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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