Table Of ContentUrbanClimate24(2018)299–310
ContentslistsavailableatScienceDirect
Urban Climate
journalhomepage:http://www.elsevier.com/locate/uclim
Remote sensing retrieval of urban land surface
temperature in hot-humid region
⁎
YurongShi,YufengZhang
StateKeyLaboratoryofSubtropicalBuildingScience,DepartmentofArchitecture,SouthChinaUniversityofTechnology,NO381.Wushanroad,
Tianhedistrict,510640Guangzhou,PRChina
a r t i c l e i n f o a b s t r a c t
Articlehistory: Withtherapidurbanizationandtheglobalclimatechange,theurban
Received19October2016 climateproblemsbecomeincreasinglyseriousinthehot-humidregion
Receivedinrevisedform3January2017 ofChina.Thisstudyestablishedthesatellite-basedremotesensingre-
Accepted4January2017 trievalmethodsforurbanlandsurfacetemperature(LST)basedonthe
mono-windowalgorithmandtheestimationmethodsofgroundemis-
Keywords: sivityandatmospherictransmittance.ThroughretrievingLSTofHaizhu
Landsurfacetemperature districtinGuangzhouinasunnysummerday,itwasfoundthatthere-
Urbanclimate
trievalmethodswereabletodistinguishthesurfacetemperaturevaria-
Remotesensing
tionsfordifferentunderlyingurbansurfaces.Bycomparisonwiththe
Mono-windowalgorithm
fieldobservations,itwasfoundthattheerroroftheretrievalmethods
Fieldobservation
wasabout1.0°CandthemethodswereapplicableforLSTretrievalin
thecitiesinthehot-humidregion.Byapplyingtheretrievalmethods,
itshowedthatthecoolingeffectsofwaterandvegetationonsurround-
ingurbanlandswereeffectivewithindistancesof250mand350m,re-
spectively.Thewatercoolingeffectcorrelatedwithitsareaandwidth
positively,andthevegetationLSTcorrelatedwithitscanopydensity
negatively.Thepresentstudyprovidesreliabletechniquesforobserving
andevaluatingurbanLSTandusefulguidanceforplanninganddesignof
urbanclimateinthehot-humidregion.
©2017ElsevierB.V.Allrightsreserved.
1.Introduction
Withtherapidurbanizationandtheglobalclimatechange,theurbanenvironmentandclimateproblems
becomeincreasinglyseriousinChina.Inthehot-humidregionofChina,thesummerislong,hotandhumid,
andtheurbanclimateproblemissevere.Ononehand,mortalityincreaseswithurbanwarming.Forinstance,
⁎ Correspondingauthor.
E-mailaddresses:[email protected](Y.Shi),[email protected](Y.Zhang)
http://dx.doi.org/10.1016/j.uclim.2017.01.001
2212-0955©2017ElsevierB.V.Allrightsreserved.
300 Y.Shi,Y.Zhang/UrbanClimate24(2018)299–310
themeandailycountofnon-accidentaldeathreached32.6during2006to2009andthetemperature-mortal-
ityrelationshipshowedaUshapeinGuangzhou,abigcityinthehot-humidregionofChina(Wuetal.,2013).
Ontheotherhand,buildingenergyconsumptionsincreasewithurbanwarming.Forexample,a10%increase
inair-conditioningdemandwascausedbytheheatislandinHongKong(Chan,2011).Theneighborhoodmi-
croclimate,asthemicro-scaleofurbanclimate,relatestopeople'sdailylifeveryclosely(Rothetal.,1989).Itis
thusimportantandnecessarytostudytheurbanclimateandtheneighborhoodmicroclimateinthehot-
humidregionforimprovinglivingenvironmentalquality,reducingbuildingenergyconsumptionandmitigat-
ingurbanheatisland.
Previousstudiesonurbanclimateandneighborhoodmicroclimatearemainlyfocusedonfiledstudyofin-
dividualcasesandhardtobegeneralized.Ontheotherside,theadvancedtechnologiesofremotesensingare
mainlyappliedtolarge-scalefieldslikeagricultureandocean.Onlyafewstudieshavebeencarriedoutinthe
citiesofSingapore(Nichol,1996),Tel-Aviv(Saaronietal.,2000),SaltLakeValley(Gluchetal.,2006)and
China(HeandXu,2012;Ye,2009;WangandZhang,2010).Theprimaryworkofutilizingremotesensing
tourbanclimateandneighborhoodmicroclimatestudiesistoacquirelandcharacteristicparametersre-
trievedbyremotesensingtechniques.Landsurfacetemperature(LST)hasbeenprovedtobeanimportant
factorcontrollingthephysical,chemicalandbiologicalprocesseson Earth'ssurface(QinandKarnieli,
1999).Intherecentyears,theremotesensingtechniquesthatbasedonsatelliteandaircrafthavebeenad-
vancedgreatlyandappliedextensivelyinurbanclimate,forinstance,VoogtandOke(1997,1998)and
Chenetal.(2014)havedonemanyworksonurbanclimatebyremotesensingtechniques.Byusingthere-
motesensingtechniques,itcanacquiretheurbanclimaticinformationquicklyandbroadlyandprovidea
largenumberofneighborhoodmicroclimaticdatabytakingitsadvantagesonmacro,multibandandhigh
resolution.
ThispaperaimedatestablishingremotesensingretrievalmethodsforurbanLSTbasedonpreviousstudies
andtestingperformancesoftheretrievalmethodsthroughfieldobservationsinthecityinthehot-humidre-
gionofChina.Furthermore,thecoolingeffectsofwaterandvegetationonurbanLSTwillbequantitatively
studiedbyusingthevalidatedmethods.Thisstudyprovidesreliablemethodsforremotesensingretrieval
ofurbanLST,andusefulguidanceforurbanclimateplanninganddesigninthehot-humidregion.
2.Retrievalmethods
2.1.Mono-windowalgorithm
Therearethreemethods,i.e.,atmosphericcorrectionmethod(alsocalledradiativetransferequation),
mono-windowalgorithm(Qinetal.,2001),andsinglechannelalgorithm(Jimenez-MunozandSobrino,
2003),appropriateforretrievingLSTbyusingthesatellitethermalbanddata.Theatmosphericcorrection
methodneedstoknowtheinsituatmosphericprofiledata.Itiscomplicatedandhardtoacquireandhuge
errorwouldbeproducedbyitsestimation.Themono-windowalgorithmandthesinglechannelalgorithm
arebothderivedfromthethermalradiancetransferequation,whicharesimple,needingfewinputsand
withrelativelylesserror.SomestudiescomparedthethreemethodswiththefieldobservationsintheChi-
nesecitiesofBeijing(Baietal.,2008)andGansu(Mengetal.,2005),andconcludedthattheaccuracyof
themono-windowalgorithmwashighestforretrievingLST.Therefore,themono-windowalgorithmwasde-
terminedtobethemethodforretrievingurbanLSTinthepresentstudy.
Themono-windowalgorithmretrievesLST(T)byknowinggroundemissivity,effectivemeanatmospher-
s
ictemperatureandatmospherictransmittancebyEqs.(1)–(3):
T ¼½að1−C−DÞþ½bð1−C−DÞþCþD(cid:2)T −DT (cid:2)=C ð1Þ
s 6 0
C¼ετ ð2Þ
D¼ð1−τÞ½1þð1−εÞτ(cid:2) ð3Þ
whereaandbareconstants,a=−67.355351,andb=0.458606;T isthebrightnesstemperatureinK;T is
6 0
the effective mean atmospheric temperature in K; ε is ground emissivity; τ is atmospheric
transmittance.
Y.Shi,Y.Zhang/UrbanClimate24(2018)299–310 301
T canbecalculatedbyusingtheformulasprovidedforvarioussatellites.T canbeevaluatedbyanalysisof
6 0
thestandardatmosphereprofiledataandthevariationsofatmosphericmoistureandairtemperaturewith
theincreasingelevation.Themethodsforestimationsofεandτarepresentedinthefollowingsections.
2.2.Groundemissivityestimation
Fortheremotesensingimages,landsurfacescanbedividedintowater,soil,vegetationandmixedpixels.
Themixedpixelsaremostcommoninthesensingremoteimagesofurbansurfaces.TheNDVIthreshold
methodproposedbyValorandCaselles(1996)isthemostwidelyusedmethodtoestimategroundemissivity
ofthemixedpixels.ItestimatesgroundemissivitybyusingthevaluesofNDVIandgreencoverageratio.
ThepresentstudyadoptedtheimprovedmodelproposedbyLietal.(2004)toobtaingreencoverageratio
(P)ofthemixedpixels:
v
h (cid:1) (cid:3)i
P ¼ ðNDVI−NDVI Þ= NDVI −NDVI ð4Þ
v Soil Veg Soil
whereNDVIisnormalizeddifferentialvegetationindex;NDVI andNDVI arenormalizeddifferentialveg-
Soil Veg
etationindexofsoilandvegetation,setasempiricalvaluesof0.05and0.7,respectively.
TheNDVIthresholdmethodwaspromotedbyQinetal.(2004),whichwasadoptedtoestimateground
emissivityoftheurbanlandsurface.
ε¼P R ε þð1−P ÞR ε þdε ð5Þ
v v v v m m
whereεisgroundemissivity;ε andε areemissivityofvegetationandbuildingsurface,respectively,setas
v m
empiricalvaluesof0.986and0.970;dεisinteractionofthermalradiationbetweenvegetationandbuilding
surface,ignoredwhenthelandsurfaceisrelativelyeven;R andR aretemperatureratioofvegetationand
v m
buildingsurfaces,calculatedbytheempiricalequations:
R ¼0:9332þ0:0585P ð6Þ
v v
R ¼0:9886þ0:1287P ð7Þ
m v
CombiningEqs.(5)–(7),groundemissivityofthemixedpixelsintheurbanremotesensingimagescanbe
estimatedby:
ε¼0:9589þ0:086P −0:0671P 2 ð8Þ
v v
2.3.Atmospherictransmittanceestimation
Atmospherictransmittanceisaffectedbyvariousfactorslikeairpressure,airtemperature,watervapor
content,aerosolcontent,andconcentrationsofCO ,O ,CO,NH etc.Theatmosphericsimulationprograms
2 3 4
suchas6S,LOWTRANandMODTRANcanbeusedtoestimateatmospherictransmittance.Inaddition,
manystudiesfoundthatwatervaporcontentisakeyfactoraffectingatmospherictransmittance,andthe
higherthewatervaporcontent,thelowertheatmospherictransmittanceis.MaoandQin(2004)mentioned
thatitwouldimprovethereal-timeperformanceofatmospherictransmittanceestimationbyusingtherela-
tionshipbetweenwatervaporcontentandthermalinfraredbandtransmittance.
ThewatervaporcontentwasestimatedusingKaufmanandGao's(1992)modelinthepresentstudyas
follows:
0 (cid:1) (cid:3)1
α− ln ρ19 2
ω¼@ ρ2 A ð9Þ
β
whereωiswatervaporcontenting/cm2;αandβarecoefficient,setas0.02and0.651,respectively;ρ2and
ρ19arethewavelengthofband2(0.841–0.876μm)andband19(0.915–0.965μm)forMODISsatellite.
302 Y.Shi,Y.Zhang/UrbanClimate24(2018)299–310
3.Researchmethods
3.1.Climateandsite
Guangzhou,locatedatlatitude23°08′Nandlongitude113°19′E,isatypicalexampleofacityinthehot-
humidregionofChina.Thelongsummersarehotandhumidwithplentifulrainfallandthewintersare
warmandshort.Themonthlymeantemperatureis28.4°Candthehumidityis83%inJulyand13.3°Cand
74%,respectively,inJanuary.Theprevalentwindissoutheastwindinsummerandnorthwindinwinter
andtheannualsunshinehoursis1906h.ThepresentstudywasconductedinGuangzhou.
ThesitetobestudiedislocatedinthewestpartofHaizhudistrictinGuangzhouasshowninFig.1.Thesite
isatypicalresidentialareaandconsistsofmanyneighborhoods.Mostbuildingsarehigh-riseandincompact
arrangement,andfewarelow-rise.Thegreeningiscontinuouslydistributed.Thereareverdanttreesinthe
neighborhoodsandsparsetreesalongbothsidesoftheroads.Theexteriorsurfacesofbuildingsaremainly
lighttilingandfewareplaster.Theroadwaysarecoveredbyasphalt.
3.2.Dataacquisitionandprocess
Currently,thesatellitesofMODIS,ASTER,Landsathavethermalbanddata,ofwhich,thedataofLandsatis
easytoacquireandinhighresolution.TheLandsathasthreethermalbandsensors,TM,ETM+,andTIRS,and
theirspatialresolutionsare120m,60m,100m,respectively.TheLandsatthermalbanddatawasusedinthe
presentstudy.BycheckingthedatapassedthroughGuangzhouinrecenttime,itwasfoundthatthedataat
GTM2:51am(localTime10:51am),September18,2016,wasappropriateforLSTanalysisbecauseitwas
sunnyandcloudlessatthattime.
TheLSTofthewholeHaizhudistrictinGuangzhouwasretrievedbyusingthemethodsinSection2,and
thedetaileddataacquisitionandprocessareillustratedasfollows.
3.2.1.Spectralradiance
Thespectralradiance(L )ofthermalband10fromTIRSsensorwasacquiredbyusingthefunctionofra-
6
diometriccalibrationofthesoftwareENVI.
Fig.1.Visibleimagemapofthestudiedsite.
(FromQuickbirth,2010).
Y.Shi,Y.Zhang/UrbanClimate24(2018)299–310 303
3.2.2.Brightnesstemperature
Thebrightnesstemperature(T )thatcorrespondstothespectralradiance(L )wascalculatedbyusingthe
6 6
approximateformofPlanck'sradiancefunction:
T ¼K = lnð1þK =L Þ ð10Þ
6 2 1 6
whereK andK wereacquiredfromtheheaderfileoftheLandsatsatellitedata,fortheTIRSdata,K =
1 2 1
774.89W/(m2·μm·sr),K =1321.08K.
2
3.2.3.Effectivemeanatmospherictemperature
Qinetal.(2003)providedfourlinearrelationshipsbetweenT andT forvariousstandardatmosphere
0 a
conditions.Therelationshipforthetropicalstandardatmosphereconditionwasusedinthepresentstudyac-
cordingtoDeng(2010),thatis:
T ¼17:9769þ0:91715T ð11Þ
0 a
whereT isnearsurfaceairtemperatureandcanbereplacedbythelocalmeteorologicalobservationdata
a
ofdailymeanairtemperatureatthedayofsatellitepassing.
3.2.4.Atmospherictransmittance
ThewatervaporcontentwascalculatedbyEq.(9).Qinetal.(2003)andGuetal.(2011)studiedthechang-
esofatmospherictransmittancewithwatervaporcontent(0.4–6.4g/cm2)forthemid-latitudeatmosphere
conditionsbyusingLOWTRAN7andMODTRAN4,andfoundthatthegoodlinearrelationshipsexistedbe-
tweenatmospherictransmittanceandwatervaporcontent.ThisstudyutilizedMODTRAN4tocalculatethe
atmospherictransmittanceinthethermalbandof10.40–12.50μmbyusingtheatmosphericprofiledata
ofsubtropical30°NinJuly.
3.2.5.Groundemissivity
ThegroundemissivityofthesitewasestimatedbythemethodsinSection2.2,i.e.,theNDVIvaluewascal-
culatedbysoftwareENVI,thegreencoverageratiowasobtainedbyEq.(4),andthegroundemissivitywas
estimatedbyEq.(8).
3.3.Fieldobservations
SincefieldobservationofLSTforthewholesitewasnotfeasible,asmallandrepresentativepatchofthe
sitewasselectedasshowninFig.2a.Thelandsurfaceofthepatchconsistsofbuildings,vegetation,and
Fig.2.Theobservedpatchandthemeasurementmethod.aTheobservedpatch.bThethermalinfraredcamera.
304 Y.Shi,Y.Zhang/UrbanClimate24(2018)299–310
roads.Thebuildingsaremiddle-riseandhigh-rise.Thegreencoverageratiorangesfrom4%to28%andthe
roadscover20%ofthesurface.Allofthesecharacteristicsareclosetothoseofthewholesite,indicating
thattheselectedpatchisrepresentativetothesiteintheaspectoflandsurface.
ThermalInfraredimagesofthepatchwerephotographedatthetimewhentheLandsatsatellitepassed
through.TheinstrumentisNECAvioTH9260ThermalInfraredcamera,anditsmeasuringrangeis−40to
500°C,precisionis±2°Candresolutionis0.06to0.12°C.Thecamerawasfixedattheroofofatallbuilding
neartothepatch(Fig.2b),84mabovetheground,takingphotosfromtoptobottom.Theviewanglewasap-
proximatedtobe30°.TheLSTofthepatchwasobtainedafteremissivitycorrection,ofwhichtheemissivity
wassettobe0.97accordingtotheretrievalresults.
4.Results
4.1.Theretrievalparameters
Themeannear-surfaceairtemperature(T)was28.7°CforthesiteatthetimetheLandsatsatellitepassed
a
through,basedonwhich,theeffectivemeanatmospherictemperature(T )wasobtainedas294.8K.The
0
watervaporcontentwas4.5g/cm2,whichisinagreementwiththepreviousfindingsinthehot-humidregion.
Theatmospherictransmittancewas0.39,theNDVIvalueschangedfrom−0.03to0.43,andthegroundemis-
sivitywasinrangeof0.955–0.986.
Fig.3showstheresultsofgreencoverageratioforthesite.Itisshownthatinthemajorityofthesitethe
greencoverageratiowasbetween4%and28%.
4.2.LSTretrievals
TheretrievedLSTimagemapofthewholeHaizhudistrictisshowninFig.4,ofwhich,thescopesmarkedin
blacklinearethestudiedsiteinthepresentpaper.ThevisibleimagemapisalsoprovidedasshowninFig.5.
ItcanbeseenfromFig.5thatthePearlRiverrunsthroughtheHaizhudistrictbyseparatingintotwo
branchesandconvergesafterwards.FortheareathatsurroundedbythePearlRiver,thewestpartshows
greatdevelopmentintensityandhighbuildingdensity,whiletheeastpartshowssmalldevelopmentinten-
sityandlargegreenspace.Abovethesurroundedareabuildingsarebuiltintensively,whilebelowthe
surroundedareabuildingsarebuiltloosely.CombingFigs.4and5,itcanbeknownthatthereexistedgood
relationshipsbetweenLSTanddevelopmentintensityandlandtype,showingthatLSTwasthehighestfor
theintensivelydevelopedareas,followedbythelessdevelopedareasandroads,andLSTofgreenand
waterareasweresmallest.Thisindicatedthattheproposedretrievalmethodswereabletodistinguishthe
surfacetemperaturevariationsfordifferentunderlyingurbansurfaces.
TheanalysisofthescopemarkedinblacklineinFig.4showedthatthemean,maximumandminimumLST
ofthesitewere39.8°C,47.9°Cand34.2°C,respectively.ItcanbeseenfromFig.5thatLSTofthesitewas
higherthanotherareasofHaizhudistrict.Thisprobablybecausethatthebuildingdensityofthesiteislarger.
Fig.3.Greencoverageratioofthesite.(Forimagemap,thedeeperthegreencolor,thelargerthegreencoverageratiois)
Y.Shi,Y.Zhang/UrbanClimate24(2018)299–310 305
Fig.4.LSTretrievalimagemapoftheHaizhudistrictinGuangzhou.
(GTM2:51am,September18,2016).
TheabovefindingswerefurtherconfirmedbycomparingFig.4withtheLCZclassificationmapofHaizhu
districtshowninFig.6.ItcanbeseenthatLSTwasthehighestfortheLCZofcompactbuildings,followedby
theLCZofopenbuildings,andLSTofwaterandvegetationweresmallest.Inaddition,therewerestillsome
areaswithlow-risebuildingsbuthighLST,andsomeareaswithcompactandmiddle-risebuildingsbutlow
LST,indicatingthatLSTmaybenotonlyaffectedbybuildingdensityandheight,butalsobysurfacepavement
andgreening.
Fig.5.VisibleimagemapoftheHaizhudistrictinGuangzhou.
306 Y.Shi,Y.Zhang/UrbanClimate24(2018)299–310
Fig.6.LCZclassificationmapofHaizhudistrictinGuangzhou.
(http://geopedia.world/#T4_L107_x12622485.993643802_y2641625.479021549_s11_b17).
4.3.LSTobservations
Fig.7showstheLSTobservationsforthepatch.Thehighertemperatureswereobservedforthesurfaces
directlyexposedtosolarradiation,liketheroofandpartoftheroads;whilethelowertemperatureswereob-
servedforthesurfacesnotexposedtodirectsolarradiation,likewallsandvegetation.Themeansurfacetem-
peraturesofroof,road,wallsandvegetationwere40.7°C,32.5°C,31.8°Cand31.5°C,respectively.
4.4.Validationofretrievalmethods
AsthewidthofLandsatimagewas185km,theremightbeacertainviewdifferencethatwasestimatedas
azenithangleof3°betweenpixelofimagemarginandcentralscanningline(Qinetal.,2003).Accordingto
Fig.7.Thermalinfraredimageofthepatch.
(GTM2:51am,September18,2016).
Y.Shi,Y.Zhang/UrbanClimate24(2018)299–310 307
this,thesurfacesthatcouldbeobserveddirectlyfromaverticalangle,includingtheroofs,roads,andvegeta-
tion,wereconsideredandtheiraveragetemperaturewasobtainedas36.8°Cbasedonthefieldobservations.
Thecomparisonbetweentheretrievalresultsandthemeasuredresultsreporteda3°Cdifferencebetween
them.Thepreviousstudiesindicatedthataverageerrorofthemono-windowalgorithmwaswithin1.1°C.
ConsideringtheaccuracyofthermalInfraredcamerawas±2°C,webelievedthatthepresentstudyvalidated
theresultsofpreviousstudies,thatis,theerrorofthemono-windowalgorithmforurbanLSTretrievalwas
about1.0°C.Theabovefindingsindicatethattheretrievalmethodsbasedonthemono-windowalgorithm
andtheestimationmethodsforgroundemissivityandatmospherictransmittanceareapplicableforurban
LSTretrievalinGuangzhouandothercitiesinthehot-humidregion.Hereinafter,thecoolingeffectsof
waterandvegetationontheurbanLSTwereanalyzedbyusingthevalidatedretrievalmethods.
4.5.Watercoolingeffect
TheimagemapsofwaterandvegetationinGuangzhouwereextractedbytheNDVIthresholdmethodas
showninFig.8.TheNDVIvalueswerefrom−1to−0.5forwater,and0.2–0.8forvegetation.
TwelveriversinGuangzhouwithvariousareas,lengthsandwidthswereselectedassamplestobestudied.
Takingwaterareaascenter,thebufferzoneswerebuiltevery50moutward(totally10bufferzonesand
500mawayfromthecenter),andthetemperaturedrop,namelythemeanLSToftheurbanlandina
500mrangeoutsidethebufferzoneminusthemeanLSTofthebufferzonewasobtainedbyusingARCGIS.
ThechangeoftemperaturedropwithbufferdistanceforwaterisshowninFig.9.
Itcanbeseenthatwhenthebufferdistancebecamegreater,thetemperaturedropdecreasedrapidlyini-
tially,andthentendedtobestablewithathresholdof250m,thatistosay,thecoolingeffectofwateronits
surroundingurbanlandswassignificantwithinadistanceof250m.
TakingtheLSTdifferencebetweenwateranditscoolingurbansurface(300mrange)astheindexofwater
coolingeffect,therelationshipbetweenwatercoolingeffectandwaterarea,lengthandwidthwereanalyzed
asshowninFig.10.Thewatercoolingeffectwascorrelatedwiththewaterareaandwidthpositively,withthe
correlationcoefficientsof0.865and0.879,respectively,whileindependentofthewatershape.
Fig.8.ExtractedimagemapsofwaterandvegetationinGuangzhou.aWater.bVegetation.
308 Y.Shi,Y.Zhang/UrbanClimate24(2018)299–310
Fig.9.Changeoftemperaturedropwithbufferdistanceforwater.
4.6.Vegetationcoolingeffect
ThirteenpiecesofforestryinGuangzhouwithdifferentarea,shape,andcanopydensitywereselectedas
samplestobestudied.Thedefinitionoftemperaturedropandtheanalysismethodkeptthesamewiththose
ofwater.TheresultsareshowninFig.11.
Itcanbeseenthatwhenthebufferdistancebecamegreater,thetemperaturedropdecreasedrapidlyini-
tially,andthentendedtobestablewithathresholdof350m.Furtheranalysisontherelationshipsbetween
Fig.10.Relationshipsbetweenwatercoolingeffectandwaterareaandwidth.
Fig.11.Changeoftemperaturedropwithbufferdistanceforvegetation.
