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FusionofMODIS,VIIRS,andLandsatsnowcoverdatatocreateestimatesofsnowwaterequivalent
EdwardBair1,KarlRittger2,Rajagopolan Balaji2,WilliamKleiber2,KatBormann3,andBillDoan4
1UniversityofCalifornia,SantaBarbara;2UniversityofColorado,Boulder;3JetPropulsionLaboratory;4ArmyEngineerR&DCenter
MODISVIIRSScienceTeamMeeting,MODISLandScienceAnalysis,CypressBallroom10/17/1810:10am
Whydoweneedaccuratesnowcoverestimates?• Abillionpeopleworldwidedependonsnowandicemeltforwater(Barnettetal.2005)
• Snowcoverinthemountainsvariesdramatically,bothspatiallyandtemporally
• Forwaterresources,thatvariabilityneedstobecapturedtoaccuratelymodelbasin-widesnowwaterequivalent(SWE)
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Selkowitz etal.(2014)
Thegeneralproblem• Satellite-bornesensorscanhavehightemporalorhighspatialresolution,butnotboth.
• Forexample,considerfractionalsnow-coveredarea(fSCA)fromthisimageryovertheHimalaya.TheleftimageisfromdailyMODISTerraat500mwhiletherightimageisfromLandSat 8at30m,butisonlyavailableevery16days.
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SWEreconstruction• SWEisbuiltupinreverse,frommeltouttoitspeak• Potentialmelt𝑀" iscalculatedusingourParallelEnergyBalancemodel(ParBal)
• Potentialmeltisspreadaroundapixelandconvertedtomelt𝑀 using:𝑀 =𝑓%&'×𝑀"
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Basin-wideSWEreconstructedwithParBal andmeasurementsfromASOintheupperTuolumneBasin,CAUSA
AsummaryofourcurrentapproachforfSCA
1. WeusespectralunmixingforfSCA andothersnowsurfaceproperties,specificallyMODISSnowCoveredAreaandGrainSize(Painteretal.2009)andVIIRSCAG(MODSCAGforVIIRS).
2. MODSCAGshows9%vs.23%RMSEwhencomparedtoastandardproductfSCA (MOD10A1v5),validatedusingLandSat 7(Rittger etal.2013).
3. Wealsosmoothandgap-fillusingweightedsplinesbasedonviewinggeometry(Dozieretal.2008).
5Dozieretal.2008
Problemswithourcurrentapproachthatcanbehelpedwithimprovedspatial&temporalresolution• Snowclouddiscriminationremainsanissue,seeD.Halletal.poster#127:• Opticallythickcloudsarebrighterinallbandsthansnow,butthinclouds/snowcanbespectrallyinseparablefromothernon-snowmixtures,especiallyat0.5-1kmresolution.
• MODSCAGgrainsizesaretoosmallatlowerelevations(seeimagetotheright)
• Snowalbedoretrievalsneedwork,andperformbestonpure(unmixed)pixels• nosnowalbedostandardproductformixedpixels
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MODISandVIIRSbothperformsimilarlyatmappingfSCA,validationwithLandSat 8
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Ourproposedapproach:Bayesianfusion
• 𝛷*+ - quantilefunction,transformationtorealvalueswithnormaldistributions
• 𝑌 𝑠, 𝑡 - modelrealizations,with𝑠 aslocationand𝑡 astime• 𝜇 𝑠, 𝑡 - meanfunctionbasedonphysiographicvariables• 𝑓+ … 𝑓2 - nonlineartransformations• 𝑋+ …𝑋2 - space-timefeatures(e.g.Sobelfilter,sharpeningkernel)• 𝜀(𝑠, 𝑡) - space-timeerror
• Uncertaintyisexpressedthroughconditionallysimulatedensembles• Flexibleintermsofnumberoffeaturesemployed 8
𝛷*+ 𝑌 𝑠, 𝑡 = 𝜇 𝑠, 𝑡 + 𝑓+ 𝑋+ 𝑠, 𝑡 +𝑓9 𝑋9 𝑠, 𝑡 + ⋯+𝑓" 𝑋" 𝑠, 𝑡 + 𝜀 𝑠, 𝑡
Bayesianfusionexample
ExampleofdownscaledMODISimageryusingBayesianfusion:• (a)Original,MODISfSCA at500mspatialresolution;(b)Fusedproduct,trainedoffdatafromotherdays;(c)Validation,LandSat 8fSCA at30mspatialresolution.
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(a) (b) (c)
FusedfSCA productshavebeentriedbefore• Durandetal.(2008)usedalinearprogramapproachtofuseMOD10AV.4binarysnowcoverwithfSCA fromLandSat 7.
• ComparedtousingMODISfSCAalone,theyreporta51%reductioninMeanAbsoluteErrorwhenrunthroughaSWEreconstructionmodel(moreonthislater).
• ThisstudyshowedpromisingresultsforfSCA fusion,buthasseveralsignificantdrawbacks:• Linearprogramissimple–constraintsarelinearanduncertaintyisnotaddressed
• BinaryfSCA isinherentlybiased• LandSat 7saturatesissuesinsnow(8bitvs12bitradiances) 10
Smallcircles– MOD10AV.4Largecircles– LandSat 7Dottedline– fusedproduct
Durandetal.(2008)
UtahNevada Colorado
WyomingIdaho
Arizona New Mexico
China
India
Pakistan
Tajikistan
United States
Canada
Mexico Cuba
China
India
2,200 km
260 km
(a)
(b)
Studyareas
SnowcoveredMODISimageryofstudyareas:upperColoradoRiverBasin(a),upperIndusRiverBasin(b)
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UtahNevada Colorado
WyomingIdaho
Arizona New Mexico
China
India
Pakistan
Tajikistan
United States
Canada
Mexico Cuba
China
India
2,200 km
260 km
(a)
(b)
Fiveplannedphases1. FusionofMODISandVIIRS:500mfSCA andalbedo2. DownscalingandfusionwithLandSat:30mfSCA andalbedo3. ReconstructedSWEinbothstudyareas4. Leveragingotherfundedwork:machine-learningbasedSWEestimatesin
bothstudyareas5. Leveragingotherfundedwork:Modelready(HECHMS)snowandice
estimatesforupperIndus
12AnnualmeltintheupperIndus,2014
Wheredoesmachinelearningfit?Topredicttoday’sSWE• Reconstructionisaccuratebutcanonlybedoneafterallthesnowmelts
• UsereconstructedSWEtotrainmachinelearningmodelsthatusepredictorsavailablefortoday
• Specifically,baggedtrees(randomforests)andneuralnetworkswereused
• Thosemodelswereusedtopredicttoday’sSWEthroughoutAfghanistan
• 20%oftrainingdata(reconstructedSWE)washeldoutforvalidation
• Nash-Sutcliffeefficiencyis0.68forallyears,indicatingsubstantialimprovementoverameanforecast 13
Top:BaggedtreepredictorimportanceBottom:BaggedtreebiasandRMSE,validatedusing20%holdout
Bairetal.(2018)
References• Bair,E.H.,A.AbreuCalfa,K.Rittger,andJ.Dozier(2018),
Usingmachinelearningforreal-timeestimatesofsnowwaterequivalentinthewatershedsofAfghanistan,TheCryosphere,12(5),1579-1594,doi:10.5194/tc-12-1579-2018.
• Barnett,T.P.,Adam,J.C.,andLettenmaier,D.P.(2005).Potentialimpactsofawarmingclimateonwateravailabilityinsnow-dominatedregions.Nature 438, 303-309.doi:10.1038/nature04141.
• Dozier,J.,Painter,T.H.,Rittger,K.,andFrew,J.E.(2008).Time-spacecontinuityofdailymapsoffractionalsnowcoverandalbedofromMODIS.AdvancesinWaterResources 31, 1515-1526.doi:10.1016/j.advwatres.2008.08.011.
• Durand,M.,Molotch,N.P.,andMargulis,S.A.(2008).Mergingcomplementaryremotesensingdatasetsinthecontextofsnowwaterequivalentreconstruction.RemoteSensingofEnvironment 112, 1212-1225.doi:10.1016/j.rse.2007.08.010.
• Painter,T.H.,Rittger,K.,Mckenzie,C.,Slaughter,P.,Davis,R.E.,andDozier,J.(2009).Retrievalofsubpixelsnow-coveredarea,grainsize,andalbedofromMODIS.RemoteSensingofEnvironment 113, 868-879.doi:10.1016/j.rse.2009.01.001.
• Rittger,K.,Painter,T.H.,andDozier,J.(2013).AssessmentofmethodsformappingsnowcoverfromMODIS.AdvancesinWaterResources 51, 367-380.doi:10.1016/j.advwatres.2012.03.002.
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