SummaryofMODISMaintenanceActivities

(SeniorReviewSummaries)

MiguelRomán(USRA/EfSI)MODIS/VIIRSMeasurementTeamCo-Lead

withinputsfromtheTerra/AquaMODISMaintenancePIsGSFCCode619MODAPSsupportteams

These are updated annually, so users know the current validation status of the MODIS and VIIRSproducts.

Statements do not have to be updated annually, but the PIs need to verify the status information posted on their status pages are current.

Status of MODIS / VIIRS Land Product Validation Statements

Transition of validation status information from LandVal to MODLAND – Deprecation of LandVal

Background• MODISLandValidation(LandVal)andMODLANDwebsitesevolvedseparately,bothactivesince2000withstableURLs.• MODISLandValidationwasalargeconcertedeffortpost-launch,websiteservedprimarilyasalinktomanydifferent

datasetscollectedattheEOSCoreSites(includingMODISsubsets)andtoMODISproductvalidationstatusinformation.• LandValsitenowoperatesalmostsolelyasasourcefortheProductvalidationstatusinformation(perwebsitetraffic

monitoring)• Mostofthecoresitevalidationdatawerecollectedinthe2000-2010timeframe,mostwerestoredattheLPDAACand

arenolongeravailablethere(asnotedonthewebsitehomepage)Plan• TomovetheproductvalidationstatuspagesfromLandValintotheMODLANDwebsite.Willprovideredirectsforthe

manyunknownlinksouttheretothesiteforatime,butwillnotifytheLPandNSIDCDAACSofthenewlinks.• LandValwebsitewillbedeprecatedatsomepointwhichhasyettobedetermined.Ifanyonehasanyneedstoretain

informationhostedthere,pleasecontactJaimeNickeson(jaime.nickeson@nasa.gov).

Very high-resolution commercial imagery available for NASA-funded research

• The National Geospatial-Intelligence Agency’s extensive archive of commercial satellite data are available to NASA investigators free of cost

• Licensed under NextView contract (can be shared with those supporting USG interests)• 4 active sensors available, plus historical IKONOS and QB. MS and Pan (0.5 to 5 m

resolution), as well as SWIR w WV3, some CAVIS (MODIS bands), extensive global coverage.

• GSFC already has over 7M high res scenes and 3.5 Pb of these data in house, and access to future collections.

• Go to http://cad4nasa.gsfc.nasa.gov to register and submit requests. Non-NASA need grant number to register.

MODISSRProductsuiteCollection6:(Releasedin2015)Bands1through7250m,500m,0.05deg.Daily,8daysStatusandUpdates:• MODISSRcollection6(LaSRC:LandSurfaceReflectanceCode)isthebasisfora

varietyofSRproduct(VIIRS,AVHRR,Landsat,Sentinel2)assuringconsistencyandtraceabilityintheSRproductsfrommultiplesatellites/instruments.

• ValidationstageIV(AERONET)andcross-comparisonwithMODISison-going.ACIX-II(Landsat8/S2)ison-going.

RecentPublications:• Guillevic,P.C., etal.,2019.ImpactoftheRevisitofThermalInfraredRemoteSensing

ObservationsonEvapotranspirationUncertainty—ASensitivityStudyUsingAmeriFluxData. RemoteSensing, 11(5),p.573.

• Santamaría-Artigas,A.E.,etal.,2019.EvaluationofNear-SurfaceAirTemperatureFromReanalysisOvertheUnitedStatesandUkraine:ApplicationtoWinterWheatYieldForecasting. IEEEJournalofSelectedTopicsinAppliedEarthObservationsandRemoteSensing.

• Villaescusa-Nadal,J.L.,etal.,2019.SpectralAdjustmentModel'sAnalysisandApplicationtoRemoteSensingData. IEEEJournalofSelectedTopicsinAppliedEarthObservationsandRemoteSensing.

• Franch,B.,etal.,2019.Remotesensingbasedyieldmonitoring:ApplicationtowinterwheatinUnitedStatesandUkraine. InternationalJournalofAppliedEarthObservationandGeoinformation, 76,pp.112-127.

• Becker-Reshef,I.,etal.,2018.PriorSeasonCropTypeMasksforWinterWheatYieldForecasting:AUSCaseStudy. RemoteSensing, 10(10),p.1659.

StatusofMODISSurfaceReflectance(MOD09)

0

1000

2000

3000

4000

5000

6000

7000

GooglescholarcitationscontainingMODISsurfacereflectance(asofNovember13,2019)about63000total. 5

6

MODISVISuite(inits19thyear)Collection6:(Releasedin2015)Collection7: (Manychanges)

VIIRSVISuite(inits7thyear)Collection 1: (Released in 2018)ConsistentwithMODISproductsuiteCollection2:(Changesandpossible375mproduct)

StatusandUpdates:– ImprovedQA-drivencompositingalgorithm– ImprovedQAandViewAnglecompositingscheme– OngoingAlgorithmsenhancementtoaddressupstreamchangesand/orissues– Regularandopportunisticvalidation(usingNEONandDroneData)– TimeseriesfullycharacterizedwithexplicitMODIST/Acontinuitytransfer

functions– VIIRSVIproductorphanedandnolongerfunded,thePI/SCFcontinuesto

supporttheAlgorithm/productsuiteduetoitscriticalvaluetothesciencecommunity(thousandsofusersandtensofagenciesandprivatecompaniesdependonthiseffort).

KnownIssues:• Theuseofpre-composited8-daysurfacereflectanceinputscontinuesto

causeconsistencyissuesthatwillbeaddressedinC7(backtodailyinputs)

RecentPublications:• Jarchow,C.J.,Didan,K.,Barreto-Muñoz,A.,Nagler,P.L.,&Glenn,E.P.(2018).

ApplicationandComparisonoftheMODIS-DerivedEnhancedVegetationIndextoVIIRS,Landsat5TMandLandsat8OLIPlatforms:ACaseStudyintheAridColoradoRiverDelta,Mexico. Sensors, 18(5),1546.

a)

b)

c)

d)MODIS/VIIRSVITimeSeriesoverselectbiomesillustratingthestabilityandconsistencyoftheproductsuite(thereisasystematicdifferenceatthelowerendoftheVIrangewithVIIRShigherthanMODIS).a&b)HarvardForest,c)Amazon,d)Saharadesert

StatusoftheMODISandVIIRSVITimeSeriesKamel Didan1, Armando Barreto1, Compton Tucker2, Jorge Pinzon3

1University of Arizona, 2Goddard Space Flight Center, 3SSAI/GSFC

StatusandLong-termplansfortheVITimeSeries

ForthemostpartVIIRSisalmostidenticaltoMODIS(notwithstandingresolution)withR2 >96%.DifferencesbetweenVIIRSandMODISareminoraveragingaround2%and1.7%VIunitsforNDVIandEVIrespectively.Diff.standarddeviation(ameasureoftheTimeSeriescontinuityerror)isaround0.057forNDVIand0.0386EVI.Therearestillsomechallengesthatweplantoaddresswithlatercollections(clouds,WLmask,etc..)

OnlineplatformforVIValidationandAcrosssensorcontinuityhttps://vip.arizona.edu/tools/NEON/

GlobalVITimeSeriesMulti-sensorOnlinePlatformhttps://vip.arizona.edu/viplab_data_explorer.php

Futureplans• ArobustandInternalLWmasktoavoidcurrentproblems(nearshores

land)• Internalcloudmaskandfinerresolution

(375m,requestedbysomeofourusers)forVIIRS

• Newerlong-termCMGdatabases• BacktodailyforMODIS• PrototypinganexperimentalZERO

CLOUD productsuitewithGapfilling• AimingatValidationStage4forMODIS

and2/3forVIIRS

OpportunisticvalidationandcontinuityanalysiswithNEON

MCD19ProductSuiteCollection6:(SinceMay,2018)• MCD19A1: SurfaceReflectance

– DailyL31km:BRFinbands1-12;Snowgrainsizeandsnowfraction;– DailyL3500m:BRFinbands1-7;

• MCD19A2: Atmosphericproperties– DailyL31km:CM,AOD,CWV,PlumeInjectionHeight(fordetectedsmoke)

• MCD19A3: BRDF/Albedo– 8-DayL31km:RTLSBRDF,instantaneousalbedoinbands1-8;

StatusofMODISMCD19

AODColumnWVRGBSRSnowFrac,1kmRGBKiso (RTLS)

DOY

230

DOY

60, 2

005

StatusandUpdatesforC6.1:• Dailygap-filled250mBRFinbands1-2(Red-NIR);• CMG(0.05° products)

KnownIssues:• NA

SelectedRecentPublications:• Lyapustinetal.,2018.MODISCollection6MAIACAlgorithm,Atm.Meas.Techniques,doi:10.5194/amt-2018-141.• Lyapustinetal.,2019,MAIACThermalTechniqueforSmokeInjectionHeightFromMODIS,IEEEGeosci.Rem.Sens.Lett.,pp.1-5,2019doi:

10.1109/LGRS.2019.2936332.• H. Jethva, O. Torres, R.D.Field, A. Lyapustin, R. Gautam, V.Kayetha, Connecting Crop Productivity, Residue Fires, and Air Quality over Northern India, Scientific

Reports, 9:16594, 2019.• Q.Di,H.Amini,L.Shi,I.Kloog,R.Silvern,J.Kelly,M.B.Sabath,C.Choirat,P.Koutrakis,A.Lyapustin,Y.Wang,L.J.Mickley,J.Schwartz.Anensemble-

basedmodelofPM2.5concentrationacrossthecontiguousUnitedStateswithhighspatiotemporalresolution,Environ.Int.,130(2019).8

MODISMAIACAlgorithm

Chowdhury, S., S. Dey, L. Di Girolamo, K.R. Smith, A. Pillarisetti, A. Lyapustin, Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset, Atm. Environ., 204, 142-150, 2019.

(B)

MAIAC AOD shows best correlation and lowest rmse against AERONETamong operational aerosol products (Schutgens et al., 2019; climatemodeling community)

Pollution in Dehli from agricultural residue burning in upwind states of Punjab and Haryana (for ref.: the US AQ standard is 35µg/m3)

(C)

H. Jethva, O. Torres, R.D.Field, A. Lyapustin, R. Gautam, V.Kayetha, Connecting Crop Productivity, Residue Fires, and Air Quality over Northern India, Scientific Reports, 9:16594, 2019.

A 25% increase in rice production in Punjab and post-harvest fires during 2002-2016 result in dramatic deterioration of air quality in populous Indo-Gangetic plane with associated 43% growth in aerosol loading and near 60% rise in ground particulate matter (in New Dehli)

(A)

9

MODISBurnedAreaProductCollection6:(released2017)

•MCD64A1: MonthlyL3500mSINGrid•MCD64CMH: MonthlyCMG(released2018)

StatusandUpdates:• Stage-3validationcomplete.

KnownIssues:• None.

RecentPublications:• Boschetti,L.,Roy,D.P.,Giglio,L.,Huang,H.,Zubkova,M.,andHumber,M.L.,2019,Globalvalidationofthecollection6MODISburnedareaproduct. RemoteSensingofEnvironment,235:111490.

• Zubkova,M.,etal.,2019,ChangesinfireactivityinAfricafrom2002to2016andtheirpotentialdrivers.GeophysicalResearchLetters,46,1-11.

• Giglio,L.,Boschetti,L.,Roy,D.P.,Humber,M.L.,andJustice,C.O.,2018,TheCollection6MODISburnedareamappingalgorithmandproduct.RemoteSensingofEnvironment,217:72–85.

StatusofMODISBurnedArea

Stage-3ValidationVoxels 10

MODISActiveFireProductsCollection6:(released2015)

•MOD14/MYD14: Terra/AquaL2Swath•MOD14A1/MYD14A1:L3Daily500-mSINGrid•MOD14A2/MYD14A2:L38-day500mSINGrid•MCD14ML: Monthlyfirelocations

StatusandUpdates:• Widelyusedmatureproduct.• Stage-2validated.

KnownIssues:• None.

RecentPublications:• Vadrevu,K.P.,Lasko,K.,Giglio,L.,Schroeder,W.,Biswas,S.,andJustice,C.O.,2019,TrendsinvegetationfiresinsouthandsoutheastAsiancountries.ScientificReports,9:7422,1-13.

• Giglio,L.,Schroeder,W.,andJustice,C.O.,2016,Thecollection6MODISactivefiredetectionalgorithmandfireproducts.RemoteSensingofEnvironment,178,31-41.

StatusofMODISActiveFire

Congo’srainforestisgettingsmaller.TheEconomist,19Oct.2019

11

12

MODISSnow-CoverProductCollection6.1:

•New-- M*D10A1F: DailyCloud-gap-filledproductMODIS/Terra/AquaL3500mSINGrid

•M*D10_L2revisedalgorithmanddatacontent,improvedsnowcoverdetectionStatusandUpdates:• Snowcoveralgorithm:revisedlowvisiblereflectancescreenand

addedtwoalgorithmQAbitflags• ProductuserguidesupdatedforC6.1KnownIssues:• Investigatingcloud/snowconfusionandeffectofaerosolsonthe

snowcoveralgorithmRecentPublications:• Hall,D.K.,G.A.Riggs,NicoloE.DiGirolamoandMiguelO.Román,2019:EvaluationofMODISand

VIIRSCloud-GapFilledSnow-CoverProductsforproductionofanEarthScienceDataRecord,HydrologyandEarthSystemSciences (acceptedfollowingminorchanges).

• O’Leary,Donal,DorothyHall,MichaelMedler,andAquilaFlower,2018:Quantifyingtheearlysnowmelteventof2015intheCascadeMountains,USAbydevelopingandvalidatingMODIS-basedsnowmelttimingmaps,FrontiersofEarthScience 12(4):693-710.

StatusofMODISCryosphereProductsDorothyHall1 andGeorgeRiggs2

1ESSIC/UniversityofMaryland,2SSAI

Cloud

MODISCloud-Gap-Filled(CGF)ProductisNowinProductionDorothyHall1,2 andGeorgeRiggs3,2

1ESSIC/UniversityofMaryland,2NASA/GSFCCryosphericSciencesLab,3SSAI

Thecloud-gapfilled(CGF)MODISproductprovidesatimeseriesofdailycloud-freesnow-covermapsat500-mresolutionfrombothTerraandAqua

DailySnowMapwithCloud– mostsnowisobscured

Cloud-GapFilledDailySnowMap– noclouds

5

-5

0

Meanchangein#daysofsnowcoverperyear

TheMODISTerraCGFtimeseriesshowsatrendof~4fewerdaysofsnowcoverforthispartofthewesternU.S.over18years

Halletal.,inpreparationTrendofnumberofdaysofsnowcoverperpixel(Feb– May)

2001– 2018

Cloud

CloudCloud

13

Meanchangein#daysofsnowcoverperyear

MODISCloud-Gap-Filled(CGF)Snow-CoverMapsShowaTrendof~11FewerDaysofSnowCoverintheGreatSaltLakeBasin,2001– 2018

Halletal.,inpreparation

Trendofnumberdaysofsnowcoverperpixel(Feb– May)over18years

GreatSaltLake

GreatSaltLakeBasinintheWasatchandUinta

Mts.

5

-5

0

*basinsoftheWeber,BearandJordanrivers

Utah

Wyo.

IdahoMODISTerracloud-gapfilledsnow-coverbuild-upanddepletioncurvesfromtheGreatSaltLakeBasin,Utah*,2001- 2018

Averageof18yrs

40°lat.

14

15

StatusandUpdates:• MODISLST&Eswath(L2)andsinusoidal(A1/A2)productsreleasedinCollection6(Fall2018).• MODISLST&Egriddedproducts(C1,C2,C3)inprocessingandreleasedwithCollection6.1(Fall2019)

MOD21LST&EProducts:Collection6:(ReleasedFall2018)

• MxD21L2:Daily5-minL2Swath1km• MxD21A1:DailyL3Global1km• MxD21A28-dayL3Global1km

Collection6.1:(inprocessing)• MxD21C1:Daily0.05degreeClimateModelingGrid(CMG)• MxD21C2:8-day0.05degreeClimateModelingGrid(CMG)• MxD21C3:Monthly0.05degreeClimateModelingGrid(CMG)

KnownIssuesandConcerns:• MOD21Terraproductsonlyproduceduntil2005inCollection6duetoissueswithband29calibration.• Currentlytwodifferentproductstreams(MxD11/MxD21)withatotalof10differentproducttypes• NocurrentplanforwardtoretireMxD11suiteofproducts• ValidationshowsMxD21productaddressescoldLSTbiasofMxD11overaridregions,withproducts

havingsimilaraccuracyovervegetatedregions.Publications/Documentation:• Hulley,G.,Shivers,S.,Wetherley,E.,&Cudd,R.(2019).NewECOSTRESSandMODISLandSurfaceTemperatureDataRevealFine-ScaleHeat

VulnerabilityinCities:ACaseStudyforLosAngelesCounty,California.RemoteSensing,11(18).• Hulley,G.C.,Malakar,N.,Islam,T.,Freepartner,R,(2017),NASA’sMODISandVIIRSLandSurfaceTemperatureandEmissivity Products:A

ConsistentandHighQualityEarthSystemDataRecord,IEEETGRS,DOI:10.1109/JSTARS.2017.2779330.• Malakar,N.K.,andG.C.Hulley (2016),Awatervaporscalingmodelforimprovedlandsurfacetemperatureandemissivityseparationof

MODISthermalinfrareddata,RemoteSensingofEnvironment,182,252-264• UserguideandATBDavailableat:https://modis.gsfc.nasa.gov/data/dataprod/mod21.php

StatusofMODISLST&E

NewMODISMYD21LSTdetectsrisingextremetemperaturetrendsandheatvulnerabilityinLosAngeles,CA

NewMYD21LSTproductpinpointshotspotsandregionsmostvulnerabletoheatstressinurbanareas.Heatvulnerabilityindex(HVI)mapsarecurrentlyusedbytheLAcountysustainabilityofficetoadviseonimplementingeffectiveclimateadaptionandmitigationstrategies.

[Hulley etal.2019,Rem.Sens.]LowerincomecommunitiesaredisproportionalityaffectedbytheeffectsofextremeheatinL.A. 16

HistoryStatusofMODISLAI/FPAR

üAll are in nominal operation

üStatus of MODIS LAI/FPAR ProductCollection 3: November 2000 – December 2002 / OBSOLETE!Collection 4: March 2000 – December 2006 / OBSOLETE!Collection 5: February 2000 – March 2017 / OBSOLETE!Collection 6: February 2000 – Present (Released since 2015)

MOD15A2H: MODIS/Terra 8-Day L4 Global 500 m SIN Grid V006

MYD15A2H: MODIS/AQUA 8-Day L4 Global 500 m SIN Grid V006

MCD15A2H: MODIS/Terra+Aqua 8-Day L4 Global 500 m SIN Grid V006

MCD15A3H: MODIS/Terra+Aqua 4-Day L4 Global 500 m SIN Grid V006• L2G–lite surface reflectance at 500m resolution as MOD09GA input instead of

reflectance at 1km resolution MODAGAGG • New multi-year land cover product at 500m resolution in place of the 1km

resolution static land cover product17

MODIS LAI/FPAR Product suite• Collection 6: (Released in 2015)• Terra (MOD), Aqua (MYD), and Terra and Aqua (MCD)• 8 days for MOD/MYD, 4 days for MCD• 500mStatus and Updates:• MODIS LAI/FPAR collection 6 uses 500m Surface

Reflectance and land cover instead of 1km in collection 5.• New 3 or 4 years land cover instead of static land cover• Validation at stage 2 has been achieved for the MODIS

collection 6 LAI product.Known Issues:• NoneRecent Publications:• Chen et al., 2019. China and India lead in greening of the

world through land-use management. Nature Sustainability 2, 122–129. https://doi.org/10.1038/s41893-019-0220-7

• Xu et al., 2018. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sens. Environ., doi:10.1016/j.rse.2018.02.049

• Chen et al., 2017. Prototyping of LAI and FPAR Retrievals from MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data. Remote Sensing, doi:10.3390/rs9040370

• Yan et al., 2016. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements, Remote Sensing, doi:10.3390/rs8050359

• Yan et al., 2016. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison, Remote Sensing, doi:10.3390/rs8060460

Status of MODIS LAI/FPAR

“One-third of the global vegetated area is greening and 5% is browning…Two-thirds of this greening is from croplands and forests in about equal measure …The greening is most notably in China and India, which together account for nearly one-third of the observed total net increase in green leaf area globally. ” 18

MODISGPP/NPPandET/PETproducts.Collection6:– MXD17A3H: MODIS/Terra-AquaAnnuallyL4500mSINGrid– MXD17A2H: MODIS/Terra-Aqua8-dayL4500mSINGrid– MXD16A3: MODIS/Terra-AquaAnnuallyL4500mSINGrid– MZD16A2: MODIS/Terra-Aqua8-dayL4500mSINGrid

StatusandUpdates:• Thecollection6productsarenotavailableduetocloudcontaminationproblems.• ThenewGapfilledGPP/NPPandET/PETproducts(collection6.1)aregoingtobe

availablesoon.• Acomparisonandvalidationofthemethodwillbecarriedoutwiththenewdata.

KnownIssues:• Importantgapsduetocloudcontaminationinheavilycloudedareasin(collection6).

RecentPublications:• Sánchez-Ruiz,S.,Moreno-Martínez,Á.,Izquierdo-Verdiguier,E.,Chiesi,M.,Maselli,F.,&Gilabert,M.A.(2019).Growingstockvolumefrommulti-temporallandsat imagerythroughgoogleearthengine.InternationalJournalofAppliedEarthObservationandGeoinformation,83,101913.

• He,M.,Kimball,J.S.,Yi,Y.,Running,S.,Guan,K.,Jensco,K.,...&Maneta,M.(2019).Impactsofthe2017flashdroughtintheUSNorthernplainsinformedbysatellite-basedevapotranspirationandsolar-inducedfluorescence.EnvironmentalResearchLetters,14(7),074019.

• He,M.,Kimball,J.S.,Yi,Y.,Running,S.W.,Guan,K.,Moreno,A.,...&Maneta,M.(2019).Satellitedata-drivenmodelingoffieldscaleevapotranspirationincroplandsusingtheMOD16algorithmframework.RemoteSensingofEnvironment,230,111201.

• Robinson,N.P.,Jones,M.O.,Moreno,A.,Erickson,T.A.,Naugle,D.E.,&Allred,B.W.(2019).RangelandProductivityPartitionedtoSub-PixelPlantFunctionalTypes.RemoteSensing,11(12),1427.

StatusofMODISGPP/NPPandET/PETproducts.

Absoluteerror(mm/y)inMOD16(ET)duetocloudcontamination

%Gapsduringthegrowingseason

1000

500

0 0

50

100

19

ImprovementsonMODISEvapotranspiration(MOD16)andGPP/NPP(MOD17)OperationalDataSetsUsingGap-filledClimatologicalFPAR/LAI

Maosheng Zhao,AlvaroMoreno,Sudipta Sarkar,SadashivaDevadiga andStevenW.Running

• Cloud cover and aerosols difficultobtaining valid retrievals in 8-dayoperational FPAR/LAI which in turncreate “gaps” in MOD17 andMOD16 products (A).

• Collection 6.1 MOD16 and MOD17products use a back up enhancedclimatological FPAR/LAI (EHCFL)when unreliable FPAR/LAIestimates are present.

• The pre-computed EHCFL improvesstandard mean/medianclimatologies approaches.

• EHCFL maximizes global MOD16and MOD17 usefulness and reducedrastically the errors due to cloudcontamination(B) .

(B)

(A)

0

MeanannualdatagapsinMOD17/MOD16(%)

Absoluteerror(mm/y)inMOD16(ET)duetocloudcontamination

%Gapsduringthegrowingseason

1000

500

0 0

50

100

20

Amethodologytoderiveglobalmapsofplanttraitsusingremotesensingandclimatedata

AlvaroMoreno,Gustau Camps-Valls,JensKattge,NathanielRobinson,MarkusReichstein,…,StevenW.Running(2018).RemoteSensingofEnvironment,218,69-88

Plant traits are an important part of the MODISGPP/NPP and ET/PET algorithms (MOD17 andMOD16) . But also in most of DGVMs, ESMs andGCMs.Their contribution in all these models is oversimplified (one value per PFT) and constitutes asignificant source of uncertainties (A).We present and validate a combined remotesensing and biogeographic approach tospatializing estimates of key leaf traits (B) andtheir respective uncertainties (C).

SLA

LNC

LPC

(B) (C)

(A)

21

Satellitedata-drivenmodelingoffieldscaleevapotranspirationincroplandsusingtheMOD16algorithmframework

MingzhuHe,JohnS.Kimball,Yonghong Yic,StevenW.Running,Kaiyu Guan,AlvaroMoreno,Xiaocui Wue,MarcoManet (2019).RemoteSensingofEnvironment.

• We refined the MOD16 ET algorithmto better represent C3 and C4croplands

• Enhancements include refined model,dynamic land cover and 30-mvegetation inputs (Landsat based).

• Results show enhanced ET accuracyand lower bias over diverse CONUScrop types.

• Improved representation of field scale(30-m) ET heterogeneity (A), (B)

(B)Original500mMOD16algorithm

(A)RefinedLandsat30mMOD16algorithm

22

MCD43BRDF,Albedo,andNBARproductsfromTerra/AquaMODIS

Crystal Schaaf`1, Zhuosen Wang2,3 Qingsong Sun2, Yan Liu1, Zhan Li1, Angela Erb1, Arthur Elmes1, Charlotte Levy1

1 School for the Environment, University of Massachusetts Boston, Boston MA, USA2 Terrestrial Information Systems Lab, NASA Goddard Space Flight Center, Greenbelt, MD, USA3 Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

MCD43D61:DOY250,2010ShortwaveBroadbandWSA

MCD43GF: DOY250, 2010Shortwave Broadband WSA

1.0 0.0

MODISBRDFAlbedoNBARProductsThe MODIS products rely on high quality multi-date, multi-angle surface reflectances to retrieve a dailysurface BRDF for each pixel. This BRDF is then used to produce White Sky Albedo (bihemispherical albedounder isotropic illumination), Black Sky Albedo (directional hemispherical albedo under local solar noonillumination), and Nadir BRDF-Adjusted Reflectance (NBAR). Extensive QA fields are provided. Albedo isretrieved as either a snow albedo or a snow-free albedo depending on the condition of the daily day ofinterest.CollectionV006:

• MCD43A:500mSINgrid• MCD43A1:BRDF/AlbedoModelParameters• MCD43A2:BRDF/AlbedoQuality• MCD43A3:Albedo• MCD43A4:NBAR• MCD43C:0.05degreeCMG• MCD43C1:CMGBRDF/AlbedoModelParameters• MCD43C2:CMGBRDF/AlbedoModelSnow-FreeParameters• MCD43C3:CMGAlbedo• MCD43C4:CMGNBAR• MCD43D:30Arc-SecondCMG(1– 40)• MCD43GF:CMGGap-FilledSnow-Free

StatusandUpdates:• DuetothedetectorfailuresinMO/YD09Bands5and6,newmandatoryQAvalueshavebeencreated

fortheproducts,indicatingwhetherornotthesebandscouldbeusedinprocessing.Newnarrow-to-broadbandcoefficientshavebeencalculatedforsuchcases.

• SnowfreeGapFilledV006products(MCD43GF)areavailablefromtheLPDAACforyears2001-2017

Gap-FilledvsStandardCMGProduct

23

StatusofTerra/AquaMODISBRDF,AlbedoandNBAR

Bruegge,C.,Coburn,C.,ElmesA.,Helmlinger,M.,Kataoka,F.,Kuester,M.,Kuze,A.,Ochoa,T.,Schaaf,C.,Shiomi,K.andSchwandner,F.(2019).Bi-DirectionalReflectanceFactorDeterminationoftheRailroadValleyPlaya.RemoteSensing,inpress.

VicariouscalibrationofBRFinRailroadValleyPlayawasperformedwithMCD43BRDFvalues.AspeciallymodifiedMCD43codewasusedduetocompensateforunderlyingMOD/YD09reflectancevaluesmistakingexceptionallybrightsurfacesforaerosol.

ThespecialMODISproduct,whichisproducedignoringtheaerosolQAflag,isfoundtoagreewithinsitumeasurementswithin4%.

PARABband,nm

MODISband,nm

FracDiff30W

FracDiff 20E

444 446 0.023 −0.053

551 556 −0.002 −0.009

860 862 0.016 −0.037

1650 1631 0.011 −0.029

GraphshowsvicariouscalibrationofMCD43vsPARABOLAinstrumentinsitu

‘Special’ProductOperationalProduct

RailroadPlaya

24

GreenlandIceSheetMeltAlbedo

https://nsidc.org/greenland-today/2019/07/a-record-melt-event-in-mid-june/

Broadbandwhite-skyalbedointheseimages,derivedfromtheModerateMCD43A3,showstherapiddarkeningofthewesternedgeoftheicesheetduringspring/summer2019.Therecentextensivemelteventexacerbatedearlymeltingintheablationarea,wherebareicebecomesexposedbyearlywarming.

MCD43productsfeaturedon‘GreenlandToday’blogoverthesummer,helpinghighlighttheexceptionalmeltseason.Futureworkwillexploremelt-relatedalbedoconsequencesoverGreenlandfortheentireMODIStimeseries.

Animationshows3/16/2019to10/17/2019

25

100%Non-TreeVegetation

100%TreeCover

100%Non-Vegetated

MODISMOD44BVegetationContinuousFields:AFunctionalBaselineforBiogeochemicalParameterizations

CharleneDiMiceli,JohnTownshend,RobertSohlberg

UniversityofMaryland

ü Sub-pixelestimatesoflandscapecomponents.ü Annualresultsfor2000– present.ü Nominalspatialresolutionof250m.ü DerivedwithdailyL2Gdataandmachinelearning.ü Fullyautomatedwithembeddederrorestimates.ü Algorithmcanbeappliedtoothersensorsystems.

ü Popularusewithinthecarboncommunity.ü Improvesspatialestimatesofproductivity,

roughness,disturbance,surfacewater,etc.ü 2400uniqueusercitationssince2000.ü 1040citationsinthepastfiveyears.ü 420citationsinjustthepasttwoyears. 26

RecentMaintenanceActivity

ü RoutineproductionofC6annualproductsreleasedeachApril.

ü User’sGuideandATBDup-to-date.ü AvailableviatheLP-DAAC.ü Severalcodeupdatesto

accommodatechangesinmetadataandupstreamsurfacereflectanceinputproducts.

ü Allcodecurrentlyacceptedintheproductionsystem.

ü StandingbyforC6.1reprocessingandqualityassurancechecks.

ü Productqualityremainsstable.

MODISMOD44BVegetationContinuousFields:AFunctionalBaselineforBiogeochemicalParameterizations

NewScienceActivity

ü WorkunderwaytouseVCFcapabilitiestoestimateglobaldisturbance.ü Forestdegradationandfragmentationresultinginadverseimpacton

provisionofecologicalgoodsandservices.ü Fragmentationhotspotswhere>30%of25km2 tileshavechanged

frominteriorforest(>1kmfromforestedge)toedge-impactedforest(<1kmfromforestedge):

CharleneDiMicelicdimicel@umd.edutel:301-780-3967

27

MODISMOD44BVegetationContinuousFields:AFunctionalBaselineforBiogeochemicalParameterizations

A. Logging and regrowth. Oregon.

B. Many roads and small clearings break up interior forest into smaller patches. West Virginia.

C. Forests in the Far North are naturally fragmented.

D. Patches of forest are surrounded by agricultural clearings. Nicaragua.

A. B. C. D.

FutureNeeds&Opportunities

ü Extensiveusebyclimateandcarbonmodelingcommunitiesdemonstratestheuniquecontributionoffractionalcoverproducts.ü TheproductwouldbenefitfromuseofnewlyavailableLidardata.Canopyheightandstructuredatawouldimprovetraining

andallowretrievalofamuchrequested“shrubs”layer.ü Newmachinelearningtechniquesareavailabletoprovideadynamicwaterlayerwhichcapturestheseasonalsignal.ü ThecurrentcodecouldexpandtheVCFrecordtoVIIRSwithminimalnewinvestment,primarilyQA/QCandvalidation.ü Weplantodevelopecologicalapplicationsinconcertwiththelandmanagementcommunity.ü Asillustratedbelow,afforestationismorecomplicatedthaniscurrentlyunderstood.Fragmentationandedgeeffects– both

anthropogenicandclimate-driven– degradeecologicalservicesevenastotalforestarearemainsstable.

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MODISDSRandPARProductsaddedin2017Collection6:

• MCD18A1: MODIS/Terra+Aqua DailyL35kmDSRSINGrid• MCD18A2: MODIS/Terra+Aqua DailyL35kmPARSINGrid

Collection61:• Underprocessing• MCD18A1,MCD18A2:1km• MCD18C1,MCD18C2: CMG,0.05°

KnownIssues:• ProgrammingerrorsinC6overestimatingDSRandPAR

StatusandUpdates:• C61codesfixedtheprogrammingerrors• Spatialresolutionwasimprovedfrom5kmto1km• Addednew0.05degreeCMGproductsofDSRandPAR• ImprovedLUTwithbetterrepresentationofclouds• AddingVIIRSasadditionaldatatobettercapturediurnalchanges

RecentPublications:• Wang,D.,Liang,S.,Zhang,Y.,Gao,X.,Brown,M.,&Jia,A.(2019).A

newsetofMODISlandproducts(MCD18):downwardshortwaveradiationandphotosynthetically activeradiation.ScienceofRemoteSensing,submitted

• Huang,G.,Li,Z.,Li,X.,Liang,S.,Yang,K.,Wang,D.,&Zhang,Y.(2019).Estimatingsurfacesolarirradiancefromsatellites:Past,present,andfutureperspectives.RemoteSensingofEnvironment,233,111371

• Zhang,Y.,He,T.,Liang,S.,Wang,D.,&Yu,Y.(2018).Estimationofall-skyinstantaneoussurfaceincidentshortwaveradiationfromModerateResolutionImagingSpectroradiometer datausingoptimizationmethod.RemoteSensingofEnvironment,209,468-479

StatusofMODISDSRandPAR(MCD18)

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Fielddatacollection• Startingwithdataof2018• BSRN,AmeriFlux,EuropeanFlux,OzFlux 119stationsOtherproductsforintercomparison• CloudsandtheEarth'sRadiantEnergySystem(CERES)• GlobalLAnd SurfaceSatellite(GLASS)

Validationapproaches• Temporalandspatialaggregation• Effectsofinputsurfacereflectancedata• Effectsofdailyoverpasscounts

ExtensivevalidationofMCD18C6

Topfigures: scatterplotsbetweenMCD18dataandinsitumeasurementsforinstantaneousDSR,dailyDSR,instantaneousPARanddailyPAR.Rightfigure:AccuracyofMODISdailyDSRproduct(MCD18A1)asfunctionsofthecountsofdailyMODISoverpass.

RadiometricCalibrationTerra&AquaMODIS: Collection6.1 (2013–2019)SNPPVIIRS: Collection1(Archive5110) (2013–2019)NOAA-20VIIRS: Collection2(Archive5200) (2018–2019)

SurfaceReflectanceValidationTerra&AquaMODIS: Collection6 (2013–2019)SNPPVIIRS: Collection1(Archive5000) (2013–2019)NOAA-20VIIRS: noimagery

StatusandUpdates:• RadiometricCalibrationTestSite(RadCaTS)operationalsince~2013.• RadCaTScurrentlyoneoffiveRadCalNetsites(www.radcalnet.org).• Routinedailydownloadandweeklyprocessingofdata.

KnownIssues:• RadCaTSbiaswithMODISBand3(466nm).

RecentPublications:• Czapla-Myers,J.S.,andAnderson,N.J.,"IntercomparisonoftheGOES-16and-17

AdvancedBaselineImagerwithlow-Earthorbitsensors."Proc.SPIE11127(2019).• Bouvet,M.,Thome,K.,Berthelot,B.,Bialek,A.,Czapla-Myers,J.,Fox,N.P.,Goryl,

P.,Henry,P.,Ma,L.,Marcq,S.,Meygret,A.,Wenny,B.N.,andWoolliams,E.R.,“RadCalNet:ARadiometricCalibrationNetworkforEarthObservingImagersOperatingintheVisibletoShortwaveInfraredSpectralRange,”RemoteSensing,11(20),2401(2019).

• Czapla-Myers,J.S.,Coburn,C.A.,Thome,K.J.,Wenny,B.N.,andAnderson,N.J.,"DirectionalreflectancestudiesinsupportoftheRadiometricCalibrationTestSite(RadCaTS)atRailroadValley."Proc.SPIE10764,9(2018).

StatusofMODISandVIIRSSurfaceReflectanceValidation

J.Czapla-Myers,UniversityofArizona

RadiometricCalibrationTerra&AquaMODIS(02): Collection6.0 (2000–2019)SNPPVIIRS: Collection1(Archive5110) (2012–2019)

LandSurfaceTemperatureandEmissivityValidationTerra&AquaMODIS(11): Collection6.0 (2000–2019)

StatusandUpdates:• LakeTahoeSiteoperationalsince1998• SaltonSeaSiteoperationalsince2008• Collection6.0Terrathermalbands31,32andAquathermalbands29,31,32

within+/- 0.25(TopFigure),Terraband29steadydriftsince2009inCollection6.0(BottomFigure).

• Collection6.1willrequirecalibrationofMODISandVIIRSmidandthermalinfrareddataandproducts(02,11,21).

• Collection6.1IncludesMOD/MYD21LST&Eproductforfirsttime.• MOD21willbeaffectedbydriftandonlyCollection6.1shouldbeused.• VIIRSthermalbandswithin0.25K

KnownIssues:• CrosstalkinMODISTerraBand29.MaybefixedinCollection6.1• BiasinMODISTerramidinfraredbands

StatusofMODISandVIIRSLandSurfaceTemperatureandEmissivityValidation

SimonJ.Hook,NASA/JPL

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Delta

Tem

peratureK(B

29-B3

1)

Time

DeltaTemperatureM29-M31atLakeTahoeandSaltonSeaCY2000-2019,v6.x

Terra Aqua Poly.(Terra) Poly.(Aqua)

-1.5

-1.25

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

1.25

1.5

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 AllYears

Del

ta (V

-O) B

right

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Tem

pera

ture

(K)

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Delta Brightness Temperature in TIR Channels for MODIS Aqua at Lake Tahoe and Salton Sea CY2000-2019, vz0-30 v6.x

b29 b31 b32

M14 M15 M16

Band 31: 11.01 μm 1% radiance change ≈ 0.65K