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VisibleInfraredImagingRadiometerSuite(VIIRS)375mActiveFireDetectionandCharacterization

AlgorithmTheoreticalBasisDocument1.0

December2016

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TableofContents1. SCIENCERATIONALEFORTHEPRODUCT.............................................................................................22. THEALGORITHM.....................................................................................................................................22.1. TECHNICALBACKGROUNDANDHERITAGE.................................................................................22.2. ALGORITHMINPUT.........................................................................................................................32.3. ALGORITHMDESCRIPTION............................................................................................................62.3.1. CLOUDANDWATERPIXELCLASSIFICATION..........................................................................62.3.2. FIXEDTHRESHOLDTESTS.........................................................................................................72.3.3. POTENTIALBACKGROUNDFIRES.............................................................................................72.3.4. AVOIDINGBRIGHTREFLECTIVETARGETS..............................................................................72.3.5. CANDIDATEFIREPIXELS...........................................................................................................82.3.6. CONTEXTUALFIREDETECTIONTESTS....................................................................................92.3.7. SECONDARYTESTS..................................................................................................................102.3.8. NIGHTTIMESOUTHATLANTICMAGNETICANOMALYFILTER..........................................102.3.9. PERSISTENCETEST..................................................................................................................112.3.10. FIRERADIATIVEPOWERRETRIEVAL.................................................................................12

3. PRODUCTDESCRIPTION......................................................................................................................133.1.LEVEL2ACTIVEFIREDATA...............................................................................................................133.1.1. FILEFORMAT............................................................................................................................133.1.2. DATACONTENT.......................................................................................................................133.2. QA/METADATA..........................................................................................................................15

4. PRODUCTASSESSMENT.......................................................................................................................164.1. THEORETICALFIREDETECTIONCURVES.................................................................................164.2. VALIDATIONAPPROACH.............................................................................................................174.3. VALIDATIONRESULTS.................................................................................................................17

5. USERGUIDANCE...................................................................................................................................206. ASSOCIATEDPUBLICATIONS...............................................................................................................207. REFERENCES.........................................................................................................................................20Writtenby:WilfridSchroeder&LouisGiglioDepartmentofGeographicalSciencesUniversityofMarylandEmail:wschroed@umd.edu

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1. SCIENCERATIONALEFORTHEPRODUCTThisdocumentdescribestheVisibleInfraredImagingRadiometerSuite(VIIRS)375mactivefiredetectionproduct.TheVIIRSinstrumentwasfirstlaunchedon28October2011onboardtheSuomiNationalPolar-orbitingPartnership(S-NPP),whichwasplacedinasunsynchronousorbitatanaltitudeof829kmandwith1:30pm/1:30amequatorialcrossingtimes.ThatinstrumentwillbefollowedbyothersimilarsensorsonboardtheJointPolarSatelliteSystem(JPSS)seriesofsatellitesoperatedjointlybyNASAandNOAA.VIIRSisawhiskbroomscanningradiometerwithaswathwidthof3060km,providingglobalwall-to-wallcoverageevery12horlessdependingonthelatitude.Itconsistsofamultispectralinstrumentincludingfivespectralchannels(0.6<>12.4µm)at375m(I-bands)and16spectralchannels(0.4<>12.5µm)at750m(M-bands),inadditiontoalight-sensitive(0.5<>0.9µm)day-and-nightbandat750m(DNB). Comparedtoothercoarserresolution(≥1km)satellitefiredetectionproducts,theVIIRS375mdataprovidegreaterresponseoverfiresofrelativelysmallarea,aswellasimprovedmappingoflargefireperimeters.Consequently,thedataarewellsuitedforuseinsupportoffiremanagement(e.g.,nearreal-timewildfirealertsystems),aswellasotherscienceapplicationsrequiringimprovedfiremappingfidelity.Thisproductconsistsofahybridalgorithmcombiningqualitiesofthe375mand750mVIIRSdata.Thehigherresolutiondata(channelsI1-I5)aretheprimarydriversofthefiredetectioncomponent,whereasthe750mdata(specificallythedual-gainM13channel)areusedprimarilyinthesub-pixelfireradiativepower(FRP)retrievals.The375mfirealgorithmsupersedesthebaselineVIIRS750mactivefiredetectionandcharacterizationdata,whichwasoriginallydesignedtoprovidecontinuitytothe1kmEarthObservingSystemModerateResolutionImagingSpectroradiometer(EOS/MODIS)activefiredatarecord.

2. THEALGORITHM2.1. TECHNICALBACKGROUNDANDHERITAGE

ActivelyburningfiresoftenshowawiderangeoftemperaturesspanningseveralhundredKelvininassociationwithflamingandsmolderingphasesofcombustion.Typically,coolersmolderingfiresshowtemperaturesbetween450and850K,whereashighertemperaturesrangingfrom800Ktoupwardsof1200Kprevailduringthemoreintenseflamingphase[LobertandWarnatz,1993].Fueltypeandmoisture,andambientconditions(airtemperature,wind,andrelativehumidity)arekeyfactorsregulatingbiomasscombustion.Whenmoderatespatialresolutionsensorsareconsidered,mid-infrared(4µm)spectralchannelsarethemostresponsivetoactivelyburningfirescapturingmostoftheradiometricsignalfromsmolderingandflamingphasesofcombustionduringbothdayandnighttimepartsoftheorbit.ThepeakinemittedfireradiantenergyonchannelI4makesthatchannel(andsimilarlychannelM13)responsivetosmallsub-pixelfiresoccurringoveracool(≤300K)background.Consequently,intenseactivefires(>1000K)

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occupyingfractionalpixelareasassmallas10-4maybedetected.Inadditiontofacilitatingthedetectionofsub-pixelactivefires,therateofradiativeenergyreleasedbyfiresobservedinthe4µmregionisfoundtobedirectlyrelatedtothebiomassconsumedperunittime[Kaufmanetal.,1998;Woosteretal.,2003]. TheVIIRSactivefiredetectiondatabuildontheEOS/MODISfireproductheritageusingamulti-spectralcontextualalgorithmtoidentifysub-pixelfireactivityandotherthermalanomaliesintheLevel1(swath)inputdata[Kaufmanetal.,1998].ThebaselineVIIRS750mactivefiredetectionproductwasoriginallydesignedmirroringtheMODISCollection4FireandThermalAnomaliesalgorithm(MOD14/MYD14),althoughlackingkeyoutputsciencedatalayerssuchasthe2DfiremaskandFRPretrievals[Csiszaretal.,2014;Giglioetal.,2003].ThatalgorithmwaslaterreplacedwiththeMODISCollection6algorithmequivalentincludingalloutputsciencedatalayers[Giglioetal.,2016].ThatproductisavailablethroughvariousVIIRSdataoutletsprovidingdirectreadout(NASA’sInternationalPolarOrbiterProcessingPackage[IPOPP]),nearreal-time(NOAA’sS-NPPDataExploitation[NDE]),andsciencedataaccess(NASA’sLandScienceInvestigator-ledProcessingSystem[LandSIPS]).TheVIIRS750mfireproductgenerationandavailabilitywillcontinueuntilfurthernotice. Thealgorithmdescribedinthisdocumentwasproposedduringtheearlypost-launchperiodfollowingthesuccessfulapplicationofthe375mdataforactivefiredetection.ThatnewapplicationconstitutedarepurposingoftheVIIRS375m(I)channels,asnoneofthosewereoriginallydesignedforactivefiredetection.Mostimportantly,abnormalradiometricconditionsinvolvingdifferentpixelsaturationscenariosarefrequentlyobservedintheprimarymid-infraredchannelI4therebyrequiringspecialhandlingofthedata.BuildingontheMOD14/MYD14algorithm,severalmodificationswereimplementedinordertoaccommodatetheuniquecharacteristicsassociatedwiththeVIIRS375mdata.Detailedalgorithmdescriptionisprovidedinthefollowingsections.Theinformationcontainedinthisdocumentiscomplementedbytheoriginalpeer-reviewedpublicationdescribingtheinitialimplementationoftheVIIRS375mglobalalgorithm[Schroederetal.,2014].

2.2. ALGORITHMINPUT TheVIIRS375mfireproductusesinputdatafromallfive375mchannels(I1-I5)andthedual-gain750mmid-infrareddata(channelM13),inadditiontotheircorrespondingqualityflags(QF1)(Table1).TheproductevolvedfromtheSchroederetal.[2014]VIIRS375mglobalfirealgorithm,incorporatinganancillaryland-waterclassificationmask,FRPretrievalsbasedonthemethodologydescribedinWoosteretal.[2003],amongotherrefinements.ThehigherresolutionVIIRS375mdataprovidethebasisforthedetectionofactivefiresandotherthermalanomalies,whereasthe750mdataareusedinthecalculationofsub-pixelFRPaswellastodiscriminatepotentialfalsealarmsassociatedwithnoiseintheinputfire-sensitive375mmid-infrared(I4)channeldata.

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Table1:ListofVIIRSchannelsusedasinputtothe375mactivefiredetectionalgorithm.ThecorrespondingVIIRSLevel1Bdataqualityflags,terrain-correctedgeolocationandquarterlyland-watermaskdatacomplementthelistofinputfilesused.

ChannelSpatial

Resolution(m)

Spectralresolution(μm)

PrimaryUse

I1 375 0.60–0.68 Cloud&waterclassificationI2 375 0.846–0.885 Cloud&waterclassificationI3 375 1.58–1.64 WaterclassificationI4 375 3.55–3.93 FiredetectionI5 375 10.5–12.4 Firedetection&cloudclassification

M13* 750 3.973–4.128 FRPretrieval,firedetectionoverwaterandacrosstheSouthAtlanticmagneticanomalyregion

*Aggregated(750×750mnominal)&un-aggregated(250×750mnominal)dataareused The375mdatadescribethenominalresolutionafternativepixelsarespatiallyaggregated(Figure1).Theaggregationschemechangesacrossthreedistinctimageregions.Inthefirstregion(nadirto31.59oscanangle),threenativepixelsareaggregatedinthealongscan(cross-track)directiontoformonedatasampleintheLevel1image.Inthesecondregion(31.59oto44.68oscanangle),twonativepixelsareaggregatedtoformonedatasample.Finallyinthethirdandlastregion(44.68oto56.06o-edgeofswath)onenativepixelwillresultinonedatasample.Allfive375mchannelsareaggregatedonboardthespacecraftbeforethedataaretransmittedtothegroundstations.Theinput750mdual-gainM13channeldataundergoasimilaraggregationschemealthoughthedatareductionisperformedafterthegroundstationsreceivethenativeresolutiondatafromthesatellite.Inordertomaximizeperformance,thealgorithmusesbothaggregatedandun-aggregatedM13data.

Figure1:SpatialresolutionofVIIRSimagerdata(Ibands)asafunctionofscanangle.Thethreedistinctregionsdescribedataaggregationzonesextendingfromnadirtotheedgeoftheswath. Giventheuniquespatialandspectralresolutionofthedata,thefiredetectionalgorithmwascustomizedandtunedinordertooptimizeitsresponseoversmallfireswhilebalancingtheoccurrenceoffalsealarms.Frequentsaturationofthemid-infraredI4channeldrivingthedetectionofactivefiresdemandsadditionaltestsandproceduresinordertoavoidpixelclassificationerrors.Pixelsaturationoccursmost

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oftenoverlargeand/orintenseheatsources(e.g.,wildfires)andistypicallyidentifiedintheinputdatawiththeuseofthecompanionqualityflag.Undermoreextremeconditions,verylargeactivefires(e.g.,crownfires)cangreatlyexceedtheeffectivesaturationtemperatureonchannelI4leadingtoacompletefoldingofthedigitalnumber(DN)associatedwiththeaffectedpixel.ApplicationofthenormalcalibrationparameterstothoseanomalousDNvaluesresultsinabnormallycoldbrightnesstemperaturevaluesequaltoornearthelowendofthatchannel’sdynamicrange(208K).Thecompanionqualityflagsmayalsobeusedtoproperlyidentifyandprocessthosepixels.AthirdandmorechallengingscenariodescribingchannelI4saturationinvolvesthemixingofsaturatedandunsaturateddataduringonboardaggregation.Suchoccurrencesresultinartificiallylowbrightnesstemperaturesaccompaniedbynominalqualityflagsfortheaffectedpixels.Underthoseconditions,complementarychannelI5datamaybeusedtotryandidentifythecorruptedchannelI4pixels.Overall,thelow(≈358K)effectivesaturationtemperatureonchannelI4resultsin≈9%discernablefirepixelsaturationrateassociatedwithallthreescenariosabove(inadditiontoayetunknownpercentageofmoresubtleandthereforeindistinguishablesaturation).Consequently,sub-pixelfirecharacterizationshouldbeavoidedinthatchannel.Thatlimitationisaddressedintheproductwiththeuseofco-locatedM13dual-gainchanneldata.Thecombinationofhigher(≈659K)saturationtemperatureandlowerspatialresolutionresultsinextremelyrarepixelsaturationoccurrenceintheM13datamakingitsuitableforsuchapplication. AnotheranomalousconditionaffectingtheI4channelinvolvestheoccurrenceofspuriousbrightnesstemperaturedataasaresultoftheSouthAtlanticmagneticanomaly.Thegeographicareawheretheproblemismostcommonlyfoundextendsfrom110°W<>11°Eand7°N<>55°S(Cabreraetal.,2005;Casadioetal.,2012).TheimpactofthemagneticanomalyisevidencedbyartificiallyhighbrightnesstemperaturevaluesoccurringpredominantlyinthenighttimeI4channeldata.Theseoccurrencesaretypicallyassociatedwithnominaldataqualityandthereforecannotbereadilyidentifiedusingtheavailablequalityflags.Onaverage,individualchannelI4pixelsaffectedbytheanomalymaydepartfromthebackgroundby15–30Ktherebycreatingsimilarradiometricresponsesassociatedwithactualnighttimefire-affectedpixelsatbothabsoluteandcontextuallevels.NodiscernableimpactonnighttimeI5channeldataqualitywasfoundassociatedwiththemagneticanomaly. Currently,processingofthefirealgorithmislimitedtotheLevel2(swath)productoutput,whichhassimilardatastructureandformattoMODISLevel2(MOD14/MYD14)fireproduct.Itincludesatwo-dimensionalfiremaskandqualityassurancesciencedatasets,plussparsearraysdescribingindividualfirepixelinformation(e.g.,FRP)andadditionalgranuleattributes.Alternativetext(ASCII)filesaregeneratedforeachLevel2granulecontainingbasicfiredetectioninformationinaGeographicInformationSystem(GIS)-friendlyformat.SupplementaryLevel3(tiled)and4(ClimateModelingGrid)productoutputswillbeaddedinthefuture.

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2.3. ALGORITHMDESCRIPTIONTheVIIRSfirealgorithmusesacombinationoffixedandcontextualteststodetectactivefiresandotherthermalanomaliesinbothdaytimeandnighttime(solarzenithangle≥90o)partsoftheorbit.ThecurrentimplementationevolvedfromtheworkofSchroederetal.(2014),incorporatingnewelementsinresponsetousers’requirements(e.g.,sub-pixelFRPretrievals).Thedetectioncriteriaarebasedonmulti-spectraltestsusingprimarilythemid-infrared(channelI4)andlongwave-infrared(channelI5)data,complementedbycloudandwaterclassificationschemesasdescribedbelow.

2.3.1. CLOUDANDWATERPIXELCLASSIFICATIONThecloudclassificationschemebuildsontheMODISfireproduct[Giglioetal.,2003;2016]andisdesignedtomaskopticallythickclouds.Theresultingcloudmaskismadeintentionallyliberalinordertominimizefiredetectionomissionerrorsundertranslucentclouds(e.g.,cirrus)andinpartiallycoveredpixels.Cloud-coveredpixelsareidentifiedinthedaytimedatausingthefollowingcriteria:BT5<265KORρ1+ρ2>0.9ANDBT5<295KORρ1+ρ2>0.7ANDBT5<285KwhereρiandBTiarethetop-of-atmospherereflectanceandbrightnesstemperatureinVIIRS375mchanneli,respectively.Nighttimecloudpixelsareidentifiedusing:BT5<265KANDBT4<295KPixelsidentifiedascloudsskipanysubsequentfiredetectionprocessingandarealsoexcludedfrombackgroundcharacterization.Complementingthecloudmasking,waterpixelsareclassifiedusing:ρ1>ρ2>ρ3Thetestabovecansuccessfullymaskmostwaterbodiesinthedaytimedataalthoughittendstoomitsediment-filledwaterpixelsalongshorelines,andcausecommissionerrorsoverburnscars.Thoselimitationshavenoobservableimpactontheoverallfirepixeldetectionandcharacterizationperformance.TheinternalwatermaskcomplementstheavailableVIIRSland-watermaskwhichbuildsontheMODIS250mwaterclassificationproduct[Carrolletal.,2009].Allwaterpixelsundergosubsequentprocessingtoallowdetectionofgasflaresandotherthermalanomalies.

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2.3.2. FIXEDTHRESHOLDTESTSFirepixelsarefirstidentifiedinthedayandnighttimedatausingacombinationoffixedthresholdtestsbasedontheobservationscenario.FirepixelsdetectedusingthesetestsshowastrongerradiometricsignatureineitherchannelI4orI5data,andtendtobeunequivocallyassociatedwithactivefiresorhighintensitythermalanomalies.Firepixelsdetectedusingthesetestsareinitiallyassigneda‘highconfidence’class.Incaseofunsaturatednighttimedata,thefollowingtestisused:BT4>320KANDQF4=0 (nighttimeonly)WhereQF4istheVIIRSchannelI4qualityflagvalue.Saturateddaytimeandnighttimefirepixelsareidentifiedusingthefollowingcriteria:BT4=367KANDQF4=9ANDQF5=0 (daytimeornighttime)ANDBT5>290KANDρ1+ρ2<0.7 (daytimeonly)Finally,casesinvolvingfoldingofchannelI4dataareidentifiedusing:ΔBT45<0ANDBT5>325KANDQF5=0 (daytimeonly)ORΔBT45<0ANDBT5>310KANDQF5=0 (nighttimeonly)ORBT4=208KANDBT5>335K (nighttimeonly)WhereΔBT45isthebrightnesstemperaturedifferencebetweenchannelsI4andI5.

2.3.3. POTENTIALBACKGROUNDFIRESPotentialbackgroundfirepixelscanaffectthedetectionandcharacterizationofindividualfirepixelsandthereforemustbeidentifiedandmaskedoutaccordingly.Thefollowingtestsareusedtoidentifythosepixels:BT4>335KANDΔBT45>30K (daytimeonly)ORBT4>300KANDΔBT45>10K (nighttimeonly)Inadditiontothetestsabove,pixelsassociatedwithfoldingofchannelI4data(typicallycharacterizedbyartificiallylowBT4)arealsoconsideredbackgroundpixels.

2.3.4. AVOIDINGBRIGHTREFLECTIVETARGETSSolarreflectionoverbrightsurfaces(e.g.,sandbanksalongriverbeds)caninducehighbrightnesstemperaturesonchannelI4daytimedata,causingpotential

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confusionwithactivefires.Thoseareasareidentifiedandavoidedusingthefollowingcriteria:ρ3>0.3ANDρ3>ρ2ANDρ2>0.25BT4≤335K Sunglintisaformofrecurringobservationphenomenonalsoknowntoleadtofalsealarmsinsatellitefiredetectionproducts.Examplesoffalsealarm-proneareasassociatedwithsolarreflectionincludelargemetallicrooftopsinindustrialparks,small/undetectedwaterbodies,andotherbrightsurfacesinurbanareas.Inordertoreducethefrequencyofthoseoccurrences,pixelsidentifiedwiththefollowingtestsareassigneda‘sunglint’classandnotconsideredforcandidatefirepixelselection(Section2.3.5):cosθg=cosθv×cosθs−sinθv×sinθs×cosϕθg<15oANDρ1+ρ2>0.35ORθg<25oANDρ1+ρ2>0.4Whereθvandθsaretheviewandsolarzenithangles,respectively,andϕistherelativeazimuthangle.

2.3.5. CANDIDATEFIREPIXELSCandidatefirepixelsareselectedusingrelativelyliberaltestsinordertoincludeallpotentialpixelsshowingthermalanomaliesonchannelI4accordingto:BT4>BT4SANDΔBT45>25K (daytimeonly)ORBT4>295KANDΔBT45>10K (nighttimeonly)WhereBT4Sisadynamically-adjustedbackgroundvaluecalculatedusingchannelI4brightnesstemperaturedatabasedona501×501samplingwindowcenteredonthecandidatefirepixel.Thisinitiallarge-areasamplingaccommodatesvariationsinbackgroundconditions,addingflexibilitytocandidatefirepixelselection.Itisintendedtoimprovealgorithmsensitivitytofiresoccurringincolderhighlatituderegions,whilereducingfalsealarmratesinlowerlatitudesconsistingofwarmerbackground.Thelargeareabackgroundsamplingexcludesallpixelspreviouslyclassifiedascloud,waterbodies,andpotentialbackgroundfirepixels,aswellasanypixelwithnon-zeroqualityflagincludingfillvaluesassociatedwithbowtiedeletionsamples[Wolfeetal.,2013].BT4Sisdefinedas:BT4M=MAX[325,M+25]KBT4S=MIN[330,BT4M]K

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WhereMistheBT4medianvaluecalculatedforthe501×501window.Thesamplingwindowmustcontainaminimumof10validobservationsotherwiseBT4Sissetto330K.Furthermore,BT4SisonlyderivedfordaytimedataallowingthecandidatefirepixelbrightnesstemperatureonchannelI4tovarybetweenaminimumof325Ktoamaximumof330Kinordertoaccommodatescene-dependentchangesinbackgroundconditions.Nighttimebackgroundconditionsarefoundtobelessvariable,thereforethealgorithmusesasinglefixedvaluetodefinecandidatefirepixelsatnight.

2.3.6. CONTEXTUALFIREDETECTIONTESTSThecontextualtestsuseadynamicsamplingwindowtocharacterizethebackgroundconditionsaroundeachindividualcandidatefirepixel.Thesamplingwindowisallowedtovaryfromaminimumof11×11elementscenteredonthecandidatepixel,toamaximumof31×31elementsuntil≥25%or≥10validpixelsareencountered.Validpixelsexcludeclouds,backgroundfirepixels,non-nominalqualitydata,andarelimitedtosame-classpixels(i.e.,candidatefirepixelsoverland(water)useland(water)backgroundpixelsonly).Candidatefirepixelslackingproperbackgroundcharacterizationareassignedthe“unclassified”class.Daytimecandidatefirepixelshaving≥4backgroundfirepixelsinthesamplingwindow,orhavingbackgroundfirepixelsinexcessof10%ofthevalidbackgroundpixelsmustgothroughthefollowingtest:ρ2>0.15ANDBT’4B<345KANDδ’4B<3KANDBT4<BT’4B+6×δ’4BWhereBT’4Bandδ’4Barethemeanbrightnesstemperatureandmeanabsolutedeviation,respectively,calculatedusingthepotentialbackgroundfirepixels.Candidatefirepixelsthatsatisfythecriteriaaboveareexcludedfromfurtherprocessingandassignedafire-free(waterorland)pixelclass.Thetestsbelowdescribethesubsequentdaytimeandnighttimedataprocessingcriteriaappliedtotheremainingcandidatefirepixels:Daytime:ΔBT45>ΔBT45B+2×δ45BANDΔBT45>ΔBT45B+10KANDBT4>BT4B+3.5×δ4BANDBT5>BT5B+δ5B–4KORδ’4B>5KNighttime:ΔBT45>ΔBT45B+3×δ45BANDΔBT45>ΔBT45B+9K

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ANDBT4>BT4B+3×δ4BWhereΔBT45BandBTiBdenotethemeanchannelI4-I5brightnesstemperaturedifferenceandthemeanbrightnesstemperatureonchanneli,respectively,calculatedusingthevalidbackgroundpixels;δ45Bandδ4BarethemeanabsolutedeviationcalculatedforchannelI4-I5brightnesstemperaturedifferenceandchannelI4,respectively,alsousingthevalidbackgroundpixeldata.Candidatefirepixelsmeetingthecriteriaaboveareassigneda‘nominal’confidencefirepixelclass.

2.3.7. SECONDARYTESTSTwoadditionaltestsareappliedtothedatainorderto(i)identifyresidualfirepixelsnotdetectedwiththecriteriaabove,and(ii)markdownpotentiallowconfidencefirepixels. Thefirsttesttargetslesscommonpixelsaturationandfoldingscenariosusingthefollowingcriteria:BT5≥325KORBT4=355KORΔBT45<0K Pixelsthatmeetsuchcriteriaandhaveoneormoreadjacentfirepixelsof‘nominal’or‘high’confidence(amongtheeightimmediateneighbors)areassigneda‘low’confidencefireclass. ThesecondtesttargetsresidualfalsealarmsoccurringalongSunglintareas.Thefollowingtestisappliedtoall‘nominalconfidence’firepixels:ΔBT45≤30KORθg<15o Pixelsmeetingtheabovecriteriawillbeassigneda‘lowconfidence’firepixelclassifoneofthefollowingconditionsapply:

(i) twoormoreadjacent‘Sunglint’pixelsarefound(ii) noadjacent‘highconfidence’pixelsarefoundandBT4islessthan15K

higherthanadjacentpixels

2.3.8. NIGHTTIMESOUTHATLANTICMAGNETICANOMALYFILTERNoiseassociatedwiththeSouthAtlanticmagneticanomalyisparticularlypronouncedinthemid-infraredchannelI4datadrivingthedetectionoffiresandthermalanomalies.Thenoiseconditionisfoundpredominantlyatnight,withonlyfewandsparseoccurrencesduringtheday.GiventherandomnatureandradiometricsignalcharacteristicsoftheresultingchannelI4noise,whichoftenmimicthoseofactualfires,severalspuriousfirepixelsarenormallyproducedovertheaffectedregion(Figure2).Dataartifactsareoftenlinkedtosingle,stand-alonepixelsshowingabnormallyhighbrightnesstemperatureonchannelI4.Inordertoaddresstheproblem,thealgorithmusesco-locatedun-aggregatedM13brightness

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temperaturedatatoindependentlyverifynighttimefirepixels.Despitetheirsimilarspectralcharacteristics,channelM13showssignificantlylowerratesofdatacontaminationcomparedtochannelI4.Inadditiontothat,channelI4andM13occurrencesarefoundtobepredominantlyindependentfromeachother. ThefilterfirstcreatesamodifiedaggregatedM13dataarraybyreplacingthemeanvalueaggregationschemewithamaximumvaluemethod.Thisapproachminimizesfiresignallossasaresultofthenormalaggregationscheme.Thesuspicious375mfirepixelissubsequentlyco-locatedtothemodifiedM13dataarray,afterwhichthecoincidentM13aggregatedpixelisselectedforfurtheranalysis.Inordertobeconfirmedasa‘nominal’or‘high’confidencefirepixel,thecoincidentM13pixelbrightnesstemperaturemustbe≥2KthanalloftheadjacentM13pixels.Pixelsthatfailedthattestaredowngradedtoafire-free(‘water’or‘land’)classandmarkedwithauniquequalityflag(seeTable3).

Figure2:SpuriousVIIRS375mfiredetectionsassociatedwiththeSouthAtlanticmagneticanomalyduring01-30August2013(adaptedfromSchroederetal.[2014]).

2.3.9. PERSISTENCETESTSomeresidualnoiseintheinputdatamaypropagatethroughthealgorithmleadingtofewandisolatedspuriousdetectionsmosteasilyfoundoveroceanwaters.SuchcasesaretypicallyassociatedwithrandomsensornoiseorwithinfrequentmanifestationofSouthAtlanticmagneticanomalyondaytimedata.Inordertoimprovehandlingofthosecases,thealgorithmincludesapersistencetestappliedtofirepixelsdetectedoverwater.Basedonthattest,‘low’,‘nominal’and‘high’confidencefirepixelsdetectedoverwatermustshowdistinguishableheatsignatureonchannelM13.ThetestusesthesameapproachandmodifiedM13aggregateddataarraydescribedinSection2.3.8,requiringaslightlyhigherbrightnesstemperaturedifferenceof2.5KbetweenthetargetM13pixelandtheadjacentones.Pixelsfailingthatinitialtestmustshowtemporalpersistenceconsistingofaminimumof3co-locateddetectionsintheprevious30daysinordertobeconfirmed.PixelslackingM13channelheatsignatureorpersistenceindicationaredowngradedtofire-free(‘water’or‘land’)pixelsandmarkedupwithuniquequalityflags(bits19-21onTable3).

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2.3.10. FIRERADIATIVEPOWERRETRIEVALBecauseofthefrequentfirepixelsaturationinthemid-infraredI4channel,fireradiativepowerretrievalsarecalculatedusingco-locatedM13channeldata.Theapproachutilizes375mdatatoidentifyfirepixelsandtoassistintheselectionofvalidbackgroundpixels.Co-locatedM13aggregatedradiancescoincidingwiththefire-affectedandbackgroundpixelsareusedintheFRPretrievalfollowingWoosteretal.[2003],whichisrepresentedby(assumingunitatmospherictransmittanceandsurfaceemissivity):

!"# = !" !!" − !!"!!

WhereAisthepixelareawhichvariesasafunctionofscanangle,σistheStefan-Boltzmannconstant(5.67×10-8Wm-2K-4),aisachannel-specificconstant(VIIRSM13=2.88×10-9Wm-2sr-1µm-1K-4),andL13andL13BaretheM13channelfirepixelandmeanbackgroundradiances,respectively.Despitebeingextremelyrare,M13pixelsaturationmayoccuroververylargeandintenseactivefires.Normally,thatconditionwilltriggertheappropriatequalityflagfortheaffectedpixelintheinputdata,whichmaycarryafill(non-usable)radiancevalue.Inthatevent,thefirepixelmaystillbedetected(grantedthatthealgorithmisabletoresolveitusingtheavailabledata)whereastheFRPretrievalwillbesettozero.OthersituationsinvolvingchallengingFRPretrieval(e.g.,insufficientbackgrounddata)mayalsoresultinfirepixelsaccompaniedbynullFRPvalues.Wenotethatsuchcasesareratherinfrequent.Asinglepixel750mFRPretrievalisdividedamongthenumberofcoincident375mfirepixels,witheachsub-pixelreceivingthesameresultingvalueinMW(Figure3).

Figure3FRPcalculationusingacombinationofVIIRS375mand750mdata.Theformerisusedtoidentifyfire-affected(solidanddashedred),cloud(solidblue),waterpixels(dashedblue),andvalidbackgroundpixels(gray;inthiscaserepresentingfire-freelandsurface).Co-locatedM13channelradiancedata(750m;blackdashedoutline)coincidingwithfirepixel(redshade)andvalidbackgroundpixels(gray-only)areusedintheFRPcalculation.Inscenario1,thesingle750mretrieval(centerpixel;FRP)isassignedtothesinglecoincident375mfirepixel(solidred;FRPi,whereiisthe375mfire-affectedsub-pixelindex).Inscenario2,thesingle750mFRPretrievalissplitbetweenthetwocoincident375mfire-affectedsub-pixels,sothatFRPi=FRP÷2.

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3. PRODUCTDESCRIPTIONTheVIIRSlandproductsuiteiscomposedofLevel2(swathprojection),Level3(tiled,withsomemulti-temporaldata),andLevel4(griddeddatameetingclimatemodelingcommunityrequirements)datasets.Currently,theactivefiredatasetisrestrictedtoLevel2processingcarryingsimilarcharacteristicsastheinputL1Bdataingestedbythealgorithm.Thedataarestoredinswathprojectionwithindividualgranulescomprisinganorbitsegmentofapproximately6min.VIIRSLevel3&4firedataproductsshouldbecomeavailableinthenearfuture.

3.1. LEVEL2ACTIVEFIREDATA3.1.1. FILEFORMAT

VIIRSactivefiredataareoutputinNetCDF4.2fileformat.Level2filesalsoshareseveraloftheL1Bglobalattributes(includingnomenclature);filescanbemanipulatedusingstandardNetCDF-enabledsoftware.Filenameconventionisasfollows:VNP14IMG.AYYYYDDD.HHMM.VVV.yyyydddhhmmss.ncWhere:VNP14IMG=VIIRS375mactivefireproductidentifierYYYY=yearofdataacquisitionDDD=JuliandayofdataacquisitionHHMM=hourandminuteofdataacquisitionyyyydddhhmmss=dataprocessingtime(year,Julianday,hour,minute,second)

3.1.2. DATACONTENTTheVIIRSactivefirealgorithmoutputcontains25primarysciencedatasets,inadditiontothealgorithm’squalityflag(seeSection3.2).Theindividualsciencedatasets(SDSs)arenamedasfollows:‘firemask’ =imageclassificationarray(2D)‘FP_line’ =granulelineoffirepixel‘FP_sample’ =granulesampleoffirepixel‘FP_latitude’ =latitudeoffirepixel(degrees)‘FP_longitude’=longitudeoffirepixel(degrees)‘FP_T4’ =channelI4brightnesstemperatureoffirepixel(kelvin)‘FP_T5’ =channelI5brightnesstemperatureoffirepixel(kelvin)‘FP_MeanT4’ =channelI4meanbackgroundbrightnesstemperature(kelvin)‘FP_MeanT5’ =channelI5meanbackgroundbrightnesstemperature(kelvin)‘FP_MeanDT’ =meanbackgroundI4-I5brightnesstemperaturedifference(kelvin)‘FP_MAD_T4’ =backgroundchannelI4brightnesstemperaturemeanabsolute deviation(kelvin)‘FP_MAD_T5’ =backgroundchannelI5brightnesstemperaturemeanabsolute deviation(kelvin)

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‘FP_MAD_DT’=backgroundI4-I5brightnesstemperaturedifferencemeanabsolute deviation(kelvin)‘FP_power’ =fireradiativepower(MW)‘FP_M13’ =channelM13radianceoffirepixel(W.m-2.sr-1.µm-1)‘FP_MeanM13’=channelM13meanbackgroundradiance(W.m-2.sr-1.µm-1)‘FP_AdjCloud’=numberofadjacentcloudpixels‘FP_AdjWater’=numberofadjacentwaterpixels‘FP_WinSize’ =numberofadjacentwaterpixels‘FP_confidence’=detectionconfidence(7=low,8=nominal,9=high)‘FP_day’ =dayflagforfirepixel(0=night,1=day)‘FP_SolZenAng’=solarzenithangleoffirepixel(degrees)‘FP_SolAzAng’=solarazimuthangleoffirepixel(degrees)‘FP_ViewZenAng’=viewzenithangleoffirepixel(degrees)‘FP_ViewAzAng’=viewazimuthangleoffirepixel(degrees) The‘firemask’SDSconsistsofan8-bitintegertwo-dimensionalarraywiththesamenumberofelementsastheinputL1Bdataarray(Figure4).Firemasksgeneratedfromthestandard6-minutefileshave6,400samples(constant)and202<>203scanstotaling6,464<>6,496rows(variablenumberofscanspergranuleisdesignedtoaccommodate≈6minutedatasegments).Distinctpixelclassesareusedforland,water,cloudandfirepixels,plusadditionalclassesindicatingnon-processedpixelsandpixelswithundefinedclassification(‘unclassified’)(Table2).Thelatterdescribesthosecaseswhenbackgroundstatisticscannotberetrievedpreventingproperpixelclassification.Firepixelconfidenceclasses(‘low’,‘nominal’and‘high’)arerepresentativeoftheobservationconditionsassociatedwitheachdetection(seeSection2).Theadditionaldatasetsoutputbythealgorithmconsistofindividualsparsearrayscontainingimageline,column,longitude,latitude,FRP,detectionconfidence,amongotherparametersforallfirepixelsdetected.

Figure4:S-NPP/VIIRS375mactivefiredetectionclassificationproduct(mask)derivedforagranuleacquiredon22November2015at1035UTCoverpartsofnorthernMadagascarandsoutheastAfrica(left).Rightpanelshowsmagnifiedsubsetcontainingland(green),water(blue),clouds(white)andfire(red)pixels.Glint(cyan)andbowtiedeletion(black)pixelsarealsovisibleinthelargeimage.

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Table2:VIIRS375m‘firemask’datasetclasses.PixelClass Definition0 Notprocessed1 Bowtiedeletion2 Sunglint3 Water4 Clouds5 Land6 Unclassified7 Lowconfidencefirepixel8 Nominalconfidencefirepixel9 Highconfidencefirepixel

3.2. QA/METADATAAtwo-dimensionalarraycomplementsthefiremaskoutputprovidingqualityassurance(QA)informationforeverypixelprocessed.TheQAdataarestoredin32-bitunsignedintegerformatpopulatedwithseveralfieldsthattogethercanbeusedtoreconstructsomeofthekeyobservationconditionspertinenttoeachpixelanalyzed.Bits0-6describetheoverall(nominal/non-nominal)qualityofallinputfilesused,followedbybits7-18describingprimaryandsecondaryfiredetectiontests.Bits19-22areusedtomarkpixelsassociatedwithdetectionoverwater(persistencetest)and/orbowtieconditions,whereasbit23-31arereservedforfutureuse.Table3:VIIRS375mfiredetection‘algorithmQA’datasetbitsanddefinition.

Bit Description0 ChannelI1quality(0=nominal(ornighttime),1=non-nominal)1 ChannelI2quality(0=nominal(ornighttime),1=non-nominal)2 ChannelI3quality(0=nominal(ornighttime),1=non-nominal)3 ChannelI4quality(0=nominal,1=non-nominal)4 ChannelI5quality(0=nominal,1=non-nominal)5 Geolocationdataquality(0=nominal,1=non-nominal)6 ChannelM13quality(0=nominal,1=non-nominal)7 Unambiguousfire(0=false,1=true[nightonly])8 Backgroundpixel(0=false,1=true)

BT4>335KANDΔBT45>30KORsaturation/folding(day)BT4>300KANDΔBT45>10KORsaturation/folding(night)

9 Brightpixelrejection(0=false,1=true)ρ3>30%ANDρ3>ρ2ANDρ2>25%ANDBT4≤335K

10 Candidatepixel(0=false,1=true)BT4>325KANDΔBT45>25K(daytime)BT4>295KANDΔBT45>10K(nighttime)

11 Scenebackground(0=false,1=true)BT4>MIN([330,BT4M])(day)

12 Test1(0=false,1=true)ΔBT45>ΔBT45B+2×δ45B(day)ΔBT45>ΔBT45B+3×δ45B(night)

13 Test2(0=false,1=true)ΔBT45>ΔBT45B+10K(day)ΔBT45>ΔBT45B+9K(night)

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14 Test3(0=false,1=true)BT4>BT4B+3.5×δ4B(day)BT4>BT4B+3×δ4B(night)

15 Test4(0=false,1=true)(day)BT5>BT5B+δ5B–4KORδ’4B>5K

16 Pixelsaturationcondition(0=false,1=true)(day)BT5≥325KORBT4=367KORΔBT45<0

17 Glintcondition(0=false,1=true)(day)ΔBT45≤30KORGlint(θg)<15o

18 PotentialSouthAtlanticmagneticanomalypixel(0=false,1=true)19 Firepixeloverwater(0=false,1=true)20 Persistencetest(0=false,1=true)

BT13-MAX[BT13B]<2.5K21 Persistencetest(0=false,1=true)

Numberofpreviousco-locateddetections<322 Residualbowtiepixel(0=false,1=true)23-31 Reservedforfutureuse

4. PRODUCTASSESSMENT4.1. THEORETICALFIREDETECTIONCURVES

AtheoreticalfiredetectionenvelopewascalculatedbysimulatingdifferentfirescenariosappliedtoactualVIIRS375mglobalimagery.Firesweresimulatedassumingareasrangingfrom2to250m2,andtemperaturesrangingfrom400to1200K.Fireradianceswerederivedat2m2and10KintervalsforbothI4andI5channelsusingtheinstrument'sspectralresponsefunctions,andassumingblackbodyemission.Atotalof10daytimeand10nighttimeVIIRSL1BgranulesacquiredduringAugust2013wererandomlyselectedcoveringdifferentgeographicareas,includinglowandhighlatituderegions,withvariablelevelsoffireactivity.Foreveryimage,10pixelswereselectedalongnadirandapartfromeachother,andwhenpossible,nearareasoffireactivityinordertobestrepresentregionalfire-proneconditions.Simulatedfireradiancesandactualbackgroundradianceswerearea-weightedtoproviderealisticBT4andBT5pixelvaluesrepresentativeofactualobservationconditions.Theactivefiredetectionalgorithmwasthenappliedtothedatacontainingsimulatedactivefirepixelsurroundedbygenuinelyobservedbackgroundpixels.Figure5showsthe50%probabilityofdetectioncurvesderivedforthealgorithmusingtheglobaldaytimeandnighttimedatasample.Improvednighttimeperformanceresultedfromthemorehomogeneousbackgroundconditions,whichtendstoenhancethealgorithm'sresponsetorelativelysmallheatsources.

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Figure5:Theoretical50%probabilityoffiredetectioncurvesderivedfortheVIIRSalgorithmasafunctionoffireareaandtemperatureusingdaytimeandnighttimedata(adaptedfromSchroederetal.[2014]).

4.2. VALIDATIONAPPROACHThevalidationapproachadoptedfortheVIIRSactivefiredatabuildsontheheritageEOS/MODISmethodology,whichconsistedontheuseofcoincidentreferencefiredataderivedfromhigherspatialresolutionsensors[Morisetteetal.,2005;Schroederetal.,2008].However,theearlyafternoonorbitdescribedbyVIIRSisamajorimpedimentlimitingtheuseofavailableLandsat-classsensors(typicallyon≈10amorbits)duetoprohibitivelylargetemporalseparationbetweensame-daydataacquisitions[CsiszarandSchroeder,2008].Asanalternative,referencedatasetsderivedfromairbornemappinginstrumentsareused,complementedbyfieldcampaignsandotherqualitativeinformationoriginatedfromfireactivityreports.Additionally,expertimageanalystsprovidevaluableinputforthecalculationofcommissionerrorratesassociatedwiththeoccurrenceoffiredetectionpixelsinurbanareasusingavailablehigh-resolutionvisibleimagery(e.g.,GoogleEarth).

4.3. VALIDATIONRESULTSDataverificationandvalidationwasperformedforselectedsitesacrosstheglobe,includingdedicatedfieldcampaignsexploringsmall-to-mediumsize(<500ha)prescribedfires(seeforexample:Dickinsonetal.[2015]).Useofnear-coincidentairbornereferencefiredatashowsgoodoverallcorrespondencewithVIIRSdaytimeandnighttimefiredatageneratedformedium-to-largesizewildfiresasdepictedinFigure6.

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Figure6:Airbornereferencefiredata(USDA/NationalInfraredOperations[NIROPs])overlaidonnear-coincidentVIIRSdaytime(left)andnighttime(right)375mfiredetectiondataacquiredon06and07August2013,respectively.OutlineofVIIRS750mbaselinefiredetectionproductisshownforreference(adaptedfromSchroederetal.[2014]). Theoccurrenceoffiredetectionoverurbanareas(potentialcommissionerrors)wasassessedbySchroederetal.[2014]andwaslowerthan1.2%fornominal/highconfidencepixelsforallsitesanalyzed.Lowconfidencepixelsrespondedforapproximately10%ofallglobalfirepixelsproducedandshowedhigheroccurrenceofurbandetections,peakingat40%overeasternChinawherenumerousindustrialparksarefound. ComparisonanalysesofFRPretrievalsderivedusingtheapproachaboveandthoseobtainedfromtheVIIRS750mfireproduct(afterreconciliationofthetwodatasets)showsgoodagreement(R2=0.99)albeitwithslightly(≈1%)lowervaluescalculatedforthelatter.Thisdifferenceisattributedtoimprovedsamplingofthebackground,whichoftenresultsincleanerdataandhigherFRPestimates.GiventhespectralresolutionoftheM13channel,inparticularconcerningthepartialoverlapwithaCO2absorptionbandinthe4µmregion,atmosphericattenuationeffectsmaydoublecompared,forexample,tothecorrespondingMODISmid-infrareddata(channels21/22)usedintheMOD14/MYD14products(Figure7).WhilethischaracteristiccouldleadtosystematicunderestimationofVIIRSFRPvaluescomparedtocoincidentMODISdata,otherfactorssuchaspixelsize/geometry,dataaggregationandpointspreadfunctioncombinetocreatevariableeffectsonFRPretrievalsandtheresultingcorrelationamongproducts(Figure8).DetailedassessmentofFRPretrievalsiscurrentlylimitedtofieldvalidationcampaignsthatarefewandsparse.Dataverificationandvalidationanalysesshallexpandasnewreferencedatabecomeavailable.

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Figure7:SpectralresponsefunctionsforVIIRSI4andM13,andMODISB21/22mid-infraredchannels,andthecorrespondingatmospherictransmittancecalculatedusingMODTRANassumingU.S.standardatmosphericconditions(toppanel).BottompanelshowsthecorrespondingnetatmospherictransmittanceasafunctionoftheapplicableVIIRSandMODISsensorzenithangles.

Figure8:VIIRSM13radiance-basedFRPretrievalsplottedagainstnear-coincidentAqua/MODISFRP(MYD14).Leftpanelshowstop-of-atmosphere(TOA)data;rightpanelshowssamedataafteratmosphericcorrectionusingMODTRAN®andMERRA0.5oglobalanalysisdata.

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5. USERGUIDANCEVIIRSfiredatausersareencouragedtoconsultthedatausersguideforadditionalinformationondataaccessibilityandhandling,andfrequentlyaskedquestions.

6. ASSOCIATEDPUBLICATIONSCsiszar,I.,Schroeder,W.,Giglio,L.,Ellicott,E.,Vadrevu,K.,P.,Justice,C.O.,andWind,B.(2014).ActivefiresfromtheSuomiNPPVisibleInfraredImagingRadiometerSuite:ProductStatusandfirstevaluationresults.JournalofGeophysicalResearch:Atmospheres,doi:10.1002/2013JD020453.

Giglio,L.,Schroder,W.,andJustice,C.(2016).TheCollection6MODISactivefiredetectionalgorithmandfireproducts.RemoteSensingofEnvironment,178,31-41.

Schroeder,W.,Oliva,P.,Giglio,L.,andCsiszar,I.(2014).ThenewVIIRS375mactivefiredetectiondataproduct:Algorithmdescriptionandinitialassessment.RemoteSensingofEnvironment,143,85-96.

7. REFERENCESCsiszar,I.,andSchroeder,W.(2008).Short-termobservationsofthetemporaldevelopmentofactivefiresfromconsecutivesame-dayETM+andASTERimageryintheAmazon:Implicationsforactivefireproductvalidation.IEEEJournalofSelectedTopicsinAppliedEarthObservationsandRemoteSensing,1(4),248-253.

Cabrera,J.,Cyamukungu,M.,Stauning,P.,Leonov,A.,Leleux,P.,Lemaire,J.,etal.(2005).FluxesofenergeticprotonsandelectronsmeasuredonboardtheOerstedsatellite.AnnalesGeophysicae,23,2,975-2,982.

Casadio,S.,Arino,O.,andSerpe,D.(2012).GasflaringmonitoringfromspaceusingATSRinstrumentseries.RemoteSensingofEnvironment,116,239-249.

Dickinson,M.B.,Hudak,A.T.,Zajkowski,T.,Loudermilk,L.E.,SchroederW.,etal.(2015).Measuringradiantemissionsfromentireprescribedfireswithground,airborneandsatellitesensors–RxCADRE2012.InternationalJournalofWildlandFire,doi:10.1071/WF15090.

Giglio,L.,Descloitres,J.,Justice,C.O.,andKaufman,Y.J.(2003).AnenhancedcontextualfiredetectionalgorithmforMODIS.RemoteSensingofEnvironment,87,273-282.

Kaufman,Y.J.,Justice,C.O.,Flynn,L.P.,Kendall,J.D.,Prins,E.M.,Giglio,L.,etal.(1998).PotentialglobalfiremonitoringfromEOS-MODIS.JournalofGeophysicalResearch,103(D24),32,215-32,238.

Lobert,J.M.,andWarnatz,J.(1993).Emissionsfromthecombustionprocessinvegetation.In:FireintheEnvironment:TheEcological,Atmospheric,andClimaticImportanceofVegetationFires(Editors:P.J.CrutzenandJ.G.Goldammer),JohnWiley&SonsLtd.

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Morisette,J.T.,Giglio,L.,Csiszar,I.,andJustice,C.O.(2005).ValidationoftheMODISactivefireproductoverSouthernAfricawithASTERdata.InternationalJournalofRemoteSensing,26(19),4239-4264.

Schroeder,W.,Prins,E.,Giglio,L.,Csiszar,I.,Schmidt,C.,Morisette,J.T,.andMorton,D.(2008).ValidationofGOESandMODISactivefiredetectionproductsusingASTERandETM+data.RemoteSensingofEnvironment,112,2711-2726.

Wolfe,R.E.,Lin,G.,Nishihama,M.,Tewari,K.P.,Tilton,J.C.,andIsaacman,A.R.(2013).SuomiNPPVIIRSprelaunchandon-orbitgeometriccalibrationandcharacterization.JournalofGeophysicalResearch:Atmospheres,118,doi:10.1002/jgrd.50873.

Wooster,M.J.,Zhukov,B.,andOertel,D.(2003).Fireradiativeenergyforquantitativestudyofbiomassburning:derivationfromtheBIRDexperimentalsatelliteandcomparisontoMODISfireproducts.RemoteSensingofEnvironment,86,83–107.