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MODISCollection6(C6)LAI/FPARProductUser’sGuide

(Updated:April21,2020)

Contents

1.Definitions Page22.SummaryofChangesinC6 Page23.AlgorithmDescription Page24.StandardMODISProducts Page45.HowtoObtaintheData Page66.Contentoftheproductfile Page67.Policies Page118.ContactInformation Page119.RelatedPapers Page11

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1.DefinitionsLeafareaindex(LAI;dimensionless)isdefinedastheone−sidedgreenleafareaper

unitgroundareainbroadleafcanopiesandasone−halfthetotalneedlesurfaceareaperunitgroundareainconiferouscanopies.STD LAI is the estimated retrieval uncertainty, i.e., “true LAI” can differ from its

retrievalcounterpartby±STDLAI(SeeFigure1).Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR;

dimensionless)isdefinedasthefractionofincidentphotosyntheticallyactiveradiation(400−700nm)absorbedbythegreenelementsofavegetationcanopy.STDFPARis theestimatedretrievaluncertainty, i.e., “trueFPAR”candiffer fromits

retrievalcounterpartby±STDFPAR(SeeFigure1).2.SummaryofChangesinC6• UsesL2G–litesurfacereflectanceat500mresolutionas(MOD09GA1)inputinplace

ofreflectanceat1kmresolution(MODAGAGG2)inCollection5.Anintermediatedailysurface reflectance product (MOD15IP3) at 500 m resolution is created fromMOD09GAbeforebeingusedforLAI/FPARretrieval.

• Productsaregeneratedataspatialresolutionof500m.• Usesimprovedmulti-yearlandcoverproduct.3.AlgorithmDescriptionThe MODIS LAI/FPAR algorithm consists of a main Look-up-Table (LUT) based

procedurethatexploitsthespectralinformationcontentoftheMODISred(648nm)andnear-infrared (NIR,858nm)surface reflectances,and theback-upalgorithmthatusesempirical relationships between Normalized Difference Vegetation Index (NDVI) andcanopy LAI and FPAR. The LUT was generated using 3D radiative transfer equation[Knyazikhinetal.,1998]. Inputstothealgorithmare(i)vegetationstructuraltype,(ii)sun-sensor geometry, (iii) BRFs at red (648 nm) and near-infrared (NIR, 858 nm)spectral bands and (vi) their uncertainties (Table 1). Figure 1 illustrates the main

1MOD09GA is a MODIS daily surface reflectance product, which provides daily atmosphericallycorrected surface reflectance at 500 m resolution in seven spectral bands. MOD09GA can beaccessedviaReverbtool(PleaserefertotheSection5.HowtoObtaintheData)2MODAGAGG is a MODIS daily aggregated surface reflectance product, which provides dailyatmospherically corrected surface reflectance at 1 km resolution in seven spectral bands.MODAGAGGisnotanarchivedproduct.3MOD15IPistheintermediateMODISdailysurfacereflectanceproductat500mresolution,whichis preprocessed from the daily MOD09GA surface reflectance product, for LAI/FPAR production.ThisproductisanequivalentofMODAGAGGinC5andnotarchived.

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algorithm:foreachpixelitcomparesobservedandmodeledspectralBRFsforasuiteofcanopy structures and soil patterns that represent an expected range of typicalconditions for a given biome type. All canopy/soil patterns and corresponding FPARvaluesforwhichmodeledandobservedBRFsdifferwithinaspecifieduncertaintylevelareconsideredasacceptablesolutions.ThemeanvaluesofLAI,FPAR,theirdispersions,STDLAIandSTDFPAR,arereportedasretrievalsandtheiruncertainties[Knyazikhinetal., 1998]. In the case of dense canopies, the reflectances saturate, and are thereforeweaklysensitivetochangesincanopyproperties.Thereliabilityofparametersretrievedunder the condition of saturation is low, that is, the dispersion of the solutiondistribution is large. Suchretrievalsare flagged inQA layers (Table5).When theLUTmethod fails to localizea solution, theback-upmethod isutilized.Thealgorithmpath(main or backup) is archived in QA layers (Table 5). Analyses of the algorithmperformance indicate thatbestquality,highprecisionretrievalsareobtained fromthemainalgorithm[Yangetal.2006b;Yangetal.2006c].Thealgorithmpathisthereforeakeyqualityindicator.The algorithm has interfaces with the MODIS Surface Reflectance Product

(MOD09GA) and theMODIS Land Cover Product (MCD12Q1). Technical details of thealgorithmcanbefoundintheAlgorithmTheoreticalBasisDocument(ATBD)4.

A

B

Figure1.Schematicillustrationofthemainalgorithm.PanelA:Distributionofvegetatedpixelswith respect to their reflectancesat redandnear-infrared (NIR) spectralbandsfromTerraMODIStileh12v04.Apointonthered-NIRplaneandanareaaboutit(yellowellipse defined by a𝜒#distribution) are treated as themeasured BRF at a given sun-sensorgeometryanditsuncertainty.Eachcombinationofcanopy/soilparametersandcorrespondingFPARvalues forwhichmodeled reflectancesbelong to theellipse is an

4ATBDforMODISLAI/FPARproductcanbedirectlydownloadedfrombelowlink:http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf

Red$

Near(Infrared

$

Soil$line$(LAI=0)$

Prob

abili

ty d

ensit

y

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acceptable solution. Panel B: Density distribution function of acceptable solutions.ShownissolutiondensitydistributionfunctionofLAIforfivedifferentpixels.ThemeanLAIanditsdispersion(STDLAI)aretakenastheLAIretrievalanditsuncertainty.Thistechnique is used to estimate mean FPAR and its dispersions (STD FPAR). From[Knyazikhinatal,1998].Table1.Theoreticalestimatesofuncertainties(%)intheBRFsusedintheC6LAI/FPARalgorithm

BiomeTypeUncertainty

Red(648nm) NIR(858nm)Biome1(Grasses/Cerealcrops) 20% 5%Biome2(Shrubs) 20% 5%Biome3(Broadleafcrops) 20% 5%Biome4(Savanna) 20% 5%Biome5(EvergreenBroadleafforest) 30% 15%Biome6(DeciduousBroadleafforest) 30% 15%Biome7(EvergreenNeedleleafforest) 30% 15%Biome8(DeciduousNeedleleafforest) 30% 15%4.StandardMODISProductsThe standardMODISC6 LAI/FPARproducts (M*D15A*H) are at 500−meter spatial

resolutionandincludeLAI/FPARretrievals fromTerraMODIS,AquaMODISandTerraMODIS+Aqua MODIS Combined. The temporal compositing periods are 8 and 4 days(Table2).

Table2.DescriptionoftheStandardMODISLAI/FPARproducts

OfficialName Platform RasterTypeSpatialResolution

TemporalGranularity

MOD15A2H Terra Tile 500m 8DayMYD15A2H Aqua Tile 500m 8Day

MCD15A2H Terra+AquaCombined

Tile 500m 8Day

MCD15A3H Terra+AquaCombined

Tile 500m 4Day

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TheMODISLAI/FPARproductsusetheSinusoidalgridtillingsystem(Figure2).Tilesare10degreesby10degreesattheequator(Table3).Thetilecoordinatesystemstartsat (0, 0) (horizontal tile number, vertical tile number) in the upper left corner andproceedsright(horizontal)anddownward(vertical).Thetileinthebottomrightcorneris(35,17).Table3.DatasetcharacteristicsoftheMODISLAI/FPARproducts

Characteristics C6Product

TemporalCoverageMOD15:February18,2000–MYD15&MCD15:July4,2002–

Area ~10x10lat/longFileSize ~0.8MBcompressedProjection SinusoidalDataFormat HDF−EOSDimensions 2400x2400rows/columnsResolution 500meterScienceDataSets(SDSHDFLayers) 6

Figure2.MODISSinusoidalTilingSystem

MODISproductfilenames(i.e.,thelocalgranuleID)followanamingconventionthat

gives useful information regarding the specific product. For example, the filenameMOD15A2H.A2006001.h08v05.006.2006012234657.hdfindicates:

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ü MOD15A2H–ProductShortNameü .A2006001–JulianDateofAcquisition(A−YYYYDDD)ü .h08v05–TileIdentifier(horizontalXX,verticalYY)ü .006–CollectionVersionü .2006012234657–JulianDateofProduction(YYYYDDDHHMMSS)ü .hdf–DataFormat(HDF−EOS)TheMODIS LAI/FPAR products have two sources ofmetadata: the embeddedHDF

metadata, and the external ECS metadata. The HDF metadata contains valuableinformationincludingglobalattributesanddataset−specificattributespertainingtothegranule. The ECS (generated by the EOSDIS Core System) .met file is the externalmetadata file in XML format, which is delivered to the user along with the MODISproduct.ItprovidesasubsetoftheHDFmetadata.SomekeyfeaturesofcertainMODISmetadataattributesincludethefollowing:

ü TheXdimandYdimrepresenttherowsandcolumnsofthedata,respectively.ü TheProjection andProjParams identify the projection and its corresponding

projectionparameters.ü TheSinusoidalProjectionisusedformostofthegriddedMODISlandproducts,

andhasauniquespheremeasuring6371007.181meters.ü The UpperLeftPoinitMtrs is in projection coordinates, and identifies the very

upperleftcorneroftheupperleftpixeloftheimagedata.ü The LowerRightMtrs identifies the very lower right corner of the lower right

pixeloftheimagedata.Theseprojectioncoordinatesaretheonlymetadatathataccuratelyreflecttheextremecornersofthegriddedimage.

ü ThereareadditionalBOUNDINGRECTANGLEandGRINGPOINT fieldswithinthemetadata, which represent the latitude and longitude coordinates of thegeographictilecorrespondingtothedata.

5.HowtoObtaintheDataNASA EARTHDATA (https://earthdata.nasa.gov/): This tool provides access to a

completedatarecordofallMODISproductsavailablefromtheLPDAAC.

6.ContentoftheproductfileTheMODISLAI/FPARproductisat500−meterresolutioninaSinusoidalgrid.Science

Data Sets provided in the product include LAI, FPAR, quality ratings, and standarddeviationforeachvariable,STDLAIandSTDFPAR(Table4).

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Table4.ScientificDataSetsincludedintheMODISLAI/FPARproduct

ScientificDataSets(HDFLayers)(6)

Units BitTypeFillValue

ValidRange

MultiplyByScaleFactor

Fpar_500m Dimensionless8−bitunsignedinteger

249−255 0−100 0.01

Lai_500m Dimensionless8−bitunsignedinteger

249−255 0−100 0.1

FparLai_QC Classflag8−bitunsignedinteger

255 0−254 N/A

FparExtra_QC Classflag8−bitunsignedinteger

255 0−254 N/A

FparStdDev_500m5 Dimensionless8−bitunsignedinteger

248−255 0−100 0.01

LaiStdDev__500m5 Dimensionless8−bitunsignedinteger

248−255 0−100 0.1

6.1.DescriptionofQCSDSQualitycontrol (QC)measuresareproducedatboth the file (containingoneMODIS

tile)andatthepixellevelsfortheM*D15A*Hproduct.Atthetilelevel,theseappearasaset of EOSDIS core system (ECS) metadata fields. At the pixel level, quality controlinformation is representedby2data layers (FparLai_QCandFparExtra_QC) in the filewithM*D15A*Hproduct.NotethattheLAI/FPARalgorithmisexecutedirrespectiveofinputquality.ThereforeusershouldconsulttheQClayersoftheLAI/FPARproducttoselectreliableretrievals.

5ThemainalgorithmemploysaLUTmethodsimulatedfroma3-Dradiativetransfermodel.TheLUTmethodessentially searches forLAI/FPARs fora specific setof solarandviewzenithangles,observed BRFs at certain spectral bands and biome types. The outputs are the LAI/FPARmeanvalues (i.e.,Lai_500m/Fpar_500mscientificdata)averagedoverallacceptable solutions,and thestandarddeviation(i.e.,LaiStdDev/FparStdDevscientificdata)servingasameasureofthesolutionaccuracy.

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Table5.ValuesofFparLAI_QC(8−bit)

BitNo. ParameterName BitComb.

FparLai_QC

0 MODLAND_QCbits0

Goodquality(mainalgorithmwithorwithoutsaturation)

1 Otherquality(back−upalgorithmorfillvalues)

1 Sensor0 Terra1 Aqua

2 DeadDetector0

Detectorsapparentlyfineforupto50%ofchannels1,2

1 Deaddetectorscaused>50%adjacentdetectorretrieval

3−4

CloudState(inheritedfromAggregate_QCbits{0,1}cloudstate)

00 0SignificantcloudsNOTpresent(clear)01 1SignificantcloudsWEREpresent

10 2Mixedcloudpresentinpixel

11 3Cloudstatenotdefined,assumedclear

5−7 SCF_QC(five−levelconfidencescore)

0000Main(RT)methodused,bestresultpossible(nosaturation)

001 1Main(RT)methodusedwithsaturation.Good,veryusable

0102Main(RT)methodfailedduetobadgeometry,empiricalalgorithmused

0113Main(RT)methodfailedduetoproblemsotherthangeometry,empiricalalgorithmused

1004Pixelnotproducedatall,valuecouldn’tberetrieved(possiblereasons:badL1Bdata,unusableMOD09GAdata)

Note, in theFparLai_QC, the fieldMODLAND is thestandardonecommon to theall

MODLAND products and specifies the overall quality of the product. Also, several bitfields in the M*D15A*H QA are passed-thru from the corresponding bitfields of theMOD09GAsurfacereflectancesproduct(CloudState,LandSea,etc.).ThekeyindicatorofretrievalqualityoftheLAI/FPARproductisSCF_QCbitfielddthatrepresentsalgorithmpath.M*D15A*Hbitpatternsareparsedfromrighttoleft.Individualbitswithinabitword

are read from left to right. The following example illustrates the interpretation of

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FparLai_QC. Let assume a single pixel’s value from FparLai_QC layer is 64, thus thisdecimal value can be converted to a binary value of 1000000 as shown in Figure 3.Interpretationofbit-stringsisalsoshowninFigure3basedonTable5.

Figure3.ExampleofFparLai_QCbit-stringanditsinterpretation

Table5.ValuesofFparExtra_QC(8−bit)

BitNo. ParameterName BitComb. FparExtra_QC

0−1 LandSeaPass−Thru

00 0LANDAggrQC(3,5)values{001}

01 1SHOREAggrQC(3,5)values{000,010,100}

102FRESHWATERAggrQC(3,5)values{011,101}

11 3OCEANAggrQC(3,5)values{110,111}

2Snow_Ice(fromAggregate_QCbits)

0 Nosnow/icedetected1 Snow/icedetected

3 Aerosol0

Noorlowatmosphericaerosollevelsdetected

1 Averageorhighaerosollevelsdetected

4Cirrus(fromAggregate_QCbits{8,9})

0 Nocirrusdetected

1 Cirruswasdetected

5 Internal_CloudMask0 Noclouds1 Cloudsweredetected

6 Cloud_Shadow0 Nocloudshadowdetected1 Cloudshadowdetected

7 SCF_Biome_Mask0 Biomeoutsideinterval<1,4>1 Biomeininterval<1,4>

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ExampleforinterpretationofFparExtra_QCbit-stringsisshowninFigure4.

Figure4.ExampleofFparExtra_QCbit-stringanditsinterpretation

6.2.DescriptionofFillvalueforSDSsUsing the MODIS land cover product (MCD12Q1), each 500m pixel is classified

according to its status as a land or non-land pixel. A number of non-terrestrial pixelclassesarenowcarriedthroughintheproductdatapixels(notQA/QCpixels)whenthealgorithmcouldnotretrieveabiophysicalestimate(Table6and7).Table6.LAIandFPARFillvalueLegends

Value Description

255Fillvalue,assignedwhen:theMOD09GAsurfacereflectanceforchannelVIS,NIRwasassignedits_Fillvalue,orlandcoverpixelitselfwasassignedFillvalue255or254

254 landcoverassignedasperennialsaltorinlandfreshwater253 landcoverassignedasbarren,sparsevegetation(rock,tundra,desert)252 landcoverassignedasperennialsnow,ice251 landcoverassignedas“permanent”wetlands/inundatedmarshlands250 landcoverassignedasurban/built−up249 landcoverassignedas“unclassified”ornotabletodetermine

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Table7.STDLAIandSTDFPARFillValueLegends

Value Description

255Fillvalue,assignedwhen:theMOD09GAsurfacereflectanceforchannelVIS,NIRwasassignedits_Fillvalue,orlandcoverpixelitselfwasassigned_Fillvalue255or254

254 landcoverassignedasperennialsaltorinlandfreshwater253 landcoverassignedasbarren,sparsevegetation(rock,tundra,desert)252 landcoverassignedasperennialsnow,ice251 landcoverassignedas“permanent”wetlands/inundatedmarshlands250 landcoverassignedasurban/built−up249 landcoverassignedas“unclassified”ornotabletodetermine248 Nostandarddeviationavailable,pixelproducedusingbackupmethod7.PoliciesPlease find the currentMODIS−relatedDatapolicies on theMODISPolicies page at

https://lpdaac.usgs.gov/lpdaac/products/modis_policies.For informationonhowtociteLPDAACdata,pleaseseeourDataCitationspageat

https://lpdaac.usgs.gov/about/citing_lp_daac_and_data.

8.ContactInformationRangaMyneniDepartmentofGeographyandEnvironment,BostonUniversityEmail:ranga.myneni@gmail.comWeb:http://cliveg.bu.edu9.RelatedPapersAhletal.,2006.MonitoringSpringCanopyPhenologyofaDeciduousBroadleafForest

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Ganguly et al., 2008. Generating vegetation leaf area index earth system data recordsfrommultiple sensors. Part 2: Implementation, Analysis and Validation. RemoteSens.Environ.,112(2008)4318–4332,doi:10.1016/j.rse.2008.07.013

Gaoetal.,2008.AnAlgorithmtoProduceTemporallyandSpatiallyContinuousMODIS–LAITimeSeries.Geophys.Res.Lett.,doi:10.1109/LGRS.2007.907971.

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Shabanovetal.,2003.TheeffectofspatialheterogeneityinvalidationoftheMODISLAIandFPARalgorithmoverbroadleafforests,RemoteSens.Environ.,85:410–423.

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