Dynamics of the Terra Nova Bay Polynya: The potential of multi-sensor satellite observations

19 Pages·2016·4.77 MB·English

RemoteSensingofEnvironment187(2016)30–48
ContentslistsavailableatScienceDirect
Remote Sensing of Environment
journal homepage: www.elsevier.com/locate/rse
Dynamics of the Terra Nova Bay Polynya: The potential of multi-sensor
satellite observations
ThomasHollandsa,⁎,WolfgangDierkinga,b
aAlfredWegenerInstitute,HelmholtzCentreforPolarandMarineResearch,27570Bremerhaven,Germany
bArcticUniversityofNorway,9019Tromsø,Norway
a r t i c l e i n f o a b s t r a c t
Articlehistory: Researchonprocessesleadingtoformation,maintenance,anddisappearanceofpolynyasinthePolarRegions
Received27April2016 benefitssignificantlyfromtheuseofdifferenttypesofremotesensingdata.TheSentinelsoftheEuropean
Receivedinrevisedform13September2016 SpaceAgency(ESA),togetherwithothersatellitemissions,provideavarietyofdatafromdifferentpartsofthe
Accepted2October2016
electromagneticspectrum,atdifferentspatialscales,andwithdifferenttemporalresolutions.Inacasestudy
Availableonline10October2016
wedemonstratetheadvantageofmergingdatafromdifferentspaceborneinstrumentsforanalysingicecondi-
tionsandicedynamicsinandaroundthefrequentlyoccurringTerraNovaBayPolynya(TNBP)intheRossSea
Keywords:
intheAntarctic.Startingwithalistofpolynyaparametersthataretypicallyretrievedfromsatelliteimages,we
Seaice
Polynya assesstheusefulnessofdifferentsensortypes.Onregionalscales(several100km),passivemicrowaveradiom-
Multi-sensorsatelliteobservations etersprovideaviewonthemutualinfluenceofthethreeRossSeapolynyasonseaicedriftanddeformationpat-
TerraNovaBay terns.Opticalsensorswithmeter-scaleresolution,ontheotherhand,allowverylocalizedanalysesofdifferent
Microwaves polynyazones.Thecombinationofdifferentrangesoftheelectromagneticspectrumisessentialforrecognition
ThermalIR andclassificationoficetypesandstructures.Radarimagestogetherwithdatafromthermalinfraredsensors,op-
Opticalimages
eratedattenstohundredsofmetersresolution,improvetheseparationoftheoutletzoneofthepolynyafromthe
Icetypeclassification
adjacentpackice.Thedirectcomparisonofradarandpassivemicrowaveimagesrevealsthevisibilityofde-
Icedriftretrieval
formedicezoneinthelatter.AsequenceofradarimageswasemployedtoretrieveicedriftaroundtheTNB,
whichallowsanalysingthetemporalchangesofthepolynyaareaandtheextensionandstructureoftheoutlet
zoneaswellasicemovementsanddeformationthatareinfluencedbythekatabaticwinds.
©2016TheAuthors.PublishedbyElsevierInc.ThisisanopenaccessarticleundertheCCBYlicense(http://
creativecommons.org/licenses/by/4.0/).
1.Introduction productionishigh,andatmosphereCO2issequesteredintotheocean
byphysical-chemicalprocesses(Willmottetal.,2007).
Inthispaperwedealwithconcurrentmulti-sensorsatelliteobserva- Polynyasoccurinice-coveredoceanregionsintheArcticandAnt-
tionsofafrequentlyoccurringcoastalpolynyaintheTerraNovaBay, arctic,mostlyininaccessibleplaces.Hence,remotesensingprovides
whichislocatedintheRossSea/Antarctica.Themotivationistoassess anessentialtoolforgatheringdataaboutpolynyas.Onemajorquestion
thegainthatcanbeachievedintheresearchofpolynyaevolutionanddy- instudiesdealingwithpolynyadynamicsiswhichparameterscanbe
namicswhencombiningdataofESA'sdifferentSentinelsatellitemissions providedbymeansofremotesensing?Hereweaddresstheuseofremote
(e.g.,https://sentinels.esa.int),whichcarryvarioussensorssuchasimag- sensingdataforprocessstudiesandparameterretrievals,consideringvar-
ingradar,multi-spectralinstruments,andthermalradiometers.Coastal ioussatellitesensors,which(a)coverawiderangeoftheelectromagnetic
polynyasarehighlydynamicareasofopenwaterandrecentlyformed spectrumfromvisibletomicrowavefrequencies,(b)areoperatedondiffer-
icethatdevelopbetweenthecoastandtheoffshorepackice.Froma entspatialscales,and(c)deliverdataatdifferenttemporalintervals.
geoscienceandbiochemicalpointofviewtheyareoflargeinterestbe- Becauseoftheirindependencefromcloudcoverageandfrequent
cause(a)theyarelocationsofstrongheatandmoistureexchangebe- dataacquisitionsoveragivenarea,passivemicrowaveradiometers
tweenatmosphereandocean;(b)coolingeffectsandtheformationof (PMR)arepreferredsatellitesensorsformonitoringpolynyas(e.g.,
frazilicecauselocaldensitychangesandmixingofthewatervolume Kernetal.,2007;Kern,2009).Methodshavebeendevelopedtoesti-
below, which are processes that may affect ocean stratification on matethepolynyaarea(e.g.,MarkusandBurns,1995;Hunewinkelet
localandevenregionalscales;(c)indaylightthebiologicalprimary al., 1998), and the thickness of thin ice that forms in the polynya
(Martinetal.,2004;Martinetal.,2005).Thepolynyaextentisdirectly
determinedfrommeasuredmicrowaveintensityratios,usingthresh-
⁎ Correspondingauthor.
oldsforseparatingopenwaterandthinicefromthickeroffshorepack
E-mailaddresses:[email protected](T.Hollands),[email protected]
(W.Dierking). iceandlandoriceshelves(e.g.,Willmesetal.,2010).Theicethickness
http://dx.doi.org/10.1016/j.rse.2016.10.003
0034-4257/©2016TheAuthors.PublishedbyElsevierInc.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/).
T.Hollands,W.Dierking/RemoteSensingofEnvironment187(2016)30–48 31
hastobecalculatedindirectlyusingempiricalrelationshipsthattypical- satellites,significantlyreducethetemporalgapsbetweendataacquisi-
lyareestablishedbycomparisonwithcomplementarydata,e.g.,from tions.IthasalsotobenotedthattheswathwidthsofSARsystemsare
infrared sensors. Considering the small width of several polynyas limitedbetweenafewtensand500km.
(here,“small”meansb1–10km),inparticularintheirearlyevolution Inthestudiesmentionedaboveseveralproblemswererecognized
phase,thedrawbackofsatellitePMRsistheircoarsespatialresolution regardinglimitationsofthesensor'scapacitytoprovidecertaininfor-
(presentlybetween5and40km,dependentoninstrument,frequency, mationandparameters.Forrealisticsimulationof,e.g.,polynyawidth
andimagingmode).Thisleadstocontaminationeffectsatthepolynya and ice production rate, the extent of the open water and thin ice
edges(e.g.,signalmixturesoflandandopenwaterorthinandpack areasaswellasthethinicethicknessdistributionhavetobedetermined
ice).TheadvantageofPMRsisthatvariationsofpolynyaextentand withhighaccuracy.Thisisdifficulttoachieveatcoarsespatialresolu-
thin icethicknesscan be continuously monitored in thelong term tionand/orinsufficientsignalcontrastbetweenopenwateranddiffer-
withonlysmalltemporalgapsofroughly24h,butoftenless.Recentsat- enticetypes.Oneexampleisthemisinterpretation ofPMRsignals
ellite-bornePMRsaretheAdvancedMicrowaveScanningRadiometer sinceiceshelves,icebergs,fasticeandthinicehavesimilarmicrowave
(AMSR-2)ontheJapaneseGCOM-W1satelliteandtheSpecialSensor characteristics,andtheirlocationsatagiventestsitechangecontinu-
MicrowaveImagerSounder(SSMIS)carriedonboardthespaceborne ouslyduetocalving,breakupanddrifting.TIR-dataareoftenconsider-
platformsoftheDefenseMeteorologicalSatelliteProgram(DMSP).Al- ablyinfluencedbyatmosphericconditions.Thephysicalpropertiesof
thoughoperatedatsimilarcoverageandspatialresolution,datafrom seaicefoundinoneregionmaydifferfromthoseofseaiceinotherre-
scatterometerssuchasQuikSCAThaveonlybeenusedoccasionally gions;henceautomatedalgorithmsforparameterretrievalfromsatel-
(e.g.,Willmesetal.,2010).Thecoarse-resolutionradarbackscattering litedata,developedforonelocaltestsite,cannotbeappliedglobally
coefficientsobtainedfromQuikSCATaremoredifficulttointerpretin ineverycase.Inthesingle-frequencysingle-polarizationSARimages
termsofpolynyaextentandicethicknesssincetheyarealsoverysensi- usedhitherto,thehighlyvariablesignalsbackscatteredfromthinice
tivetoicesurfaceroughnessvariations.Hence,QuikSCATdataareof (bare,rafted,coveredwithfrostflowers)complicateitsautomatedsep-
minorimportanceforthedevelopmentofrobustretrievalalgorithms. arationfromopenwaterandpackice.
Despitetheirsensitivitytothepresenceofclouds,theusefulnessof Basedonthereferencescitedabove,theconclusionsregardingfu-
thermalinfrared(TIR)imagesfortheretrievalofpolynyasizeandthin turesatelliteobservationstrategiesoverpolynyasare:
ice thickness has also been demonstrated in several studies (e.g.,
Willmes et al., 2010;Krumpen et al.,2011; Ciappa et al.,2012).In – Concurrentdataacquisitionsusingcomplementarysensors(optical,
mapsofsurfacetemperature,coldiceandwarmeropen watercan thermal,radar)improvethesegmentationandclassificationofdif-
oftenbeeasilyseparated.Fortheestimationofthinicethickness,ather- ferentzonesinandoffshorefromapolynya.
modynamicicegrowthmodelisemployedforwhichthesurfacetem- – Regionalandlocalinteractionmechanismsatsitesoffrequentpo-
peraturehastobeprovidedasinputparameter(e.g.,YuandLindsay, lynyaoccurrencesrequiresatelliteimageswithdifferentspatialcov-
1996;Krumpenetal.,2011).Majordifferencesbetweenthetempera- erageandresolution(e.g.,PMRversusSAR).
tureintheTIR-andthemicrowave-regimearisebecausetheformeris – Ahigherdataacquisitionfrequencyisneededforsensorsproviding
influencedbyaverythinskinlayer(forsalinewater,e.g.,thethickness imageswithhighspatialresolutioninordertoresolvethedynamics
variesbetween1μmand1mmforwavelengthsbetween2and16μm), oflocalprocesses.
whereasthelatterisdeterminedbylayersof0.04–0.5cminthickness
(frequenciesbetween1and20GHz).OneadvantageofTIR-datais
thattheyareavailableathighspatialresolution,typicallyontheorder Inthisstudy,weanalyseexamplesdemonstratingthepotentialofthe
of1kmandbetter,andthattheyareoperatedatlargeswathwidths, mostrecentandfuturesatellitemissionsforimprovedmonitoringofpo-
whichdecreasesthetimebetweenacquisitionsoveragivenpolynya lynyasandfortheretrievalofparameterscharacterizingpolynyaevolution.
(e.g., the swath width of the Moderate Resolution Imaging Otherexamplesfortheuseofdatafrommultiplesensorsforstudiesof
Spectroradiometer MODIS is 2330 km compared to 1445 km for polynyadynamicsareprovided,e.g.,inCiappaandPietranera(2013),
AMSR-E). Willmesetal.(2010),orDruckeretal.(2003).Theyfocusinparticular
AscomplementarydatasourcetoTIR-imaging,syntheticaperture ontheretrievalofgeophysicalparameters.Inourstudy,wealsopayat-
radar(SAR)sensorsprovideanevenbetterspatialresolutionbetween tention on the usefulness of image processing techniques that can be
b10mand150m,dependentonradarfrequencyandimagingmode. regardedaspreparatorysteptoincreasetherobustnessandreliabilityofre-
Sincetheyareoperatedatmicrowavefrequencies,theiradvantageis trievalalgorithms.InSection2wegiveabriefoverviewaboutpolynya
thattheydeliverdataindependentofcloudcoverageanddaylight–in parametersthatcanberetrievedfromremotesensingdata.Weintro-
contrasttooptical(“visiblerange”)sensorswhichotherwisecanbeop- ducethebackgroundabouthowthoseparametershavebeenusedto
eratedatcomparablespatialresolutions.Atpresent,EarthObservation analysepolynyaprocesses,tosimulatetheiropeningandclosing,and
(EO)satelliteSARsystemsoperateatfrequenciesbetween1.2GHz(L- toestimateiceproductionrates.Themotivationistoprovidethereader,
band)and10GHz(X-band),andusedifferentpolarizationcombina- whoisnotfamiliarwiththerequirementsofpolynyaresearch,withthe
tions(mostlyoflineartype:HH,HV,VH,VV,withH-horizontal,V-ver- informationnecessarytoassesstheprosandconsofdifferentsatellite
tical,thefirstletterindicatingthetransmitted,thesecondthereceived sensorsandretrievalmethods.Section3dealswiththelocalandregion-
polarization).SARimageshavebeenappliedforvalidatingalgorithms alenvironmentalconditionsinandaroundourtestsite,theTerraNova
derivedforPMR-andTIR-sensors(e.g.,Willmesetal.,2010;Ciappa Bay,andSection4providesinformationaboutthesatellitedataweused
andPietranera,2013;MorelliandParmiggiani,2013).Alsoopticalim- forouranalyses.Wedemonstratethegainofcombiningdifferentimage
agesoraerialphotographyareusefulinthiscontext(e.g.,Willmeset typesforaqualitativeanalysisoficeconditionsinSection5.Multi-sen-
al.,2010).Haarpainteretal.(2001),whodevelopedamodelforsimulat- soriceclassificationisdiscussedinSection6,andthedeterminationof
ingtheevolutionoftheStorfjordenpolynya(Svalbard),tooktimeseries icedriftanddeformationpatternsisdealtwithinSection7.Atthe
ofSARimagesacquiredbytheEuropeanRemoteSensing(ERS-2)satel- endweprovideadiscussionofadditionalaspectstobeconsidered,
liteformanualclassificationofseaicetypes.Besidesvariationsofpolyn- followedbytheconclusions.
yashapeandsize,alsoicedriftcanbedeterminedfromsequencesof
opticalorSARimages(e.g.,Druckeretal.,2003).Themajorproblem 2.Polynyaparametersobtainedfromremotesensing
withpastsatelliteSARmissionswasthatdataacquisitionsoverpolyna
siteswereonlyirregularandwithlargetimegaps.Constellationmis- Inthissectionweprovideexamplesofpolynyaparametersthathave
sions such as Sentinel-1 and Sentinel-2, consisting of two or more been directly retrievedor indirectly determined from observations
32 T.Hollands,W.Dierking/RemoteSensingofEnvironment187(2016)30–48
usingremotesensingtechniques.Thepurposeofretrievalsistoprovide between different ice growth stages or ice types. Williams et al.
parametersfordescribingthelong-termdynamics,climatology,andas- (2007),theirFig.1)distinguishfourdifferentmeasuresofthepolynya
sociatedchangesofapolynya(e.g.,Kern,2009),tocompareobserva- width,namely(1)theregionofopenwater,(2)theregionofopen
tions with model simulations (e.g., Hollands et al., 2013) or to andpartiallyopenwater,(3)the“practicalwidth”whichencompasses
determineparametersthatareneededforrunningthemodels(e.g., allicecoverwhichistoothinforsafetravel,andfinally(4)the“full
Druckeretal.,2003). width”whichincludesallicethatisthinnerthantheoffshorepackice.
AccordingtoWillmottetal.(2007)onedistinguishestwomajorap- The degree of details that can be achieved in separating different
proaches for modelling the evolution of a polynya. These are flux zonesdependsnotonlyonspatialresolutionbutinthecaseofSAR,
modelsandgeneralcirculationmodels.Theformerarebasedontheas- e.g.,alsoonthefrequencyandpolarization.
sumptionthatawind-generatedcoastalpolynyaattainsamaximum
size,whichisdeterminedbythebalancebetweentheiceproduction
intheopenwaterzoneandthefluxoftheoffshorepackiceoutofthe 2.2.Polynyaandcoastlineshape
polynya.Theadvantageoffluxmodelsisthatitiseasiertoidentifysin-
gledominantprocessesthatinfluencepolynyadevelopment.However, Amorecomprehensivelookatpolynyaevolutionrequiresconsider-
itisextremelydifficulttoincorporateallpossiblefeedbackmechanisms, ingtheshapeofthecoastlineinfrontofwhichthepolynyaislocated.
whicharemoreorlesswellintegratedinoceancirculationmodelsin- Besides of the orientation of single coastline segments, the steady
cludingdynamic-thermodynamicseaicecomponents(e.g.,Hollands stateshapeofacoastalpolynya(morepreciselyofitsoffshoreopen-
etal.,2013).Iceconcentration,thickness,anddriftvelocityareamong wateredge)isinfluencedbymagnitudeuanddirectionαofthemove-
theprognosticvariablesofsuchmodels.Thepolynyaisthendefined mentoffree-driftingfrazilice,andbymagnitudeUanddirectionθofthe
asanareaforwhichtheiceconcentrationisbelowacertainthreshold consolidatedthiniceinthepolynya(e.g.,Darbyetal.,1995;Krumpenet
(Willmottetal.,2007). al.,2011,theirFig.2).Darbyetal.defineanalong-shorelengthscaleL
C
onwhichthepolynyaadjuststoitssteady-statewidth(seetheirFig.2
2.1.Polynyaarea explainingthepracticaldeterminationofL ).Thisparameterdepends
C
bothonθandα.CoastlinevariationsthatareshorterthanL arenotmir-
C
Themostobviousvariablethatcanberetrievedfromsatelliteimag- roredintheshapeoftheopen-wateredge.ThemagnitudeofL canvary
C
eryistheextentofthepolynya.Besidesbeinganessentialparameterre- betweenzeroandtensofkilometres(Darbyetal.,1995).Hencesatellite
latedtopolynyaformation,evolutionanddecay,thisinformationis imageswithspatialresolutionsontheorderof100mandlessandwith
neededtovalidatesimulationsbothwithfluxandcirculationmodels, aclearaccentuationofthecoastlineandtheopen-wateredgeareneed-
andfortuningcoupledpolynya-atmosphericmodelsornumericalsim- ed.Darbyetal.(1995)presentedsimulationsfortheTerraNovaBayPo-
ulationsofthethermohalinecirculationinducedbypolynyas(Ciappaet lynyawiththeactualcoastlinegeometry,whichrevealedarealistic
al.,2012).Withknowledgeofthepolynyaextentitisinprinciplepossi- shapeoftheopen-wateredge.
bletoquantifyheatlosses,newiceproductionandsaltfluxes.When
usingdatafromPMR,thesimplestmethodistodefineathresholdfor
theiceconcentration,belowwhichthecorrespondingresolutioncell
isregardedpartofapolynya(e.g.,MorelliandParmiggiani,2013).
Thiscorrespondstothedelimitationofpolynyasinsimulationscarried
outwithcirculationmodels(seeabove).Anotherpossibilityistoem-
ploy the polynya signature simulation method (PSSM) to separate
thinice,openwater,andthickice(MarkusandBurns,1995;Kernet
al.,2007;Kern,2009;Willmesetal.,2011;Adamsetal.,2013).Thepo-
lynyaextentisthenthesumofthethiniceandopenwaterareas.Kern
etal.(2007)comparedresultsobtainedfromthePSSMappliedtoSSMI
data(89GHzand37GHzchannelswithspatialresolutionsof15km×
13kmintheformerand37km×28kminthelattercase)withicecon-
centrationestimatesfromAMSR-E89GHzdata(spatialresolution6
km×4km).TheyfoundthatAMSR-Eiceconcentrationsof25–40%
correspondedtoareasclassifiedasopenwaterusingthePSSM,andcon-
centrationsof65–80%werefoundforareasofPSSMclass“thinice”.
(Notethatthetheoreticalconcentrationsare0and100%,respectively).
Kernetal.(2007)explainedthiswiththedifferentfrequenciesandspa-
tialresolutionsusedfortheestimationandclassification.Theresult
demonstratestheneedofachievingmoredetailedspatialinformation
abouttheiceconditionsinandaroundapolynya.
Becauseoftheirhigh-resolutionmodes,SAR,opticalandTIRsensors
enablemoresubtleanalysesoficeconditionsespeciallyforsmallerpo-
lynyas(widthsb10km)andtheseparationofopenwater,fraziland
greaseice,closedthinice,andtheoffshorepackice(Dokkenetal.,
2002;Willmesetal.,2010;CiappaandPietranera,2013;Hollandset
al.,2013).Examplesfromourstudywillbepresentedbelow.Inthe
caseofSARimages,visualclassificationisachievedbyconsideringthe
backscattered radarintensityandimagetexture,whicharerelated
mainlytotheicesurfacestructureonscalesbetweenafewmillimeters
tocentimeters(“small-scale”surfaceroughness)and10sofmeters
(e.g.,iceridges,raftingzones).Inthecaseoflowsalinityice,theinflu-
enceofvolumeinclusionsandstructurehasalsotobetakenintoac-
Fig.1.TNBPandadjacentregions,showingmajorseaicezoneswithdominantdrift
count. Temperature variations are related to thickness changes directionsandlocationsoftheoutletglaciersmentionedinthetext.
T.Hollands,W.Dierking/RemoteSensingofEnvironment187(2016)30–48 33
Fig.2.SatelliteimagesshowingthelocalenvironmentaroundtheTNBP.Therespectivesensorwithacquisitiondateandtimingisgivenintheupperrightcornerofeachimage,the
coverageisabout150km×150km.
2.3.Iceproductionrate inthemeteorologicaldata(Willmesetal.,2010).WhenusingPMR,the
distinctionofdifferenticethicknessclassesishamperedduetothe
Fromageophysicalpointofview,iceproductionratesinpolynyas coarsespatialresolutionofthosesensors(Willmesetal.,2010,their
arethemostinterestingparametersincetheyhavetobetakenintoac- Fig.7).Thevalidationofthicknessretrievalsoverthinpolynyaiceisex-
countforestimatingvariationsbetweeniceproductioninagivenarea tremelydifficult,evenwithairborneinstrumentssuchaselectromag-
andicetransportoutofthisarea.Theestimationoficeformationre- netic probes since their measurement uncertainty is too large
quirestodeterminethesizeofthepolynya(or–better–ofthedifferent (Willmesetal.,2010).Insummary,theestimationoficethicknessand
waterandicezonesinthepolynya,seeabove)andtoretrievetheice iceproductionratesfromremotesensingdataandtheirvalidationre-
thickness.Thesurfaceheatlossandicevolumeproductioncanbecalcu- mainverychallenging.
latedifcorrespondingmeteorologicaldataareavailable(e.g.,Willmeset
al.,2010,Krumpenetal.,2011).Underlyingassumptionsarethatthe 2.4.Iceformation,drift,anddeformation
sumofradiativeandturbulentfluxesattheicesurfaceisbalancedby
theconductiveheatfluxintheice,thatallheatlossatthesurfaceis Asmentionedabove,driftvelocityanddirectionoffrazilandconsol-
usedforiceformation,andthattheoceanicheatfluxissmall.Thelatter idatedpolynyaicearerequiredinfluxmodels(e.g.,Darbyetal.,1995;
isoftenvalidinpolynyasbecausethewholewatercolumnisclosetothe Krumpenetal.,2011).Theicedriftisaprognosticvariableofcirculation
freezingpointduringwinterinthecontinentalshelfregion(Tamuraet models(e.g.,Hollandsetal.,2013).Whilethedriftoffraziliceinthe
al.,2008).TheuseofTIRandPMRforicethicknessretrievalandtheir openwaterareaofpolynyascannotberetrievedwithrecentsatellite
prosandconswerementionedintheintroductionabove.UsingTIR, systems,thisisoftenpossibleforconsolidatedpolynyaandoffshore
goodresultscanbeobtainedforicethicknessesbelow0.5m,ifthever- packice.Differentretrievalalgorithmsareavailablethatcanbeapplied
ticaltemperatureprofileintheiceislinearandtheiceissnow-free onsequencesofsatelliteimages(SAR,TIR,VIS).Oneofthemostpopular
(Druckeretal.,2003).Alargesourceoferrorsarisesfromuncertainties approaches is a multi-scale multi-resolution pattern matching
34 T.Hollands,W.Dierking/RemoteSensingofEnvironment187(2016)30–48
algorithmrequiringanimagepairasinput,whichwasusedbyHollands complexmixtureofdifferentbranchesfromLarsen,Reeves,Priestly
etal.(2013)andresultsinacceptableaccuraciesof3to5pixelsforthe andDavidGlacier.TheDrygalskiIceTongue,whichisanextensionof
retrieveddisplacementvectorsduringfreezingconditions(Hollands theDavidGlacier,blocks northwarddriftingseaicethatoriginates
and Dierking, 2011). However, the approach requires recognizable fromMcMurdoSoundandthecoastofRossIsland.Hence,thelength
radarsignaturevariationsondifferentspatialscalesthatcanbefound and orientation of the ice tongue influences shape and size of the
inboththefirstandsecondimage(seenextparagraph).Therelatively TNBP.Ruscianoetal.(2013)separatetwodifferentstagesofpolyniaac-
largetemporalgapbetweenbothimages(rarelylessthanoneday tivities:increasinganddecreasingiceproductioninMarch–Juneand
withmostrecentSARsystemswhenoperatedinhigh-resolutionimag- December–February,respectively,andaperiodofmaximumefficiency
ingmode)isoftenaproblemconsideringthefastchangesoficecondi- inseaiceproductionfromJulytoNovember.Theopenwaterareaof
tionsoffshorefromevolvingpolynyas.Thetemporalacquisitionrateis theTNBPduringwintertimeestimatedfromTIR-data rangesfrom
higherwithrecentlylaunchedorplannedsatelliteconstellations(such 1000to1300km2(butpeaksof8500km2wereobserved).Thecom-
asCosmoSkymed,Sentinel-1,RadarsatConstellation). binedopenwaterandthiniceareaobtainedfromPMRislargerbyafac-
IntheiranalysisofthePeasemodel,e.g.,Druckeretal.(2003)em- tor of about 2 (Ciappa et al., 2012). Ciappa and Pietranera (2013)
ploySARdatatodeterminetheadvectionofthepackiceawayfrom characterizethedifferentphasesofpolynyaevolution.Whentheiceis
thepolynyaregion.AlsousingSARimagery,Hollandsetal.(2013)re- pushedawayfromthecoast(openingphase),frazilicethatisorganized
trievedthedriftvelocitiesofconsolidatedthiniceandofpackicein asicestreaksparalleltothewinddirectionoccursintheopenwater
theRonnePolynyaarealocatedintheWeddellSea.Theynotethatthe area. Using high-resolution SAR imagery, Ciappa and Pietranera
accuracydependscriticallyonthepresenceofrecognizableicestruc- (2013)measureddistancesbetweenthestreaksrangingfrom300to
tures,whichmaybeburiedunderwetsnowduringmeltingconditions, 800m.Theyalsorecognizedwavesoflengthsbetween30and70m,
ormaychangeveryquicklybecauseofstrongmovementsanddeforma- whichmodulatedthestreaksandthefrazilandgreaseiceaccumulated
tionsofnewlyformediceinthepolynya.Inbothcases,theretrievedice attheoffshorepolynyamargin.Whenthewindspeeddecreases,the
velocitiesarelessreliablethanfortheoffshorepackiceunderfreezing gapsbetweenthestreaksbecomesmaller.Progressivefreezingfrom
conditions. theoffshoreedgeofthepolynyatothecoastcharacterizestheclosing
Gallée(1997)usedacoupledatmosphere–polynyamodeltostudy phase.ThissuggeststhatthedetectionofthepolynyaareawithIR-sen-
theair-seainteractionsovertheTerraNovaBayPolynyainwinter.He sorsorPMRismorereliableduringthegrowingphase,whenlargeareas
pointedoutthatthereisaneedforhavingabetterknowledgeoffrazil ofunfrozenwaterarepresent,thanduringtheclosingphasewhenlarge
iceevolution(consolidationoffrazilintopancakes,frazilherding).In areasofthinsea-iceprevail.
hismodelheusedasimplecavitatingfluidbehaviourfortheseaicerhe- Gallée(1997)statesthatthepolynyasizeispoorlycorrelatedwith
ologyinthepolynya,whichmeansthatthe“modelice”revealsdiver- thelarge-scalewindforcing,suggestingthatitsmainforcingisthekat-
gence or shear but does resist convergence. Hence the effects of abaticwind.Regionaliceconditionsareinfluencedbywindsblowing
ridgingandraftingarenotincludedinthesimulations. acrosstheRossIceShelf(VanWoert,1999).Gallée(1997)foundthat
theopenwarmwaterareasignificantlyinfluencestheatmosphericcir-
3.TerraNovaBay culationinthecoastalzone.MorelliandParmiggiani(2013),whocom-
binedsatelliteobservationsandmodelsimulations,obtainedasimilar
3.1.Largescalesituation result,whichrevealsthattheheatingoftheairassociatedwiththepo-
lynyaincreasesthespeedofthekatabaticwindoverthepolynyaafterit
IntheAntarcticOcean,theRossSeaPolynya(RSP)isthelargestpo- hascrossedthecoastline.This,inturn,isinlinewiththestudybyVan
lynyawithawinterareaofarond20,000km2(BarberandMassom, Woert(1999)whoshowedthatchangesofsensibleandlong-wave
2007,theirTable23).Twosmallerpolynasarelocatedinthewestern heatfluxescanexplainuptoabout50%oftheobservedvariancein
partoftheRossSea,oneinTerraNovaBay(theTerraNovaBayPolynya, thepolynyaopenwaterfraction.Differentstagesofcloudinesscanex-
inthefollowingdenotedasTNBP,withameanareaof1300km2and plainanother8–10%ofthevariance.Ciappaetal.(2012)foundmost
maxima up to 5000 km2 (Van Woert, 1999)) and the other in the cloudyperiodsduringphasesoflimitedpolynyaactivitiesatlowwind
McMurdo Sound (MSP, with an area about 2/3 of TNBP, see Kern conditions.Fluctuationsoftheopenwaterareacanoccurwithinvery
(2009),Table2).Tamuraetal.(2008)foundthatthehighesticeproduc- shorttimeintervalsintheorderofhours,andpeaksofarealgrowing
tionoftheAntarcticOceanoccursintheRossSea.Fromthe1990stothe rate may exceed 300 km2 during extreme wind gusts (Ciappa and
2000s,theiceproduction(involume)decreasedbyabout30%duetoa Pietranera,2013).
decreaseinpolynyaareasandduetoatmosphericwarming.Tamuraet TIRimagescanbeusedtoanalysethevariabilityofstrongsurface
al.(2008)supposethatthenegativetrendiniceproductionisonerea- winds(Bromwich,1989)oversnow-coveredregions.Theimagesmay
sonfortherecentfresheningoftheAntarcticBottomWater.Druckeret revealdelimitedwarmerareasindicatingthepresenceofstrongkata-
al.(2011)reportthattheiceproductionintheRossSeaapproximately baticairstreams.Thereasonwhythoseairstreamsmanifestthemselves
equalstheiceexport.Theformerwasestimatedfrom36GHzAMSR-E inthethermalsignatureistheintenseverticalmixingoftheairand
data,thelatterwascalculatedforfluxgatesparalleltothe1000-m driftingsnow.InlandfromthecoastofTerraNovaBayCiappaetal.
isobaths,usingmapsofdailyicemotionretrievedfrom89GHzAMSR- (2012)recognizedthesignaturesofkatabaticwindsdescendingfrom
Edata.From2003to2008,theaverageannualiceproductionofthe theglaciersflowingtowardsthebay.Theyfoundthatwhenthepolynya
RSPamountedto510–730km3,oftheTNBP70–111km3,andofthe isopen,airflowfromtheReevesGlacierisalwaysstrong.Theopeningis
MSP11–80km3(numbersaregivenperfreezingseason).Thetotalice largewhensurfacewindsoftheotherthreeglacierscontributetothe
production in these three polynyas accounts for 20% to 50% of the katabaticwindfloworiginatingfromtheReevesGlacier.Becauseof
totalseaicevolumeintheRossSea. thiscomplexairflowstructure,itisdifficulttoexplaintheTNBPevolu-
tion using a one-dimensional flux model, instead, a 2-dimensional
3.2.LocalconditionsinTerraNovaBay modelisneeded.
ThecoastalpolynyainTerraNovaBayisorientedineast-westdirec- 4.Data
tion.ItisboundedbytheDrygalskiIceTongueinthesouthandbythe
CampbellIceTongueinthenorth(seeFig.1).Thepolynyaisgenerated ThedatathatweusedforouranalysiswereacquiredovertheTNBP
andmaintainedbypersistentkatabaticwindswithspeedsofuptoN inSeptember2009(Table1).Thedatasetconsistsofhigh-andcoarse-
40m/s,whichpushthebayiceoffshore.Thekatabaticwindfieldisa resolution imagery. The former includes SAR data (ALOS PALSAR,
T.Hollands,W.Dierking/RemoteSensingofEnvironment187(2016)30–48 35
EnvisatASAR),opticaldatafromEO1-ALI(EarthObserving1Mission – separationoficetypesandstructuresinandadjacenttothepolynya
AdvancedLandImager,inthefollowing‘optical’isusedsynonymously on localscale employingnear-simultaneously acquiredimagery
with‘visiblerange’),MODISopticalandnear-infrared(NIR),AVHRR withhighspatialresolutionfromSAR,TIR,andopticalsensors,
(TIR),andAATSRthermal(TIR)images.Coarse-resolutionproducts – interpretationofregionalicecovercharacteristicsafew100km
arefromapassivemicrowaveradiometer(PMR),namelyAMSR-E.De- aroundthepolynya,basedonPMR-data,andlinkinglocaltoregional
tailsaboutsensorsandimagesareprovidedinTable2.Inparticular conditions,
forSeptember10,2009,favouriteconditionsforinter-comparisonsof – monitoringofthepolynyaevolutionandofvariationsofpolynyapa-
differentsensorsaregivensincePALSAR,ASAR,AATSR,MODIS,and rameterssuchasitssize,shape,orrelativearealfractionsofdifferent
EO1-ALIdatawereallacquiredwithinashorttemporalwindow. zones(i.e.openwater,fraziliceherding,accumulatedand/orconsol-
TheASARandPALSARdataweregeocodedandcalibratedusingthe idatedice).
commercial SARscape software. The high-resolution data were re-
projectedtoanAntarcticPolarStereographicProjectionwithaCentral
Longitudeof180°EandaStandardLatitudeof71°S.Ifcomparisonsof Whencombiningdatafromdifferentrangesoftheelectromagnetic
fixedtargets(e.g.,coastlinesandislands)revealedslightdiscrepancies, spectrumonehastoconsiderthesensitivitiesoftherespectivemea-
therespectiveimageswereco-registeredtothecorrespondingEnvisat suredquantity(brightnesstemperatureT ,backscatteringcoefficient
B
ASARscene.Allimageswereresampledtoaresolutionof100m× σ0,temperatureT,reflectivityR)tothephysicalpropertiesoftheim-
100m. agedarea.Sensitivitiestosensorconfigurations,suchase.g.,frequency,
BothAVHRRandAATSRseasurfacetemperature(SST)temperatures polarization,ordirectionofmeasurement,havetobetakenintoaccount
are based on the combination of the 11 μm- and 12 μm-band. The aswell.
AVHRRSSTwasevaluatedfollowingKeyetal.,1997.Forthecalculation
of the AATSR SST the ESA VISAT Software was used. In contrast to 5.1.Localanalysis
AVHRR,the AATSRsensoracquiresoneimagein forwarddirection
(lookingslantedthroughtheatmosphere)andasecondonenadir- High-resolutionSARandAVHRRimagesare,e.g.,usedinthestudy
looking,inordertocompensateforatmosphericeffectsinthecalcula- byMassometal.(2001)foranalysingtheiceconditionsaroundthe
tionsoftheSST(ESA,2002;Corlettetal.,2006).Thecomparisonof MertzGlacierPolynya.InFig.2,multi-sensordataacquiredoverthe
theAVHRRandAATSRdatashowedvariabledifferencesonapoint-to- TerraNovaBayareshown:A–EnvisatASAR,B–ALOSPALSAR,C–
pointscale(whichispartlycausedbyicemovementsinthetimeinter- MODIS Band1,andD–EnvisatAATSR,covering an area of 22,500
valof2hbetweendataacquisitions).Onaveragethedatacompared km2, with spatial resolutions between 100 m (PALSAR) and 1 km
well,allowingsolidconclusionsintheframeofouranalysis. (AATSR).Thetemporaldifferencebetweenthefirstandlastdatatake
Thehigh-resolutionimagesweresupplementedbyAMSR-E89GHz is1:39h.Majorstructuresoftheicecovercanbeidentifiedintheim-
datafortheperiodSeptember5–12,bothatoriginalandatspatiallyen- ages,e.g.,thecoastline,thelandice,theicetongue,theopenwater
hancedresolution(thedatasetthatweusedisdescribedinLongand areaofthepolynya,andsinglesmallerandlargericefloes(compare
Stroeve,2011).Incontrasttothespatialresolutionof5.4×5.4km/ alsotoFig.1).Theiceformedinthepolynyaisfirstexportedtowards
pixelintheoriginal89GHzdata,thespatiallyenhancedversionhasa eastbutthenisdeflectednortheast.Wetermthisbandoficeexport
resolutionof2.2×2.2km.Inenhancementalgorithms,theantennapat- “outletzone”.Inthevisiblerange(MODISBand1),majorpartsofthe
ternisde-convolvedforreconstructionoftheunderlyingbrightness outletzonearecoveredbyclouds.Icefloesappearlightgrey,andthin-
temperaturedistributiononahigher-resolutiongrid.Resolution-en- ner ice areas dark grey. In particular the belt starting south of the
hancementtechniques,however,provideimprovedresolutionatthe Drygalskiicetongueandthengraduallyturningtowardsnortheastis
expenseofanincreasednoiselevel(LongandDaum,1998).Neverthe- characterizedbyheavilybrokenice,consistingofsmallerandlarger
less,theenhancedimagesallowedtheidentificationofmoredetailsin thickerfloes(lightgrey)andthinice.Wetermthiszone“deformation
theareaaroundthepolynya.Weusethebrightnesstemperaturedata belt”.Itiscarriedawaytotheeastduetoshearingforcesimposedby
asobtained,withoutconsideringanyatmosphericeffectsthatinfluence theiceintheoutletzoneoftheTNBPandpushedtothenorthbythe
highfrequencybandsliketheemployed89GHzchannel. iceoriginatingfromtheRSPandtheMSP(compareFig.1).Smaller
clouds are visible over different parts of the scene. The dark area
abovetheDrygalskitongueistheopenwaterzoneoftheTNBP,with
5.Qualitativeanalysesofpolynyaconditions streaksoffraziliceweaklyshowinguptowardstheseaiceedgetoits
right.Thethermalimage(Fig.2D)revealsacoarserspatialresolution
Withourdatasetitispossibletocarryoutqualitativeanalysesin- butresemblestheMODISsceneinmajorparts.Thecloudcovercan
cludingthefollowingelements: lessclearlybedistinguished.Mostpresumablythedistributionofclouds
changedinthe90minbetweentheacquisitionsofMODISandAATSR
data.Theopenwaterzoneappearsverybright.Itistheareawiththe
Table1
SatelliteimagesusedforthecasestudypresentedinSections5–7.Sensorabbreviations highesttemperatureintheregion.Thestreaksoffrazilicearehardlyvis-
areexplainedinthetext. ible,whichmaybepartlyattributabletothecoarserspatialresolutionof
thethermaldata,butmostprobablyindicatesthatthetemperaturedif-
Date Time(UTC) Sensor
ferencebetweentheformingicecrystalsandthewateratthesurfaceis
06.09.2009 19:41:47 EnvisatASARWSScene verylow.TheiceintheoutletzoneoftheTNBPiswarmerthaninthe
07.09.2009 19:10:28 EnvisatASARWSScene
adjacenticezones,whichmeansthatitisrelativelythin.Alsothethin
07.09.2009 21:19:36 EO1ALI
iceareasinthedeformationbeltappearbright.Comparedtothereflec-
09.09.2009 19:47:34 EnvisatASARWSScene
10.09.2009 12:39:57 EnvisatAATSRScene tance(Fig.2C)orradarbrightness(Fig.2,AandB),thetemperaturevar-
10.09.2009 19:16:19 EnvisatASARWSScene iationsseemtobemostsuitablefordelimitingtheoutletzone.Inthe
10.09.2009 19.39:20 ALOSPALSARScanSAR SARimages,alsotheiceunderthecloud-coveredareasisvisible.Thein-
10.09.2009 20:35:00 MODIS
formationcontentoftheC-andL-bandimageissimilar,buttheice
10.09.2009 20:55:37 EnvisatAATSRScene
11.09.2009 20:19:36 ALOSPalSARScanSAR coverstructureismorepronouncedatL-band.Thisismoreaconse-
11.09.2009 20:24:20 EnvisatAATSRScene quenceofthesignaturecontrastsofdifferenticetypesandstructures
Daily(5.–12.9.2009) AMSR-E89GHz–Horizontal thanofthedifferentspatialresolutions(PALSAR100m,ASAR150m).
Daily(5.–12.9.2009) AVHRR
Theappearanceoftheopenwaterzonedependsontheradarfrequency
36 T.Hollands,W.Dierking/RemoteSensingofEnvironment187(2016)30–48
Table2
SpecificationsforthesensorslistedinTable1.
Sensor Wavelength Frequency Swath Spatialresolution Parameter
width
EnvisatASARWS 5.62cm 5.33GHz 400km 150×150m Backscatteringcoefficient
(C-Band) [dB]
ALOSPALSARScanSAR 23.61cm 1.27GHz 250–350 100×100m Backscatteringcoefficient
(L-Band) km [dB]
AMSR-E89GHz, 89GHz(W-Band) 1445km 5.4×5.4km(2.2×2.2kmafter Brightnesstemperature[K]
H-polarization enhancement)
ModisBand1 620–670nm 2330km 250×250m Reflectance
ModisBand2 841–876nm 2330km 250×250m Reflectance
AATSR 3.7,10.8, 512km 1×1km SST[K]
12μm
AVHRR 0.58–12.5μm 2900km 1.1×1.1km Radiance/SST[K]
EO1ALI 0.433–2350μm(7bands) 37km 30×30m(10×10mpanchromatic) Reflectance
0.48–0.69μm
panchromatic
andonwindspeedanddirection.AtL-band(Fig.2B),arimisclearly andtheirdistancetooneanotherincreasewiththedistancefromthe
visibleattheseaiceedgeeast(totheright)oftheopenwaterareaof coast.MorelliandParmiggiani(2013)pointoutthatthewindspeedis
theTNBP,whichismoredifficulttoidentifyintheotherimages(A,C, largerwhenmovingawayfromthecoastbecauseoftheheatreleased
D).Weinterpretthisrimasanarrowzoneofaccumulatingfrazilice. bytheopenwaterzoneofthepolynya.Consideringtheresultsofexper-
Inbothradarscenes,thestreaksoffraziliceintheopenwaterzoneof imentalstudiesonLangmuircirculationjustmentionedabove,thein-
theTNBParerecognizable.Thinsmoothice(darkerintheradarimag- creasingdistancebetweenstreaksandtheirbroadeningconformsto
ery)andthickericefloes(brighter)canbebetterdistinguishedatL- thewindspeedincreasewithdistancefromthecoast.
band. The outlet zone reveals alternating bright, partly banded ice InFig.4,theiceandwatersurfacetemperaturesintheTerraNova
zones,anddarkzonesconsistingofbrokenlevelice.Fromthisstructure BayforSeptember10,2009,derivedfromAVHRRdata,arepictured.
andtheavailablesequenceofSARimagesweconcludethatrelatively WehereselectedAVHRRandnotAATSRdatasinceforthatdatethefor-
smoothlevelicezonesdevelopinthepolynyaareaatphasesofcalm merprovideabetterspatialcoverageofthesouthwestpartoftheRoss
wind and break due to external forces exerted by the adjacent ice Sea,whichwetakeadvantageofintheanalysispresentedinSection5.2.
masses, while being pushed to the northeast when the katabatic Thelocationsofautomatedweatherstationsareindicatedbyredtrian-
windsincreaseinstrength.Thebrightzones,withlargepartsoffine- gles.ForthedayoftheAVHRRdataacquisition,thelocalairtempera-
grainedimagetexture,arecharacteristicforroughicesurfaces.Based tures measured at 2 m height above surface at the stations were:
on pairs of SAR images (revealing similar grey-tone patterns) and LaurieII:−44.6°C,CapeBird:−27.5°C,Manuela:−26.5°C,Eneide:
high-resolution optical images acquired on other days, we assume −23°C.Adetailedcomparisonbetweenthestationdataandthetem-
thatthesebrightareasconsistofaccumulatedfrazilandgreaseiceat peraturesretrievedfromAVHRRthermalimageryisbeyondthescope
theopenwatermarginthatisconsolidatedatlargerdistancesfrom ofourstudyhere,butwenotethataone-to-onecorrespondencecan
theopenwaterzone,eventuallyformingaheavilyraftedicecoverand hardlybeexpectedforanumberofreasons.Nevertheless,thestation
brokenicefloes.Thebandingisthenduetodifferentstagesofcompac- dataprovideanadditionalpossibilitytojudgethetemperaturecondi-
tionand/orsurfaceroughness.Thedarkiceareaintheupperleftcorner tionsintheregionshowninFig.4.Thezonesofhighertemperatures
ofbothSARimagesisfastice(confirmedbyouranalysisofasequenceof (yellow)delimittherangeofinfluencefromtheRSP(lowerright),the
SARimagesdiscussedbelow). MSP(inasmallpartnorthwestfromstationCapeBird),andtheTNBP.
Anotherexample is shownin Fig. 3,in which ahigh-resolution TheopenwaterzoneoftheTNBPisindicatedbythebrownishcolour.
(10m)EO1ALIimageissuperimposedonanASARscene.Theformer Verywellreflectedisalsothedeformationbeltwithsmallerandlarger
wasacquired2:09hlaterthanthelatter.Theopticaldatarevealtheor- colder(thicker)icefloesandwarmer(thinner)icebetweenthem.The
ganizationoffrazilandgreaseiceasstreaks.Windgeneratedwaves darkblueareaintheupperleftcorneroftheimageisafasticezone.
travelthroughthestreaks(zoom-in,Fig.3),whichwasalsorecognized Thesouthernpartofthiszoneisbreakingoff.Thisprocessmanifestsit-
by Ciappa and Pietranera (2013) in high-resolution (5 m) COSMO selfinthecrackofhighertemperature.
SkyMedSARimages.Inourwide-swathASARscenewithaspatialreso- Thejointanalysisofoptical(near-infrared),thermal,andradarim-
lutionof150m,theicestreaksarealsovisible,althoughmoredifficultto ageryacquiredwithinshorttimeintervals(about2handless)demon-
identify.Thelocation,width,andshapeofindividualstreakscanchange strates the great advantage that multi-sensor data offer for the
withinminutesdependentonlocalwindconditions.Hence,theydonot qualitativeretrievaloficeconditionsandanalysisofcertainprocesses.
matchperfectlybetweentheALIandtheASARimageconsideringthe Weregardthecombinationofthermalandradardatauseful,sincethe
timeintervalbetweentheiracquisitions. formerpermitarelativelyclearseparationofthinnerandthickerice
AccordingtoCiappaandPietranera(2013)thegapsbetweenthe (withthedisadvantageofbeinghamperedbycloudcover),whereas
streaksdependonwindspeed.Lackingsatellitephotostakenfordiffer- thelatteremphasizethesurfaceroughnessandnarrowdeformation
entwindspeeds,wecannotexaminethisdependency.Theicestreaks structures.Inourcase,thelowerfrequencyL-bandimageofferssome
areunveiledbyLangmuircirculation.Thorpe(2004)reportsthatthe advantages with respect to the identification of ice structures and
separationscalesandlengthsofLangmuircellsincreasewithwind types.However,werefrainfromrecommendingL-bandimageryfor
speed.Inexperimentalstudiesithasbeenobservedthatthereisaten- allcases.Anygeneral(global)preferenceofeitherlow-(L-band)or
dencyforlargerspacingbetweenthestreakswithhigherwindspeeds higherfrequencyradar(C-orX-band)forthediscriminationofthin
(Plueddemannetal.,1996,theirFig.5),buttherearealsocleardevia- icetypeshasyetnotbeenformulated(Dierking,2010;Dierkingand
tionsfromthisrelationship.OnereasonisthatLangmuircirculation Busche,2006).If,e.g.,multi-yeariceoccursinicefieldsaroundapolyn-
doesnotonlydependonthewindstressbutalsoontheStokesdrift(av- ya(morefrequentlyintheArctic,butmulti-yearicepersistsalsointhe
eragevelocityof afluidparcel)ofsurfacewaves. Inourscene,the easternRossSea),C-bandmaybeabetterchoicefordiscriminationand
streaksinthenorthernpartarebroader.Thewidthofindividualstreaks classificationoficetypes(e.g.,Dierking,2013).Opticalimagesareuseful
T.Hollands,W.Dierking/RemoteSensingofEnvironment187(2016)30–48 37
Fig.3.EO1ALIopticalimageoverlaidonanEnvisatASARWSscene.BothwereacquiredonSep.7,2009,theformerat19:10:28UTC,thelatterat21:19:36.Thescenecoversanareaof
about60kmineast-westand100kminnorth-southdirection.Thezoom-inatthebottom(right)demonstratesthatoceanwavescanwellberecognizedintheicestreaks.
toseparatesnow-coveredandsnow-freeice,andtheyimprovethereli- RossSea(Section3andFig.1).Fig.5showsanimagesequenceacquired
abilityoftheretrievaloficeconditionsfromSARimages.Theexample withtheAMSR-Efrom5thto12thofSeptember2009,representingthe
showninFig.3alsodemonstratesthatprocessesshapingtheicecondi- brightnesstemperatureT thatwasmeasuredat89GHz,H-polariza-
B
tionsinandaroundpolynyas(inthiscasethewindshapingtheice tion,andmappedwithaspatialresolutionof2.2km.Onecanidentify
streaks)canonlybefullyconceivedifadditionalmeteorologicaland theRossIceShelfatthebottomoftheindividualimages(whichisin
oceanographicdataareavailable. thesouth),andVictoriaLandwiththeDrygalskiicetongueontheleft
(west).TheRSPcanbespottedattherightbottom,theMSPleftofthe
5.2.Regionalanalysis bottomcenter,andtheTNBPabove(northof)theDrygalskiicetongue.
InthemiddleoftheimageabowshapedfeatureoflowerT -values
B
Ontheregionalscale,meteorologicalandiceconditionscausea showsup,whichseparatesthesouth-easticecoverinfluencedby(and
complexinteractionofthethreepolynyas(RSP,MBP,TNBP)inthe partly originating from) the RSP and the north-west sea ice cover
38 T.Hollands,W.Dierking/RemoteSensingofEnvironment187(2016)30–48
surface,andinthecaseofseaiceonsalinity,brinevolumefraction,
brinepocketshapeandspatialdistribution(ShokrandSinha,2015).
At90HGHz,εofopenwateris0.528,fornewice0.573,andforfirst-
year ice 0.886 (Shokr and Sinha, 2015, Table 8.10). Their Fig. 8.32
showsthatεincreasessignificantlyduringthefirst10mmgrowthof
ice,butthenreachingvaluestypicalforfirst-yearice.Hence,weexpect
largervariationsofεonlyinareasofopenwater,greaseandthinice,e.g.,
intheoutletzoneandinleads.Fig.7showsagraphrelatingT andT ,
ice B
whichrevealsslightlygrowingT -valueswithincreasingT forthe
B ice
zonesoffastice,smoothicefloes,androughfloes(blueinFig.4).For
thezonesofaccumulatedice(yellowandorangeinFig.4),aclearrela-
tionshipbetweenT andT wasnotfound:theintervalofT ismainly
ice B ice
clusteredbetween−20°Cand−27°C,thecorrespondingrangeofT
B
from218Kto239K,whichresultsinarangeofεbetween0.86and
0.97, typical for first-year ice of varying roughness (see Shokr and
Sinha,2015,Fig.8.37demonstratingtheeffectofsurfaceroughness).
ThehighestvaluesofT (−4to−5°C)arefromthefraziliceand
ice
openwaterzoneofthepolynya,inwhichT variesbetween200and
B
220K.Here,wedidnotcorrectfortheemittinglayerthickness.There-
sultindicatesthepresenceofraftedandbrokenice.Forthefastice,level
ice,andthinicefloes(bluishcolorsinFig.4)wealsoobtainemissivities
offirst-yearicethickerthanafewcentimeters.
ThewarmeroutletzonesoftheRSPandtheTNBPinFig.5(covering
theopenwaterzoneandthepolynyaicepushedoffshore)arecharac-
terizedbyspatialbrightnesstemperaturevariationsbetween215K
Fig.4.IcesurfacetemperaturederivedfromAVHRR4and5(11and12μm).Theimage and235KonSep.10(thedateisselectedwithreferencetoFig.4).
wasacquiredonSep. 10,2009. Thespatial resolution is 1.1km. Average surface ThecolderdeformationbeltrevealsvariationsofT between195K
B
temperaturesfordifferenticetypesare:1fastice(darkblue) −40°C±2.9°C2level and215K(blue-yellow)withinterspersedwarmer(thinner)ice(or-
icefloes(blue) −35°C±1.8°C3thinicefloes(lightlue) −31°C±1.8°C4
ange)overtheperiodfromSep.5to12.OnSep.12,however,itde-
accumulationzone −24°C±1.8°Candrefrozenleads(lightyellow)Thepartially
openwaterzoneofthepolyna(brown)revealedameantemperatureof−5°C. creasedinwidth.Thedeformationbeltasazoneofshearbetweenthe
southeastRSPiceandthenorthwestTNBPicecanbeidentifiedover
thewholeimagesequenceshowninFig.5.Itscontrastinbrightness
movingawayfromtheTNBP(seealsoFig.1).Fromacomparisonofthe temperaturemayberelatedtothemagnitudeoftheforcesexertedon
AMSR-EdatawiththecorrespondingSARimagesavailableforthisperi- it,whichcausesstrongericedeformation.Unfortunately,wedonot
od(seeexampleshowninFig.6),wefoundthatthisfeaturecorre- havethenecessarydata(iceandwindconditions,oceancurrents)to
spondstothedeformationbeltthatisalsovisibleinFigs.2and4. provethishypothesis.
Themicrowavebrightnesstemperatureistheproductoftheemis- TheT -patternobservedintheAMSR-E89GHzdatacanalsoberec-
B
sivityεandthephysicaltemperatureToftheimagedarea.Theemitted ognizedinthe36GHzchannel,althoughthesignaturecontrastisless.
radiationisfromtheuppericelayer.Itsthicknessisdeterminedbythe Thismaybecausedbythelowerspatialresolutionofthe36GHzchan-
penetrationdepth,whichvariesbetweenafewmillimeterstodecime- nel.Thedeformationbeltcanbeobservedregularlyovertheyears.
ters,dependentonicephysicalpropertiesandmicrowavefrequency.
Forafrequencyof89GHz,Mathewetal.(2008)givevaluesof28cm 6.Separationoficezones
fordrysnow,4cmformulti-yearice,and0.9cmforfirst-yearice.The
averagephysicaltemperatureoftheemittinglayerdiffersfromtheair Inthissectionwedealwiththesegmentationandclassificationof
orsurfacetemperature.Itdependsonthemicrophysicalpropertiesof distincticezonesintheTerraNovaBay.Inandaroundapolynyadiffer-
thesnowandiceandvarieswithmicrowavefrequency(duetothedif- entstagesofnewiceformationarefound,e.g.,accumulationsoffrazil
ferentpenetrationdepths).Mathewetal.(2008)determinedthe“emit- andgreaseiceduetocompressionalforcesexertedbythekatabatic
tinglayertemperature”asalinearfunctionoftheairtemperaturefor wind,andconsolidatedthinlevelicethatmayrevealdeformationstruc-
theArctic.Forfirst-yearice,theirresultsrevealthattheformerranges tures(rafting,ridging).Inourstudy,theaimofsegmentationandclas-
from-19°Cto−8°Cat89GHzforairtemperaturesbetween−40°C sification (the latter is linking segments and actual ice conditions)
and−10°C.Weassumethattheseresultsareapproximatelyvalid mustbetoreconstructdifferentstepsintheevolutionofapolynya.
alsoforiceconditionsintheTerraNovaBay.Iftheicesurfacetempera- Theindividualicezonesmayhavecompletelydifferentpropertiesre-
tureattwodifferentfirst-yearicelocationsdiffersby30°C,thecorre- gardingsaltrelease,heatexchange,anddeformation.Foranexample
spondingchangeofT wouldhenceonlybeabout10K,ifεremains ofclassificationwecombinethenear-simultaneousmulti-sensorsatel-
B
constant.ThevariationsoftheicesurfacetemperatureT onSeptem- liteacquisitionsovertheTNBPshowninFig.2,whichhavecomparable
ice
ber10,showninFigs.2and4fortheTerraNovaBay,rangefromap- spatialresolutions(here,wedonotconsiderthecoarse-resolutionPMR
proximately−45°Cto−15°C(−2to−4°Cinthepolynyazoneof data).
partiallyopenwater,seeFig.7below).Thebrightnesstemperatures Inafirststep,welookedatthesegmentationpotentialofdifferent
T inthecorrespondingareaarebetweenapproximately195Kand setsofdiscriminationrules(supervisedhierarchicalapproach)andof
B
235K.Thepolynyazoneisnotrecognizableinthemicrowaveimage, unsupervisedclusteringalgorithms(suchasISODATA).Inbothcases,
whichweattributetothecoarseresolutionandthemixed-pixeleffect. spatiallyvaryingmeanvaluesandvariancesofthedirectlymeasured
Consideringthattherangeoftheemittinglayertemperatureissmaller quantities(reflectance,temperature,backscatteringcoefficient)arede-
thantheoneoftheicesurface,thevariationsofT mustalsobeinflu- termined.Afterdividingtheimagesintodifferentsegments,theyhave
B
encedbytheemissivity.Theemissivityεoficedependsontheicetem- tobelinkedwithactualiceclassesinasecondstep.Forsupervisedclas-
perature,surfaceroughness,porosity,snowwetness,snowgrainsize, sificationsanoperatoridentifiesareasofinterestintrainingdatathat
icelayersinthesnow,presenceofslushorsuperimposediceonthe mirrorthestatisticalcharacteristicsofsingleisolatedicezones.Several
T.Hollands,W.Dierking/RemoteSensingofEnvironment187(2016)30–48 39
Fig.5.SequenceofimagesfromAMSR-E89GHz,H-polarization,fortheperiodfromSep.5to12,showingthebrightnesstemperaturesTBatanenhancedresolutionof2.2km.Bluecolors
indicatelowervaluesofTB,redcolorshighervalues.
approachesareavailable,spanningtherangefromvariousdistance allowsagooddifferentiationofthedifferenticetypesandfloes,L-
measuresthatareusedtoassociateeachpixelwithacertainsegment, bandrevealsastrongerbackscatterforhighlydeformedregions(e.g.
tohighlycomplexsystemssuchasneuralnetworksorsupportedvector deformedpolynyaiceordeformationssuchasraftingandridges).SAR
machines.Themanualidentificationoftrainingclassesistimeconsum- imagesacquiredunderfreezingconditionsshowmanymoredetails
ingandambiguous(duetothesubjectivecomponentofhumaninter- abouticestructure(floes,cracks,brashiceetc)thanopticalandthermal
ference). An alternative is the use of unsupervised techniques. A sensors,becausetheradarsignalspenetratethroughdrysnow.The
popularapproachinthisgroupistheISODATAalgorithm.Suchap- thermalsensoreasestheseparationofopenwater,thinandthickice
proachescanhandlemultidimensionaldataandclassifymulti-sensor withoutbeinghamperedbytoomanysmallstructuraldetails.
data.Theadvantageisthattheyoftendetectsegmentsthatmightbedif- TheidealclassificationintheareaofTNBPshouldcomprisethefol-
ficulttorecognizebyvisualanalysis.Whilevisualseparationofseg- lowingelements.
mentsisstillpossiblewhencombiningdataoftwoorthreedifferent
channels,sensormodes,orsensors,itbecomesimpossibleinfouror 1. Thepackicezoneconsistsofdifferentlysizedthickericefloeswith
moredimensions.Thedisadvantageofunsupervisedmethodsisthat interspersedthinice.Thickericefloesreveallowersurfacetempera-
theirresultsareoftendifficulttolinktoaclassificationschemethatis turesandmoderatetohighbackscatteringintensityduetodifferent
optimallyadaptedtotheactual(ice)conditions.ForourISODATAclas- stages of deformation and fracturing. Thin ice manifests itself
sificationwefoundthatasupervisedpost-adjustmentwasnecessaryto throughhighertemperaturesandlowerbackscatteringcoefficients.
adapttheautomatedsegmentationtotheactuallypresenticecondi- 2. Theoutleticerevealsasequencewithalternatingaccumulated,part-
tions,whichdevaluatestheadvantageofunsupervisedtechniques.In lybandediceandbrokenicefloesthatpartlyrevealsignsofridging
thissection,wefocusthereforeexclusivelyonthesupervisedsegmenta- andrafting.Thesurfacetemperatureishighanddoesnotshowany
tion.Landandcloud-coveredareasareexcludedfromtheclassification differencesbetweenaccumulatediceandicefloezones.Itgradually
procedure. decreaseswithdistancefromthepolynya.Thebackscatterintensity
SincetheMODISNIRreflectanceimage(Fig.2)ismoredifficulttoin- ishighoveraccumulatedice(indicatingaroughsurface)andlower
terpretthanthethermalAATSRdataandmoreseverelyaffectedby overtheicefloezones.
clouds, we decided to include only the latter into a segmentation 3. Theopenwaterpolynyazonecanbeeasilyrecognizedbecauseofits
scheme,togetherwiththeC-andL-bandradarimages.WhileC-band veryhightemperature.Thebackscatteredintensitydependsonthe