New Scheme for Validating Remote-Sensing Land Surface Temperature Products with Station ...

24 Pages·2017·5.96 MB·English

remote sensing
Article
New Scheme for Validating Remote-Sensing Land
Surface Temperature Products with Station Observations
WenpingYu1,*,MingguoMa1,* ID,ZhaoliangLi2,JunleiTan3andAdanWu3
1 ChongqingEngineeringResearchCenterforRemoteSensingBigDataApplication,SchoolofGeographical
Sciences,SouthwestUniversity,No.2TianshengRoad,BeibeiDistrict,Chongqing400715,China
2 ICube,Uds,CNRS(UMR7357),300BldSebastien-Brant,CS10413,67412Illkirch,France;[email protected]
3 HeiheRemoteSensingExperimentalResearchStation,NorthwestInstituteofEco-Environmentand
Resources,ChineseAcademyofSciences,320DonggangWestRoad,Lanzhou730000,China;
[email protected](J.T.);[email protected](A.W.)
* Correspondence:[email protected](W.Y.);[email protected](M.M.)
Received:27September2017;Accepted:20November2017;Published:24November2017
Abstract: Continuousland-surfacetemperature(LST)observationsfromground-basedstationsare
an important reference dataset for validating remote-sensing LST products. However, a lack of
evaluationsoftherepresentativenessofstationobservationslimitsthereliabilityofvalidationresults.
In this study, a new practical validation scheme is presented for validating remote-sensing LST
products that includes a key step: assessing the spatial representativeness of ground-based LST
measurements. Threeindicators,namely,thedominantland-covertype(DLCT),relativebias(RB),
and average structure scale (ASS), are established to quantify the representative levels of station
observationsbasedontheland-covertype(LCT)andLSTreferencemapswithhighspatialresolution.
WevalidatedMODISLSTsusingstationobservationsfromtheHeiheRiverBasin(HRB)inChina.
Thespatialrepresentativeevaluationstepsshowthattherepresentativenessofobservationsgreatly
differsamongstationsandvarieswithdifferentvegetationgrowthandotherfactors.Largedifferences
inthevalidationresultsoccurwhenusingdifferentrepresentativelevelobservations,whichindicates
alargepotentialforlargeerrorduringthetraditionalT-basedvalidationscheme. Comparisonsshow
thatthenewvalidationschemegreatlyimprovesthereliabilityofLSTproductvalidationthrough
high-levelrepresentativeobservations.
Keywords: spatialrepresentativeness;heterogeneity;validation;land-surfacetemperatureproducts
(LSTs);observations;HiWATER;remotesensing
1. Introduction
Land-surfacetemperature(LST)isanimportantparameterrelatedtothesurfaceenergyandwater
balanceatlocalandglobalscalesandhasprincipalsignificanceforapplicationssuchasmonitoringthe
climate,hydrologicalcycle,andvegetation[1]. Satelliteremotesensingprovidesarepetitivesynoptic
view in short intervals of the global land surface and is a vital tool for monitoring the LST of the
Earth. Withthedevelopmentofremote-sensingtechnology,manyLSTproductshavebeenprovided
bydifferentgroupsbasedonretrievalfromdifferentsatellitedata[2–5]. Thefirstlong-termglobal
sensingLSTdataset,theNOAA/NASAPathfinderAVHRRLanddataset(PAL)[2],wasreleasedin
1994. ThesecondgenerationAVHRRLandPathfinderΠ(PALΠ)wasarefinementproductfromthe
PALreleasedin2000[3]. SunandPinkerestimatedLSTproductsfromaGeostationaryOperational
EnvironmentalSatellite(GEOS)in2003[4]. TheLSTsfromSpinningEnhancedVisibleandInfrared
Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) was retrieved in 2008 [6]. As a
partofNASAEarthObservingSystem(EOS)project,MODISLSTshaveplayedanimportantrolein
recentstudies,especiallyinregionalstudies,becauseofthesuitabletemporalandspatialresolution,
RemoteSens.2017,9,1210;doi:10.3390/rs9121210 www.mdpi.com/journal/remotesensing
RemoteSens.2017,9,1210 2of24
acceptableaccuracy,andaccessibilityoftheseLSTs. Therefore,MODISdailyLST/LSEproductswith
1-kmspatialresolutionarevalidatedinthisstudy.
Remotely sensed LSTs must be appropriately and precisely evaluated to ensure effective
application [7]. Mainly two types of methods exist for validating LST products retrieved from
thermal-infraredsatellitedata: temperature-basedmethods(T-based)andradiance-basedmethods
(R-based)[8–14]. ThemainadvantageofR-basedmethodsisthattheyworkduringboththedaytime
andnighttimebecauseinsituLSTobservationsarenotrequired,andfindingvalidationsiteswith
smallspatialvariationsinland-surfaceemissivityisrelativelyeasy[14]. However,theatmospheric
andwatervaporprofilesatvalidationsitesfromradiosondeballoonsthataresynchronouslylaunched
with the satellite are a necessary dataset, which limits the implementation of this method for
long-termandlarge-regionvalidation.Therefore,T-basedmethodsremaincommon,andground-based
measurements are still the primary source of datasets to directly validate remotely sensed LSTs.
However,wecannotperformadirectcomparisonwithapixelgrid,especiallyforacoarse-resolution
productoverheterogeneousareas,becauseofthespatialheterogeneityanddifferentscalesbetween
ground-basedobservationsandremotelysensedLSTpixels.
Agenerallyacceptedmethodisasystematicsite-to-networkmethod,whichdeeplydevelops
an in situ sampling strategy and upscaling approaches to acquire the truth at the pixel scale over
a heterogeneous surface based on multiscale, multi-platform and multi-source observations [15].
Thisapproachemploysbothfieldmeasurementsfromnodesofawirelesssensornetwork(WSN)and
high-resolutionremote-sensingdatafromsynchronoushigh-resolutionsatellitesorairbornesensors
to establish a site-specific relationship and generate high-resolution LST reference maps over the
validationarea[15]. TheseLSTreferencemapsarethentreatedasbenchmarkstoobtainmultiscale
validationdatasetsbyupscalingmethods[16–18]. However,onlyafewhigh-resolutionLSTreference
mapscanbesynchronouslyobtainedforagivenregionbecauseofcostlimitationsandtherevisiting
cyclesofsatelliteswithhighresolution,whichisthegreatestchallengetowardstheglobalvalidation
ofLSTproducts,especiallyintermsoftemporalconsistencyinproductvalidation.
IncontrasttomorecomplicatedR-based,site-to-networkmethodswithlimitedLSTreference
maps, simple T-based methods are directly based on existing global, long-term ground LST
measurementsandareanimportantsupplementforvalidation. SimpleT-basedmethodshavebeen
widelyusedtovalidateremotelysensedLSTproductsathomogeneousstations[8,13,19].Whendirectly
validating LST products with spatial resolutions above hundreds or even thousands of meters by
ground-basedmeasurements,theerrorfromthescalemismatchchangeswiththeland-covertype(LCT)
andtheproportionsofmixturesinpixelgridsreducethereliabilityofthevalidationandhinderthe
applicationofground-basedLSTmeasurementsduringthevalidationofremotelysensedLSTproducts.
Colletal. havepointedoutthatduringtheday, LSTcanvaryby10Kormoreoverafewmeters
inaheterogeneoussurface[9]. Ground-basedLSTmeasurementsfromtwotypesLSTobservation
instrumentswithdifferentfieldofview(FOV)wereselectedtodiscussthescalemismatchimplications
forvalidationofremotesensingLSTproductsinthestudybyYuetal.,andthevalidationresultsshow
thatthereisanextra26.9%intheerror>3Krangecausedbythe41.5FOVdifference[20].Therefore,
wemustassessthespatialrepresentativenessofstationobservationsatagivenspatialresolutionto
reliablyvalidateremotelysensedLSTs. Recently,severalmethodshavebeenusedtoassessthespatial
representativenessofdifferentland-surfaceparameters,suchastheleafareaindex[21],surfacesolar
radiation[22],bidirectionalreflectancedistributionfunction(BRDF)/albedo[23],airtemperature[24]
and air quality [25], which are observed by ground stations. These methods are based on two
factors: thepoint-to-areaconsistencyandthespatialheterogeneity[21]. Thepoint-to-areaconsistency
indicatorcanbecalculatedthroughtwomethods. Thefirstinvolvesdirectlycomparingthefootprint
of the ground-station observations to the corresponding product pixels [26] or the average value
of the corresponding area [27]. In the second approach, the observational representativeness is
determinedbytheaveragedifferencebetweenagivenstationanditsneighboringstationsbasedon
multi-temporalobservationsfrommultiplestations[9,28]. Thesemi-varianceisusuallyselectedto
RemoteSens.2017,9,1210 3of24
describethespatialrepresentativenessbyanalyzingthespatialheterogeneityaroundthestations[29].
Afirst-orderstatisticalalgorithmisanimportantspatialheterogeneityindicator,forexample,using
window-size analysis to assess the spatial variation of the landscape around a given station [30].
Spatialrepresentativenessassessmentshavebeenwidelyimplementedtovalidatesatellite-albedo,
evapotranspiration,andLAIproducts[21,23,26,31,32]. However,fewrepresentativenessassessments
Remote Sens. 2017, 9, 1210 3 of 24
existforstationLSTobservations,whichincreasestheuncertaintyofthevalidationofLSTproducts,
heterogeneity around the stations [29]. A first-order statistical algorithm is an important spatial
particularlyforsimpleT-basedimplementations,andhinderstheapplicationofstationobservations.
heterogeneity indicator, for example, using window-size analysis to assess the spatial variation of the
ThispaperpresentsanewmethodologyforvalidatingLSTproductsthatfocusesonquantifying
landscape around a given station [30]. Spatial representativeness assessments have been widely
the spatiaiml prelepmreensteend tatot ivvaelnideastes soafteslltiatet-iaolbnedoob, seevravpaottriaonnsspirtaotioimn, parnodv LeAtIh peroadcuccutsr a[2c1y,23a,n26d,31r,e3l2i]a. bility of
LST prodHuocwtevvaerl,i dfeawt iroepnr.eseTnthaetivteenremss a“sssepssamtieanltsr eexpisrte fsoer nsttaatitoinv eLnSTe sosb”serrveafteiorsns,t wohmiche ainscureraesmes ethnet s of the
degree touwncheritcahingtyr oofu tnhed v-balaidsaetdiono bofs LeSrTv aptrioodnusctcs,a pnarrteicsuolalvrlye ftohr esismuprlreo Tu-bnadseidn gimLpSleTmebnytaetixontes,n adnidn g to the
hinders the application of station observations.
satellite footprint. This validation technique assesses the spatial characteristics of the LST, and
This paper presents a new methodology for validating LST products that focuses on quantifying
the seasonal representativeness changes within a statistical framework. A scheme that is based
the spatial representativeness of station observations to improve the accuracy and reliability of LST
on spatiaplrroedpurcet sveanlidtaattiiovne. nTehses teirnmd i“csaptaotirasl riesprperseensteantitveednesisn” trhefiesrsp tao pmeera,saunredmetnhtes nof tthhee dgergardeei ntog criteria
are outlinwehdicihn gdroeutnadil-.baAseldl othbseersvtaattioionns scafnr ormesoltvhee thHe esiuhreroWunadtinegrs LhSeTd bAy lelxietedndTinegle tmo ethtrey saEtexllpiteer imental
footprint. This validation technique assesses the spatial characteristics of the LST, and the seasonal
Research (HiWATER) [33] are selected for applying the validation strategy. The study area and
representativeness changes within a statistical framework. A scheme that is based on spatial
data-processing procedure are introduced in Section 3. In Section 4, the representativeness of the
representativeness indicators is presented in this paper, and then the grading criteria are outlined in
givenstatdioetnaiol. bAsellr vthaet iostnastioisnsa sfsroemss etdhet oHveaihleid aWteateMrsOheDd ISAlVlie5dd TaeillyemLeStrTy pErxopderuimctesn(taMl OReDse/aMrchY D11A1).
Therepres(HeniWtaAtiTvEeRn)e [s3s3]a asrsee sseslemcteedn tfoarn adppLlySinTgp threo dvaulicdtavtiaonli dstarattieogny.a Trheea sltsuodya naraelay aznedd daatnad-prdoicsecsusisnsge dinthis
Section. Fpinroacleldy,utrhe earceo innctrloudsuiocends ianr eSescutimonm 3a. rIinz eSdec.tion 4, the representativeness of the given station
observations is assessed to validate MODIS V5 daily LST products (MOD/MYD11A1). The
representativeness assessment and LST product validation are also analyzed and discussed in this
2. Methodology
Section. Finally, the conclusions are summarized.
2.1. NewValidationScheme
2. Methodology
TheLSTisaland-surfaceparameterwithgreatspatialandtemporalheterogeneity,whichcreates
2.1. New Validation Scheme
manychallengesfor“point-to-pixel”comparisons. Localchangesinthesurfacetemperaturewithin
The LST is a land-surface parameter with great spatial and temporal heterogeneity, which
andbetweendifferentecosystemsintroducescalemismatcherrors. Moreover,thesepatternschange
creates many challenges for “point-to-pixel” comparisons. Local changes in the surface temperature
seasonallyandareparticularlydifficulttoidentifyduringperiodsofrapidlychangingsurfaceconditions.
within and between different ecosystems introduce scale mismatch errors. Moreover, these patterns
Thereforec,h“apnogein set”asmonealalys uanredm aree nptasrtaicluolnarelya drieffincuoltt stou ifdfeicniteifny tdtuorinvga pliedriaotdes soaf trealpliidtely- dchearnivgiendg sLuSrfTacree trievals,
especiallycornedmitiootnes-. sTehnesreinfogreL, “SpToinptr”o mdeuacsutsrewmeitnhts maloondee arrae tneoat nsudffilcoiewnt rtoe svoalliudtaitoe nsa(teilllliutes-tdrearitveedd iLnSTF igure1).
retrievals, especially remote-sensing LST products with moderate and low resolution (illustrated in
Thistemporalmismatchcanbesolvedbyincreasingtheobservation-acquisitionfrequencyofstations.
Figure 1). This temporal mismatch can be solved by increasing the observation-acquisition frequency
Theschemethatisdevelopedheretovalidateremote-sensingLSTproducts(seeFigure1)attempts
of stations. The scheme that is developed here to validate remote-sensing LST products (see Figure 1)
to solve sapttaetmiapltsm tois smolvaetc shpaetifafle mctissmdautcrhin egffevctasl diduaritnigo nv.aliTdahteionk.e Tyhein ketyh iins tshcihs escmheemies isto toa asssseessss tthhe e spatial
representasptiavteianl eresps,rewsehnitcahtiviesnbesass, ewdhoicnh rise mbaosetde -osne nresminogted-saetnasiwngi tdhatha iwghiths phiagthia slpraetisaol lruestiooluntitohna tthaatr eclosely
related toatrhe ecloLsSelTy. reInladteidc atot othres LaSrTe. pInrdoicpaotosresd arteo prqoupaosnetdif tyo qthuaenstipfya tthiae lspreatpiarle rseepnretsaetnivtaetinveensse,ssa, nandd then the
then the grading criteria are designed before selecting appropriate ground-based measurements for
gradingcriteriaaredesignedbeforeselectingappropriateground-basedmeasurementsforvalidation.
validation.

Figure1.FNigeuwres 1c. hNeemw escfhoermlea nfodr lsaundrf saucrefatceem tepmepreartauturree ((LLSSTT) )vvalaidliadtiaotni obnasebda soend thoe nastshesesmasesnet sosfm loecanl toflocal
spatialrepspreatsiealn rteaptrievseennteastisve(snietses l(esivtee lle)v.eIln). Itnh ethsec shcheemmee,, LLCCTT isi sthteh aebabrbebvriaetvioina toifo lnanodf-cloavnedr -tycpoev, earndty pe,and
NDVI is the abbreviation of normalized difference vegetation index.
NDVIistheabbreviationofnormalizeddifferencevegetationindex.
RemoteSens.2017,9,1210 4of24
2.2. IndicatorsforAssessingSpatialRepresentativeness
Three indicators are proposed to describe the spatial characteristics for a specific parameter,
mainlyincludingtheconsistencyfrompointstopixelsandthespatialheterogeneitywithinpixels.
These indicators are calculated on high-resolution images, which are much easier to access, thus
simplifyingtheprocess. TheLSTisadirectparameterforassessingtherepresentativenessofagiven
station’sLSTobservations. However,obtainingtemporalhigh-spatial-resolutionLSTmatchesforLST
productswithlowerresolutionisdifficult. Therefore,high-resolutionland-covertype(LCT)andthe
normalizeddifferencevegetationindex(NDVI),whichcanindicatethesurfaceconditionsandtheir
changes,arechosenasadditionalsupportingparameterstoevaluateground-basedLSTobservations
inadditiontoLSTdatawithahighspatialresolution.
IftheLCTsobservedbystationsdonotmatchthedominantLCTsinthepixels,thesestationLST
observationscannotrepresenttheLSToftheLCTsinthepixels. Thus,thedominantLCT(DLCT),
whichisgivenbythepercentageoftheobservedLCTthroughoutthepixel’sarea,isdefinedas
M(s)
DLCT= ×100 (1)
N(s)
where s is the product pixel, M(s) is the area with the LCT that is observed by the given station,
andN(s)isthetotalareaoftheLCTsintheLSTproduct’spixelgrid. Whenusingahigh-resolution
LCT map, the DLCT can also be described as the percentage of fine pixel numbers covered by
station-observed LTC to total LCT pixel numbers in the LST product’s pixel range. A high DLCT
indicatesthattheLCTobservedbyastationisconsistentwiththatintheLSTproduct’spixelandlow
heterogeneitywithintheproductpixelbecausethemixingrateofLCTsinthepixelisnotlarge.
Wedevelopedarelativebias(RB)indicatortoassesshowcloseaground-basedLSTmeasurement
istothevalueofthecorrespondingpixelarea. Accordingtothehigh-spatial-resolutionLSTreference
images, therelativebiasisusedtodescribethedifferencebetweentheLSTvalue T(s) atastation
andtheaverageLSTvalueT(s)intheproductpixel’sarea. Ifweconsiderthecomparabilitybetween
differentrangesofLSTvalues,theRBisdefinedas
|T(s)−T(s)|
RB= ×100 (2)
T(s)
wheresistheproductpixelandRBdependsonbothitsresolutionandtheresolutionofthereference
LSTimage. Thisindicatorcanquantifythecertaintyoftheground-basedmeasurementstotheproduct
pixel area LST values. A smaller RB indicates more spatially representative observations for the
correspondingpixelatthespecificspatialresolutionofs.
Thetwoaboveindicatorsmainlymeasurethepoint-to-areavalueconsistency,sotheheterogeneity
ofthespatialdistributionoftheLSTwithinapixel,whichiscorrelatedwiththevegetationgrowth,must
beseasonallyquantified. IntermsofthespatialautocorrelationofLSTparameters,semivariogramis
oneofthemostcommonlyusedandefficientgeostatisticalanalysistoolsforquantitativelyevaluating
spatial variations. Semi-variance and related geostatistical kriging were developed from mining
researchduringthelate1950sandhavebeenwidelyusedafterapublicationbyJournelandHuijbregts
in1978[34,35]. Thesegeostatisticaltechniqueshavebeenusedinmanyscientificprojects,suchasin
describingthedistributionanddensityofplantsandanimals[36,37]andindeterminingthespatial
scalesofvariationandsamplingstrategiesinremotesensing[38–40]. Inregionalizedvariabletheory,
thesemi-variancemeasuresthedissimilarityofaspatialvariableobservedatdifferentlocations. The
semi-varianceiscalculatedbytheaveragesquareddifferencebetweenobservationsZ(x )andZ(x ),
i j
whichareseparatedbydistanceh,asdescribedbelow:
r(h) = 1 ∑ (Z(x )−Z(x ))2 (3)
2N(h) i j
||xi−xj||=h
RemoteSens.2017,9,1210 5of24
where2N(h)isthenumberofobservationpairs,whichareseparatedbyadistanceh,orlag,asintervals
tocalculatethesemi-variance. Inthisstudy,thevariogramestimatorr(h)iscomputedondiscretized
point values from high-spatial resolution LST pixels, and then a variogram model is established
asaparametricfunctionalapproximationbasedonthesesemi-variancevalues. Severaltheoretical
variogrammodeltypesexist, includinglinearmodels, sphericalmodels, exponentialmodels, and
Gaussianmodels. Amongthesemodels,sphericalmodelsarethemostwidelyusedvariogrammodels
fortheirstrongfittingandgeneralizationcapabilitiesandarerecommendedforassessingthespatial
representativenessofobservations[23,32]. Theisotropicsphericalvariogramthatisusedtoestimate
thevariogramisasfollows:
 (cid:18) (cid:16) (cid:17)3(cid:19)
 c +c× 1.5× h −0.5× h for0≤ h ≤ a
r (h) = 0 a a (4)
sph
 c +c forh > a
0
In Equation (4), it is obvious that the r (h) generally increases from a nonzero value to a
sph
relativelystableconstantvaluewithh. Whenh =0,ther (h)valueisanonzerovaluec ,namely,
sph 0
r (0) = c , and c isthenuggetofthevariogram. Thestableconstantvalueisthesill (c +c) of
sph 0 0 0
thevariogram. Whenr (h)reachesthesill,thevalueofthevariablehisa,namely,therangeofthe
sph
variogram. Thekeyparametersrange(a),nugget(c ),andsill(c +c)canbeobtainedfromfitting
0 0
Equation(4). aisthemaximaldistancebetweenthetwocorrelatedpointsandindicatestheaverage
structural scale (ASS) of the given area. The nugget c indicates the level of Z(x )’s randomness,
0 i
which may be caused by internal variations in Z(x ) over a smaller distance h than the sampling
i
distanceormaybederivedfromthesamplingerror. Thesill(c +c)representsthelargestextentof
0
theregionalizedvariation. LiandReynolds[41]introducedtheproportionofstructuralvariation,
whichisbasedonsubtractingthevariogramnugget(c )fromthesill(c)andthendividingbythesill,
0
todiscussthedefinitionandquantificationofecologicalheterogeneity. However,theheterogeneity
ofLSTparametersconsiderablyvariesovertimewhencomparedtothatofecologicalparameters,
andreferencehigh-resolutionLSTmapsmaynotbecompletelysynchronouswiththevalidatedLST
products. Therefore,theASSisintroducedbasedontherangeofthevariogrammodel,whichreflects
theaveragestructuralscaleofthegivenareaandthesizeofthehomogeneousarea. AlargeASSvalue
indicatesthatthestationobservationsrepresentalargehomogeneousarea.
3. DataInstructionandPreparation
3.1. Ground-BasedLSTMeasurements
Inthisstudy,weselectedtheHeiheRiverBasin(HRB),whichisthesecondlargestinlandvalley
in China’s arid regions, to evaluate MODIS LSTs with 1-km resolution. The HRB is located in the
northernaridregionwithin97.1◦E–102.0◦Eand37.7◦N–42.7◦N.Glaciers,frozensoils,alpinemeadows,
forests, irrigated crops, riparian ecosystems, deserts, and gobi are distributed from upstream to
downstreamregions(seeFigure2). TheHRBwasselectedasanexperimentalwatershedtorevealthe
processesandmechanismsoftheecohydrologicalsysteminaninlandriverbasin. Alliedtelemetry
experimentssuchastheHeiheBasinFieldExperiment(HEIFE)[42]andWatershedAlliedTelemetry
ExperimentalResearch(WATER)[43]havebeenconductedintheHRB,andtheHiWATER[33]project
has been ongoing since 2012. The stations that collect watershed hydrological observations cover
a wider range than those in previous studies and provide a large amount of ecohydrological data
forevaluation. EighteenatmosphericstationsfromHiWATERarescatteredaroundtheHRBregion.
Longwave-radiationdataforeighteenstationsfrom2013to2014wereselectedtoobtainground-based
LSTstoevaluatetheMODISLSTs. ThelocationsofthestationsareshowninFigure2. Theinformation
for the stations is listed in Table 1, and environmental photos of these sites are shown in Figure 3.
TheARC,ARS,ARY,DSL,JYL,HZS,HCG,andEBZstationsarelocatedintheupstreamarea;the
DMZ,GBZ,HZZ,SDZ,andSSWstationsarelocatedinthemidstreamarea;andthedownstreamarea
RemoteSens.2017,9,1210 6of24
containstheSDQ,HJL,HYZ,NTZ,andLTZstations. TheLCTsofthesestationsarealltypicaltypesin
thethreesignificantlydifferentareas.
Remote Sens. 2017, 9, 1210 6 of 24

FiFgiugruere2 .2.S Stutuddyya arreeaa aanndd llooccaattiioonnss ooff tthhee vvaalliiddaatitoionn stsatatitoionns.s .
TTabablele1 1..I nInffoorrmmaattiioonn ffoorr tthhee ssttaattiioonnss iinn tthhisis sstutuddyy. .
SSttaattiioonn NNaammee LLoonnggiittuuddee//◦°EE LLataittiutudde/e◦/N°N AAltlittiutudde/em/m HHeeiigghhtt//mm FFoooottpprriinntt 11/m/m LLaannddssccaappeess
A’rou superstation (ARC) 100.46 38.05 3033 5 37.32 Alpine meadow
A’rousuperstation(ARC) 100.46 38.05 3033 5 37.32 Alpinemeadow
A’rou sunny slope station
100.52 38.09 3559 6 44.78 Alpine grassland
(AA’rRoSu) sunnyslope 100.52 38.09 3559 6 44.78 Alpinegrassland
station(ARS)
A’rou shade station (ARY) 100.42 37.99 3538 6 44.78 Alpine grassland
DA’arsohuaslohnagd estsatatitoionn (D(ASRLY) ) 19080.9.452 373.89.983 33573785 66 4444.7.788 ASwlpainmepg mraesasldaonwd
JDinayshanalgolninggs statatitoionn( D(JSYLL)) 9180.19.511 383.78.385 33777050 66 4444.7.788 SAwlpaimnep mmeeaaddooww
Huangzangsi station (HZS) 100.19 38.23 2660 6 44.78 Cropland (wheat)
Jinyanglingstation(JYL) 101.11 37.85 3700 6 44.78 Alpinemeadow
Huangcaogou station (HCG) 100.73 38.00 3186 6 44.78 Alpine grassland
Huangzangsistation(HZS) 100.19 38.23 2660 6 44.78 Cropland(wheat)
E’bo station (EBZ) 100.94 37.96 3407 6 44.78 Alpine grassland
DHaumanagnc asuogpoeurstsatatitoionn (D(HMCZG)) 110000..7337 383.80.086 31158169 162 8494.5.778 AClrpoipnleangdra (smslaanizde)
GE’obboi ssttaattiioonn ((EGBBZZ)) 110000..9340 373.89.689 31450771 66 4444.7.788 AGlopbini Degesraesrts land
HDaumazahnasizuip deerssteartti ostnat(iDoMn Z) 110000..3372 383.88.677 11571296 21.25 1889.6.567 CDreospelratn sdte(pmpaei ze)
(HZZ)
Gobistation(GBZ) 100.30 38.89 1571 6 44.78 GobiDesert
Wetland station (SDZ) 100.45 38.97 1460 6 44.78 Wetland
SHhueanzshhaaiwziod deseesretrst tsattaiotinon(H ZZ) 100.32 38.77 1726 2.5 18.66 Desertsteppe
100.49 38.79 1582 6 44.78 Desert
(WSSetWla)n dstation(SDZ) 100.45 38.97 1460 6 44.78 Wetland
PShoepnuslhuas wfooredsets setrattion (HYZ) 101.12 41.99 927 24 179.14 Populus forest
100.49 38.79 1582 6 44.78 Desert
Cstraotipolnan(SdS sWta)tion (NTZ) 101.13 42.01 919 6 44.78 Cropland
Barren-land station (LTZ) 101.13 41.99 931 6 44.78 Bare soil
Populusforeststation(HYZ) 101.12 41.99 927 24 179.14 Populusforest
Euphrates poplar
Croplandstation(NTZ) 101.13 42.01 919 6 44.78 Cropland
Sidaoqiao station (SDQ) 101.12 41.99 935 10 74.64 olive and Tamarix
Barren-landstation(LTZ) 101.13 41.99 931 6 44.78 Bmairxeedso fiolrest
EEuupphhrraatteess ppooppllaarr
MSidixaeodq ifaooresstta tsitoantio(SnD (QH)JL) 110011..1123 414.19.999 993259 2104 17749..6144 oolliivvee aanndd TTaammaarriixx
mmiixxeedd ffoorreesstt
1 “Footprint” refers to the diameter of the footprint; “Height” indicates the installationE huepihgrahtte.s poplar
Mixedforeststation(HJL) 101.13 41.99 929 24 179.14 oliveandTamarix
mixedforest
Eighteen meteorological towers are located in the HRB region, consisting of three superstations
1“Footprint”referstothediameterofthefootprint;“Height”indicatestheinstallationheight.
and fifteen ordinary stations. Pyrgeometers are deployed at these 10-m to 35-m high meteorological
station towers to measure longwave radiation (see Figure 3 and Table 1). At least two pyrgeometers
EighteenmeteorologicaltowersarelocatedintheHRBregion,consistingofthreesuperstations
are positioned on a single tower: one facing upward and the other facing downward. The field-of-
andfifteenordinarystations. Pyrgeometersaredeployedatthese10-mto35-mhighmeteorological
view (FOV) of the upward-facing pyrgeometer is nearly 180°, while that of the downward-facing
stationtowerstomeasurelongwaveradiation(seeFigure3andTable1). Atleasttwopyrgeometers
pyrgeometer is 150°. Therefore, the effective diameter of the FOV of the pyrgeometers on a 10-m to
arepositionedonasingletower: onefacingupwardandtheotherfacingdownward. Thefield-of-view
35-m tower is approximately 2.5–24 m with a 6-m average mounting height, and the diameters of the
ground-observation footprints are shown in Table 1. The pyrgeometers are sensitive to the spectral
range from 4.5 to 42 µm in the longwave band. All the instruments at each station were calibrated
RemoteSens.2017,9,1210 7of24
(FOV) of the upward-facing pyrgeometer is nearly 180◦, while that of the downward-facing
pyrgeometeris150◦. Therefore,theeffectivediameteroftheFOVofthepyrgeometersona10-mto
35-mtowerisapproximately2.5–24mwitha6-maveragemountingheight,andthediametersofthe
ground-observationfootprintsareshowninTable1. Thepyrgeometersaresensitivetothespectral
rangefrom4.5to42µminthelongwaveband. Alltheinstrumentsateachstationwerecalibrated
beforeandafterfielddeployment. Field-routingexamswereimplementedoncepermonth. Assurance
andqualitycontrolprovidedthebestpossibledataforthelevel-2dailydata. Allthesedataandrelated
informationcanbefoundattheHiWATERwebsite[44]. Alltheground-basedmeasureddatafromthe
eighteenRHemioWte SAenTs.E 20R17,s 9i,t 1e2s10w ere10-minaveragedvalues. Thelongwaveradiationdataw7 oef r2e4 selected
accordingtothefieldviewingtimefortheMODISLST.TheLSTisrelatedtoland-surfacelongwave
before and after field deployment. Field-routing exams were implemented once per month.
radiationAascscuorarndcien agntdo qtuhaelitSyt ecofanntr-oBl oplrtozvmidaedn nthlea bwes[t1 p,4os5s]i:ble data for the level-2 daily data. All these
data and related information can be found at the HiWATER website [44]. All the ground-based
measured data from the eighteenL ↑H=iWεAbTδETR4 s+ite(s1 w−erεeb )10×-mLin↓ averaged values. The longwave (5)
radiation data were selected according to the field viewing time for the MODIS LST. The LST is
where L↑reilsatethd eto slaunrdfa-scuerfaucpe wlonegllwinavge lroadnigatwioanv aeccorraddiniagt tioo nth,e εSbtefiasnt-Bhoeltzsmurafnanc leawb r[1o,4a5d]:b and emissivity, δ
isStefan-Boltzmann’sconstant(5.67 ×(cid:1838)10=−8(cid:2013) W(cid:2012)(cid:1846)·(cid:2872)m+−(12·−K(cid:2013)−)4)×,(cid:1838)an d L↓ istheatmosphericdo(5w) nwelling
↑ (cid:3029) (cid:3029) ↓
longwaveradiationatthesurface. Therefore,theground-measuredLSTcanbeestimatedfromstation
where (cid:1838) is the surface upwelling longwave radiation, (cid:2013) is the surface broadband emissivity, (cid:2012) is
longwave-radiat↑ionobservationsbythefollowingequat(cid:3029)ion:
Stefan-Boltzmann’s constant (5.67 × 10−8 W∙m−2∙K−4), and L is the atmospheric downwelling
↓
lloonnggwwaavvee -rraaddiiaattiioonn aotb tsheer svuartifoances. T bTyh =ethree(cid:20)f ofoLrle↑l,o t−whein( gg1r oe−uqnuεdabt-)imo×ne:a sLu↓re(cid:21)d14 LST can be estimated from station (6)
s
εbδ (cid:2869)
InEquation(6),L↑andL↓areobtain(cid:1846)e(cid:3046)d=fr(cid:3428)o(cid:1838)↑m−t(h1e(cid:2013)−g(cid:2012)(cid:2013)r(cid:3029)o)u×n(cid:1838)d↓(cid:3432)-(cid:2872)b asedmeasurements. Sevenn(6a) rrowband
(cid:3029)
emissivities exist in MOD/MYD11B1 LST/LSE products. ε can be estimated from these MODIS
In Equation (6), (cid:1838) and (cid:1838) are obtained from the gbround-based measurements. Seven
↑ ↓
narrowbannadrroewmbiasnsdi vemitiiesssiv[i4ti5e]s: exist in MOD/MYD11B1 LST/LSE products. (cid:2013) can be estimated from these
(cid:3029)
MODIS narrowband emissivities [45]:
ε =0.2122×ε +0.3859×ε +0.4029×ε (7)
b (cid:2013) =0.2122×2(cid:2013)9 +0.3859×(cid:2013) 31+0.4029×(cid:2013) 32 (7)
(cid:3029) (cid:2870)(cid:2877) (cid:2871)(cid:2869) (cid:2871)(cid:2870)
whereεbisthebroadbandemissivity,andε29,εε31,aεndε32aεrethenarrowemissivityproductsofMODIS
bands29,w3h1e,rea n(cid:2013)(cid:3029)d i3s 2thteh bartoaadrbearnedt reimevisesidviftyro, amndt he29M, O31D, aInSd da3y2 /arnei gthhet nLarSrTowa legmoisrsiitvhimty p(rModOucDts/ oMf YD11B1
MODIS bands 29, 31, and 32 that are retrieved from the MODIS day/night LST algorithm
LST/LSE).
(MOD/MYD11B1 LST/LSE).

Figure3.Cont.
RemoteSens.2017,9,1210 8of24
Remote Sens. 2017, 9, 1210 8 of 24

Figure3.FiPghuroet o3.s Pohfottohse of1 8theH 1e8i hHeeiWhe aWteartserhsehdedA Alllliieedd TTeelelmemetreyt rEyxpEexripmeernitmal eRnetsaealrRche s(HeaiWrcAhT(EHR)i WATER)
stations.Tsthateiotnws.o Thpey trwgoe opmyrgeetoemrsettehras tthwate wreerue suesdedt too rreeccoorrdd thteh leonlognwgawvea rvadeiartaiodni awtieoren dwepeloreyedde apt laony edatan
average height of 6 m, with one facing upwards and the other facing downwards.
averageheightof6m,withonefacingupwardsandtheotherfacingdownwards.
3.2. Remote-Sensing Data
3.2. Remote-SensingData
3.2.1. MODIS Data
3.2.1. MODISADs aat acomponent of NASA’s Earth Observing System (EOS) project, two MODIS instruments
were placed onboard the Terra and Aqua satellite platforms to provide information for global
AsacomponentofNASA’sEarthObservingSystem(EOS)project,twoMODISinstrumentswere
atmosphere-, land- and oceanic-process studies [46]. MOD/MYD22_L2, MOD/MYD11A1,
placedonMboOaDr/dMtYhDe1T1Be1rr aanadn MdOADq/MuaYDsa0t7e_lLl2it earpe laaltl fdoarimly sLtSoT pprroodvuidctes ibnafsoedrm ona ttihoenrmfoalr-ignflroabreadl adtamta osphere-,
land- andfroomc eMaOnDicI-Sp. rMoOceDs/sMYstDu1d1_ieLs2 w[4a6s] r.etrMievOedD b/yM a YgeDne2r2a_lizLe2d, sMpliOt-wDin/dMowY Dalg1o1rAith1m, wMitOh D1-/kmM YD11B1
and MOD/MYD07_L2 are all daily LST products based on thermal-infrared data from MODIS.

MOD/MYD11_L2 was retrieved by a generalized split-window algorithm with 1-km spatial
resolution [47]. MOD/MYD11A1 is tile-based and gridded in the sinusoidal projection from
MOD/MYD11_L2. MOD/MYD11B1wasobtainedusingtheday/nightLSTalgorithmat5-kmspatial
resolution[48]. MOD/MYD07_L2wasretrievedbytheatmosphericteamusingstatisticalregression
methods [49]. In this study, we focus on the collection of 5 MOD/MYD11A1 products, which are
morewidespread. Uncertaintiesfromthesatellitemeasurementsandimprovementsintheoriginal
RemoteSens.2017,9,1210 9of24
MODISLSTsforcloudydaysarebeyondthescopeofthispaper.Toeliminateeffectsfromtheinversion
algorithmandclouds,onlypixelswithhigh-qualityMODISLSTswereselectedfortheevaluation
basedonaqualitycontrolflagvalueof0. ThenarrowemissivityproductsfromMOD/MYD11B1
LST/LSEwereselectedtoestimatetheland-surfacebroadbandemissivityε andobtainground-based
b
LSTs[45]. ThenarrowemissivitiesfromMOD/MYD11B1LST/LSEat5-kmresolutionwereresampled
to1-kmspatialresolutiontomatchtheevaluatedMODISLSTs.
3.2.2. High-Spatial-ResolutionImages
Land-cover images with a spatial resolution of 30 m (data doi:10.3972/hiwater.155.2014.db,
downloadedfromtheHiWATERlandcoverdatasets[50],whichwereproducedbyZhongetal.[51,52],
wereselectedtoobtaintheDLCTina1-kmLSTproductpixel. Thisdatasetwasmainlybasedon
charge-coupled device (CCD) data from the Huan Jing 1 (HJ-1) satellite, which was launched on
6 September 2008, by the China Center for Resources Satellite Data and Application (CRESDA).
HJ-1/CCDhasthreevisiblebandsandonenear-infraredband[53]. Thisdatasetprovidesmonthly
land-covermapsoftheHRBfrom2011to2015with30-mspatialresolution. LCTschangewiththe
seasons,andthesechangesaresimilaracrossconsecutiveyears,sotheDLCTproductsin2013were
collectedtocalculatetheDLCTindicator.
Landsat8,whichiscalledtheLandsatDataContinuityMission,isextendingthedistinguished
40-yearrecordsofLandsat-seriessatellitesandhasenhancedcapabilities,suchasaddingnewspectral
bandsinthevisibleandthermal-infraredwavelengthsandimprovingthesignal-to-noiseratioand
radiometric resolution of the sensor [54]. The Landsat 8 satellite includes two instruments: an
Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). High-resolution LST maps
wereretrievedfromtheLandsat8TIRSdataandOLIdata. BeforeretrievingtheLSTmaps,theLandsat
OLIandTIRSimageswerepreprocessed,includingradiometriccalibrationandatmosphericcorrection
basedonthecorrectionmodelintheENVIsoftware. MonthlyNDVImapswereobtainedtoassessthe
relationshipsbetweenthemonthlychangesintheindicatorsandvegetationgrowth. TheNDVImaps
werebasedonthevisibleredband(R,band4)andthenear-infraredband(NIR,band5)accordingto
thefollowingequation:
NIR−R
NDVI= (8)
NIR+R
Inthisstudy,theLSTdatawereestimatedfromtheTIRSaboardLandsat8basedonapractical
split-window (SW) algorithm developed by Du et al. [55]. The SW algorithm can be expressed
asfollows:
T = b +(cid:18)b +b 1−ε +b ∆ε(cid:19)Ti+Tj +(cid:18)b +b 1−ε +b ∆ε(cid:19)Ti−Tj +b (cid:0)T −T(cid:1)2 (9)
0 1 2 ε 3 ε2 2 4 5 ε 6 ε2 2 7 i j
whereTisLST,T andT aretheTOAbrightnesstemperaturesinthethermal-infraredchannelsiand
i j
j,respectively; εistheaverageemissivityofthetwochannels(i.e.,ε = 0.5(+ε )); ∆εisthechannel
j
emissivitydifference(i.e.,ε =0.5(ε −ε ));andb (k =0, 1, ... 7)arethealgorithmcoefficientsfrom
i j k
the simulated dataset. In this algorithm, the coefficients were determined based on atmospheric
water-vaporsubranges,whichwereobtainedthroughamodifiedsplit-windowcovariance-variance
ratiomethod. Thechannelemissivitieswereacquiredfromnewlyreleasedgloballand-coverproducts
at30mandafractionofthecalculatedvegetationcoverfromvisibleandnear-infraredimagesthat
wereobtainedbyLandsat8.
Theeffectofheterogeneitychangesdependingontheseason,soweselectedLandsat8datafrom
September2013toAugust2014witha16-daytemporalresolutiontocalculatetheRB,andASS.Atotal
of92Landsat8imagesforallthestationsintheHRBweredownloadedfromthefollowingUSGS
website[56]. Thestatisticalresultswerebasedonper-monthaveragestoeliminateinvaliddatafrom
cloudcover.
RemoteSens.2017,9,1210 10of24
4. ResultsandDiscussion
4.1. SpatialRepresentativenessClassification
SincetheDLCTandRBcanmeasurepoint-to-pixelvalueconsistency,andASScanassessthe
spatial patterns for a given station, respectively. Therefore, all three indicators were combined
to describe the representativeness including point-to-pixel consistency and spatial heterogeneity.
Thespatialrepresentativenesswasalsoclassifiedbasedonthesethreeindicators. TheDLCTindicator
determines the representativeness of the station’s LCT in the product pixel. When the LCT in the
viewfootprintofastationisnotdominantwithintheproductpixel,thestationobservationscannot
berepresentativeofthepixel,andtheLSTvalueofallothervegetation-covertypesmaybeignored,
evenifthepoint-to-areaLSTconsistencyishighatthestationsometime. Whenapixelhasalarge
DLCTvalue,theRBandASSsubsequentlywoulddeterminethespatialrepresentativenesstogether.
Presumably,thestation-observedLSTsrepresentidealdataforLSTproductvalidationiftheRBvalueis
smallandtheASSvalueislarge. IftheRBandareASSvaluearebothsmall,thestationmayhavesome
spatialrepresentation. Insomeextremecases,thestationobservingareaisanaverageheterogeneity
sub-areasintheproductspixel,whichmeansthestationobservationsarerepresentativeforpixels,
althoughthesurfaceisheterogeneousintheseproductspixels. Finally,iftheRBislargebuttheASSis
small,theobservationsprobablydifferfromthevaluesofthepixel.
ReasonablethresholdsfortheDLCT,RBandASSarerequiredtodeterminetherepresentativeness
levelofagivenstation’sobservations. Theemissivityproductsoftheversion5collectionofMODIS
LST/LSEproductsareretrievedbasedontheLCTofapixel,andtheLCTisclassifiedasthelandcover
ofthispixelbasedontheclassificationruleforMODISland-coverproducts(MCD12Q1)ifthearea
percentofoneLCTinapixelishigherthan60%[57]. Thus,60%wasdefinedasthethresholdofthe
DLCTinthisstudy. TheRBwasusedtoevaluatethedifferencebetweentheland-basedmeasurements
withintheviewfootprintateachstation(inTable1,theviewfootprintsareshowninthesixthcolumn)
andthemeanpixelvalueatthestationlocations. TheidealRBvalueisclosetozero. However,the
thresholdoftheRBisnotuniqueanddependsonthespatialresolutionoftheLSTproducts,theview
footprintofthestationmeasurements,andtheretrievalaccuracyofthehigh-spatial-resolutionLST
mapsthatareusedtoevaluatetherepresentativeness. Thus,areasonablethresholdfortheRBwas
0.5%inthisstudyforMODISLSTproductswith1-kmspatialresolution,astationviewfootprintabove
30manda1-Kretrievalerrorfromthehigh-resolutionLSTmapitself[55]. TheASSiscalculatedfrom
variogrammodelsbasedonthesemi-varianceina3-km×3-kmareathatiscenteredatagivenstation
andcanindicatethegreatestdistanceoverwhichthevalueatapointonthesurfaceisrelatedtothe
valueatanotherpoint. TheASSdefinesthemaximumneighborhoodoverwhichcontrolpointsshould
beselectedtoestimateagridnodetotakeadvantageofthestatisticalcorrelationamongobservations
andcandescribethespatialdistributionofLSTsandquantifytheaverageLSTspatialstructuresin
thegivenarea. Inthisstudy,themeasurementsfromstationswereusedtoevaluatetheMODISLSTs
witha1-kmspatialresolution. Therefore,areasonableASSindicatorshouldbelargerthanthespatial
resolutionoftheLSTproducts,thatis,1kminthisstudy.
Thespatialrepresentativenessofthestation’sLSTobservationswasclassifiedintofivedifferent
levels based on the difference-constraining degrees of the three indicators and their thresholds.
ThelevelsandtheirdescriptionsarepresentedinTable2.