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remote sensing
Article
Development of Himawari-8/Advanced Himawari
Imager (AHI) Land Surface Temperature
Retrieval Algorithm
Youn-Young Choi and Myoung-Seok Suh *
Department of Atmospheric Science, Kongju National University, 56, Gongjudaehak-ro, Gongju-si,
Chungcheongnam-do 32588, Korea; choiyoyo@smail.kongju.ac.kr
*Correspondence: sms416@kongju.ac.kr; Tel.: +82-41-850-8527
Received: 25 October 2018; Accepted: 8 December 2018; Published: 12 December 2018
Abstract:
We developed land surface temperature (LST) retrieval algorithms based on the time of
day and water vapor content using the Himawari-8/AHI (Advanced Himawari Imager) data, which
is the Japanese next generation geostationary satellite. To develop the LST retrieval algorithms,
we simulated the spectral radiance using the radiative transfer model (MODTRAN4) by applying the
atmospheric profiles (SeeBor), diurnal variation of LST and air temperature, spectral emissivity
of land surface, satellite viewing angle, and spectral response function of Himawari-8/AHI.
To retrieve the LST from Himawari-8 data, a linear type of split-window method was used in
this study. The Himawari-8 LST algorithms showed a high correlation coefficient (0.996), and a
small bias (0.002 K) and root mean square error (RMSE) (1.083 K) between prescribed LSTs and
estimated LSTs. However, the accuracy of LST algorithms showed a slightly large RMSE when
the lapse rate was larger than 10 K, and the brightness temperature difference was greater than
6 K. The cross-validation of Himawari-8/AHI LST using the MODIS (Terra and Aqua Moderate
Resolution Imaging Spectroradiometer) LST showed that annual mean correlation coefficient, bias,
and RMSE were 0.94, +0.45 K, and 1.93 K, respectively. The performances of LST algorithms were
slightly dependent on the season and time of day, generally better during the night (warm season)
than during the day (cold season).
Keywords: land surface temperature; Himawari-8; split-window method; MODIS
1. Introduction
Land surface temperature (LST) is affected by the solar zenith angle (SZA), albedo, land cover,
soil moisture, and so on [
1
–
3
], and is an important biophysical parameter of the Earth’s surface that
regulates sensible and latent heat fluxes between the surface and the atmosphere. It is therefore
important to obtain quantitative and periodic observational LST data for use in a variety of studies,
such as those analyzing surface urban heat islands of large cities, making drought predictions for
agricultural purposes, and estimating soil moisture [4–9].
LST is highly variable, both spatially and temporally, owing to nonuniform surface properties
such as vegetation, altitude, and soil moisture, and it is thus not possible to make in situ observations
that are sufficiently accurate and high resolution [
10
,
11
]. Thus, LST is regularly observed only at
very few special observatories. Currently, LST data, which are required by a variety of applications,
are obtained from satellite data observed at a high spatial resolution and short time intervals.
Attempts to retrieve LST using satellite data have been the basis of many studies conducted
since the 1970s, and some studies have focused on techniques for retrieving sea surface temperatures
(SSTs) from thermal infrared radiation and extending them to LST [
12
–
17
]. Specifically, a number of
Remote Sens. 2018,10, 2013; doi:10.3390/rs10122013 www.mdpi.com/journal/remotesensing
Remote Sens. 2018,10, 2013 2 of 20
algorithms have been developed for retrieving LST from data acquired by different satellites, such
as National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High-Resolution
Radiometer (AVHRR), Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS),
and Landsat Thematic Mapper (TM), which provide adequately high temporal and spatial resolution
data, regardless of geographical features [
15
,
18
–
23
]. Furthermore, with the substantial advances
made in the spatial resolution, temporal frequency and radiometric resolution of geostationary
meteorological satellites, many studies have begun to employ associated data to retrieve LST [
24
–
29
].
In Korea, the National Meteorological Satellite Center (NMSC) operationally retrieves LST using
the Communication, Ocean, Meteorological Satellite (COMS), which is Korea’s first geostationary
multipurpose satellite [30–32].
A new generation of meteorological satellites equipped with improved sensors have recently been
developed and launched by countries such as the United States, China, Japan, and the European Union
(EU), including the Geostationary Operational Environmental Satellite-16 (GOES-16, previously known
as GOES-R), Meteosat Third Generation (MTG), Himawari-8/9, and Feng Yun-4A (FY-4A)
[33–35]
. Also,
the Korean Meteorological Administration (KMA) plans to launch the GeoKompsat-2 Atmosphere
(GK-2A), which is equipped with an Advanced Meteorological Imager (AMI), in December 2018. The
United States’ GOES-16 Advanced Baseline Imager (ABI) group of NOAA’s National Environmental
Satellite, Data, and Information Service (NESDIS), and the EU’s Sentinel-3 Sea and Land Surface
Temperature Radiometer (SLSTR) group specify that LST is one of the primary outputs [
28
,
29
,
36
].
Although various studies on surface–atmosphere interactions are being performed in East Asia and
there is a growing demand for high-resolution and high-accuracy LST data, there is a limited number
of countries or institutes that officially provides LST data. Although the KMA provides LST data
retrieved from the COMS satellite, its mission ends in 2019. Therefore, retrieval and services of LST
data from the Himawari-8/Advanced Himawari Imager (AHI) and GK-2A/AMI data are necessary to
provide the LST continuously and utilize the new capability of these two satellites [37].
GK-2A/AMI is the geostationary meteorological satellite that will replace the COMS when
it is launched in 2018. In this study, we developed an LST retrieval algorithm using data from
Himawari-8/AHI with the aim of providing LST data for the East Asia region and the GK-2A/AMI
observation region. The contents of this paper are as follows. Data properties and research methods
relating to the LST retrieval algorithm are described in Section 2, and the LST retrieval processes and
results are presented in Section 3. We also present direct and indirect validation results using in situ
data and other satellite based LST data in Section 3. Furthermore, we discuss the current retrieval
status, limitations of the algorithm, and plans for future improvements in Section 4.
2. Data and Methods
2.1. Data
In this research, we used Himawari-8/AHI data provided by the KMA/NMSC. Himawari-8 is
Japan’s next-generation geostationary meteorological satellite that was launched in 2014 and has been
in operation since July 2015. It is located at 140.2
◦
E longitude and is equipped with an AHI sensor
that has 16 channels. To retrieve LST from Himawari-8/AHI data, we used data from channels 13
(Ch. 13, 10.41 µm) and 15 (Ch. 15, 12.38 µm) and their spectral response functions (SRFs), in addition
to land/sea and cloud masking data provided by the NMSC. Figure 1shows the SRFs of the thermal
infrared channels of Himawari-8/AHI. The Himawari-8/AHI has observation periods of 10 min,
full disk observation modes, and spatial resolutions of approximately 2 km at nadir. The observation
area of Himawari-8/AHI is characterized by different land cover types, including tropical forests and
desert areas, and by different climate conditions according to the geographic location and season.
Figure 2shows the spatial distribution of Himawari-8 LST over the full disk observation area. The
analysis period used in this study was one year, from September 2015 to August 2016. The output from
the GK-2A cloud detection algorithm provided by the NMSC was applied to the Himawari-8/AHI
Remote Sens. 2018,10, 2013 3 of 20
data as cloud masking data [
38
], and the temporal and spatial resolutions of the cloud masking data
are 1 h and approximately 2 km at its nadir, respectively.
RemoteSens.2018,10,xFORPEERREVIEW 3of20
TheLSTretrievalmethodsfromsatellitedatacanbesimplydividedintotwotypesbasedonthe
assumptionofknownlandsurfaceemissivity(LSE)[13,14,39,40]andLSTretrievalwithunknown
LSE[16,41–48].Inthisresearch,weusedthegeneralizedsplit‐windowmethodtoretrievetheLST
andassumedthatLSEwasalreadyknown[13,23].TheLSEdatausedinthisstudywerederivedfrom
[49],whichisthemodifiedversionofthevegetationcovermethod(VCM)inReference[50],andthese
werethenre‐griddedfortheHimawari‐8observationarea.ThetemporalresolutionoftheLSEis8
daysandthespatialresolutionis2km.
Figure1.RelativespectralresponsefunctionofHimawari‐8/AHIinfraredchannels.
Figure2.SampleimageofLSTretrievedfromHimawari‐8/AHIdata.
LSTretrievalmethodstypicallyconsiderseveralatmosphericandlandsurfacefactorsthataffect
LSTwithintheareaobservedbythesatellite.Simulateddataarethenconstructedbyperformingthe
radiativetransfermodel(RTM)usingvariousconditionsincludingatmosphericprofiles,viewing
geometry,andLSEandreferenceLSTdata.Furthermore,amultipleregressionbetweenthereference
LSTandtheestimatedbrightnesstemperatureofsatellitewasthenconducted[51–53].TheSeeBor
version5.0datawereusedastheatmosphericprofiles[54,55];thesedataprovideprofilesof
temperature,moisture,andozone,andrepresent15,704globalprofilesobtainedfromNOAA‐88,the
ECMWF60Ltrainingset,TIGR‐3,ozonesondes,andradiosondes[55].
InsituobservedLSTdatafortheHimawari‐8observationareaareverylimited.Inthisstudy,
weuseddatafromoneinsituBaselineSurfaceRadiationNetwork(BSRN)stationoverTateno(Japan)
tovalidatetheretrievedLST.Itwasthusnecessarytoconductacross‐validationforthealgorithmto
verifyitsabilitytoretrieveLST,andMODIS(MOD/MYD11_L2SwathProductscollection6)high‐
Figure 1. Relative spectral response function of Himawari-8/AHI infrared channels.
RemoteSens.2018,10,xFORPEERREVIEW 3of20
TheLSTretrievalmethodsfromsatellitedatacanbesimplydividedintotwotypesbasedonthe
assumptionofknownlandsurfaceemissivity(LSE)[13,14,39,40]andLSTretrievalwithunknown
LSE[16,41–48].Inthisresearch,weusedthegeneralizedsplit‐windowmethodtoretrievetheLST
andassumedthatLSEwasalreadyknown[13,23].TheLSEdatausedinthisstudywerederivedfrom
[49],whichisthemodifiedversionofthevegetationcovermethod(VCM)inReference[50],andthese
werethenre‐griddedfortheHimawari‐8observationarea.ThetemporalresolutionoftheLSEis8
daysandthespatialresolutionis2km.
Figure1.RelativespectralresponsefunctionofHimawari‐8/AHIinfraredchannels.
Figure2.SampleimageofLSTretrievedfromHimawari‐8/AHIdata.
LSTretrievalmethodstypicallyconsiderseveralatmosphericandlandsurfacefactorsthataffect
LSTwithintheareaobservedbythesatellite.Simulateddataarethenconstructedbyperformingthe
radiativetransfermodel(RTM)usingvariousconditionsincludingatmosphericprofiles,viewing
geometry,andLSEandreferenceLSTdata.Furthermore,amultipleregressionbetweenthereference
LSTandtheestimatedbrightnesstemperatureofsatellitewasthenconducted[51–53].TheSeeBor
version5.0datawereusedastheatmosphericprofiles[54,55];thesedataprovideprofilesof
temperature,moisture,andozone,andrepresent15,704globalprofilesobtainedfromNOAA‐88,the
ECMWF60Ltrainingset,TIGR‐3,ozonesondes,andradiosondes[55].
InsituobservedLSTdatafortheHimawari‐8observationareaareverylimited.Inthisstudy,
weuseddatafromoneinsituBaselineSurfaceRadiationNetwork(BSRN)stationoverTateno(Japan)
tovalidatetheretrievedLST.Itwasthusnecessarytoconductacross‐validationforthealgorithmto
verifyitsabilitytoretrieveLST,andMODIS(MOD/MYD11_L2SwathProductscollection6)high‐
Figure 2. Sample image of LST retrieved from Himawari-8/AHI data.
The LST retrieval methods from satellite data can be simply divided into two types based on the
assumption of known land surface emissivity (LSE) [
13
,
14
,
39
,
40
] and LST retrieval with unknown
LSE [
16
,
41
–
48
]. In this research, we used the generalized split-window method to retrieve the LST and
assumed that LSE was already known [
13
,
23
]. The LSE data used in this study were derived from [
49
],
which is the modified version of the vegetation cover method (VCM) in Reference [
50
], and these were
then re-gridded for the Himawari-8 observation area. The temporal resolution of the LSE is 8 days and
the spatial resolution is 2 km.
LST retrieval methods typically consider several atmospheric and land surface factors that affect
LST within the area observed by the satellite. Simulated data are then constructed by performing
the radiative transfer model (RTM) using various conditions including atmospheric profiles, viewing
geometry, and LSE and reference LST data. Furthermore, a multiple regression between the reference
LST and the estimated brightness temperature of satellite was then conducted [
51
–
53
]. The SeeBor
version 5.0 data were used as the atmospheric profiles [
54
,
55
]; these data provide profiles of
temperature, moisture, and ozone, and represent 15,704 global profiles obtained from NOAA-88,
the ECMWF 60 L training set, TIGR-3, ozonesondes, and radiosondes [55].
In situ observed LST data for the Himawari-8 observation area are very limited. In this study,
we used data from one in situ Baseline Surface Radiation Network (BSRN) station over Tateno (Japan)
Remote Sens. 2018,10, 2013 4 of 20
to validate the retrieved LST. It was thus necessary to conduct a cross-validation for the algorithm
to verify its ability to retrieve LST, and MODIS (MOD/MYD11_L2 Swath Products collection 6)
high-quality LST data were used for the indirect validation of retrieved LST data [
56
]. The intensive
validation of MODIS LST data with in situ observed LST data throughout the United States showed
that the root mean square error (RMSE) is less than 1.0 K [
57
–
60
]. MODIS LST data are swath data
with a spatial resolution of 1 km at nadir, and LST data are obtained at 5-min intervals as the polar
orbiting satellite, Terra/Aqua moves along its orbit.
2.2. Methodology
The Himawari-8/AHI LST retrieval algorithm developed in this research consisted of four
sequential steps, as shown in Figure 3. To produce the LST database that corresponds to Himawari-8/
AHI satellite observations, simulation data were generated using a variety of atmospheric and surface
conditions prescribed in the RTM. The RTM used in this study is MODTRAN4 and the detailed
documentation about the MODTRAN4 is found in Reference [
54
]. The accuracy of LST retrievals
is mainly influenced by the air temperature (Ta) lapse rate near the surface and the moisture in the
atmosphere [
11
,
23
,
24
,
32
,
61
]. In consideration of this, we separated the simulation data generated
through the MODTRAN 4 simulation according to the air temperature lapse rate (difference between
LST and Ta) and atmospheric moisture amount derived from the brightness temperature difference
(BTD) between channels 13 and 15, and then determined the LST retrieval coefficients. The LST
retrieval formulas were then used to estimate LST, which was subsequently compared to the reference
LST to evaluate the accuracy of LST retrieval formulas. The developed LST retrieval formulas
were directly applied to Himawari-8/AHI data to retrieve LST, and MODIS LST data were used
for indirect validation.
RemoteSens.2018,10,xFORPEERREVIEW 4of20
qualityLSTdatawereusedfortheindirectvalidationofretrievedLSTdata[56].Theintensive
validationofMODISLSTdatawithinsituobservedLSTdatathroughouttheUnitedStatesshowed
thattherootmeansquareerror(RMSE)islessthan1.0K[57–60].MODISLSTdataareswathdata
withaspatialresolutionof1kmatnadir,andLSTdataareobtainedat5‐minintervalsasthepolar
orbitingsatellite,Terra/Aquamovesalongitsorbit.
2.2.Methodology
TheHimawari‐8/AHILSTretrievalalgorithmdevelopedinthisresearchconsistedoffour
sequentialsteps,asshowninFigure3.ToproducetheLSTdatabasethatcorrespondstoHimawari‐
8/AHIsatelliteobservations,simulationdataweregeneratedusingavarietyofatmosphericand
surfaceconditionsprescribedintheRTM.TheRTMusedinthisstudyisMODTRAN4andthe
detaileddocumentationabouttheMODTRAN4isfoundinReference[54].TheaccuracyofLST
retrievalsismainlyinfluencedbytheairtemperature(Ta)lapseratenearthesurfaceandthemoisture
intheatmosphere[11,23,24,32,61].Inconsiderationofthis,weseparatedthesimulationdata
generatedthroughtheMODTRAN4simulationaccordingtotheairtemperaturelapserate
(differencebetweenLSTandTa)andatmosphericmoistureamountderivedfromthebrightness
temperaturedifference(BTD)betweenchannels13and15,andthendeterminedtheLSTretrieval
coefficients.TheLSTretrievalformulaswerethenusedtoestimateLST,whichwassubsequently
comparedtothereferenceLSTtoevaluatetheaccuracyofLSTretrievalformulas.ThedevelopedLST
retrievalformulasweredirectlyappliedtoHimawari‐8/AHIdatatoretrieveLST,andMODISLST
datawereusedforindirectvalidation.
Figure3.FlowchartofLSTretrievalprocessfromHimawari‐8/AHI.
ToconstructsimulationdatathroughtheRTMsimulationsneededforthedeterminationofthe
LSTretrievalcoefficientsofmultipleregressionequations,considerationofvariousimpactingfactors
isnecessary[26,30,51,52,62–65].Inthisresearch,weusedtheSRFoftheHimawari‐8/AHIand2818
profilesfromthe15704SeeBorv5.0profiles,forwhichtheviewingzenithangle(VZA)ofHimawari‐
Figure 3. Flowchart of LST retrieval process from Himawari-8/AHI.
Remote Sens. 2018,10, 2013 5 of 20
To construct simulation data through the RTM simulations needed for the determination of
the LST retrieval coefficients of multiple regression equations, consideration of various impacting
factors is necessary [
26
,
30
,
51
,
52
,
62
–
65
]. In this research, we used the SRF of the Himawari-8/AHI
and 2818 profiles from the 15704 SeeBor v5.0 profiles, for which the viewing zenith angle (VZA)
of Himawari-8/AHI is less than 50
◦
. This angle was chosen because the quality of the LST data is
significantly lower with an increase in the VZA. The RTM conditions used in these simulations are
shown in Table 1.
Table 1. Input conditions of RTM simulation according to impacting factors.
Impacting Factors Conditions
Atmospheric Profiles 2818 SeeBor profiles (version 5)
(Viewing zenith angle < 50 ◦)
Land Surface Temperature Day: Ta −2KtoTa+16K(astepof2K)
Night: Ta −6KtoTa+2K(astepof2K)
Air Temperature Ta0= Ta + (LST −Ta)/2
Land Surface Emissivity
εch13: 0.9478–0.9968 (a step of 0.0049)
−0.012 ≤∆ε≤0.012 (a step of 0.004)
If (εch15 ) > 1, εch15 = 0.9999
RTM simulations were conducted separately for day and night. We assumed that diurnal
variations of LST (Ta
−
6 K to Ta + 16 K) were greater than that of air temperature (Ta). Because the
observation area of Himawari-8/AHI is composed of various types of land cover, such as desert and
semi-desert [
66
], the conditions for separating day from night were based on the larger LST diurnal
variations during day time than night time, as shown in previous studies (Day: Ta
−
2 K to Ta +16
K; Night: Ta
−
6 K to Ta + 2K) [
22
,
30
,
67
]. To minimize the errors caused by fixed Ta, we included
diurnal variations in Ta based on the assumption that they were equal to half of the LST variation. The
lapse rate clearly depends on the land surface conditions (such as land cover, leaf area index, and soil
moisture) for the same SZA. We used overlap conditions in the range [Ta
−
2 K:Ta + 2 K] between
day and night to consider the diversity of the lapse rate. Furthermore, to account for the different
effects of water vapor, we developed LST retrieval equations separately according to total water vapor.
In this process, separation criteria were derived manually by visually inspecting RMSE variations
according to the amount of water vapor (can be represented BTD) in one regression equation. In this
study, separation of day and night was based on the temperature lapse rate in the RTM simulation as
shown in Figure 4; however, the SZA of each pixel was used to separate day from night when LST was
retrieved from Himawari-8/AHI data. As in many studies, threshold angles for the day and night
separation are 80
◦
and 100
◦
, respectively. The pixels with SZA between 80
◦
and 100
◦
were regarded as
the dawn and twilight period, and the LST of these pixels were recalculated as the linear average of
the two LST algorithms (the day and night LST algorithms).
Figure 4shows the RMSE values according to factors affecting LST retrieval. Results show that
RMSE values are mostly affected by the BTD, regardless of the other impacting factors. To reflect the
strong impact of BTD on LST retrieval, we developed the LST retrieval algorithm as a function of
BTD and constructed dry (BTD
≤
0 K), normal (0 K
≤
BTD
≤
6 K), and moist (BTD > 6 K) algorithms
because the BTD stands for the total atmospheric water vapor.
Remote Sens. 2018,10, 2013 6 of 20
RemoteSens.2018,10,xFORPEERREVIEW 5of20
8/AHIislessthan50°.ThisanglewaschosenbecausethequalityoftheLSTdataissignificantlylower
withanincreaseintheVZA.TheRTMconditionsusedinthesesimulationsareshowninTable1.
Table1.InputconditionsofRTMsimulationaccordingtoimpactingfactors.
ImpactingFactorsConditions
AtmosphericProfiles2818SeeBorprofiles(version5)
(Viewingzenithangle<50°)
LandSurfaceTemperatureDay:Ta−2KtoTa+16K(astepof2K)
Night:Ta−6KtoTa+2K(astepof2K)
AirTemperatureTa′=Ta+(LST−Ta)/2
LandSurfaceEmissivity
𝜀:0.9478–0.9968(astepof0.0049)
−0.012∆ε0.012(astepof0.004)
If(𝜀)>1,𝜀=0.9999
RTMsimulationswereconductedseparatelyfordayandnight.Weassumedthatdiurnal
variationsofLST(Ta−6KtoTa+16K)weregreaterthanthatofairtemperature(Ta).Becausethe
observationareaofHimawari‐8/AHIiscomposedofvarioustypesoflandcover,suchasdesertand
semi‐desert[66],theconditionsforseparatingdayfromnightwerebasedonthelargerLSTdiurnal
variationsduringdaytimethannighttime,asshowninpreviousstudies(Day:Ta−2KtoTa+16K;
Night:Ta−6KtoTa+2K)[22,30,67].TominimizetheerrorscausedbyfixedTa,weincludeddiurnal
variationsinTabasedontheassumptionthattheywereequaltohalfoftheLSTvariation.Thelapse
rateclearlydependsonthelandsurfaceconditions(suchaslandcover,leafareaindex,andsoil
moisture)forthesameSZA.Weusedoverlapconditionsintherange[Ta−2K:Ta+2K]betweenday
andnighttoconsiderthediversityofthelapserate.Furthermore,toaccountforthedifferenteffects
ofwatervapor,wedevelopedLSTretrievalequationsseparatelyaccordingtototalwatervapor.In
thisprocess,separationcriteriawerederivedmanuallybyvisuallyinspectingRMSEvariations
accordingtotheamountofwatervapor(canberepresentedBTD)inoneregressionequation.Inthis
study,separationofdayandnightwasbasedonthetemperaturelapserateintheRTMsimulation
asshowninFigure4;however,theSZAofeachpixelwasusedtoseparatedayfromnightwhenLST
wasretrievedfromHimawari‐8/AHIdata.Asinmanystudies,thresholdanglesforthedayandnight
separationare80°and100°,respectively.ThepixelswithSZAbetween80°and100°wereregarded
asthedawnandtwilightperiod,andtheLSTofthesepixelswererecalculatedasthelinearaverage
ofthetwoLSTalgorithms(thedayandnightLSTalgorithms).
Figure4showstheRMSEvaluesaccordingtofactorsaffectingLSTretrieval.Resultsshowthat
RMSEvaluesaremostlyaffectedbytheBTD,regardlessoftheotherimpactingfactors.Toreflectthe
strongimpactofBTDonLSTretrieval,wedevelopedtheLSTretrievalalgorithmasafunctionof
BTDandconstructeddry(BTD≤0K),normal(0K≤BTD≤6K),andmoist(BTD>6K)algorithms
becausetheBTDstandsforthetotalatmosphericwatervapor.
Figure4.DistributionofRMSEsbetweenLSTcalculatedfromradiativetransfermodelsimulated
brightnesstemperatureandprescribedLSTaccordingtodifferentimpactingfactors:(a)brightness
Figure 4.
Distribution of RMSEs between LST calculated from radiative transfer model simulated
brightness temperature and prescribed LST according to different impacting factors: (
a
) brightness
temperature difference (BTD) and surface lapse rate, (
b
) emissivity difference and BTD, (
c
) satellite
viewing zenith angle (VZA) and BTD.
In this research, a linear type of split-window method was developed to retrieve LST from Himawari-8/
AHI data. The split-window method uses the difference in absorption between two adjacent infrared
channels to correct for atmospheric effects. This algorithm is expressed as a combination of simple
linear formulas with various impacting factors (e.g., BTD, LSE, and VZA) [
19
,
23
,
68
]. Split-window
methods provide relatively high accuracies and retrieval efficiencies; they are therefore applied to the
various satellites and used in many LST retrievals [24,26,28,32,35,68,69]:
LST =c0+c1BTCh.13 +c2(BTCh.13 −BTCh.15 )+c3(secθ−1)+c4(1−ε)+c5∆ε(1)
where
BTch.13
and
BTch.15
are the brightness temperatures of channels 13 and 15, respectively;
θ
is the
VZA;
ε
is the mean LSE of infrared channels 13 and 15;
∆ε
is the difference in LSE between channels 13
and 15; and
c0
,
c1
,
c2
,
c3
,
c4
, and
c5
are the regression coefficients for each LST retrieval formula. Here,
channels 13 and 15 refer to channels 13 (10.41
µ
m) and 15 (12.38
µ
m), respectively, of Himawari-8/AHI.
Coefficients of multiple regression algorithms according to the lapse rate and water vapor amount are
shown in Table 2.
Table 2. Coefficients of Himawari-8/AHI LST retrieval coefficients according to the conditions.
Conditions c0c1c2c3c4c5
Day
Moist
67.1857
0.7448 2.07 1.096 63.061 −
75.1606
Normal 8.926 0.9651 0.9364 −
0.1385
56.8638 −
63.8708
Dry
15.3567
0.9461 1.1996 −1.411 48.5137 −
68.3093
Night
Moist
44.5826
0.8205 2.0427 1.6411 58.5399 −
59.1371
Normal
12.1778
0.9535 0.9278 −0.095 51.2696 −
51.8349
Dry
20.3004
0.9279 1.0879 −
1.4883
47.2503 −
61.7212
Validation methods of the retrieved LST from satellites are temperature-based (T-based),
radiance-based (R-based), and cross-validation-based methods [
10
]. In this study we used T-based
and cross-validation methods. T-based validation methods use LST from an in situ observation site
within the satellite observation area to validate the accuracy of the estimated LST [
27
,
70
–
72
]. In situ
observed LST is point observation data; hence, the land surface that corresponds to the satellite’s
spatial resolution must be uniform; however, as the surface is typically complex, spatial representation
problems can occur. Cross-validation methods verify the retrieved LST using LST obtained from
other satellites, which is useful in areas where it is difficult to apply T-based and R-based validation
methods [
57
]. The spatial and temporal variability of LST is large; therefore, when satellite-retrieved
LST is validated using cross-validation methods, rigorous spatial–temporal collocations must be
performed. It is therefore necessary to consider the differences between the two satellites, such as
Remote Sens. 2018,10, 2013 7 of 20
their observation time, spatial resolution, cloud detection, and the LSE data used when retrieving the
LST. The cross-validation method has the advantage of being able to compare a certain point in an
area observed by two satellites, but it also has limitations in that the LST data obtained from the other
satellite for validation can also contain errors. To evaluate LST accuracy, most previous research has
used both temperature-based validation and the cross-validation method, or has used cross-validation
for areas where there are no in situ observatories [18,32,57,58,73].
For temporal collocation, Himawari-8 cloud mask information is provided at regular intervals
from the NMSC; therefore, we used MODIS LST information observed at the same time (within
±
5 min). For spatial collocation, we took a simple mean of the clear pixels from the nearest 3
×
3
MODIS pixels surrounding a Himawari-8 pixel (if more than five clear pixels were present).
3. Results
3.1. Results of Radiative Transfer Model Simulation
To evaluate the accuracy of the Himawari-8/AHI LST retrieval algorithms, we compared the
estimated LST obtained using regression equations with the reference LST inputted to the RTM (shown
in Table 1). Figure 5shows the scatter plot and histogram of these values obtained from the RTM
reference LST and retrieved LST.
RemoteSens.2018,10,xFORPEERREVIEW 7of20
3.Results
3.1.ResultsofRadiativeTransferModelSimulation
ToevaluatetheaccuracyoftheHimawari‐8/AHILSTretrievalalgorithms,wecomparedthe
estimatedLSTobtainedusingregressionequationswiththereferenceLSTinputtedtotheRTM
(showninTable1).Figure5showsthescatterplotandhistogramofthesevaluesobtainedfromthe
RTMreferenceLSTandretrievedLST.
Figure5.(a)Scatterplotand(b)histogramshowingdifferencesbetweenthereferenceLSTand
estimatedLSTusingtheHimawari‐8/AHILSTretrievalalgorithm.
Asshowninthescatterplot,thereisagoodmatchoverawiderangefrom250Kto330K.The
correlationcoefficient,bias,andRMSEwere0.996,0.002K,and1.083K,respectively;theseresultsall
confirmthatLSTwaswellestimated.However,problemsoccurredinthe300Kto320Krange,where
theLSTalgorithmunderestimatedLSTcomparedtothereferenceLST.Thefrequencydistributionof
biasshowedanalmostnormaldistributionwith0Kasthecenter,andnosystematicerrorwas
observed.
Figure6showsthedistributionofRMSEsforHimawari‐8LSTretrievalalgorithmsbasedon
variousimpactingfactors.Theareasshowninwhiteindicatealackofdataforarange,andtheRMSE
couldthereforenotbeshown.Ingeneral,theRMSEincreasedandtheaccuracydecreasedwhenthe
BTDwasgreaterthan6K.Ch.13wasmore(less)sensitivetoaerosols(watervapor)thanCh.15.
Therefore,thebrightnesstemperaturedifferencebetweenCh.13andCh.15indicatetherelative
amountsofaerosolsandwatervaporintheatmosphere:largerpositiveandnegativevaluesindicate
largeramountsofwatervaporandaerosolsintheatmosphere,respectively.Moreover,whenthe
VZAwasabove40°andtheBTDwasabove8K,therewasasignificantincreaseintheRMSE.Thisis
becausewhenageostationarysatellitelocatedattheequatorobservestheEarth’ssurface,thereisan
increaseintheopticalpathlengthtotheEarth’ssurfacewithanincreaseintheVZA;theatmospheric
attenuationeffectthenincreases,whichlowerstheretrievalaccuracy.Theseeffectsaremore
significantinSouthEastAsiabecauseofthecombinedeffectsofalargeVZAandalargewatervapor
content.ThedistributionofRMSEaccordingtotheBTDandlapseratevariesgreatly,ratherthanthe
emissivitydifference.
Figure 5.
(
a
) Scatter plot and (
b
) histogram showing differences between the reference LST and
estimated LST using the Himawari-8/AHI LST retrieval algorithm.
As shown in the scatter plot, there is a good match over a wide range from 250 K to 330 K.
The correlation coefficient, bias, and RMSE were 0.996, 0.002 K, and 1.083 K, respectively; these
results all confirm that LST was well estimated. However, problems occurred in the 300 K to 320 K
range, where the LST algorithm underestimated LST compared to the reference LST. The frequency
distribution of bias showed an almost normal distribution with 0 K as the center, and no systematic
error was observed.
Figure 6shows the distribution of RMSEs for Himawari-8 LST retrieval algorithms based on
various impacting factors. The areas shown in white indicate a lack of data for a range, and the
RMSE could therefore not be shown. In general, the RMSE increased and the accuracy decreased
when the BTD was greater than 6 K. Ch. 13 was more (less) sensitive to aerosols (water vapor) than
Ch. 15. Therefore, the brightness temperature difference between Ch. 13 and Ch. 15 indicate the
relative amounts of aerosols and water vapor in the atmosphere: larger positive and negative values
Remote Sens. 2018,10, 2013 8 of 20
indicate larger amounts of water vapor and aerosols in the atmosphere, respectively. Moreover, when
the VZA was above 40
◦
and the BTD was above 8 K, there was a significant increase in the RMSE.
This is because when a geostationary satellite located at the equator observes the Earth’s surface,
there is an increase in the optical path length to the Earth’s surface with an increase in the VZA;
the atmospheric attenuation effect then increases, which lowers the retrieval accuracy. These effects are
more significant in South East Asia because of the combined effects of a large VZA and a large water
vapor content. The distribution of RMSE according to the BTD and lapse rate varies greatly, rather
than the emissivity difference.
RemoteSens.2018,10,xFORPEERREVIEW 8of20
Figure6.DistributionofRMSEsfortheHimawari‐8LSTretrievalalgorithmsbasedonvarious
impactingfactors:(a)brightnesstemperaturedifferences(BTD)andsurfacelapserate,(b)emissivity
differenceandBTD,(c)emissivitydifferenceandsurfacelapserate,(d)satelliteviewingzenithangle
(VZA)andBTD,and(e)VZAandsurfacelapserate.
3.2.Cross‐ValidationResultsUsingMODISLST
ToevaluatetheHimawari‐8/AHILSTretrievalalgorithms,LSTswereretrievedforoneyear
fromSeptember2015toAugust2016,aperiodinwhichbothHimawari‐8/AHILevel1Bdataand
NMSCclouddetectionoutputwereavailable.AsLSTcanonlyberetrievedwhenskiesareclearand
cloudless,weusedHimawari‐8/AHIcloudmaskingdata,whichwasproducedbytheGK‐2Acloud
detectionalgorithmdevelopmentteam[38].
Figure7showsHimawari‐8andMODISLSTsat1500UTConDecember12,2015andthe
differencesbetweenthetwotemperatures.Thespatialdistributionsweresimilar(spatialcorrelation
coefficient:0.994),andthedifferencesbetweenthetwotemperaturesweremostlywithin±2K.The
MODISLSTwasretrievedforthesouth‐centralregionofChina,probablybecauseofdifferences
betweentheclouddetectionalgorithmsinthetwodatasets.Inthescatterplot,theLSTcovereda
widerangefrom250Kto300K,andmatchesweregoodforLSTsgreaterthan270K;however,the
Himawari‐8LSTshowedaslightoverestimationforLSTlessthan270KcomparedtoMODISLST.
ThebiasandRMSEofthecasewere−0.332Kand1.089K,respectively.
Figure 6.
Distribution of RMSEs for the Himawari-8 LST retrieval algorithms based on various
impacting factors: (
a
) brightness temperature differences (BTD) and surface lapse rate, (
b
) emissivity
difference and BTD, (
c
) emissivity difference and surface lapse rate, (
d
) satellite viewing zenith angle
(VZA) and BTD, and (e) VZA and surface lapse rate.
3.2. Cross-Validation Results Using MODIS LST
To evaluate the Himawari-8/AHI LST retrieval algorithms, LSTs were retrieved for one year from
September 2015 to August 2016, a period in which both Himawari-8/AHI Level 1B data and NMSC
cloud detection output were available. As LST can only be retrieved when skies are clear and cloudless,
we used Himawari-8/AHI cloud masking data, which was produced by the GK-2A cloud detection
algorithm development team [38].
Figure 7shows Himawari-8 and MODIS LSTs at 1500 UTC on December 12, 2015 and the
differences between the two temperatures. The spatial distributions were similar (spatial correlation
coefficient: 0.994), and the differences between the two temperatures were mostly within
±
2 K.
The MODIS LST was retrieved for the south-central region of China, probably because of differences
between the cloud detection algorithms in the two data sets. In the scatter plot, the LST covered
a wide range from 250 K to 300 K, and matches were good for LSTs greater than 270 K; however,
the Himawari-8 LST showed a slight overestimation for LST less than 270 K compared to MODIS LST.
The bias and RMSE of the case were −0.332 K and 1.089 K, respectively.
Remote Sens. 2018,10, 2013 9 of 20
RemoteSens.2018,10,xFORPEERREVIEW 9of20
Figure7.Spatialdistributionof(a)Himawari‐8LST,(b)MODISLST,(c)theirdifferences,and(d)
scatterplotbetweenHimawari‐8andMODISLSTforDecember12,2015,1500UTC.
Figure8showsthetwoLSTsat0300UTConFebruary8,2016.Theirspatialdistributionsand
temperaturerangesweresimilar,buttheestimatedHimawari‐8LSTtendedtobehigherthanthe
MODISLSTovertheentireanalysisarea.Inparticular,therewasasystematicwarmbias(+1.6K)
between+0Kand+4KinsoutheastChinaandTaiwan,whichisbelievedtoaffecttheoverallretrieval
accuracy.Asaresult,theRMSEandcorrelationcoefficientwerenotgoodat1.88Kand0.936,
respectively.
Figure 7.
Spatial distribution of (
a
) Himawari-8 LST, (
b
) MODIS LST, (
c
) their differences, and (
d
)
scatter plot between Himawari-8 and MODIS LST for December 12, 2015, 1500 UTC.
Figure 8shows the two LSTs at 0300 UTC on February 8, 2016. Their spatial distributions and
temperature ranges were similar, but the estimated Himawari-8 LST tended to be higher than the
MODIS LST over the entire analysis area. In particular, there was a systematic warm bias (+1.6 K)
between +0 K and +4 K in southeast China and Taiwan, which is believed to affect the overall
retrieval accuracy. As a result, the RMSE and correlation coefficient were not good at 1.88 K and
0.936, respectively.
Remote Sens. 2018,10, 2013 10 of 20
RemoteSens.2018,10,xFORPEERREVIEW 10of20
Figure8.SameasFigure7exceptforFebruary8,2016,0300UTC.
Figure9showstheHimawari‐8andMODISLSTsat1500UTConMay4,2016.Thespatial
patternsofthetwoLSTsweregenerallyingoodagreementoverallfromTibet’splateauregionto
HanoiinVietnamandthentosoutheastChina.Asaresult,theaccuracyoftheretrievedLSTfrom
Himawari‐8/AHIdatawasrelativelyreasonable(corr.:0.984,bias:−0.679K,RMSE:1.213K).The
scatterplotalsoshowedthattherewasagoodmatchbetweenthetwoLSTsforawiderangeofLSTs
from265Kto305K.
Figure 8. Same as Figure 7except for February 8, 2016, 0300 UTC.
Figure 9shows the Himawari-8 and MODIS LSTs at 1500 UTC on May 4, 2016. The spatial
patterns of the two LSTs were generally in good agreement overall from Tibet’s plateau region
to Hanoi in Vietnam and then to southeast China. As a result, the accuracy of the retrieved LST
from Himawari-8/AHI data was relatively reasonable (corr.: 0.984, bias:
−
0.679 K, RMSE: 1.213 K).
The scatter plot also showed that there was a good match between the two LSTs for a wide range of
LSTs from 265 K to 305 K.
Remote Sens. 2018,10, 2013 11 of 20
RemoteSens.2018,10,xFORPEERREVIEW 11of20
Figure9.SameasFigure7exceptforMay4,2016,1500UTC.
Figure10showsthespatialdistributionandscatterplotofLSTfor0300UTConAugust25,2016.
AdiscontinuitywasobservedinthespatialdistributionofHimawari‐8LSTcomparedtothatof
MODISoverNortheasternChinaandMongolia;thiswasrelatedtothedifferentcloud‐masking
algorithms.ThelargespatialgradientofLSTintheMongolianregionwasmainlycausedbyland
coverdifferences(desert(HunsandakeDesert),semi‐desert,andgrass).Asshowninthespatial
distributionofLSTdifferences,thescatterplotalsoshowsthatouralgorithmoverestimatesand
underestimatedLSTinarangeoflessthan290Kandgreaterthan310K,respectively.
Figure 9. Same as Figure 7except for May 4, 2016, 1500 UTC.
Figure 10 shows the spatial distribution and scatter plot of LST for 0300 UTC on August 25,
2016. A discontinuity was observed in the spatial distribution of Himawari-8 LST compared to that
of MODIS over Northeastern China and Mongolia; this was related to the different cloud-masking
algorithms. The large spatial gradient of LST in the Mongolian region was mainly caused by land cover
differences (desert (Hunsandake Desert), semi-desert, and grass). As shown in the spatial distribution
of LST differences, the scatter plot also shows that our algorithm overestimates and underestimated
LST in a range of less than 290 K and greater than 310 K, respectively.
Remote Sens. 2018,10, 2013 12 of 20
RemoteSens.2018,10,xFORPEERREVIEW 12of20
Figure10.SameasFigure7exceptforAugust25,2016,0300UTC.
TofurtherquantitativelyanalyzetheHimawari‐8LSTalgorithm,weretrievedLSTforoneyear,
fromSeptember2015toAugust2016,andcomparedtheresultswithMODISLSTcollection6
products,asshowninTable3.Aspreviouslymentioned,theHimawari‐8cloudmaskingdatacan
onlybeobtainedeveryhour;therefore,validationwasconductedonlywhenitmatchedMODISLST
inspatial‐temporalconditions,andthedatesofanalysisthusdifferedbetweenthemonths.The
correlationcoefficientbetweentheHimawari‐8LSTandMODISLSTduringthedaytimewasgreater
than0.9foreachmonthexceptNovember2015,whichshowedarelativelylowcorrelation.Overall,
thecorrelationbetweenthetwoLSTswashigherfornighttimethanforthedaytime:theRMSEfor
daytimewaslessthan2.7Kinspring(March,April,andMay),2.6Kinsummer(June,July,and
August),2.6Kinautumn(September,October,andNovember),butover2.9Kinwinter(December
andJanuary),withtheexceptionofFebruary2016.However,theRMSEatnighttimewaslessthan
1.9Kforallmonths.Thecombinedresultsofdaytimeandnighttime,themonthlybiases,andthe
RMSEsofthewinter(December,January,andFebruary:DJF)seasonwereslightlylargerthanother
seasons.TheseresultsshowthatitwasnecessarytoimprovetheaccuracyoftheHimwari‐8LST
algorithmbyconductingadetailederroranalysis,particularlyforwinter(DJF).
Figure 10. Same as Figure 7except for August 25, 2016, 0300 UTC.
To further quantitatively analyze the Himawari-8 LST algorithm, we retrieved LST for one
year, from September 2015 to August 2016, and compared the results with MODIS LST collection
6 products, as shown in Table 3. As previously mentioned, the Himawari-8 cloud masking data can
only be obtained every hour; therefore, validation was conducted only when it matched MODIS
LST in spatial-temporal conditions, and the dates of analysis thus differed between the months.
The correlation coefficient between the Himawari-8 LST and MODIS LST during the daytime was
greater than 0.9 for each month except November 2015, which showed a relatively low correlation.
Overall, the correlation between the two LSTs was higher for nighttime than for the daytime: the
RMSE for daytime was less than 2.7 K in spring (March, April, and May), 2.6 K in summer (June,
July, and August), 2.6 K in autumn (September, October, and November), but over 2.9 K in winter
(December and January), with the exception of February 2016. However, the RMSE at nighttime was
less than 1.9 K for all months. The combined results of daytime and nighttime, the monthly biases,
and the RMSEs of the winter (December, January, and February: DJF) season were slightly larger than
other seasons. These results show that it was necessary to improve the accuracy of the Himwari-8 LST
algorithm by conducting a detailed error analysis, particularly for winter (DJF).
Remote Sens. 2018,10, 2013 13 of 20
Table 3.
Results of comparison between Himawari-8 LST data and MODIS LST products (collection 6)
from September 2015 to August 2016.
Month
Day Night Total (Day + Night)
# of
Scene Corr. Bias
(K)
RMSE
(K)
# of
Scene Corr. Bias
(K)
RMSE
(K)
# of
Scene Corr. Bias
(K)
RMSE
(K)
Sep 2015 6 0.92 0.90 1.71 6 0.97 −
0.48
1.31 12 0.95 0.12 1.48
Oct 2015 5 0.92 1.45 2.57 7 0.97 0.07 1.14 12 0.95 0.53 1.62
Nov 2015 6 0.86 0.95 2.55 5 0.98 0.06 1.66 11 0.93 0.48 2.08
Dec 2015 7 0.95 1.97 2.96 5 0.99 0.40 1.48 12 0.96 1.52 2.53
Jan 2016 5 0.95 2.94 3.66 3 0.99 −
0.55
1.13 8 0.96 2.44 3.30
Feb 2016 3 0.94 1.46 2.01 3 0.98 1.09 1.89 6 0.96 1.28 1.95
Mar 2016 6 0.88 1.06 2.62 3 0.99 0.17 1.34 9 0.92 0.72 2.13
Apr 2016 8 0.90 0.50 2.63 5 0.97 0.11 1.09 13 0.93 0.33 1.94
May 2016 6 0.92 −
0.14
2.59 3 0.96 −
0.43
1.28 9 0.93 −
0.25
2.07
Jun 2016 3 0.94 −
0.82
2.54 4 0.97 −
0.38
1.26 7 0.96 −
0.49
1.58
Jul 2016 5 0.92 −
0.82
2.51 5 0.88 −
0.04
1.39 10 0.89 −
0.23
1.66
Aug 2016 6 0.91 −
0.19
1.94 4 0.95 −
0.38
1.27 10 0.94 −
0.30
1.56
Total day
and Average 66 0.92 0.92 2.54 53 0.96 −
0.01
1.34 119 0.94 0.45 1.93
LSE is impacted by various factors, such as the spatiotemporal changes in vegetation, soil moisture,
and snow cover. In this study, we used climatological LSE data, which means that the quality of
retrieved LST could be affected by the erroneous prescription of LSE data, particularly for the northern
and high mountain regions where snow is frequent in winter. It is also known that estimated MODIS
LST is about 2 K to 3 K lower than in situ data in bare soil and desert areas, and this also needed to be
considered in the analysis [
53
,
56
,
74
,
75
]. Furthermore, MODIS LST data are remotely sensed data from
a satellite, which also may have errors.
3.3. In Situ Validation Results Using Baseline Surface Radiation Network (BSRN)
To evaluate the Himawari-8/AHI LST retrieval algorithms, we validated Himawari-8/AHI LST
using ground-observed LST data obtained at Tateno station (Japan), which is located at 140.126
◦
E
and 36.058
◦
N [
76
]. Measured upward longwave radiation data from the BSRN station over Tateno
were converted into LST using the Stefan–Boltzmann law and a blackbody assumption. Ground LST
data used for validation included 185 scenes, 63 daytime cases, and 122 nighttime cases, respectively.
Figure 11 shows the scatterplot between the Himawari-8 LST and the converted Tateno station LST
according to daytime and nighttime, which shows that the two LSTs were well matched within
±
5 K.
However, Himawari-8 LST was slightly lower (higher) than Tateno station LST during the daytime
above 295 K (nighttime except for 290–300 K). The relatively large warm bias at night indicates that the
Himawari-8/AHI LST algorithm had a tendency of overestimating the LST, although this warm bias
was partly caused by the blackbody assumption of the Tateno station.
RemoteSens.2018,10,xFORPEERREVIEW 13of20
Table3.ResultsofcomparisonbetweenHimawari‐8LSTdataandMODISLSTproducts(collection
6)fromSeptember2015toAugust2016.
Month
DayNightTotal(Day+Night)
#of
SceneCorr.Bias
(K)
RMSE
(K)
#of
SceneCorr.Bias
(K)
RMSE
(K)
#of
SceneCorr.Bias
(K)
RMSE
(K)
Sep201560.920.901.7160.97−0.481.31120.950.121.48
Oct201550.921.452.5770.970.071.14120.950.531.62
Nov201560.860.952.5550.980.061.66110.930.482.08
Dec201570.951.972.9650.990.401.48120.961.522.53
Jan201650.952.943.6630.99−0.551.1380.962.443.30
Feb201630.941.462.0130.981.091.8960.961.281.95
Mar201660.881.062.6230.990.171.3490.920.722.13
Apr201680.900.502.6350.970.111.09130.930.331.94
May201660.92−0.142.5930.96−0.431.2890.93−0.252.07
Jun201630.94−0.822.5440.97−0.381.2670.96−0.491.58
Jul201650.92−0.822.5150.88−0.041.39100.89−0.231.66
Aug201660.91−0.191.9440.95−0.381.27100.94−0.301.56
Totalday
andAverage660.920.922.54530.96−0.011.341190.940.451.93
LSEisimpactedbyvariousfactors,suchasthespatiotemporalchangesinvegetation,soil
moisture,andsnowcover.Inthisstudy,weusedclimatologicalLSEdata,whichmeansthatthe
qualityofretrievedLSTcouldbeaffectedbytheerroneousprescriptionofLSEdata,particularlyfor
thenorthernandhighmountainregionswheresnowisfrequentinwinter.Itisalsoknownthat
estimatedMODISLSTisabout2Kto3Klowerthaninsitudatainbaresoilanddesertareas,and
thisalsoneededtobeconsideredintheanalysis[53,56,74,75].Furthermore,MODISLSTdataare
remotelysenseddatafromasatellite,whichalsomayhaveerrors.
3.3.InSituValidationResultsUsingBaselineSurfaceRadiationNetwork(BSRN)
ToevaluatetheHimawari‐8/AHILSTretrievalalgorithms,wevalidatedHimawari‐8/AHILST
usingground‐observedLSTdataobtainedatTatenostation(Japan),whichislocatedat140.126°E
and36.058°N[7776].MeasuredupwardlongwaveradiationdatafromtheBSRNstationoverTateno
wereconvertedintoLSTusingtheStefan–Boltzmannlawandablackbodyassumption.GroundLST
datausedforvalidationincluded185scenes,63daytimecases,and122nighttimecases,respectively.
Figure11showsthescatterplotbetweentheHimawari‐8LSTandtheconvertedTatenostationLST
accordingtodaytimeandnighttime,whichshowsthatthetwoLSTswerewellmatchedwithin±5K.
However,Himawari‐8LSTwasslightlylower(higher)thanTatenostationLSTduringthedaytime
above295K(nighttimeexceptfor290–300K).Therelativelylargewarmbiasatnightindicatesthat
theHimawari‐8/AHILSTalgorithmhadatendencyofoverestimatingtheLST,althoughthiswarm
biaswaspartlycausedbytheblackbodyassumptionoftheTatenostation.
Figure11.ScatterplotbetweenHimawari‐8LSTandTatenostationBaselineSurfaceRadiation
NetworkLST(redsquaresymbol:daytime;bluecirclesymbol:nighttime).
Figure 11.
Scatter plot between Himawari-8 LST and Tateno station Baseline Surface Radiation Network
LST (red square symbol: daytime; blue circle symbol: nighttime).
Remote Sens. 2018,10, 2013 14 of 20
4. Discussion
In this study, we developed a split-window type LST retrieval algorithm using channels 13 and
15 from Himawari-8/AHI satellite data. To develop the LST retrieval algorithm, we generated a
database using RTM simulations that considered factors affecting LST retrieval in the Himawari-8/
AHI observation area; these included the vertical atmospheric profile, LSE, VZA, and brightness
temperature differences. The RTM used in this research was MODTRAN4 [54].
The split-window method uses the difference in absorption between two adjacent infrared
channels to correct for atmospheric effects [
23
]. The next-generation geostationary meteorological
satellites (Himawari-8/AHI, GOES-16/ABI, and GK-2A/AMI) have three IR channels (Ch. 13–15)
corresponding to the atmospheric window. Of the three available IR channels, we selected channels 13
and 15, because channel 13 is less sensitive to water vapor than channel 14. Furthermore, the regression
results between Ch. 13 and Ch. 15 were better than those between Ch. 14 and Ch. 15 when using the
same conditions, as shown in Figure 12. Also, despite using a non-linear algorithm, Yamamoto et al.
(2018) showed the combined use of channels 13 and 15 lead to a lower LST estimation error due to
surface emissivity [37].
RemoteSens.2018,10,xFORPEERREVIEW 14of20
4.Discussion
Inthisstudy,wedevelopedasplit‐windowtypeLSTretrievalalgorithmusingchannels13and
15fromHimawari‐8/AHIsatellitedata.TodeveloptheLSTretrievalalgorithm,wegenerateda
databaseusingRTMsimulationsthatconsideredfactorsaffectingLSTretrievalintheHimawari‐
8/AHIobservationarea;theseincludedtheverticalatmosphericprofile,LSE,VZA,andbrightness
temperaturedifferences.TheRTMusedinthisresearchwasMODTRAN4[54].
Thesplit‐windowmethodusesthedifferenceinabsorptionbetweentwoadjacentinfrared
channelstocorrectforatmosphericeffects[23].Thenext‐generationgeostationarymeteorological
satellites(Himawari‐8/AHI,GOES‐16/ABI,andGK‐2A/AMI)havethreeIRchannels(Ch.13–15)
correspondingtotheatmosphericwindow.OfthethreeavailableIRchannels,weselectedchannels
13and15,becausechannel13islesssensitivetowatervaporthanchannel14.Furthermore,the
regressionresultsbetweenCh.13andCh.15werebetterthanthosebetweenCh.14andCh.15when
usingthesameconditions,asshowninFigure12.Also,despiteusinganon‐linearalgorithm,
Yamamotoetal.(2018)showedthecombineduseofchannels13and15leadtoalowerLSTestimation
errorduetosurfaceemissivity[37].
Figure12.ScatterplotbetweenreferenceLSTandestimatedLSTfromRTMsimulationusing
Himawari‐8/AHILSTretrievalalgorithm:(a)channels13and15,and(b)channels14and15.
Inter‐comparisonresultsbetweenHimawari‐8LSTandMODISLSTcollection6showedthat
Himawari‐8LSTsweresystematicallywarmerthanMODISLSTduringthewinterseason,asshown
inTable3.ToanalyzethesystematicwarmbiasduringdaytimeinDecember2015andJanuary2016,
thespatialdistributionofthedifferencesbetweenHimawari‐8andMODISareshowninscatterplots
forsixselecteddaytimecasesinFigures13and14.Himawari‐8LSTswereshowntobesystematically
warmerthanMODISLST,irrespectiveofthecaseusedorthegeographiclocation.Inparticular,
Himawari‐8LSTsaresignificantlywarmer(by≈3K)thanMODISLSTintheregionsofnortheast
ChinaandRussiawhereLSTwascolderthan275K.Theseresultsaresimilartothoseofaprevious
studythatretrievedLSTfromMeteosatSecondGeneration(MSG)/SpinningEnhancedVisibleand
InfraRedImager(SEVIRI)dataandproducedresultsthatweresystematicallywarmerby
approximately2.0KduringthedaytimethanMODISLST[7877].Theseresultssuggestthatthecold
MODISLSTduringdaytimeinwinterisoneofthecausesofthesystematicwarmbiasofHimawari
LST.Emissivitydifferencesbetweenthetwowerecausedbythedifferentconsiderationsofsnow
cover,andthesealsoaffectthesystematicwarmbias.Therefore,adetailedanalysisofemissivity
differencesandthecharacteristicsofMODISLSTduringdaytimeinwinterwererequiredtoanalyze
thecausesofthesystematicwarmbiasesfoundinthisstudy.
Figure 12.
Scatter plot between reference LST and estimated LST from RTM simulation using
Himawari-8/AHI LST retrieval algorithm: (a) channels 13 and 15, and (b) channels 14 and 15.
Inter-comparison results between Himawari-8 LST and MODIS LST collection 6 showed that
Himawari-8 LSTs were systematically warmer than MODIS LST during the winter season, as shown
in Table 3. To analyze the systematic warm bias during daytime in December 2015 and January 2016,
the spatial distribution of the differences between Himawari-8 and MODIS are shown in scatter plots
for six selected daytime cases in Figures 13 and 14. Himawari-8 LSTs were shown to be systematically
warmer than MODIS LST, irrespective of the case used or the geographic location. In particular,
Himawari-8 LSTs are significantly warmer (by
≈
3 K) than MODIS LST in the regions of northeast
China and Russia where LST was colder than 275 K. These results are similar to those of a previous
study that retrieved LST from Meteosat Second Generation (MSG)/Spinning Enhanced Visible and
InfraRed Imager (SEVIRI) data and produced results that were systematically warmer by approximately
2.0 K during the daytime than MODIS LST [
77
]. These results suggest that the cold MODIS LST during
daytime in winter is one of the causes of the systematic warm bias of Himawari LST. Emissivity
differences between the two were caused by the different considerations of snow cover, and these
also affect the systematic warm bias. Therefore, a detailed analysis of emissivity differences and the
Remote Sens. 2018,10, 2013 15 of 20
characteristics of MODIS LST during daytime in winter were required to analyze the causes of the
systematic warm biases found in this study.
RemoteSens.2018,10,xFORPEERREVIEW 15of20
Figure13.(a)SpatialdistributionofdifferencesbetweenHimawari‐8andMODISLST,and(b)scatter
plotbetweenHimawari‐8andMODISLSTindaytimeduringwinterforselecteddays.
Figure14.SameasFigure13exceptfortheselecteddays.
Figure 13.
(
a
) Spatial distribution of differences between Himawari-8 and MODIS LST, and (
b
) scatter
plot between Himawari-8 and MODIS LST in daytime during winter for selected days.
RemoteSens.2018,10,xFORPEERREVIEW 15of20
Figure13.(a)SpatialdistributionofdifferencesbetweenHimawari‐8andMODISLST,and(b)scatter
plotbetweenHimawari‐8andMODISLSTindaytimeduringwinterforselecteddays.
Figure14.SameasFigure13exceptfortheselecteddays.
Figure 14. Same as Figure 13 except for the selected days.
Remote Sens. 2018,10, 2013 16 of 20
There are limited available in situ observed LST data for the Himawari-8 observation area,
and this study used only one in situ set of data obtained from BSRN station over Tateno (Japan). When
converting upward longwave radiation at Tateno station to LST, we assumed the blackbody and that
the broadband surface emissivity of Tateno station was 1.0, which caused a slight underestimation of
LST. According to the previous research, which provided an R-based validation for the MSG/SEVIRI
LST algorithm, validation results showed RMSEs smaller than 1.2 K for the LSA SAF product [
78
].
The study [
78
] also showed that biases were reduced after replacing input land surface emissivity
data. To quantitatively evaluate the accuracy of the Himawari-8 LST algorithm, it is thus necessary to
provide additional validation using various in situ databases (e.g., Fluxnet, Surface Radiation Budget
Network (SURFRAD), BSRN, Atmospheric Radiation Measurement (ARM)).
The sensitivity analysis for the impacting factors, such as land surface emissivity and the channel
noises, are very important steps for the evaluation of the LST retrieval algorithms. In this study,
we did not perform the sensitivity analysis for the various impacting factors, so we could not estimate
the relative contribution of each factor, including the channel noises, for the retrieval errors of LST.
According to the Himawari-8/AHI radiometric calibration results, the current standard errors for
the standard brightness temperature (Tb) of channels 13 and 15 were less than 0.1 K when compared
to Infrared Atmospheric Sounding Interferometer (IASI)/A, IASI/B, Atmospheric Infrared Sounder
(AIRS), and Cross-track Infrared Sounder (CrIS) [79]. When we take into consider the degradation of
sensor quality with time, the sensitivity analysis to the channel noises is necessary. Therefore, we are
planning to perform the sensitivity analysis to the various impacting factors in the next study.
5. Conclusions
To retrieve LST from the GK-2A/AMI data of Korea’s next-generation geostationary
meteorological satellite, which is scheduled for launch in December 2018, an LST retrieval algorithm
was developed using the Himawari-8/AHI, as the two satellites have similar orbits and sensor
characteristics. It is considered that the LST retrieval algorithms developed in this study will contribute
to enabling LST measurements prior to the GK-2A/AMI launch in 2018. To develop the LST retrieval
algorithm, we generated a database using RTM simulations. Using reference LST and estimated LST
databases, we developed six LST retrieval equations through multiple regression and consideration
of day/night and atmospheric conditions. A comparison between LST estimated from the RTM and
reference LST used as input data found a correlation coefficient, bias, and RMSE of 0.996, 0.002 K,
and 1.083 K, respectively, which demonstrates that the developed LST retrieval algorithms provided
reasonable results.
MODIS LSTs were used to indirectly validate retrieved LST from the Himawari-8/AHI data over
a one-year period (September 2015 to August 2016). Overall, the Himawari-8 LST and MODIS LST
showed similar spatial distributions with differences between the two LSTs within
±
4 K. A comparison
between Himawari-8 LST and MODIS LST showed a spatial correlation coefficient, bias, and RMSE of
0.94, +0.45 K, and 1.93 K, respectively. Overall, the results of the Himawari-8 LST and MODIS LST
were more similar during the night (warm season) than during the day (cold season). In addition, a
validation was conducted using ground observed LST data at BSRN station over Tateno, and results
showed the performance of the current LST retrieval algorithm to be comparable with that of other LST
retrieval algorithms (corr.: 0.958; bias:
−
0.378 K; RMSE: 2.093 K). However, the Himawari LST showed
a systematic warm bias during the daytime in winter compared to MODIS LST, which is similar to the
results shown in a previous study [77].
High spatiotemporal resolution LSTs retrieved from next-generation geostationary satellite data
can provide the input data required to calculate land–atmosphere interactions in different types of
numerical/climatological models, in addition to verifying the model results. It is also considered that
they can be used in a variety of research focusing on surface urban heat islands, agricultural drought
prediction, and soil moisture estimations.
Remote Sens. 2018,10, 2013 17 of 20
Author Contributions:
Investigation, Y.-Y.C.; Methodology, M.-S.S.; Supervision, M.-S.S.; Writing-original draft
preparation, Y.-Y.C.; Writing-review and editing, M.-S.S. and Y.-Y.C. All authors contributed extensively to the
work presented in this paper.
Funding:
This work was supported by “Development of Scene Analysis & Surface Algorithms” project,
funded by ETRI, which is a subproject of “Development of Geostationary Meteorological Satellite Ground
Segment (NMSC-2018-01)” program funded by NMSC (National Meteorological Satellite Center) of KMA (Korea
Meteorological Administration).
Acknowledgments:
We are thankful to the National Meteorological Satellite Centre of Korea Meteorological
Administration (KMA) for providing Himawari-8 data.
Conflicts of Interest: The authors declare no conflict of interest.
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