Impacts of Light Use Efficiency and fPAR Parameterization on Gross Primary Production Modeling

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AgriculturalandForestMeteorology189–190(2014)187–197

ContentslistsavailableatScienceDirect

Agricultural

and

Forest

Meteorology

jo u rn al h om ep a g e :w w w . e l s e v i e r . c o m / l o c a t e / a g r f o r m e t

Impacts

of

light

use

efﬁciency

and

fPAR

parameterization

on

gross

primary

production

modeling

Yen-Ben

Cheng

a,∗

_,

_Qingyuan

_Zhang

b

_,

_Alexei

_I.

_Lyapustin

c

_,

_Yujie

_Wang

d

_,

Elizabeth

M. Middleton

e

a_Earth_Resources_Technology,_Inc.,_Laurel,_MD_20707,_USA b_Unversities_Space_Research_Association,_Columbia,_MD_21044,_USA

c_Climate_and_Radiation_Laboratory,_National_Aeronautics_and_Space_{Administration}_Goddard_Space_Flight_Center,_Greenbelt,_MD_20771,_USA d_University_of_Maryland_Baltimore_County,_Baltimore,_MD_21228,_USA

e_Biospheric_Sciences_Laboratory,_National_Aeronautics_and_Space_{Administration/Goddard}_Space_Flight_Center,_Greenbelt,_MD_20771,_USA

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received7August2013 Receivedinrevisedform 12November2013 Accepted6January2014 Keywords:

Carbonmodeling

Fractionofphotosyntheticallyactive radiation

fAPARchl Lightuseefﬁciency

a

b

s

t

r

a

c

t

Thisstudyexaminestheimpactofparameterizationoftwovariables,lightuseefficiency(LUE)andthe fractionofabsorbedphotosyntheticallyactiveradiation(fPARorfAPAR),ongrossprimaryproduction (GPP)modeling.Carbonsequestrationbyterrestrialplantsisakeyfactortoacomprehensive under-standingofthecarbonbudgetatglobalscale.Inthiscontext,accuratemeasurementsandestimatesof GPPwillallowustoachieveimprovedcarbonmonitoringandtoquantitativelyassessimpactsfrom cli-matechangesandhumanactivities.Spaceborneremotesensingobservationscanprovideavarietyof landsurfaceparameterizationsformodelingphotosyntheticactivitiesatvariousspatialandtemporal scales.ThisstudyutilizesasimpleGPPmodelbasedonLUEconceptanddifferentlandsurface param-eterizationstoevaluatethemodelandmonitorGPP.Twomaize–soybeanrotationfieldsinNebraska, USAandtheBartlettExperimentalForestinNewHampshire,USAwereselectedforstudy.Tower-based eddy-covariancecarbonexchangeandPARmeasurementswerecollectedfromtheFLUXNETSynthesis Dataset.Forthemodelparameterization,weutilizeddifferentvaluesofLUEandthefPARderivedfrom variousalgorithms.WeadaptedtheapproachandparametersfromtheMODISMOD17BiomeProperties Look-UpTable(BPLUT)toderiveLUE.Wealsousedasite-specificanalyticapproachwithtower-based NetEcosystemExchange(NEE)andPARtoestimatemaximumpotentialLUE(LUEmax)toderiveLUE.For

thefPARparameter,theMODISMOD15A2fPARproductwasused.WealsoutilizedfAPARchl,aparameter

accountingforthefAPARlinkedtothechlorophyll-containingcanopyfraction.fAPARchlwasobtainedby

inversionofaradiativetransfermodel,whichusedtheMODIS-basedreﬂectancesinbands1–7produced byMulti-AngleImplementationofAtmosphericCorrection(MAIAC)algorithm.fAPARchlexhibited

sea-sonaldynamicsmoresimilarwiththeﬂuxtowerbasedGPPthanMOD15A2fPAR,especiallyinthespring andfallattheagriculturalsites.WhenusingtheMODISMOD17-basedparameterstoestimateLUE, fAPARchlgeneratedbetteragreementswithGPP(r2=0.79–0.91)thanMOD15A2fPAR(r2=0.57–0.84).

However,underestimationsofGPPwerealsoobserved,especiallyforthecropﬁelds.Whenapplyingthe site-speciﬁcLUEmaxvaluetoestimateinsituLUE,themagnitudeofestimatedGPPwasclosertoinsitu

GPP;thismethodproducedaslightoverestimationfortheMOD15A2fPARattheBartlettforest.This studyhighlightstheimportanceofaccuratelandsurfaceparameterizationstoachievereliablecarbon monitoringcapabilitiesfromremotesensinginformation.

1. Introduction

Carbonsequestration by terrestrial plants is a key factor to

a comprehensiveunderstandingof carbon budgetat theglobal

∗ Correspondingauthorat:Code618,NASAGoddardSpaceFlightCenter, Green-belt,MD20771,USA,Tel.:+13016146636;fax:+13016146695.

E-mailaddress:[email protected](Y.-B.Cheng).

scale.Carbonassimilationthroughphotosynthesisbyvegetation,

knownasgrossprimaryproduction(GPP)attheecosystemscale,

is the largestcarbon exchange between thebiosphere and the

atmosphere(Beeretal.,2010).Accuratemeasurementsand

esti-matesofGPPwillallowustoachieveimprovedcarbonmonitoring

and to quantitatively assessimpacts from climatechanges and

humanactivities(Graceetal.,2007;Middletonetal.,2011).Remote

sensingobservationscanprovideavarietyoflandsurface

parame-terizationsformodelingphotosyntheticactivitiesatvariousspatial

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andtemporalscales,andmuchefforthasbeenputtowardsthis

goaloverthepastdecades.Many remote-sensing-basedindices

andalgorithmstrackingleafbiochemicalproperties(e.g.,

chloro-phyll,watercontent)andcanopybiophysicalproperties(e.g.,leaf

area index, LAI; fraction of photosynthetically active radiation

(PAR)absorbedbyvegetationcanopy(fPARorfAPAR)havebeen

developedtoestimateGPPwithdifferentapproachesforvarious

ecosystemswithvariablesuccess(Chengetal.,2007;Graceetal.,

2007;Heinschetal.,2006;Houborgetal.,2011;PengandGitelson, 2012;Runningetal.,2004;Sellersetal.,1997;TuckerandSellers, 1986;Turneretal.,2006;Wuetal.,2011,2009;Xiaoetal.,2010, 2004a;Zhangetal.,2009).

Theconceptofthelightuseefﬁciency(LUE)model(Monteith,

1972,1977)hasbeenwidelyusedforGPPmodeling.Theapproach

describescarbonsequestrationasaproductofthecapabilityand

efﬁciencyofvegetationtocaptureandconvertsolarradiationinto

biomassbyterrestrialvegetation.Themodelisusuallydeﬁnedas:

GPP=ε×PAR×fPAR (1)

whereεisLUEoveradeﬁnedperiodoftime.Manyprevious

stud-ieshave utilizedthemodelandcoupleditwithremote sensing

datatoestimateGPPatvariousspatialscales(Chengetal.,2009;

Droletetal.,2008;Halletal.,2012;Hilkeretal.,2012;Huntzinger etal.,2012;Middletonetal.,2009;Nicholetal.,2000;Schaefer

etal.,2012),includingtheModerateResolutionImaging

Spectrora-diometer(MODIS)Terra/AquasatelliteGPPProducts(Heinschetal.,

2006;Runningetal.,2004;Turneretal.,2006;Zhaoetal.,2005).

ThealgorithmusedtodelivertheMODISMOD17A2GPPproduct

utilizesabiome-basedlook-uptable(LUT)todeterminethe

poten-tialmaximumLUE(LUEmaxorεmax),andassumesdown-regulation

basedonambientairtemperatureandvaporpressuredeﬁcit(VPD).

TheMODISMOD17productutilizestheMODISMOD15A2fPAR

productintheprocedure.TheMODISMOD15A2algorithm

incor-poratesatmosphericallycorrectedMODISsurfacereﬂectanceand

aLUTmethodtoachieveinversionofthethreedimensional

radia-tivetransferprocess invegetationcanopies. Aback-upmethod

basedonrelationshipsbetweenthenormalizeddifference

vege-tationindex(NDVI)andLAIandfPARisusedwhentheinversion

methodfails todeliver (Knyazikhin et al., 1999,1998; Myneni

etal.,2002).ThisapproachestimatesthefAPARassociatedwiththe

entirecanopyorecosystem,comprisedofbothphotosyntheticand

non-photosyntheticcomponents.TheconceptofretrievingfAPAR

forthechlorophyll-containingcanopy activelyinvolvedin

pho-tosynthesiswasproposedbyZhangandcolleagues(Zhangetal.,

2013,2012,2009,2005).Thisretrievalwasdesignedtoaccountfor

onlythesolarenergyabsorbedbychlorophyllofthefoliageand

contributingtophotosynthesis.Thiswasaccomplishedby

inver-sionofalinkedleaf-canopyradiativetransfermodelusingsurface

reﬂectanceofﬁveorsevenMODISlandbands(Zhangetal.,2013,

2012,2009,2005).

Thevegetationphotosynthesismodel(VPM)isanothersimple

GPPmodelbasedonremotesensingobservationsand

microme-teorologicaldata(Kalfasetal.,2011;Wangetal.,2010;Xiaoetal.,

2004a,b;Xiaoetal.,2005).TheVPMmodeladaptedtheLUEconcept

andcalculatesGPPastheproductofPAR,fAPARofthe

photosyn-theticallyactivevegetation(denotedasfAPARpavorfAPARchl),and

LUE(ε).TheEnhancedVegetationIndex,EVI(Hueteetal.,2002),

wasusedasaproxyoffAPARchl(Xiaoetal.,2004a,b).LUE(ε)is

derivedasthepotentialmaximumLUE(εmax)down-regulatedby

scalarsoftemperature,water,andphenology.Theεmaxvalueis

usu-allydeterminedeitherfrompreviousstudiesorfromananalytical

approachbasedonﬂuxtowernetecosystemexchange(NEE)and

PAR.Thismodelhasbeenusedandexaminedwithvarious

vegeta-tiontypesincludingdeciduousandevergreenforests,crop(maize),

grassland,andsavannahwoodlands(Jinetal.,2013;Kalfasetal.,

2011;Wangetal.,2010;Xiaoetal.,2004a,b,2005).

TheimportanceofaccurateestimateofbothfPARandLUEfor

improvedcarbonmonitoringcapabilitieshasbeenpointedoutin

recentstudies.For instanceBonanandcolleagues(Bonanetal.,

2011)suggestedtheaccurateradiativetransferparameterization

withinthecanopy(e.g.,fPAR)isnecessarytoimproveoutput

accu-racyofthesimulatedGPPfromCommunityLandModelversion4

(CLM4).LUEhasbeenshowntohavesigniﬁcantvariationsacross

vegetationtypes(Andersonetal.,2000;Goweretal.,1999).When

appliedtomodels,largeerrorswerefoundandstemfrombiases

inLUEthataccumulateinthecalculationofannualcarbon

assimi-lation(Linetal.,2011).Therefore,inthispaper,weintegratedthe

LUEconceptwithdifferentapproachesfordeterminingthe

param-etersε andfPARtoestimatecarbonassimilation(GPP)atthree

FLUXNETsites,twoagriculturalsitesandonedeciduousforestsite.

Ourobjectivesaretovalidatethecapabilitytomodeland

moni-torGPPusingthesimpleLUEconceptandtocompareresultsfrom

differentparameterizationschemes.

2. Methods

2.1. Studysites

Inthisstudy,weinvestigatedthecapabilityofasimplemodel

based on the LUE concept to estimate GPP at three FLUXNET

sitesusingdifferentlandsurfaceparameterizations.Theﬁrsttwo

sitesaremaize-soybeanrotation cropﬁelds locatednearMead,

Nebraska, USA.The third site is the Bartlett Experimental

For-estinNew Hampshire,USA.Thesesiteswereselectedbasedon

theirphenologicalcharacteristicsandtheavailabilityofﬂuxdata,

micrometeorologicalmeasurements,andremotesensing

observa-tions.Furthermore,theyprovidedustheopportunitytoexamine

themodelondifferentvegetationtypesinbothnativeandmanaged

ecosystems.

2.1.1. Maize-soybeanrotationcropﬁelds,Nebraska(US-NE2and

US-NE3)

TwoofthethreecropﬁeldslocatedattheUniversityofNebraska

AgriculturalResearchandDevelopmentCenternearMead,NE,USA

wereselectedtostudy.Bothﬁeldshavemaize(ZeamaysL.)–

soy-bean(Glycinemax[L.]Merr.)rotations,butdependdifferentlyon

watersupply.The52.4haUS-NE2site(41.1649◦N,96.4701◦W)is

equippedwithacenterpivotirrigationsystemwhilethe65.4ha

US-NE3site(41.1797◦N,96.4396◦W)reliesonrainfall(Vermaetal.,

2005).Thesoilsaredeepsiltyclayloams.Theﬁeldswereuniformly

tilledbydiskingpriorto2001andsincethenhavebeenmanaged

asano-tillsystem.Bothﬁeldsareequippedwitheddycovariance

towersystemstomeasureenergyandCO2 ﬂuxes(Vermaetal.,

2005).Inthisstudy,thedatafrom2001through2004were

col-lectedatthetwositesandanalyzed.

2.1.2. Bartlettexperimentalforest,NewHampshire(US-BAR)

The Bartlett Experimental Forest (44.0646◦N, 71.2881◦W) is

locatedwithintheWhiteMountainsNationalForestinnorthern

centralNewHampshire,USA(Jenkinsetal.,2007).The1050ha

for-estismanagedbytheUSDAForestServiceNortheasternResearch

Station.Theforestexperiencesahumidcontinentalclimatewith

shortcoolsummersandlongcoldwinters(Jenkinsetal.,2007).

A 26.5mhigh towerwas installed for continuouseddy

covari-anceﬂuxesandmeteorologicalmeasurementsinalow-elevation

northernhardwoodstand,wheretheaveragecanopyheightwas

approximately20–22m.Inthetowerfootprint,theforestis

pre-dominantlyclassiﬁedintoredmaple(Acerrubrum),sugarmaple

(Acersaccharum),andAmericanbeech(Fagusgrandifolia),forwhich

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2007).Availabledatafrom2004through2005werecollectedfor

thisstudy.

2.2. Fluxdatacollection

Flux and meteorological variables were collected from the

FLUXNET Synthesis Dataset (http://www.ﬂuxdata.org) for the

studysites.TheSynthesis Datasetprovidesa collaboration

por-talforﬂuxandmeteorologicaldatacollectedworldwide(Agarwal

etal.,2010).Thedataarequality-checkedandgap-ﬁlledbasedon

guidelinesandpreviousstudies(Agarwaletal.,2010;Falgeetal.,

2001;MungerandLoescher,2006).NEEwasprocessedfollowing

theFLUXNETprocedure,fromwhichGPPvalueswerecomputed

(Agarwaletal.,2010).Forthemaize-soybeanrotationcropﬁelds

(US-NE2,US-NE3),dailyobservations,includingairtemperature,

vaporpressuredeﬁcit(VPD),GPP,andPAR,werecollectedfrom

2001through2004.FortheBartlettforest(US-BAR),daily

mea-surementsfrom2004and2005wereobtained.

2.3. MODISdataprocessing

The MODIS data were processed using the Multi-angle

implementation of atmospheric correction (MAIAC) algorithm

(Lyapustin et al., 2011a,b, 2012). MAIAC is an advanced

algo-rithm which uses time series analysis and a combination of

pixel-based and image-based processing to improve the

accu-racyofcloud/snowdetection,aerosolretrievals,andatmospheric

correctionbased on theBRDF model of surface. For this work,

we used MODIS Collection 6 calibrated and geolocated (level

1B) data. The derived surface reﬂectance in MODIS bands 1

(620–670nm),2(841–876nm),3(459–479nm),4(545–565nm),

5(1230–1250nm),6(1628–1652nm),and7(2105–2155nm)were

thenutilizedtocalculatefAPARchl,asshownbelow,forthestudy

sites.

2.4. Modelparameterization

FortheGPPmodel[1]utilizedinthisstudy,thePARvalueswere

obtainedfromtheFLUXNETdataset.Different approacheswere

usedtodeterminetheremainingtwoparameters,␧andfPAR.

2.4.1. Lightuseefﬁciency

TheMODISMOD17GPPproducts(Runningetal.,2004)come

fromanalgorithm thatdescribestheparameterεasthe

down-regulatedvalueofthemaximumradiationconversionefﬁciency

(εmax).Thedown-regulationisdrivenbysub-optimal

environmen-talconditionsdeterminedbyairtemperatureandVPD:

ε=εmax×Tminscalar×VPDscalar (2)

where Tminscalar and VPDscalar are dimensionless multipliers

describingrelative decreasesof εmax andphotosynthesisdueto

coldtemperatureandhighVPD.Thesescalarsaredeﬁnedassimple

linearrampfunctionsusingprescribedminimumandmaximum

environmental constraints (Tminmin and Tminmax, VPDmin and

VPDmax)basedonbroadglobalbiomeclasses.Detailed

descrip-tionscanbefoundinpreviouspublications(Heinschetal.,2003,

2006).WeadaptedtheBiomePropertiesLook-UpTable(BPLUT)

usedtoderivetheMOD17product.Thevaluesofεmaxand

parame-tersneededtocalculateTminscalarandVPDscalararelistedinTable1.

InadditiontotheBPLUTvaluesusedintheMODISMOD17

prod-uct(Table1),wealsoutilizedasite-speciﬁcanalysisbetweenNEE

andPARtodetermineεmax.Theanalysisisusuallydoneby

per-formingalinearornonlinearﬁttingtohalf-hourlyorhourlydata

collectedatﬂuxtowersites(Ruimyetal.,1995;Wofsyetal.,1993).

Theapproachhasbeenadaptedinmanypreviousstudiesto

deter-minetheεmaxvaluefordifferentsites(Wangetal.,2010;Xiao,

Table1

Biomepropertieslook-uptable(BPLUT)describingparametersusedinGPPmodel.

Site US-NE2andUS-NE3 US-BAR

Vegetationtype Crop Deciduousbroadleafforest

εmax(kgCMJ-1) 0.000680 0.001044

Tminmin(◦C) −8.00 −8.00

Tminmax(◦C) 12.02 7.94

VPDmin(Pa) 650 650

VPDmax(Pa) 4100 2500

2006;Xiaoetal.,2004a,2005).Followingthedescriptionand

exam-plesestablishedinpreviousstudies(Wangetal.,2010;Xiaoetal.,

2004a,2005),weutilizedthehourlyPARandﬂuxdatacollected

fromtheFLUXNETSynthesisDatasetandappliedanonlinear

hyper-bolicmodeltoestimateεmaxforeachofthestudysites.Thederived

εmaxvalueswerethenusedasinputinEq.(2)tocalculateε,in

addi-tiontothevaluescomputedbasedonεmaxadaptedfromMODIS

MOD17algorithm(Table1).

2.4.2. FractionofabsorbedPAR

WeutilizedtwodifferentvaluesforthefPARparameter.One

isfromtheMOD15A2LAI/fPARproduct.TheMOD15A2LAI/fPAR

product was downloaded from Oak Ridge National Laboratory

(ORNL)DistributedActiveArchiveCenter(DAAC)for

Biogeochemi-calDynamics(http://daac.ornl.gov/MODIS/).TheMODISMOD15A2

fPARproductrepresentsthefractionofPARabsorbedbythewhole

canopy.

TheconceptoffAPARassociatedwiththefoliagechlorophyll

content(fAPARchl)accountsforthePARabsorbedbychlorophyll

throughout acanopy (APARchl)usedinthephotosynthesis

pro-cess,ascomparedtothePARabsorbedbythewholecanopy(e.g.,

MOD15A2 fPAR). The fAPARchl concept partitions a vegetation

canopyintoleafandnon-leaf(referredtoasstem)components.The

leafcanbefurtherpartitionedintochlorophyll,non-photosynthetic

pigments(referredtoasbrownpigment)anddrymatterfractions.

ThePROSPECT(Baretand Fourty, 1997;Jacquemoud andBaret,

1990)leafradiativetransfermodel(RTM)andtheSAIL2(Braswell

etal.,1996)canopyRTMwerecoupledtoaccountforthe

differ-entfoliagefractionsandfullcanopies.ThefAPARvalues forthe

non-chlorophyllfoliagesector(fAPARnon-chl)andfortotalfoliage

(fAPARfoliage)canbecomputedas:

fAPARnon-chl=fAPARbrownpigment+fAPARdrymatter (3)

fAPARfoliage=fAPARchl+fAPARnon-chl (4)

EachMODISsurface reﬂectanceobservation(obs)contained

somenoise,andwasassociatedwithaspeciﬁcgeometriesforview

zenithangle(v),relativeviewazimuthangle(),andsolarzenith

angle (s), allexpressedin degrees. Therefore,we treated each

reﬂectanceobservationasasampleofthefollowingdistribution:

∼{obs(,v(1+3N(0,1)),s(1+3N(0,1)), (1+3N(0,1)))}

·(1+0.05N(0,1)) (5)

whereN(0,1)wasthenormaldistributionwithameanofzero

andSD=1.WeusedmultiplesamplesfromthisdistributioninEq.

(5).TheMarkovChainMonteCarlo(MCMC)procedurewas

imple-mentedfortheinversionofthemodelstocalculatetheparameter

fAPARchl.Adetaileddescriptionofthealgorithmcanbefoundin

previouspublications (Zhang etal., 2005,2009,2012,2013).In

earlierworks,thefAPARchlvariablewasderivedusingtheMODIS

MOD09surfacereﬂectanceproductandtheEarthObserving-1

(EO-1)Hyperionimageryandwasshowntobeausefulparameterthat

improvedcarbonmonitoring(ZhangandMiddleton,2010;Zhang

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MODISreﬂectanceband1–7wasusedtocalculatefAPARchlforthe

studysites.ThefAPARchlparameterwasthenimplementedinthe

LUEmodeltoestimateGPP.

Insummary,weutilizedtwodifferentvaluesofεmaxvalueswith

theMODISMOD17algorithm,Eq.(2), tocalculateε.We linked

theεvaluestotwodifferentfPARvalues,MODISMOD15A2fPAR

andfAPARchl,toestimateGPPateight-daytemporalscaleforall

ofthestudysites.Fourcombinationsofthedifferentlandsurface

parameterizationswereexaminedandaresummarizedinTable2.

Table2

CombinationsofLUE(ε)andfPARparameterizationexaminedinthisstudy.Note, εmaxvalueswereusedintheMODISMOD17likealgorithm(Eq.(2))todetermineε fortheGPPmodel.

εmax fPAR

CaseI MODISMOD17BPLUT MOD15A2fPAR

CaseII MODISMOD17BPLUT fAPARchl

CaseIII Analyticapproach,sitespeciﬁc MOD15A2fPAR CaseIV Analyticapproach,sitespeciﬁc fAPARchl

0 50 100 150 200 250 300 350 400 450 500 0 50 100 150 200 250 1/ 1/ 200 1 3/ 1/ 200 1 5/ 1/ 200 1 7/ 1/ 200 1 9/ 1/ 200 1 11 /1 /200 1 1/ 1/ 200 2 3/ 1/ 200 2 5/ 1/ 200 2 7/ 1/ 200 2 9/ 1/ 200 2 11 /1 /200 2 1/ 1/ 200 3 3/ 1/ 200 3 5/ 1/ 200 3 7/ 1/ 200 3 9/ 1/ 200 3 11 /1 /200 3 1/ 1/ 200 4 3/ 1/ 200 4 5/ 1/ 200 4 7/ 1/ 200 4 9/ 1/ 200 4 11 /1 /200 4 P A R ( m ol /m 2/8 d) in s it u GPP (g C /m 2/8 d )

in situ GPP PAR (a)

0 100 200 300 400 500 600 0 20 40 60 80 100 120 140 160 180 200 1/ 1/ 200 1 3/ 1/ 200 1 5/ 1/ 200 1 7/ 1/ 200 1 9/ 1/ 200 1 11 /1 /200 1 1/ 1/ 200 2 3/ 1/ 200 2 5/ 1/ 200 2 7/ 1/ 200 2 9/ 1/ 200 2 11 /1 /200 2 1/ 1/ 200 3 3/ 1/ 200 3 5/ 1/ 200 3 7/ 1/ 200 3 9/ 1/ 200 3 11 /1 /200 3 1/ 1/ 200 4 3/ 1/ 200 4 5/ 1/ 200 4 7/ 1/ 200 4 9/ 1/ 200 4 11 /1 /200 4 PA R ( m o l/ m 2/8 d) in s it u GPP (g C /m 2/8 d ) in situ GPP PAR (b) 0 50 100 150 200 250 300 350 400 450 0 10 20 30 40 50 60 70 80 90 1/ 1/ 200 4 2/ 1/ 200 4 3/ 1/ 200 4 4/ 1/ 200 4 5/ 1/ 200 4 6/ 1/ 200 4 7/ 1/ 200 4 8/ 1/ 200 4 9/ 1/ 200 4 10 /1 /200 4 11 /1 /200 4 12 /1 /200 4 1/ 1/ 200 5 2/ 1/ 200 5 3/ 1/ 200 5 4/ 1/ 200 5 5/ 1/ 200 5 6/ 1/ 200 5 7/ 1/ 200 5 8/ 1/ 200 5 9/ 1/ 200 5 10 /1 /200 5 11 /1 /200 5 12 /1 /200 5 PA R ( m o l/ m 2/8 d) in s it u GPP (g C /m 2/8 d ) in situ GPP PAR (c)

Fig.1. SeasonaldynamicsofGPP(blacksolidline)andPAR(greysolidline)acquiredfromtheFluxnetSynthesisDatasetfor(a)anirrigatedcropsite,US-NE2;(b)arainfed cropsite,US-NE3in2001through2004and(c)adeciduousforest,US-BARin2004and2005.

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 200 250 1/ 1/ 200 1 3/ 1/ 200 1 5/ 1/ 200 1 7/ 1/ 200 1 9/ 1/ 200 1 11 /1 /200 1 1/ 1/ 200 2 3/ 1/ 200 2 5/ 1/ 200 2 7/ 1/ 200 2 9/ 1/ 200 2 11 /1 /200 2 1/ 1/ 200 3 3/ 1/ 200 3 5/ 1/ 200 3 7/ 1/ 200 3 9/ 1/ 200 3 11 /1 /200 3 1/ 1/ 200 4 3/ 1/ 200 4 5/ 1/ 200 4 7/ 1/ 200 4 9/ 1/ 200 4 11 /1 /200 4 fP A R in s it u GPP (g C /m 2/8 d )

in situ GPP MOD15A2 fPAR fAPARchl (a)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 20 40 60 80 100 120 140 160 180 200 1/ 1/ 200 1 3/ 1/ 200 1 5/ 1/ 200 1 7/ 1/ 200 1 9/ 1/ 200 1 11 /1 /200 1 1/ 1/ 200 2 3/ 1/ 200 2 5/ 1/ 200 2 7/ 1/ 200 2 9/ 1/ 200 2 11 /1 /200 2 1/ 1/ 200 3 3/ 1/ 200 3 5/ 1/ 200 3 7/ 1/ 200 3 9/ 1/ 200 3 11 /1 /200 3 1/ 1/ 200 4 3/ 1/ 200 4 5/ 1/ 200 4 7/ 1/ 200 4 9/ 1/ 200 4 11 /1 /200 4 fP A R in s it u GPP (g C /m 2/8 d )

in situ GPP MOD15A2 fPAR fAPARchl (b)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 60 70 80 90 1/ 1/ 200 4 2/ 1/ 200 4 3/ 1/ 200 4 4/ 1/ 200 4 5/ 1/ 200 4 6/ 1/ 200 4 7/ 1/ 200 4 8/ 1/ 200 4 9/ 1/ 200 4 10 /1 /200 4 11 /1 /200 4 12 /1 /200 4 1/ 1/ 200 5 2/ 1/ 200 5 3/ 1/ 200 5 4/ 1/ 200 5 5/ 1/ 200 5 6/ 1/ 200 5 7/ 1/ 200 5 8/ 1/ 200 5 9/ 1/ 200 5 10 /1 /200 5 11 /1 /200 5 12 /1 /200 5 fP A R in s it u GPP (g C /m 2/8 d )

in situ GPP MOD15A2 fPAR fAPARchl (c)

Fig.2. SeasonaldynamicsofGPPacquiredfromtheFluxnetSynthesisDataset(blacksolidline),theMODISMOD15A2fPARproduct(),andfAPARchl()for(a)anirrigated

cropsite,US-NE2;(b)arainfedcropsite,US-NE3in2001through2004and(c)adeciduousforest,US-BARin2004and2005.

3. Results

ValuesofGPP(blacksolidline)andPAR(greysolidline)acquired fromtheFLUXNETSynthesisDatasetareshownintheformatof eight-dayvaluesforthethreestudysites,rotationmaize-soybean ﬁeld(US-NE2,US-NE3)andtheBartlettExperimentalForest (US-BAR),inFig.1a,b,c,respectively.Notabledifferenceswereobserved

forthetwoalternatingcroptypesinUS-NE2andUS-NE3(Fig.1a,

b).Maizeshowedmuchhigherproductivityin2001and2003than

soybeanin2002and2004.GPPin allthree sitesexhibited

pro-nounced seasonalpatterns(Fig.1)withverylow productivities

in winter, that increasedin springtimeafterplanting (for

agri-culturalsites) or green-up (for forestsite), and with peak GPP

observedinsummer,decreasinginfall.Seasonaldynamicsofthe

MODISMOD15A2fPARproduct(₎andfAPAR_chl(₎areshown

as eight-day averagevalues in Fig.2 for thethree studysites.

Allthreevariables (GPP,MOD15A2 fPAR,andfAPARchl)showed

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MOD15A2 fPAR y = 0.099x + 13.714 R² = 0.78 RMSE = 96.95 fAPARchl y = 0.1579x + 3.7855 R² = 0.94 RMSE = 96.74 0 5 10 15 20 25 30 35 40 250 200 150 100 50 0 Es m a te d GP P (g C/ m 2/8 d) in situ GPP (g C/m2_/8d) US-NE2 maize MOD15A2 fPAR fAPARchl (a) MOD15A2 fPAR y = 0.1649x + 13.943 R² = 0.61 RMSE = 54.68 fAPARchl y = 0.2678x + 2.7513 R² = 0.92 RMSE = 53.99 0 5 10 15 20 25 30 35 40 45 140 120 100 80 60 40 20 0 Es m a te d GP P (g C/ m 2/8 d) insitu GPP (g C/m2_/8d) US-NE2 soybean MOD15A2 fPAR fAPARchl (b)

Fig.3.EstimatedGPPusingMOD15A2fPARandfAPARchlwithεderivedfromtheMODISMOD17likealgorithmfortheirrigatedcropsite,US-NE2(a)maizeand(b)soybean.

MOD15A2 fPAR y = 0.1179x + 11.044 R² = 0.84 RMSE = 83.43 fAPARchl y = 0.1378x + 3.2358 R² = 0.87 RMSE = 86.02 0 5 10 15 20 25 30 35 40 200 150 100 50 0 Es m a te d GP P (g C/ m 2/8 d) in situ GPP (g C/m2_/8d) US-NE3 maize MOD15A2 fPAR fAPARchl (a) MOD15A2 fPAR y = 0.1902x + 13.659 R² = 0.72 RMSE = 44.82 fAPARchl y = 0.1991x + 5.115 R² = 0.79 RMSE = 50.33 0 5 10 15 20 25 30 35 40 45 120 100 80 60 40 20 0 Es m at e d G PP (g C/ m 2/8 d) in situ GPP (g C/m2_/8d) US-NE3 soybean

MOD15A2 fPAR fAPARchl (b)

Fig.4.EstimatedGPPusingMOD15A2fPARandfAPARchlwithεderivedfromtheMODISMOD17likealgorithmfortherainfedcropsite,US-NE3(a)maizeand(b)soybean.

thetwoagriculturalsites,US-NE2andUS-NE3,fAPARchlexhibited

seasonalpatternsthatweremoresimilartomeasuredGPPthan

thoseobtainedwithMOD15A2fPAR.However,theMOD15A2fPAR

describedunrealisticallylongergrowingseasonsthanthoseshown

byGPPorfAPARchl.TheMOD15A2fPARproductrevealedhigher

valuesinspringandfall,whileGPPsuggestedlow(orno)

photosyn-theticactivitiesintheﬁeld.AttheBartlettforestsite(US-BAR),GPP,

andfAPARchlshowedasimilarseasonalpatternandlengthof

grow-ingseason(Fig.2c),whereastheMOD15A2fPARproductdescribed

theseseasonaldynamicslessaccuratelyandexhibitedmuchhigher

valuesinwinter,earlyspring,andlatefallthanfAPARchl(Fig.2c).

TheGPPmodel(1)wasappliedusingparameterizationfromthe

MODISMOD17productalgorithm(Table1)todetermineLUE(ε)

coupledwitheitherMOD15A2fPAR(Table2,CaseI)orfAPARchl

(Case II) to model GPP for the three study sites. Comparisons

betweeninsituGPPand estimatedGPPfor theUS-NE2site are

showninFig3.ForbothmaizeandsoybeanontheirrigatedUS-NE2

site,theestimatesusingfAPARchlshowconsistentlygoodlinear

relationships(r2₌_0.92–_0.94)_with_ﬂux_tower_based_GPP₍_Fig.₃_).

EstimatedGPPusingMOD15A2productshowedlesssatisfactory

(r2₌_0.61–_0.78) _results, _with_a _noticeable _offset _for_the

regres-sionline(Fig.3).Nevertheless,clearGPPunderestimationswere

observedwitheitherfAPARchlorMOD15A2fPAR.Resultsforthe

rainfedUS-NE3sitearesummarizedinFig.4,whereGPPestimates

obtainedusingeithertheMOD15A2productorthefAPARchlwere

comparable(r2₌_0.84–_0.87_for_maize;_r2₌_0.72–_0.79_for_soybean).

Strongerregressionswereobtainedforirrigated(Fig.3)vs.rainfed

(Fig.4)ﬁeldsusingfAPARchl,buttheoppositefortheMOD15A2

product.Similar tothe US-NE2site, estimates using MOD15A2

productshowed a moreobvious offset,and clear

underestima-tionswereobservedfor bothfPARproducts(Figs.3and 4).For

theBartlettforest,estimatesusingfAPARchlshowedmuchstronger

relationshipstotower-based GPP(r2₌_0.91, _Fig.₅₎_compared_to

theMOD15A2 product(r2₌_0.57)._{Nevertheless,} _estimates_using

theMOD15A2productexhibitedslightlylowerRMSEthatusing

fAPARchl,andGPPestimateswereclosertotowervaluesthanfor

fAPARchl(Fig.5).

InadditiontousingtheεmaxvaluesadaptedfromtheMODIS

MOD17productalgorithm(Table1),wealsoappliedananalytic

approach based on NEE and PAR to determine the site

spe-ciﬁcvalueofεmax.FortheUS-NE2sites,0.004209kgCMJ−1 was

retrievedformaizeand0.002606kgCMJ−1wasusedforsoybean.

MOD15A2 fPAR y = 0.6619x + 13.925 R² = 0.57 RMSE = 17.86 fAPARchl y = 0.5698x + 5.6477 R² = 0.91 RMSE = 18.61 0 10 20 30 40 50 60 70 80 100 80 60 40 20 0 Es m at e d G P P ( g C /m 2/8 d) in situ GPP (g C/m2_/8d)

US-BAR

MOD15A2 fPAR fAPARchl

Fig.5.EstimatedGPPusingMOD15A2fPARandfAPARchlwithεderivedfromthe MODISMOD17likealgorithmforthedeciduousforestsite,US-BAR.

(7)

0 50 100 150 200 250 1/ 1/ 2 00 1 3/ 1/ 2 00 1 5/ 1/ 2 00 1 7/ 1/ 2 00 1 9/ 1/ 2 00 1 11 / 1 / 20 01 1/ 1/ 2 00 2 3/ 1/ 2 00 2 5/ 1/ 2 00 2 7/ 1/ 2 00 2 9/ 1/ 2 00 2 11 / 1 / 20 02 1/ 1/ 2 00 3 3/ 1/ 2 00 3 5/ 1/ 2 00 3 7/ 1/ 2 00 3 9/ 1/ 2 00 3 11 / 1 / 20 03 1/ 1/ 2 00 4 3/ 1/ 2 00 4 5/ 1/ 2 00 4 7/ 1/ 2 00 4 9/ 1/ 2 00 4 11 / 1 / 20 04 GP P ( g C / m 2/8 d )

in situ GPP Esmates using MOD15A2 fPAR Esmates using fAPARchl

(a)

MOD15A2 y = 0.6127x + 84.887 R² = 0.78 RMSE = 50.84 fAPARchl y = 0.9777x + 23.433 R² = 0.94 RMSE = 31.59 0 50 100 150 200 250 250 200 150 100 50 0 Es m a te d GP P ( g C /m 2 /8 d) in situ GPP (g C/m2/8d) US-NE2 maize MOD15A2 fPAR fAPARchl (b) MOD15A2 fPAR y = 0.6321x + 53.429 R² = 0.61 RMSE = 37.46 fAPARchl y = 1.0262x + 10.543 R² = 0.92 RMSE = 15.49 0 20 40 60 80 100 120 140 160 140 120 100 80 60 40 20 0 Es m a te d GP P ( g C /m 2 /8 d) in situ GPP (g C/m2/8d) US-NE2 soybean MOD15A2 fPAR fAPARchl (c)

Fig.6.insituGPPandestimatedGPPusingMOD15A2fPARandfAPARchlwith␧derivedusingsitespeciﬁcεmaxfortheirrigatedcropsite,US-NE2(a)plottedagainsttime from2001through2004,(b)comparisonsformaize,(c)comparisonsforsoybean.

FortheUS-NE3sites,0.00555kgCMJ−1wasderivedformaizeand

0.002817kgCMJ−1wasusedforsoybean.FortheUS-BARforest,

0.002006kgCMJ−1wasused.Thesesitespeciﬁcεmaxvalueswere

usedasinputtotheMODISMOD17productalgorithm,Eq.(2),to

derive_ε.FluxtowerbasedGPPandestimatedGPPusingthesite

spe-ciﬁcεmaxvaluecoupledwithMOD15A2fPAR(CaseIII)andfAPARchl

(CaseIV)areplottedasatimeseriesforthethreestudysites.

Com-parisonsareshownasregressionchartsinFigs.6–8.In general,

improvementsinthemagnitudeofGPPestimatesandreductionsin

RMSEwereobserved,especiallyfortheagriculturalsites.Estimated

GPPusingfAPARchl(CaseIV)wasveryclosetoﬂuxtowerbased

val-ues,forbothmaizeandsoybean(Figs.6and7a).FortheBartlett

forest,aslightdecreaseinRMSEwasobservedforestimatesusing

fAPARchl,butaclearincreaseinRMSEandGPPoverestimateswere

observedforestimatesusingtheMOD15A2fPARproductpaired

withthesite-speciﬁcεmax(Fig.8b).

4. Discussion

Inthis study,weutilizeddifferentapproaches toderivetwo

importantparametersneededfor carbonmodeling,LUE(ε)and

fPAR,withasimpleGPPmodel.Performanceoftheseapproaches

wasassessedforsimulatedGPP,ascomparedtoinsituﬂuxtower

GPP.

AllthreestudysitesexhibitedseasonalGPPpatterns(Fig.1),as

expected.Inboththemaize-soybeanrotationsites(US-NE2and

US-NE3),theC4plant,maize,showedhigherGPPvaluesthanthe

C3plant,soybean.Furthermore,wenoticedhigherGPPvaluesfor

irrigatedcrops(Fig.1a)thanrainfedcrops(Fig.1b)thatcouldbe

attributedtowateravailability.Fortheagriculturalsites,littleorno

greenbiomassshouldbeexpectedoutsideofthegrowingseason.

Afterplanting,clearpatternsofgrowingseasonwereobservedfrom

theearlyvegetativestage,thematureandreproductivestage,tothe

senescencestage.SeasonaldynamicsoffAPARchlcloselymatched

GPPforbothoftheagriculturalsites.TheMOD15A2fPARproduct

showedsimilarvaluestofAPARchlduringthepeakofthe

grow-ingseason(approximatelymid-July,Fig.2a,b)buthighervalues

fortherestoftheyear.ThetimeseriesofMOD15A2fPAR

prod-uct also suggestedearlier green-up and later senescence, i.e. a

longerdurationofthegrowingseason,thandeterminedfromeither

towerobservations orfAPARchl.At theBartlettforest(US-BAR),

theMOD15A2fPARproducthadhighervaluesthanfAPARchlnot

onlyoutsidegrowingseasonbutalsoduringthesummergrowing

(8)

0 50 100 150 200 250 300 1 / 1/ 20 01 3 / 1/ 20 01 5 / 1/ 20 01 7 / 1/ 20 01 9 / 1/ 20 01 1 1 /1 /2 0 0 1 1 / 1/ 20 02 3 / 1/ 20 02 5 / 1/ 20 02 7 / 1/ 20 02 9 / 1/ 20 02 11 / 1 / 2 0 0 2 1 / 1/ 20 03 3 / 1/ 20 03 5 / 1/ 20 03 7 / 1/ 20 03 9 / 1/ 20 03 11 / 1 / 2 0 0 3 1 / 1/ 20 04 3 / 1/ 20 04 5 / 1/ 20 04 7 / 1/ 20 04 9 / 1/ 20 04 1 1 /1 /2 0 0 4 GP P ( g C / m 2/8 d )

in situ GPP Esmates using MOD15A2 fPAR Esmates using fAPARchl

(a)

MOD15A2 fPAR y = 0.962x + 90.136 R² = 0.84 RMSE = 80.22 fAPARchl y = 1.1249x + 26.408 R² = 0.87 RMSE = 49.49 0 50 100 150 200 250 300 200 150 100 50 0 Es m at e d G PP (g C/ m /8 d) in situ GPP (g C/m2_/8d) US-NE3 maize MOD15A2 fPAR fAPARchl (b) MOD15A2 fPAR y = 0.7879x + 56.589 R² = 0.72 RMSE = 40.23 fAPARchl y = 0.8247x + 21.192 R² = 0.79 RMSE = 18.01 0 20 40 60 80 100 120 140 160 180 120 100 80 60 40 20 0 Es m at e d G PP (g C/ m 2/8 d) in situ GPP (g C/m2_/8d) US-NE3 soybean

MOD15A2 fPAR fAPARchl (c)

Fig.7.insituGPPandestimatedGPPusingMOD15A2fPARandfAPARchlwith␧derivedusingsitespeciﬁcεmaxfortherainfedcropsite,US-NE3(a)plottedagainsttimefrom 2001through2004,(b)comparisonsformaize,(c)comparisonsforsoybean.

MOD15A2fPARattheUS-BARsitealsoappearedlongercompared

withfAPARchlandGPP.MOD15A2fPARsuggestedaslightlyearlier

green-upandalatersenescence(Fig.1c).

For all thestudy sites little or nogreen biomass shouldbe

expectedoutsideofthegrowingseason.FluxtowerbasedGPPalso

showedlittleornophotosyntheticactivitiesbetweensenescence

andgreen-up.ThefAPARchlexhibitedseasonalvariations

match-ingtheexpectation.TheMODISMOD15A2fPARproductseemed

tohaveanoverestimationbiasforallthreesitesduringwinter,

earlyspring,andlatefall.ThefAPARchl isaparameterproposed

heretoaccountforthefractionofPARabsorbedbychlorophyll

inleaves,onlywhichwillbeusedinthephotosynthesisprocess.

Theoretically,thisparameterminimizestheeffectscausedbythe

non-greencomponentofleafandcanopy(e.g.non-photosynthetic

pigmentandstem)aswellassoilbackground.Thismightexplain

thelowervaluesoffAPARchlthanMODISfPARproduct.Thisfeature

mightalsomakefAPARchlalandsurfaceparameterthatiscloserto

the“true”physiologicalandphenologicalcondition,bothduring

andoutsidethegrowingseason.

TheresultsfromcoupledMODISfPARandfAPARchl withthe

LUE(ε)valuederived fromthealgorithmsimilartotheMODIS

MOD17productareshowninFigs.3–5.EstimatesusingfAPARchl

consistentlyproducedbetteragreementwithinsitu GPPvalues

for all thethree study sites. This suggeststhat fAPARchl might

be used as an alternative option than the standard MOD15A2

fPARproductasinputtocarbonmodelstoimproveourcurrent

capabilitiesincarbonmodelingandmonitoring.Asshowninthe

regressioncharts(Figs.3–5),estimatesusingMOD15A2fPARhad

moresigniﬁcantoffsetsthanfAPARchl.Thisismostlikelydueto

thehighervaluesofMOD15A2fPARexhibitedoutsideofthe

grow-ingseason.AgeneralunderestimationofmodeledGPPwasfound

fortwo agriculturalsites(Figs.3 and4).Thismight suggestan

underestimation of ε values for thecrops using the algorithm

similarto theMODISMOD17 product.Similar underestimation

wasalsofoundfortheUS-BARsites,butwasnotaspronounced

(Fig. 5).The differencesin performance among sitescouldalso

beattributedtodifferencesinplantfunctionaltypes(C3andC4

crops,broadleaftrees).Themodelweutilizedinthisstudytook

theabilitytocaptureandtheefﬁciencytousesolarradiationinto

accountinGPPestimates.Suchplantphysiologicalpropertiesvary

amongdifferentvegetationtypes,andmight explainthe

differ-entperformance aswe observedat thethree sites.Similarly, a

recentstudyusingplantbiochemicalpropertiesand

environmen-talconditionstoestimateGPP(Wuetal.,2011)alsoreportedthat

modelcalibrationandperformancecandependonplantfunctional

(9)

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)

in situ GPP

Esmates using MOD15A2

Esmates using

fAPARchl

(a)

MOD15A2 fPAR y = 1.272x + 26.761 R² = 0.57 RMSE = 28.84 fAPARchl y = 1.095x + 10.853 R² = 0.91 RMSE = 17.75 0 20 40 60 80 100 120 140 160 100 80 60 40 20 0 Es m at e d G PP (g C/ m 2/8 d) in situ GPP (g C/m2/8d) US-BAR MOD15A2 fPAR fAPARchl (b)

Fig.8.insituGPPandestimatedGPPusingMOD15A2fPARandfAPARchlwith␧derivedusingsitespeciﬁcεmaxforthedeciduousforestsite,US-BAR(a)plottedagainsttime from2004and2005,(b)comparisonchart.

Usingthesite-speciﬁc εmaxvalues improvedtheaccuracyof

estimated GPP values, especially for the two agricultural sites

(Figs.6and7).Aswediscussedpreviously,theMOD15A2fPAR

pro-ducedGPPoverestimationoutsideofthegrowingseasoninwinter,

earlyspring,andlatefallforboththeUS-NE2andUS-NE3sites.The

bestGPPestimateswereobtainedusingfAPARchlcoupledwithε,

determinedbythesitespeciﬁcεmaxvalueandtheMODISMOD17

algorithm(CaseIV).AttheBartlettforest,usingsitespeciﬁcεmax

alsoimprovedGPPestimatesusingfAPARchl.However,GPP

over-estimatesresultedwiththesite-speciﬁcεmaxandwithMOD15A2

fPAR.Onemaynoticefromtheregressionchart(Fig.5),thatGPP

estimatesusingMOD15A2fPARpairedwithMOD17LUE(CaseI)

wereclosetoinsituGPPvalueswhenGPPvalueswerehigh,most

likelyduringpeakofthegrowingseason.Thissuggeststhatthe

sim-plemodelofusingMOD15A2fPARandεvaluesmighthavegood

performanceforsomestudysitesduringthepeakofthegrowing

season.Oneshouldalsonotethattheεmaxvaluecanbeobtained

fromliteraturereview orderivedusingtheanalyticalapproach,

whichisaffectedbymultipleparametersintheprocessincluding

regressionmodeltypeandtheperiodoftimeofinterests(Ruimy

etal.,1995;Wangetal.,2010;Xiaoetal.,2004a).Therefore,

val-uesvaryamongvegetationtypes,studysites,anddurationtimesin

differentstudies.Furthermore,recentstudieshavesuggestedthe

importanceoftakingbothsunlitandshadedfoliageintoaccountfor

accurateLUEestimates(Heetal.,2013;Middletonetal.,2009)since

theshadedsegmentofthecanopyislikelytoexperienceless

envi-ronmentalstress(e.g.,lesssolarradiationandlowertemperature),

andhence,hashigherLUE.Therefore,analgorithmthatintegrates

sunlitandshadedfoliagemighthelptoderivemoreaccurateLUE

valuesandimproveGPPestimates(Heetal.,2013).

5. Summary

WeutilizedasimplemodelwithdifferentLUEandfPAR

esti-matestosimulateGPPforcropsandadeciduousforest.Fluxtower

basedGPPandfAPARchl showedsimilarseasonaldynamics.The

MODIS MOD15A2 fPAR showed higher values before green-up

andaftersenescence,describingalongerdurationofthegrowing

seasonthan actuallyoccurred. Estimatesusing fAPARchl

consis-tentlydeliveredbetteragreementwithinsituGPPthan MODIS

fPAR.WhenutilizingεvaluesderivedusingtheMODIS

MOD17-likealgorithm,underestimationsofGPPwereobserved,especially

attheagriculturalsites.Thebestperformance wasfoundusing

site-speciﬁc_εmaxtoderiveLUEcoupledwithfAPARchl.Thisstudy

demonstratestheimportanceofaccuratelandsurface

(10)

more studysites covering different vegetationtypes shouldbe examined.

Acknowledgements

ThisstudywassupportedbyaNASAROSESproject,GrantNo.

NNX12AJ51G“CarbonMonitoringandEcosystemFeedbacks

Pre-dictionUsingfAPARch1andtheCommunityLandModel(CLM)”

(PI, Q. Zhang), through the Terrestrial Ecology Program (Diane

Wickland,manager).TheworkofA.LyapustinandY.Wangwas

supportedbytheNASAEarthSciencesProgram.Thisworkused

eddycovariancedataacquiredbytheFLUXNETcommunity and

inparticularbythefollowingnetworks:AmeriFlux(U.S.

Depart-mentofEnergy,BiologicalandEnvironmentalResearch,Terrestrial

CarbonProgram(DE-FG02-04ER63917andDE-FG02-04ER63911),

AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly,

Car-boMont,ChinaFlux,Fluxnet-Canada(supportedbyCFCAS,NSERC,

BIOCAP,Environment Canada,and NRCan),GreenGrass, KoFlux,

LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the

ﬁnancial support to the eddy covariance data harmonization

providedbyCarboEuropeIP,FAO-GTOS-TCO,iLEAPS,MaxPlanck

InstituteforBiogeochemistry,NationalScienceFoundation,

Uni-versityofTuscia,UniversitéLavalandEnvironmentCanadaandUS

DepartmentofEnergyandthedatabasedevelopmentandtechnical

supportfromBerkeleyWaterCenter,LawrenceBerkeleyNational

Laboratory,MicrosoftResearcheScience,OakRidgeNational

Lab-oratory,UniversityofCalifornia-Berkeley,UniversityofVirginia.

Theauthorsthanktheanonymousreviewersfortheirveryvaluable

suggestionsandcritiques.

References

Agarwal,D.A.,Humphrey,M.,Beekwilder,N.F.,Jackson,K.R.,Goode,M.M.,vanIngen, C.,2010.Adata-centeredcollaborationportaltosupportglobalcarbon-ﬂux analysis.ConcurrencyComputat.Pract.Exper.22(17),2323–2334.

Anderson,M.C.,Norman,J.M.,Meyers,T.P.,Diak,G.R.,2000.Ananalyticalmodelfor estimatingcanopytranspirationandcarbonassimilationﬂuxesbasedoncanopy light-useefﬁciency.Agric.ForestMeteorol.101(4),265–289.

Baret,F.,Fourty,T.,1997.Radiometricestimatesofnitrogenstatusinleavesand canopies.In:Lemaire, G.(Ed.),Diagnosis oftheNitrogen Statusin Crops. Springer,Berlin,pp.201–227.

Beer,C.,etal.,Reichstein,M.,Tomelleri,E.,Ciais,P.,Jung,M.,Carvalhais,N., Röden-beck,N.,Arain,M.A.,Baldocchi,D.,Bonan,G.B.,Bondeau,A.,Cescatti,A.,Lasslop, G.,Lindroth,A.,Lomas,M.,Luyssaert,S.,Margolis,H.,Oleson,K.W.,Roupsard, O.,Veenendaal,E.,Viovy,N.,Williams,C.,Woodward,F.I.,Papale,D.,2010. Ter-restrialgrosscarbondioxideuptake:globaldistributionandcovariationwith climate.Science329(5993),834–838.

Bonan,G.B.,Lawerence,P.J.,Oleson,K.W.,Levis,S.,Jung,M.,Reichstein,M., Lawer-ence,D.M.,Swenson,S.C.,2011.Improvingcanopyprocessesinthecommunity landmodelversion4(CLM4)usingglobalﬂuxﬁeldsempiricallyinferredfrom FLUXNETdata.J.Geophys.Res.116(G2),G02014.

Braswell,B.H.,etal.,Schimel,D.S.,Privette,J.L.,Moore,B.,Emery,W.J.,Sulzman, E.W.,Hudak,A.T.,1996.Extractingecologicalandbiophysicalinformationfrom AVHRRopticaldata:anintegratedalgorithmbasedoninversemodeling.J. Geo-phys.Res.101(D18),23335–23348.

Cheng,Y.-B.,Middleton,E.M.,Hilker,T.,Coops,N.C.,Black,T.A.,Krishnan,P.,2009. Dynamicsofspectralbio-indicatorsandtheircorrelationswithlightuse efﬁ-ciencyusingdirectional observationsata Douglas-ﬁrforest.Measure.Sci. Technol.20(9),095107.

Cheng,Y.-B.,Zarco-Tejada,P.J.,Falk,M.,KyawThaPaw,U.,Wharton,S.,Ustin,S.L., 2007.Relationshipsbetweenmoderateresolutionimagingspectroradiometer waterindexesandtowerﬂuxdatainanoldgrowthconiferforest.J.Appl.Rem. Sens.1(23(May)),013513.

Drolet,G.G.,Middleton,E.M.,Huemmrich,K.F.,Hall,F.G.,Amiro,B.D.,Barr,A.G., Mar-golis,H.A.,2008.Regionalmappingofgrosslight-useefﬁciencyusingMODIS spectralindices.Rem.Sens.Environ.112(6),3064–3078.

Falge,E.,Baldocchi,D.,Olson,R.,Anthoni,P.,Aubinet,M.,Bernhofer,C.,Burba,G., Ceulemans,R.,Clement,R.,Dolman,H.,Grainer,A.,Grunwald,T.,Hollinger,D., Jensen,N.-O.,Katul,G.,Keronen,P.,Kowalski,A.,TaLai,C.,Law,B.E.,Meyers, T.,Moncrieff,J.,Moors,E.,Munger,J.W.,Pilegaard,K.,Rannik,U.,Rebmann,C., Suyker,A.E.,Tenhunen,J.,Tu,K.,Verma,S.,Vesala,T.,Wilson,K.,Wofsy,S.,2001. Gapﬁllingstrategiesfordefensibleannualsumsofnetecosystemexchange.Ag. For.Meteorol.107(1),43–69.

Gower,S.T.,Kucharik,C.J.,Norman,J.M.,1999.Directandindirectestimationofleaf areaindex,fAPAR,andnetprimaryproductionofterrestrialecosystems.Rem. Sens.Environ.70(1),29–51.

Grace,J.,etal.,Nichol, C.,Disney,M.,Lewis, P., Quaife,T., Bowyer,P., 2007. Canwemeasureterrestrialphotosynthesisfromspacedirectly,usingspectral reﬂectanceandﬂuorescence?GlobalChangeBiol.13(7),1484–1497. Hall,F.G.,Hilker,T.,Coops,N.C.,2012.Dataassimilationofphotosyntheticlight-use

efﬁciencyusingmulti-angularsatellitedata:I.Modelformulation.Rem.Sens. Environ.121(0),301–308.

He,M.,Ju,W.,Zhou,Y.,Chen,J.,He,H.,Wang,S.,Wang,H.,Guan,D.,Yan,J.,Li,Y., Hao,Y.,Zhao,F.,2013.Developmentofatwo-leaflightuseefﬁciencymodelfor improvingthecalculationofterrestrialgrossprimaryproductivity.Agric.Forest Meteorol.173(0),28–39.

Heinsch,F.A.,Heinsch,F.A.,Reeves,M.,Votava,P.,Kang,S.,Milesi,C.,Zhao,M., Run-ning,S.W.,2003.User’sGuide,GPPandNPP(MOD17A2/A3)Products,NASA MODISLandAlgorithm.UniversityofMotana,Missoula,MT.

Heinsch,F.A.,Zhao,M.,Running,S.W.,Kimball,J.S.,Nemani,R.R.,Davis,K.J., Bol-stad,P.V.,Cook,B.D.,Desai,A.R.,Ricciuto,D.M.,Law,B.E.,Oechel,W.C.,Kwon,H., Luo,H.,Wofsy,S.C.,Allison,L.D.,Munger,J.W.,Baldocchi,D.D.,Xu,L.,Hollinger, D.Y.,Richardson,A.D.,Stoy,P.C.,Siqueira,M.B.S.,Monson,R.K.,Burns,S.P., Flana-gan,L.B.,2006.Evaluationofremotesensingbasedterrestrialproductivityfrom MODISusingregionaltowereddyﬂuxnetworkobservations.IEEETrans.Geosci. Rem.Sens.44(7),1908–1925.

Hilker,T.,etal.,Hall,F.G.,Tucker,C.J.,Coops,N.C.,Black,T.A.,Nichol,C.J.,Sellers,P.J., Barr,A.,Hollinger,D.Y.,Munger,J.W.,2012.Dataassimilationofphotosynthetic light-useefﬁciencyusingmulti-angularsatellitedata:IIModelimplementation andvalidation.Rem.Sens.Environ.121(0),287–300.

Houborg,R.,Anderson,M.C.,Daughtry,C.S.T.,Kustas,W.P.,Rodell,M.,2011.Using leafchlorophylltoparameterizelight-use-efﬁciencywithinathermal-based carbon,waterandenergyexchangemodel.Rem.SensingEnviron.115(7), 1694–1705.

Huete,A.R.,Didan,K.,Miura,T.,Rodriguez,E.P.,Gao,X.,Ferreira,L.G.,2002.Overview oftheradiometricandbiophysicalperformanceoftheMODISvegetationindices. Rem.SensingEnviron.83(1-2),195–213.

Huntzinger,D.N.,Post,W.M.,Wei,Y.,Michalak,A.M.,West,T.O.,Jacobson,A.R.,Baker, I.T.,Chen,J.M.,Davis,K.J.,Hayes,D.J.,Hoffman,F.M.,Jain,A.K.,Liu,S.,McGuire, A.D.,Neilson,R.P.,Potter,C.,Poulter,B.,Price,D.,Raczka,B.M.,Tian,H.Q., Thorn-ton,P.,Tomelleri,E.,Viovy,N.,Xiao,J.,YuanW.,Zeng,N.,Zhao,M.,Cook,R.,2012. NorthAmericancarbonprogram(NACP)regionalinterimsynthesis:terrestrial biosphericmodelintercomparison.Ecol.Model.232(0),144–157.

Jacquemoud,S.,Baret,F.,1990.PROSPECT:amodelofleafopticalpropertiesspectra. Rem.SensingEnviron.34(2),75–91.

Jenkins,J.P.,Jenkins,J.P.,Richardson,A.D.,Braswell,B.H.,Ollinger,S.V.,Hollinger, D.Y.,Smith,M.L.,2007.Refininglight-useefficiencycalculationsfora decidu-ousforestcanopyusingsimultaneoustower-basedcarbonfluxandradiometric measurements.Ag.For.Meteorol.143(1–2),64–79.

Jin,C.,Jin,C.,Xiao,X.,Merbold,L.,Arneth,A.,Veenendaal,E.,Kutsch,W.L.,2013. Phenologyandgrossprimaryproductionoftwodominantsavannawoodland ecosystemsinSouthernAfrica.Rem.Sens.Environ.135(0),189–201. Kalfas,J.L.,Xiao,X.,Vanegas,D.X.,Verma,S.B.,Suyker,A.E.,2011.Modelinggross

primaryproductionofirrigatedandrain-fedmaizeusingMODISimageryand CO2ﬂuxtowerdata.Agric.ForestMeteorol.151(12),1514–1528.

Knyazikhin,Y.,.Glassy,J.,Privette, JL,Tian,Y.,Lotsch,A.and Zhang,Y.,1999. MODISLeafAreaIndex(LAI)AndFractionOfPhotosyntheticallyActive Radi-ationAbsorbedByVegetation(FPAR)Product(MOD15)AlgorithmTheoretical BasisDocument.

Knyazikhin,Y.,Martonchik,J.V.,Myneni,R.B.,Diner,D.J.,Running,S.W.,1998. Syn-ergisticalgorithmforestimatingvegetationcanopyleafareaindexandfraction ofabsorbedphotosyntheticallyactiveradiationfromMODISandMISRdata. J.Geophys.Res.Atmosph.103(D24),32257–32275.

Lin,J.C.,Pejam,M.R.,Chan,E.,Wofsy,S.C.,Gottlieb,E.W.,Margolis,H.A.,McCaughey, J.H.,2011.Attributinguncertaintiesinsimulatedbiosphericcarbonﬂuxesto differenterrorsources.GlobalBiogeochem.Cycle.25(2),GB2018.

Lyapustin,A.,Martonchik,J.,Wang,Y.,Laszlo,I.,Korkin,S.,2011a.Multiangle imple-mentationofatmosphericcorrection(MAIAC):1.Radiativetransferbasisand look-uptables.J.Geophys.Res.Atmosph.116(D3),D03210.

Lyapustin,A.,Wang,Y.,Laszlo,I.,Kahn,R.,Korkin,S.,Remer,L.,Levy,R.,Reid, J.S.,2011b.Multiangleimplementationofatmosphericcorrection(MAIAC):2. Aerosolalgorithm.J.Geophys.Res.Atmosph.116(D3),D03211.

Lyapustin,A.I.,Wang,Y.,Laszlo,I.,Hilker,T.,Hall,F.G.,Sellers,P.J.,Tucker,C.J.,Korkin, S.V.,2012.Multi-angleimplementationofatmosphericcorrectionforMODIS (MAIAC):3.Atmosphericcorrection.Rem.Sens.Environ.127(0),385–393. Middleton,E.M.,Cheng,Y.-B.,Hilker,T.,Black,T.A.,Krishnan,P.,Coops,N.C.,

Huemm-rich,K.F.,2009.Linkingfoliagespectralresponsestocanopylevelecosystem photosyntheticlightuseefﬁciencyataDouglas-ﬁrforestinCanada.Can.J.Rem. Sens.35(2),166–188.

Middleton,E.M., Huemmrich, K.F., Cheng,Y.-B., Margolis, H.A.,2011. Spectral bioindicatorsofphotosyntheticefﬁciencyandvegetationstress.In:Thenkabail, P.S.,Lyon,J.G.,Huete,A.(Eds.),HyperspectralRemoteSensingofVegetation.CRC Press,pp.265–288.

Monteith,J.L.,1972.Solar-radiationandproductivityintropicalecosystems.J.Appl. Ecol.9(3),747–766.

Monteith,J.L.,1977.ClimateandtheefﬁciencyofcropproductioninBritain.Phil. Trans.R.Soc.B281(980),277–294.

Munger,J.,Loescher,H.,2006.GuidelinesforMakingEddyCovarianceFlux mea-surements.Ameriﬂux.

Myneni,R.B.,etal.,Hoffman,S.,Knyazikhin,Y.,Privette,J.L.,Glassy,J.,Tian,Y.,Wang, Y.,Song,X.,Zhang,Y.,Smith,G.R.,Lotsch,A.,Friedl,M.,Morisette,J.T.,Votava, P.,Nemani,R.R.,Running,S.W.,2002.Globalproductsofvegetationleafarea

(11)

andfractionabsorbedPARfromyearoneofMODISdata.Rem.Sens.Environ.83 (1–2),214–231.

Nichol,C.J.,Huemmrich,K.F.,Black,T.A.,Jarvis,P.G.,Walthall,C.L.,Grace,J.,Hall,F.G., 2000.Remotesensingofphotosynthetic-light-useefﬁciencyofborealforest. Agric.ForestMeteorol.101(2-3),131–142.

Peng,Y.,Gitelson,A.A.,2012.Remoteestimationofgrossprimaryproductivityin soybeanandmaizebasedontotalcropchlorophyllcontent.Rem.Sens.Environ. 117(0),440–448.

Ruimy,A.,Jarvis,P.G.,Baldocchi,D.D.,Saugier,B.,1995.CO2Fluxesoverplant canopiesandsolarradiation:areview.In:Begon,M.,Fitter,A.H.(Eds.),Advances inEcologicalResearch.AcademicPress,pp.1–68.

Running,S.W.,Nemani,R.R.,Heinsch,F.A.,Zhao,M.,Reeves,M.,Hashimoto,H.,2004. Acontinuoussatellite-derivedmeasureofglobalyerrestrialprimaryproduction. BioScience54(6),547–560.

Schaefer,K.,Schwalm,C.R.,Williams,C.,Arain,M.A.,Barr,A.,Chen,J.M.,Davis,K.J., Dimitrov,D.,Hilton,T.W.,Hollinger,D.Y.,Humphreys,E.,Poulter,B.,Raczka, B.M.,Richardson,A.D.,Sahoo,A.,Thornton,P.,Vargas,R.,Verbeeck,H.,Anderson, R.,Baker,I.,Black,T.A.,BolstadvP.,Chen,J.,Curtis,P.S.,Desai,A.R.,Dietze,M., Dragoni,D.,Gough,C.,Grant,R.F.,Gu,L.,Jain,A.,Kucharik,C.,Law,B.,Liu,S., Lokipitiya,E.,Margolis,H.A.,Matamala,R.,McCaughey,J.H.,Monson,R.,Munger, J.W.,Oechel,W.,Peng,C.,Price,D.T.,Ricciuto,D.,Riley,W.J.,Roulet,N.,Tian, H.,Tonitto,C.,Torn,M.,Weng,E.,Zhou,X.,2012.Amodel-datacomparisonof grossprimaryproductivity:resultsfromtheNorthAmericancarbonprogram sitesynthesis.J.Geophys.Res.Biogeosci.117(G3),G03010.

Sellers,P.J.,Sellers,P.J.,Dickinson,R.E.,Randall,D.A.,Betts,A.K.,Hall,F.G.,Berry, J.A.,Henderson-Sellers,A.,1997.Modelingtheexchangeofenergy,water,and carbonbetweencontinentsandtheatmosphere.Science275,502–509. Tucker,C.J.,Sellers,P.J.,1986.Satelliteremotesensingofprimaryproduction.Int.J.

Rem.Sens.7(11),1395–1416.

Turner,D.P.,etal.,Turner,D.P.,Ritts,W.D.,Cohen,W.B.,Gower,S.T.,Running,S.W., Zhao,M.,Ahl,D.E.,2006.EvaluationofMODISNPPandGPPproductsacross multiplebiomes.Rem.Sens.Environ.102(3-4),282–292.

Verma,S.B.,etal.,Dobermann,A.,Cassman,K.,Walters,D.T.,Knops,J.M.,Arkebauer, T.J.,Suyker,A.E.,Burba,G.G.,Amos,B.,Yang,H.S.,Ginting,D.,Hubbard,K.G., Gitelson,A.A.,Walter-Shea,E.A.,2005.Annualcarbondioxideexchangein irri-gatedandrainfedmaize-basedagroecosystems.Ag.For.Meteorol.131(1–2), 77–96.

Wang,Z.,Xiao,X.,Yan,X.,2010.Modelinggrossprimaryproductionofmaize crop-landanddegradedgrasslandinnortheasternChina.Agric.ForestMeteorol.150 (9),1160–1167.

Wofsy,S.C.,Goulden,M.L.,Munger,J.W.,Fan,S.-M.,Bakwin,P.S.,Daube,B.C.,Bassow, S.L.,Bazzaz,A.,1993.NetexchangeofCO2inamid-latitudeforest.Science260 (5112),1314–1317.

Wu,C.,Chen,J.M.,Huang,N.,2011.Predictinggrossprimaryproductionfromthe enhancedvegetationindexandphotosyntheticallyactiveradiation:evaluation andcalibration.Rem.Sens.Environ.115(12),3424–3435.

Wu,C.,Niu,Z.,Tang,Q.,Huang,W.,Rivard,B.,Feng,J.,2009.Remoteestimationof grossprimaryproductioninwheatusingchlorophyll-relatedvegetationindices. Agric.ForestMeteorol.149(6-7),1015–1021.

Xiao,X.,2006.Lightabsorptionbyleafchlorophyllandmaximumlightuseefﬁciency. IEEETrans.Geosci.Rem.Sens.44(7),1933–1935.

Xiao,X.,Hollinger,D.,Aber,J.,Goltz,M.,Davidson,E.A.,Zhang,Q.,MooreIII,B.,2004a. Satellite-basedmodelingofgrossprimaryproductioninanevergreenneedleleaf forest.Rem.Sens.Environ.89(4),519–534.

Xiao,X.,Zhang,Q.,Braswell,B.,Urbanski,S.,Boles,S.,Wofsy,S.,Ojima,D.,2004b. Modelinggrossprimaryproductionoftemperatedeciduousbroadleafforest usingsatelliteimagesandclimatedata.Rem.Sens.Environ.91(2),256–270. Xiao,X.,Zhang,Q.,Saleska,S.,Hutyra,L.,DeCamargo,P.,Wofsy,S.,MooreIII,B.,

2005.Satellite-basedmodelingofgrossprimaryproductioninaseasonallymoist tropicalevergreenforest.Rem.Sens.Environ.94(1),105–122.

Xiao,J.,Zhuang,Q.,Law,B.E.,Chen,J.,Baldocchi,D.D.,Cook,D.R.,Wofsy,S.C.,2010.A continuousmeasureofgrossprimaryproductionfortheconterminousUnited StatesderivedfromMODISandAmeriFluxdata.Rem.Sens.Environ.114(3), 576–591.

Zhang,Q.,Middleton,E.M.,2010.IntroductiontofractionofabsorbedPARbycanopy chlorophyll(fAPARchl)andcanopyleafwatercontentderivedfrom Hyper-ion,simulatedHyspIRIandMODISimages.In:IEEEInternationalGeoscience &RemoteSensingSymposium(IGARSS),Honolulu,HI.

Zhang,Q.,Xiao,X.,Braswell,B.,Linder,E.,Baret,F.,MooreIII,B.,2005.Estimating lightabsorptionbychlorophyll,leafandcanopyinadeciduousbroadleafforest usingMODISdataandaradiativetransfermodel.Rem.Sens.Environ.99(3), 357–371.

Zhang,Q.,Xiao,X.,Braswell,B.,Linder,E.,Ollinger,S.,Smith,M.L.,Minocha,R., 2006.Characterizationofseasonalvariationofforestcanopyinatemperate deciduousbroadleafforest,usingdailyMODISdata.Rem.Sens.Environ.105(3), 189–203.

Zhang,Q.,Middleton, E.M.,Margolis, H.A.,Drolet, G.G.,Barr, A.A.,Black,T.A., 2009. Canasatellite-derived estimateof thefractionof PARabsorbedby chlorophyll(FAPARchl)improvepredictionsoflight-useefﬁciencyand ecosys-temphotosynthesisforaborealaspenforest?Rem.Sens.Environ.113(4), 880–888.

Zhang,Q.,Middleton,E.M.,Gao,B.C.,Cheng,Y.-B.,2012.UsingEO-1Hyperionto simulateHyspIRIproductsforaconiferousForest:thefractionofPARabsorbed bychlorophyll(fAPARchl)andleafwatercontent(LWC).IEEETrans.Geosci.Rem. Sens.50(5),1844–1852.

Zhang,Q.,Middleton,E.M.,Cheng,Y.-B.,Landis,D.R.,2013.Variationsoffoliage chlorophyllfAPARandfoliagenon-chlorophyllfAPAR(fAPARchl,fAPARnon-chl) attheHarvardForest,.IEEEJ.SelectedTopicsAppl.EarthObserv.Rem.Sens.,In press.

Zhao,M.,Heinsch,F.A.,Nemani,R.R.,Running,S.W.,2005.Improvementsofthe MODISterrestrialgrossandnetprimaryproductionglobaldataset.Rem.Sens. Environ.95(2),164–176.