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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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29
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)
30
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
-5
-4
-3
-2
-1
0
1
2
3
4
5
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
ness
Tem
pera
ture
(K)
Year
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
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