Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from ...

26 Pages·2017·1.41 MB·English

water
Article
Mapping Dynamic Water Fraction under the Tropical
Rain Forests of the Amazonian Basin from SMOS
Brightness Temperatures
MarieParrens1,*,AhmadAlBitar1,FrédéricFrappart2,3,FabricePapa2,4,StephaneCalmant2,
Jean-FrançoisCrétaux2,Jean-PierreWigneron5andYannKerr1
1 Centred’EtudesSpatialesdelaBIOsphère(CESBIO—UniversitédeToulouse,CNES,CNRS,IRD),
UMR5126,BPI2801,31401ToulouseCEDEX9,France;[email protected](A.A.B.);
[email protected](Y.K.)
2 Laboratoired’EtudesenGéophysiqueetOcéanographieSpatiales(LEGOS),UMR5566,Universitéde
Toulouse,CNES,CNRS,IRD,ObservatoireMidi-Pyrénées(OMP),14AvenueEdouardBelin,31400Toulouse,
France;[email protected](F.F.);[email protected](F.P.);[email protected](S.C.);
[email protected](J.-F.C.)
3 GéosciencesEnvironnementToulouse(GET),UMR5563,UniversitédeToulouse,CNES,CNRS,IRD,
ObservatoireMidi-Pyrénées(OMP),14AvenueEdouardBelin,31400Toulouse,France
4 Indo-FrenchCellforWaterSciences(IFCWS),IRD-IISc-NIO-IITMJointInternationalLaboratory,
Bangalore560012,India
5 INRA,UMR1391ISPA,F-33140Villenaved’Ornon,Bordeaux,France;[email protected]
* Correspondence:[email protected];Tel.:[phone]
AcademicEditor:Y.JunXu
Received:23February2017;Accepted:11May2017;Published:17May2017
Abstract: Inlandsurfacewatersintropicalenvironmentsplayamajorroleinthewaterandcarbon
cycle. Remotesensingtechniquesbasedonpassive,activemicrowaveoropticalwavelengthsare
commonlyusedtoprovidequantitativeestimatesofsurfacewaterextentfromregionaltoglobal
scales. However,someoftheseestimatesareunabletodetectwaterunderdensevegetationand/or
inthepresenceofcloudcoverage. Toovercometheselimitations,thebrightnesstemperaturedataat
L-bandfrequencyfromtheSoilMoistureandOceanSalinity(SMOS)missionareusedheretoestimate
floodextentinacontextualradiativetransfermodelovertheAmazonBasin. Atthisfrequency,the
signalishighlysensitivetothestandingwaterabovetheground,andthesignalprovidesinformation
fromdeepervegetationdensitythanhigher-frequencies. Three-dayand(25km×25km)resolution
mapsofwaterfractionextentareproducedfrom2010to2015. Thedynamicwatersurfaceextent
estimatesarecomparedtoaltimeterdata(Jason-2),landcoverclassificationmaps(IGBP,GlobeCover
andESACCI)andthedynamicwatersurfaceproduct(GIEMS).Therelationshipsbetweenthewater
surfaces,precipitationandinsitudischargedataareexamined. Theresultsshowahighcorrelation
betweenwaterfractionestimatedbySMOSandwaterlevelsfromJason-2(R>0.98). Goodspatial
agreementsforthelandcoverclassificationsandthewatercycleareobtained.
Keywords: waterfractionextent;L-band;AmazonBasin
1. Introduction
Terrestrialsurfacewatercoversonlyabout5%oftheEarth’sice-freelandsurface[1,2],butplays
akeyroleinglobalbiogeochemistry,hydrologyandwildlifediversity[3,4]. Consequently,itiscritical
tomonitorthedistributionofterrestrialwateratlargespatialandhightemporalscales[5–8].Thework
in[9]estimatesthatnearlytwo-thirdsofallterrestrialfreshwaterwetlandsdisappearedbetween1997
and2011. AmorerecentstudyusedthreemillionLandsatimagestoprovideahighresolutionmapof
Water2017,9,350;doi:10.3390/w9050350 www.mdpi.com/journal/water
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surfacewaterextent[10]. Theauthorsofthisstudyestimatedthat90,000km2ofpermanentsurface
waterhaddisappearedbetween1984and2015.
Severaldifferentmethodsbasedonmappingwaterbodiesfromremotesensingdatasetswereused
sincethedevelopmentoftheEarth’sspaceobservations: (1)visible;(2)infrared;(3)activemicrowave;
(2)passivemicrowaveand;(4)hybridapproach(passiveandactivemicrowave). Eachmethodoffers
varyingdegreesofsuccessinprovidingquantitativeestimatesofwetlandsandinundationextents.
Watersurfacecanbesensedbyopticalremotesensingmethods. Thesemethodstypicallyexploit
theabsorptionoflongerwavelengthsoflightinwater,especiallythenearandshortwaveinfrared
partsoftheelectromagneticspectrum[11,12]. Opticalremotesensingprovidesveryaccuratemapping
ofwaterbodies. Forexample,[13]senseslakeswithaspatialresolutionof15m,whereas[14]sensed
globalwaterbodiesat30-mresolutionusingtheLandsatdata.Themajorityofthestudiesusingoptical
remotesensingforwaterbodies’detectionprovidedonlyonesnapshotofthehydrologystage. Dueto
thelowrevisittimeoftheopticalsensors,fewmapsofalargeareaareavailable,andtheminimum
and/orthemaximumofthefloodedareaarenotalwaysobserved. Thedetectionofsuddenchanges
impacting the hydrologic cycle [10] is also not sensed with accuracy. These limitations are crucial
issues for hydrology application. However, some studies [15,16] managed to follow the temporal
dynamicsofthewatersurfaceinspecificplaces. Themostimportantlimitationoftheopticalsensorsis
theirinabilitytopenetratecloudsanddensevegetationcover,whichisessentialduringtropicalwet
seasonsovertheAmazonBasin.
Activemicrowave(scatterometersandSyntheticApertureRadar(SAR))isalsosensitivetothe
watersurfaceandhastheabilitytopenetratecloudsand,toacertainextent,vegetation. Openwater
surfacesaregenerallycharacterizedbylowbackscatteringcoefficients. Contrarytopassivemicrowave,
thesignalismorecontaminatedbythevegetation. Thespatialresolutionofscatterometersisabout
25–50km,whereastheSARprovideshigherresolution,typicallyaround10–150m. Severalstudies
haveshowntheabilityofactivemicrowavetomapsurfacewateratregionalscales,suchasoverthe
Amazonregion[17]andovertheArcticregion[18]. Satellitealtimetersareradarsthatobserveatnadir
tomeasuresurfacetopography. Theyprovideaccuratemeasurementsofwaterheightsinrivers,lakes
andwetlands[19–21]. Duetotheirhighspatialresolution,altimetersdonotprovidesufficientspatial
coveragetoanalyzethewaterbodies’temporaldynamics,exceptinpolarregions[22]. Thefuture
SurfaceWaterOceanTopography(SWOT)mission[23]intendedtobelaunchedin2021isexpectedto
provideK-bandSARinterferometry,enablingcontinentalaltimetry.
Passivemicrowavesaresensitivetothedistributionofliquidwaterinthelandscape;theycan
operatedayandnightforallweatherconditions. However,theyarelimitedbyalowspatialresolution
(approximately30km). Theycansenseonlylargewetlandsorregionswherethecumulativearea
ofsmallwetlandscomprisesasignificantportionofthefieldofview. Consequently, theyprovide
thecapabilitytomapthetemporalevolutionofsurfacewateroverthelandsurfaceduetotheirhigh
temporalresolution. Inpreviousstudies,passivemicrowavemeasurementshaveshownthecapability
tosensethedynamicsofterrestrialsurfacewateratcoarseresolution[24–31]. Thebasicprincipleof
thesurfacewatermeasurementsbasedonpassivemicrowaveisexplainedbythedifferenceofthe
emissivitybetweenthewaterandthesoil.Floodingsurfacesdecreasetheemissivityinbothvertical(V)
andhorizontal(H)polarizationandincreasethedifferencebetweenthetwopolarizations,especially
at low frequencies. This approach produces ambiguous estimation of surface water over regions
withmixturesofopenwaterandothercomplexsurfaces(topographyeffects). Theworkin[26,27]
hasextensivelystudiedtheinundationareaovertheAmazonBasinwiththeScanningMultichannel
MicrowaveRadiometer(SMMR).However,theirstudiesfocusessentiallyonarestrictedareacloseto
Manaustownfrom1979to1987.
Hybridapproachescombinethestrengthsofdifferenttypesofsensors. Forexample,altimetry
dataarecharacterizedbyahighspatialresolutionandalowtemporalresolutionandcanbecombined
with passive microwave data having low spatial resolution and a high temporal resolution to
obtain a product with both high temporal and spatial resolution. The Global Inundation Extent
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fromMultiple-Satellites(GIEMS)productsarebasedonmergeddatafrompassive(SpecialSensor
Microwave/Imager(SMM/I)),activemicrowave(EuropeanRemoteSensingsatellite(ERS))anddata
fromanopticalsensor(AdvancedVeryHighResolutionRadiometer(AVHRR))[32].
Table1presentsthemajorstudiesrelatedtotheobservationanddetectionofwaterbodiesfrom
spacebyusingthetechniquespresentedabove. Visibleandinfraredremotesensingmethodswere
extensivelyused,butprovidedstaticmapsofwaterbodiesattheglobalscaleoradynamicmapat
theregionalscale[15,33]. Alackofstudiesconcerningdynamicwatersurfaceextentfrom2013tothe
presentisclearlyidentifiedinthistable.
ThefloodplainsandwetlandsoftheAmazonRiverareimportantintermsofwatervolumeand
intermsoffluxesbetweenthelandandtheatmosphere. MappingwaterfractionundertheAmazon
tropicaldenseforestischallenging,butsensingwaterunderdensevegetationremainsakeyissuein
theremotesensingscientificcommunity.
Inthisstudy,wedevelopedamethodtomapthetemporalevolutionofthewaterbodiesatcoarse
spatialresolutionandweeklytemporalresolutionbyusingamicrowavesensoratL-band(1.4GHz)
calledSoilMoistureOceanSalinity(SMOS)overtheAmazonBasin. TheSMOSsatelliteoperatesat
L-band,anditwasshownthatthisfrequencyisthemostsuitable,beinglessimpactedbyvegetation
thanhigherfrequencies[34–36]. Originally,theSMOSsatellitewasdedicatedtosensesoilmoisture
overlandsurfacesandtheoceansalinity. TheSMOSphysicalsignal(brightnesstemperature)ishighly
impactedbythepresenceofstandingwaterovertheground.
Ourmotivationistouseacontextualradiativetransfermodelandasingledatasettoestimate
thewaterfractionoverthetropicalbasin. Theareaofstudyandthedatasetsusedinthisworkare
presentedintheSection2and3,respectively. Section4presentsthealgorithmpermittingretrieving
thewaterfractionextentfromtheSMOSdata,andSection5containstheresultsandthevalidation.
ThediscussionandconclusionsarepresentedinSections6and7.
Table1.Listofselectedscientificpapersontheobservationofthewatersurfaceoverthecontinents
fromspace.References,areaofstudyandsensorsareshown.
RemoteSensingApproach Reference AreaofStudy Sensors Frequency
[27] AmazonBasin SSMR Q-band
Passivemicrowave [29] Borealregions SSM/I-SSMR K-andKa-band
[37] NorthEurasian AMSR-E-QSCAT C-band
See[38]forareviewoftheSARtechnique
[17] AmazonBasin ENVISATSAR L-band
Activemicrowave [39] Highlatituderegions ENVISATASAR C-band
[40] Mekongbasin ENVISATASAR C-band
[41] Globalscale ENVISATASAR C-band
[22] Borealregions Topex-Poseidon C-band
[42] Globalscale SSM/I,ERS,AVHRR Ka-andC-band
[32] Globalscale SSM/I,ERS,AVHRR Ka-andC-band
Hybridapproaches
[43] Globalscale SSM/I,ERS,AVHRR Ka-andC-band
[37] Globalscale SSM/I,SSMI/S,ERS,QSAT,ASCAT Ku-andC-band
[44] OkavangoDelta AVHRR -
[45] Brahmaputra AVHRR -
[46] InnerNigerDelta MODIS -
Opticalandinfrared
[47] China MODIS -
[33] MekongDelta MODIS -
[10] Globalscale Landsat -
2. StudyAreas
This study focuses on the Amazon Basin, which is the largest tropical basin with an area of
approximately6,000,000km2 andcontributesupto15%oftheglobalriverdischargetotheocean
(approximately200,000m3s−1discharge).Withasedimentloadofthreemilliontonsnearitsmouth[48]
and drainage area covers about 6,200,000 km2, almost 5% of all of the continental masses, the
Water2017,9,350 4of26
Amazon Basin is one of the most impressive hydrological basins of the world. The Amazon is
highlyinterconnectedbyfloodplainchannels,resultingincomplexflowpatterns. Figure1presentsthe
AmazonBasinwiththemainriversandfloodplains. Coveringmorethan300,000km2,theAmazon
extensivefloodplainsplayacrucialroleforglobalclimateandbiodiversity,buttheyarestillpoorly
monitoredatalargescale,limitingourunderstandingoftheirroleinfloodhazard,carbonproduction,
sedimenttransport,nutrimentexchangeandair-landinteractions. Surfacewaterstoredinfloodplains
represents about half of the terrestrial water storage and 15–20% of the water that flowed out of
the Amazon floodplains[49–53]. Because it extends over two hemispheres, the Amazon region is
characterized by several rainfall regimes. Rainfall shows opposing phases between the Northern
andtheSouthernHemispherewitharainyseasoninaustralwinterintheNorthernHemisphereand
summerintheSouthernHemisphere. Therainfallshowsagradientfromnorthwesttosoutheastwith
decreasingrainfallamountandincreasinglengthinthedryseason. Fortheeasternpartofthebasin,
therainyseasonoccursfromMarch–May,andthedryseasonprevailsfromSeptember–November.
Forthenorthernregions,lowrainfallseasonalityisobservedwithwetconditionsthroughouttheyear.
FormoreinformationontheAmazonhydrologicalregime,see[54–56].
Figure1.MapoftheAmazonBasinwiththemainriversandfloodplains.
3. Data
Thissectiondescribesthedatausedtocomputethewaterfractionextentfrompassivemicrowave
atL-band(SMOSdata,topographydataandskintemperature)andthedatausedtocompareand
validatethisproduct(precipitationdata,staticlandcovermaps,otherdynamicwaterfractionproducts,
waterlevelfromaltimetryandinsituriverdischargedata).
3.1. L-BandBrightnessTemperaturesfromSMOS
TheSMOSmissionisajointprogramoftheEuropeanSpaceAgency(ESA),theCentreNational
d’EtudesSpatiales(CNES)andtheCentroparaelDesarrolloTeccnologicoIndustrial(CDTI)inthe
frameworkoftheEarthExplorerOpportunityMissioninitiative. Itisthefirstsatellitespecifically
dedicated to soil moisture retrievals with a passive microwave radiometer at 1.4 GHz (L-band).
The physical signal of SMOS is the Brightness Temperature (TB). This signal is highly sensitive to
thewaterundertheground[34]. Cloudsandrainhaveanegligibleeffect[57],andtheatmospheric
contributionislimitedandknown[34]. Themicrowavesignalistoalesserextentsensitivetothe
vegetation,butactually,theL-bandsignalislessimpactedbythevegetationthanhigherfrequencies.
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SMOS has a Sun-synchronous orbit at a 757-km altitude with a 06:00 LST ascending Equator
crossingtimeandan18:00LSTdescendingEquatorcrossingtime. Theglobeisfullyimagedtwice
everythreedays. ThemaininnovativefeatureofSMOSisthecapabilityformulti-incidence-angle
observations at full polarization across a 900-km swath. In this study, the SMOS Level (L) 3 TB
(RE04v300)products[58]producedbytheCentreAvaldeTraitementdesDonnéesSMOS(CATDS)
areused. ThesedataareprojectedontheEqual-AreaScalableEarth(EASE)Grid2[59]withaspatial
resolution of 25km×25km. The main differences between the SMOS L3 TB and the other lower
levelsofdataare: (i)theL3TBproductsareexpressedatthetopoftheatmosphereovertheterrestrial
referenceframe(HandV); and(ii)theyare binaveragedevery5◦ from2.5◦–62.5◦. Inthe present
study,SMOSL3TBwereusedfrom2010–2015overtheAmazonBasin. Anglesof32±5◦,37±5◦,
42±5◦ and47±5◦ inbothHandVpolarizationwereconsideredtoretrievethewaterfractionover
thetropicalbasin. TheSMOSdataweredownloadedfromtheCATDSservers(www.catds.fr).
3.2. Topography
ThedigitalelevationmodelobtainedbytheShuttleRadarTopographyMission(SRTM)[60]with
aspatialresolutionof30arcsec(approximately1km)wasusedovertheAmazonBasin. Thesedata
resultfromtheGlobal30Arc-SecondElevation(GTOPO30)computedattheU.S.GeologicalSurvey’s
EROS Data Center (USGS) and were available at https://Ita.cr.usgs.gov/GOTO30. The elevation
andtopographicindexmapsovertheAmazonwatershedswerecomputedbyaveragingallofthe
SRTMelevationvaluespresentinanSMOSpixel(Figure2). Thesedataareusedtoflagareaswithan
elevationhigherthan500and/oratopographicindexindicatedasmoderateintheSMOSflag.
3.3. SkinTemperature
The surface skin temperature produced by the European Centre for Medium-range Weather
Forecasting(ECMWF)wasusedinthisstudy. ThisproductwasobtainedbytheSMOSL3preprocessor,
whichcomputedthespatiotemporalaverageoftheECMWFreanalysisproductsontheEASE2.0grid.
3.4. PrecipitationData
Precipitation measured by the Tropical Rainfall Measuring Mission (TRMM) were used over
theentireAmazonBasinfrom2010–2015. TRMMisajointmissionbetweenNASAandtheJapan
AerospaceExploration(JAXA)Agencyandprovidesrainfallestimatesat0.25◦×0.25◦spatialresolution.
TheTRMM-3B42product[61]usesmicrowavedatatocalibratetheinfrared-derivedestimatesand
createsestimatesthatcontainmicrowave-derivedrainfallestimateswhenandwheremicrowavedata
areavailableandthecalibratedinfraredestimateswheremicrowavedataarenotavailable.
3.5. StaticLandCoverMaps
TheInternationalGeosphere-BiosphereProgramme(IGBP),theGlobeCoverlandcoverclassification
and the ESA CCI maps were used. The IGBP land cover map was obtained using images from
the Moderate Resolution Imaging Spectroradiometer (MODIS) with a spatial resolution of 0.005◦
during2001–2012[62]. TheGlobeCoverlandsurfacemapisbasedondatafromtheAdvancedVery
HighResolutionRadiometer(AVHRR)dataandisata1-kmspatialresolution. Datawereacquired
during 1992–1993 [63]. Recently, a new release of the ESA Climate Change Initiative (CCI) Land
Covermapwasmadeavailable[64]. Thenewwater/nowaterglobalmaskat150mwasbuilton
previousachievementsusingSARsystemsandfurtherimprovedthankstoacombinationwithrecent
Landsat-derivedproducts. Thisdatasetwasbasedonacquisitionsfromtheyears200–2012. Datacan
be downloaded at: http://www.esa-landcover-cci.org/?q=node/162. For the three products, the
waterclasseswereaggregatedandre-sampledontheEASEv2.0gridtoobtainthewaterfraction(%)
presentineachcelloftheEASEv2.0grid. ThethreeproductsarestaticandarepresentedinFigure2.
Water2017,9,350 6of26
Figure2.(a)Elevation(inmeters)oftheAmazonBasinfromtheSRTMdatarescaledintheEASEv2.0
grid;spatialdistributionofthewatersurfacefrom:(b)ESACCI;(c)IGBP;(d)GlobeCover;(e)average
inundationextentfromGIEMSfrom1993–2007overtheAmazonBasin;and(f)averageinundation
extentfromSurfaceWaterMicrowaveProductSeries(SWAMPS)from2010–20overtheAmazonBasin.
3.6. DynamicsandClimatoloyofWaterFractionData
The GIEMS products provided a long-term global map of inundation at coarse resolution by
merging passive and active data (SSM/I, ERS, AVHRR) from 1993–2007 at monthly time steps.
TheGIEMS productis griddedonan equalarea gridof0.25◦ × 0.25◦ atthe Equator. Overhighly
vegetated areas, the GIEMS product has some limitations. For example, over the Amazon Basin,
theGIEMSproducthasatendencytooverestimatehigherinundationfractions[32]. Thisproductis
fullydescribedin[42]. Thesedataweresubsequentlyemployedinestimatingsurfacewaterstorage
variations in large river basins [50,65,66]. To be compared with our data, the GIEMS product was
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averagedduringthefullperiodandconsideredasastaticclimatologicalproduct. Figure2showsthe
temporalGIEMSaverageovertheAmazonBasinfrom1993–2007.
The recent Surface Water Microwave Product Series (SWAMPS) data provided daily surface
watergloballyat25-kmresolutionfrom1992–2013. Thisproductwasbasedonthecombinationof
passiveandactivemicrowavesensors(SSM/I,SSMIS,ERS,QuikSCAT,ASCAT)andvisiblesensors
(MODIS).Thedataaredescribedindetailandvalidatedin[37].Inourstudy,onlydatafrom2010–2013,
coincidingwithSMOSdataavailability,overtheAmazonBasinwereconsidered. Figure2showsthe
temporalSWAMPSaverageovertheAmazonBasinfrom2010–2013.
3.7. WaterLevelfromSatelliteAltimetry
IntheAmazonBasin,waterlevel(inmeters)timeseriesforvirtualstationscalculatedfromthe
Jason-2altimetersatelliteovertheperiod2008–2012weredownloadedfromtheHydrowebdatabase
(http://hydroweb.theia-land.fr/). TheJason-2satellitewaslaunchedon20June2008inthefollow-on
missiontotheJason-1satellite(2002–2008,CNES/NASA).ItoperatesatKu-band(13.575GHz)and
C-band(5.3GHz)andhasatimeperiodof∼9.9days[67]. Thewaterlevelcomputationmethodand
thelocationofthevirtualstationsarepresentedin[68]. Inthepresentstudy,waterleveltimeseries
over83virtualstationsfrom2010–2012inclusivewereused.
3.8. InSituRiverDischargeData
Monthlyinsitudischarge(m3/s)observationsfortheAmazonRiverwereobtainedfromthe
Obidosgaugestation(1◦00’S,55◦00’W)fortheperiodofJanuary2010–March2014. Dataareavailable
onHidroweb(http://www.hidroweb.ana.gov.fr)fromtheBrazilianwateragency.
4. Methods
Thissectiondescribestheapproachusedtoderivesurfacewaterextent(expressedin%)contained
inanSMOSpixelfromtheSMOSL3TB.Thecontextualradiativetransfermodelandtheselection
oftworeferencepointsaredescribedbelow,followedbythestatisticalmethodusedtocompareand
validatetheSWAFproduct.
4.1. ContextualRadiativeTransferModeltoRetrievetheWaterFractionunderDenseForests
4.1.1. DescriptionoftheSWAFAlgorithm
Itiswellknownthatthemicrowaveemissionishighlyimpactedbythedielectricconstant[57].
Inthepresentalgorithm,weassumedthatoverthetropicalbasins,thepixelsareonlycomposedof
twocontributions: thewaterandtheforest,suchasrepresentedinFigure3.
Figure3. (a)Pixelrepresentationwiththetwocontributions: forestandwater; (b)locationofthe
“water”,the“forest”andthemixedpixels.
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Therefore,foragivenpixel,theTBfortheentirepixelisthesumofthewatercontributionand
theforestcontribution. ThewatercontributionistheTBofthewaterweightedbythefractionofthe
pixelflooded,calledtheSurfaceWAterFraction(SWAF).Inthesameway,thecontributionoftheforest
istheTBoftheforestweightedbythepartofthepixelnotflooded(1-SWAF).TheTBofthetotalpixel
canbeexpressedas:
TB(θ,p) = SWAF(θ,p)TB(θ,p) +(1−SWAF(θ,p))TB(θ,p) (1)
tot water forest
whereθistheincidenceangle, pthepolarization(p=HorV),TB(θ,p) istheTBofthetotalpixel,
tot
TB(θ,p) theTBofthewaterandTB(θ,p) theTBoftheforest.Thefractionofthewaterpresent
water forest
inanSMOSpixeldependsonboththeincidenceangleandthepolarizationandcanbeexpressedas:
TB(θ,p) −TB(θ,p)
tot forest
SWAF(θ,p) = (2)
TB(θ,p) −TB(θ,p)
water forest
TB(θ,p) isthebrightnesstemperatureobservedbytheSMOSsatellite. Thisobservationisdone
tot
overeachpixelovertheAmazonBasin.However,theTB(θ,p) valuesareextractedoveraselected
forest
pixellocatedat2.137◦ S,60.803◦ W(Figure3)andcomposedexclusivelyofforest. TheTB(θ,p)
forest
areinterpolatedintimefromtheascendingSMOSoverpass. Inthesameway,theTB(θ,p) time
water
seriesarecomputedoveraselectedpixellocatedclosetoObidos(2.142◦ S,55.449◦ W)andcomposed
ofmorethan80%water(Figure3). Indeed,anSMOSpixelcomposedonlyoffreshwatercannotbeen
found(waterfromtheoceanissalty,whichmodifiestheemissivity). Therefore,theTB(θ,p) has
water
beencomputedastheproductoftheemissivityandtheskintemperatureprovidedfromtheECMWF.
TheKleinandSwiftmodel[69]hasbeenusedtocalculatethewateremissivity. Anaverageofthe
TB(θ,p) overthefullperiodiscomputedtoaddmorestabilitytothemodel. TheTB(θ,p) is
water water
theonlycontributionthatisconstantintimeinthemodel. Detailsaboutthevalueandthetimeseries
ofboththeTB(θ,p) andTB(θ,p) arepresentedinthenextsection.
forest water
Atthisstage,anillustrationisneededforabettercomprehensionofthealgorithm. Figure4presents
thetimeseriesoftheTB(θ,p) ,TB(θ,p) andapixelannuallyfloodedconsideredas“mixed”
water forest
i.e.,composedofbothforestandwater(TB(θ,p) )locatedinFigure3.
mixed
Figure4.TimeseriesofTBatHpolarization(left)andVpolarization(right)attwoincidenceangles
(32◦±5◦and47◦±5◦):the“water”,“forest”and“mixed”pixels.
AsshowninFigure4,theTB(θ,p)valuesoverthe“forest”,the“water”andthe“mixed”pixels
differslightlyfollowingtheanglesandthepolarization. Forallofthepolarizationandtheincidence
angles,theTB(θ,p)valuesofthe“water”pixelarethelowest,whereasthevaluesofTB(θ,p)overthe
“forest”pixelarehighest. ThevaluesofTB(θ,p)overthe“mixed”pixelareincludedbetweenthetwo
contributions. Overthesixyears,thevaluesofTB(θ,p) andTB(θ,p) areveryconstantwith
water forest
time. ThisisnotthecaseforthetimeseriesofTB(θ,p) ,whichshowsannualcyclesduetothe
mixed
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annualinundationoftheAmazonRiver. Forthe“mixed”pixel,ateachdate,Equation(2)iscomputed
toobtaintheSWAFvalue. ThismethodisgeneralizedoverallofthepixelsoftheAmazonBasin(the
localizationofthe“mixed”pixelmoved,butthewaterandforestreferencepixelsarealwaysthesame).
TheSWAF(θ,p)dataarecomputedeachdaywiththeSMOSascendingoverpassandsmoothedwith
aslidingwindowof17days.
The passive interferometric technique has some limitations over the areas with complex
topography[70].Toavoidartifacts,SWAFdataarenotcomputedoverareaswithmoderatetopography
according to the SMOS flag. Moreover, pixels with a topography index estimated as moderate or
higherintheSMOSflagovertheAmazonBasinwerenotconsidered.
4.1.2. ForestandWaterTBReference
As explained in the previous section, the time series of TB values over the “water” pixel are
averagedintimetobesureoftheTB(θ,p) stability. Resultsforeachangleandpolarizationused
water
inthealgorithmarepresentedinTable2. LowervaluesofTBareobtainedinHpolarizationthaninV
polarization. InHpolarization,themeanTBoverthe“water”pixeldecreaseswiththeincreaseofthe
incidenceangles. ThereverseisobservedinVpolarization. Thesignaloverthe“water”pixelisreally
stableduringthefullperiodasshownbythestandarddeviation,whichdoesnotexceed1K.
Tocomparethevalueobtainedoverthe“water”pixel, theTBoverthe“forest”pixelarealso
averaged. However, note that these values are not used in the SWAF algorithm. For the “forest”
pixel, the mean TB values increase for decreasing incidence angles, in both H and V polarization.
ThisbehaviorismoremarkedinHpolarization. ThestandarddeviationoftheTBoverthe“forest”
pixelishigherthanthatobservedoverthe“water”pixel,butdoesnotexceed4K.Lowerstandard
deviationsareobtainedinVpolarizationthaninHpolarization,exceptat32◦±5◦.
Table2.Averageandstandarddeviation(σ)oftheTBoverthe“water”and“forest”pixelinbothH
andVpolarizationandatfourincidenceangles(32◦±5◦,37◦±5◦,42◦±5◦,47◦±5◦).
“Water”Pixel “Forest”Pixel
IncidenceAngle H-pol V-pol H-pol V-pol
Mean(K) σ(K) Mean(K) σ(K) Mean(K) σ(K) Mean(K) σ(K)
32◦±5◦ 94.52 0.51 122.58 0.64 274.43 2.71 276.61 2.72
37◦±5◦ 89.96 0.49 128.25 0.67 272.44 2.94 276.12 2.61
42◦±5◦ 84.72 0.46 135.27 0.70 271.88 3.57 275.72 2.71
47◦±5◦ 78.78 0.43 143.93 0.74 269.71 3.22 274.26 2.51
4.2. StatisticScores’Computation
In this study, we use a common set of skill scores: (i) the coefficient of correlation (r); (ii) the
p-value; (iii)thecross-correlation; (iv)thebias; and(v)theRootMeanSquareError(RMSE)value.
ThePearsoncorrelationcoefficient(r)isusedtocomparethedynamicbehavioroftheSWAFdata(x)
withthedynamicevolutionofothervariables(y):
r = (cid:113) ∑in=1(xi−(cid:113)x)(yi−y) with x = 1 ∑n xi and y = 1 ∑n yi (3)
(∑in=1(xi−x)2 (∑in=1(yi−y)2 n i=1 n i=1
withnthenumberofelementsinthexandyseries.Associatedwithther,thep-valueisalsocomputed
forthenullhypothesis. Theauthorsconsiderthatfora p-valuehigherthan0.05,correlationvaluesare
notsignificant.
The cross-correlation measures the similarity of two time series (x and y) as a function of the
displacement of one relative to the other. In this study, the displacement corresponds to the time
(inmonths). Therefore,thecross-correlationvalueisthehighercorrelationvalueobtainedifthextime
Water2017,9,350 10of26
seriesismovedbynmonthswithrespecttotheytimeseries.
Thebiasbetweentwoseries(xandy)isdefinedas:
bias = ∑n (xi−yi) (4)
n
i=1
andtheRMSEvalueisusuallyusedtodefinetheaccuracyofthedata. Itiscomputedas:
(cid:114)
∑n (x −y )2
RMSE = i=1 i i (5)
n
5. ResultsovertheAmazonBasin
ThissectionprovidestheSMOSwaterfractionresultsandanalysiswithafocusonthecomparison
andthevalidationoftheproductusingasetofmulti-sourcedatasetsdescribedinSection3.
5.1. SpatialPatternsandTemporalDynamicsoftheSWAFMaps
Thissectiondescribedthespatialandtemporalbehaviorofthewatersurfaceextentestimatedby
SMOSforthefouranglesandthetwopolarizationspresentedinSection3.
Figure5showstheSMOSwaterfraction(SWAF)averagedoverthe2010–2015periodfortheentire
AmazonBasin. Resultsarepresentedforfouranglebins: (32◦±5◦,37◦±5◦,42◦±5◦,47◦±5◦)andthe
twopolarizations(HandVpol). FromFigure5,itcanbeseenthatindependentoftheincidenceangles
andpolarizations,themajorspatialpatternsoftheAmazonBasinareobserved: theAmazonRiverand
itstributaries,theMamorefloodplaininthesouthofthebasin,theBrancofloodplaininthenorthofthe
basinandtheBalbinalakelocatedinthenorthofManaus. Foragivenangle,thespatialdistribution
oftheSWAFiscloseinHandVpolarization. However, thepercentageofwaterfractionestimate
is slightly higher at H polarization than V polarization. The major difference concerns the spatial
distributionoftheSWAFsensedatlowincidenceangle(32◦±5◦)andathighincidenceangle(47◦±5◦).
LowincidenceanglesrevealsmallpatternsofSWAFand,inparticular,thesmallerAmazonRiverwest
affluent. Conversely,SWAFsensedwithahigherincidenceangleshowsonlythemajorstructureof
thefloodedareas(AmazonRiver,RioNegroRiver,Mamoreplain,etc.).
Figure 6 shows the temporal dynamic of the SWAF for the full basin for the eight SMOS
configurations. For all of the angles and the polarizations, water surface extent exhibits a clear
seasonalcycle. TheminimumoftheinundationisobservedinMarch,whereasthemaximumofthe
floodingisreachedduringOctober. Thisobservationisvalidforalloftheanglesandpolarization.
Both in H and V polarization, for all of the incidence angles, the temporal dynamics of the SWAF
are in good agreement, except for the incidence angle of 47◦± 5◦. SWAF sensed with the highest
incidenceangleunderestimatesthewaterfractionintheAmazonBasinwithrespecttolowerangles.
InVpolarization,onaverage,theAmazonBasinislessfloodedthaninHpolarization. Theseresults
areinaccordancewithFigure5. Duringthewetseason,almost1%oftheAmazonBasinisflooded,
whereasduringthedryseason,only0.2%ofthebasinisflooded.
Figure 7 shows the monthly spatial variability of the SWAF product over the Amazon Basin.
Themonthlyaveragehasbeencomputedduringthefullperiod(2010–2015)atV-polarizationand
at 32◦± 5◦ of incidence angle. The Amazon River, its main tributaries and the major floodplains
are well represented for all of the months. The maximum of the inundation of the Amazon River
isobservedbetweenMarchandJuly. ThespatialandtemporalvariationoftheMamorefloodplain
iswelldescribedbytheSWAFdata. ThisfloodplainisinundatedfromJanuary–June,andtheleast
floodingisobservedduringSeptemberandOctober. ThemajorAmazontributariesaremoreflooded
betweenJanuaryandMay. SimilarresultsareobservedatHpolarizationandhigherincidenceangles
(notshown).