorwithoutpriorinformationfrominversionofthePROSPECTmodelforbroadleafleafandconiferneedlesamples.
Method Category Variable RMSE nRMSE Regressionequation
Inversionwithoutprior information Broadleafsamples Cm 0.0037 0.47 Y=0.52x+0.00059 Cw 0.0024 0.26 Y=0.62x+0.0019 SLA 112.25 0.83 Y=1.9x−5.2 LDMC 100.7 0.22 Y=0.58x+110 Conifersamples Cm 0.0086 0.43 Y=0.36x+0.0054 Cw 0.0053 0.21 Y=0.73x+0.0039 SLA 35.36 0.69 Y=0.68x+48 LDMC 92.2 0.20 Y=0.86x−18 Pooledsamples Cm 0.0066 0.43 Y=0.97x−0.00076 Cw 0.0044 0.24 Y=1.2x+0.00075 SLA 63.88 0.76 Y=1.2x+17 LDMC 119.9 0.26 Y=0.81x+21
Inversionwithprior information Broadleafsamples Cm 0.0018 0.23 Y=0.41x+0.0041 Cw 0.0019 0.21 Y=0.55x+0.0043 SLA 26.7 0.20 Y=1.1x−2.8 LDMC 0.376 0.09 Y=0.74x+110 Conifersamples Cm 0.0032 0.16 Y=0.24x+0.016 Cw 0.0033 0.13 Y=0.37x+0.016 SLA 9.28 0.18 Y=0.22x+38 LDMC 41.3 0.09 Y=0.33x+150 Pooledsamples Cm 0.0033 0.21 Y=0.63x+0.0047 Cw 0.0036 0.19 Y=0.72x+0.0041 SLA 21.73 0.26 Y=0.77x+16 LDMC 40.8 0.09 Y=0.65x+130
resultsofRomeroetal.(2012),whoestimatedCmof11selected
species leaf samplesby inversion of themodel after removing unrealisticcombinationsbetweentheinputparametersfromthe LUT.TheRMSEand rangevalues for Cmfor both broadleafand
conifersamplesaswellasfor thepooleddatawerewithinthe rangereportedbyAsneretal.(2011),whoestimatedtheCmfor
2871samplescollectedfromheterogeneoushumidtropicalrain forests.However,ourestimationofCmandCwyieldedarelatively
lessaccurateresult(RMSE=0.0032and0.0033)forconiferneedles thantheestimatesobtainedbyMalenovskyetal.(2006),who stud-iedNorwayspruceneedlesamplesbyusingthespectralrangeof 450–110nm.Thisisprobablybecausethelatterstudywasonmono specieswhileinoursamplesthereareacoupleofconiferspecies. ThehighRMSEbetweenthemeasuredandsimulatedspectrainthe SWIRrange(Fig.3)mightbealsotheothercause.
Overall,theleaftraitLDMCwasmoreaccuratelyestimatedthan theothervariablesinvestigatedinthisstudy.Onepossiblereason forsuchaccurateestimateofLDMCcouldbetherelativelysmaller errorsintroducedduringLDMCfieldmeasurement.Itseemstobe morechallengingtomeasureCm,CwandSLAreliablyinthefield,
partlyduetoerrorsrelatedtomeasuringtheareaofsamples. Theresultsofthemodelinversionhighlightthereliabilityand feasibilityofusingremotesensingdataforestimationofleaftraits. Thecomparisonofthespectral-basedresultsofthisstudywithfield measurementsindicatesthepotentialofremotesensingdatato estimateleaftraitsoverarangeofvegetationtypes.Thisleaf-level resultindicatesthatleaftraits,especiallySLAandLDMC,are quanti-tativelyrepresentedbyleafspectra.Previousstudiesfocusedonthe estimationofsomeorallofthedirectinputvariablesofPROSPECT modelbyusingfewbandsofthevisibleandnearinfraredregions. Herewehaveshownthatmodelinversionfromawidespectral rangecanprovideindirectand directestimatesofmultipleleaf traitsformixedmountainforests.Mostimportantly,thisleaf-level analysisoffersabasistotestthepossiblegainsandlossesincurred inscalinguptothecanopyandlandscapescale.
Inthisstudywehavedemonstratedtheinversionresultsfrom thewavelengthrange from801to2350nm.Inversionbasedon selectedspectralbands (notshown here)doesnotimprovethe retrievalaccuracy.Thisisbecauseoftheinformativenatureofthe wholeshortwavespectralregionforestimatingvariablesrelatedto
leafdrymatterandthickness.Similarresultshavebeenreported inthetropicalrainforestwhenusingthesoilleafcanopymodel (Asneretal.,2009).
TheevaluationresultsoftheinversionpresentedinFig.5and Table5demonstratedtheinherentcapacityofusingprior infor-mationinimprovingtheaccuracyoftheestimatedvariablesusing RTMinversion.Thisisinagreementwithseveralpreviousstudies (e.g.,Malenovskyetal.,2006;Dasguptaetal.,2009).Besides limit-ingtheupperandlowerboundariesofthemodelinputparameters, weusedamodifiedcostfunctionintheLUTsearch,whichtakes intoaccounttheuncertaintyofthepriorinformationonthe vari-ablestobeestimated.However,theminimumdistancebetween themeasuredand modeled spectracouldprobablybe compro-misedfortheuncertaintyanalysis,whichiswhywefoundamuch lowerRMSEbetweenmeasuredandsimulatedspectraduringthe inversionwithoutpriorinformationthanduringtheinversionwith priorinformation(Fig.6).
ItwasobservedthattheRMSEbetweenmeasuredandobserved spectrawashigherforlongerwavelengthsthanforshorter wave-lengths(Fig.4).Thiscouldbeassociatedwithseveralfactors.One possiblereasoncouldbeerrorassociatedwiththeequipmentused forthespectralmeasurement.Ourpreliminaryreliabilitytestfor adjustinggapfraction(GF)effectshowederrorinclusionduring GFcorrectionforconifersamples.ThisagreeswithYanez-Rausell etal.(2014),whotriedtoaddresstheeffectofgapsbetween sam-plesandotherfactorsaffectingspectralmeasurement ofconifer needlesbyusingtheintegratingsphere.TheGFeffectisgenerally higherintheshortwaveregionthaninthevisibleandnearinfrared region.Althoughtheerrorwassystematicandoccurredacrossall samples,itprobablycontributedtothehigherRMSEbetweenthe measuredandPROSPECTsimulatedspectrashowninFig.4.This emphasizestheneedforfuturestudiestodevelopmoreaccurate spectralmeasurementtechniques.
ThedeterminationofSLA,CmandCwrequiresaccurate
measure-mentsofleafarea.Butthecalculationofareasofirregularshape, particularlyconiferneedles,ispronetoerror.Errorsareintroduced –particularlywhencalculatingtheareaofconiferneedlesamples– bytheshadoweffectwhilescanningsamples,whileclassifyingthe scannedimagestobinaryformat,whileroundingoffpixels,bythe correctionfactorusedand byotherprocedures.Studiesshowed
Fig.5. Actualvsretrievedleafmassperarea(Cm),leafwatercontent(Cw),SLAandLDMCwithoutpriorinformation(left),broadleafsamples,usingpriorinformation(middle)
andpooledsamples,usingpriorinformation(right).Thesolidlineshowsthe1:1relation.
thattheshape of theNorwayspruce needlesvary fordifferent ageclassesandneeddifferentconversionfactors(Homolovaetal., 2013).Butwesimplyusedauniversalconversionfactorfrom lit-eraturewhichcouldbesourceoferror.Althoughitisnotpossible
toavoidalltheseerrors,infuturestudieseffortsshouldbemadeto developsimpleandfasttechniquesforcomputingneedleareaand foropticalpropertymeasurementofnarrowleafsamplesbyusing theintegratingsphere.
Fig.6. RootMeanSquareErrors(RMSE)computedbetweenthemeasuredand PROSPECTsimulatedreflectanceforthe53broadleafsampleswithandwithout usingpriorinformation.
Ingeneral,ourresultshaveconfirmedthattwoimportantleaf functionaltraitsaremeasurablewithspectralinformation.Thisin turnhighlights thepotentialtoextendthestudytocanopyand landscapescales byusingadvanced hyperspectral airborneand spacebornesensors.However,itisworthmentioningthatwhen theobservationalscalemovesfromleaftocanopyandlandscape scales,therelationshipbetweenreflectedradiationandleaftraits maychange(Ustinetal.,2009).Thespectralpropertiescausedby leaftraitsarethenaffectedbysoil,non-photosyntheticvegetation, stemcharacteristics,canopystructure,shadows,illuminationand viewangles(Robertsetal.,2004;Ollingeretal.,2008).Retrieval methodsthathavebeendesignedatleafscalearelikelytosuffer fromthesestructuralandexternalfactorswhenusedatcanopy scale(Asneret al., 1998).Therefore, furtherstudyis neededto understandtheimpactofcanopystructuralandexternalfactors (suchassunzenithandazimuthangles)otherthantheleaf func-tionaltraitsoncanopyreflectanceinordertoaccuratelyestimate thetwofunctionaltraitsfromremote-sensingdataatcanopyor landscapescale.
Acknowledgments
ThisstudywasfundedbyNuffic-Netherlandsfellowship pro-gram. We acknowledge the assistance of Dr. Nicole Pinnel in GermanRemoteSensingData Center,GermanAerospaceCenter EarthObservationCenter(DLR)andDr.HoomanLatifiinInstitute ofGeographyandGeology,UniversityofWuerzburginselectingthe testsite,andorganizingandfacilitatingthefieldcampaign.Thanks alsogototheBavarianForestNationalParksforapprovingaccess tothestudyarea,providingthecrossbowwithitsaccessories,and otherfieldandcampingfacilities.Theauthorsalsowishtothank ZhihuiWangandTiejunWanginthefacultyofGeo-Information ScienceandEarthObservation,UniversityofTwente,whoassisted withthefieldworkdesignanddatacollection.Dr.JoyBurroughwas thelanguageeditorofanear-finaldraftofthepaper.
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