Evaluation of Seven Atmospheric Profiles from Reanalysis And

remote sensing

Article Evaluation of Seven Atmospheric Proﬁles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

Jingjing Yang 1,2, Si-Bo Duan 2,*, Xiaoyu Zhang 1, Penghai Wu 3 , Cheng Huang 2,4, Pei Leng 2 and Maofang Gao 2

1 School of Environment and Resources, Shanxi University, Taiyuan 030006, China; [email protected] (J.Y.); [email protected] (X.Z.) 2 Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; [email protected] (C.H.); [email protected] (P.L.); [email protected] (M.G.) 3 Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China; [email protected] 4 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China * Correspondence: [email protected]

Received: 30 January 2020; Accepted: 28 February 2020; Published: 1 March 2020

Abstract: Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

Keywords: land surface temperature; atmospheric proﬁle; single-channel algorithm; Landsat 8 TIRS; validation

Remote Sens. 2020, 12, 791; doi:10.3390/rs12050791 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 791 2 of 24

1. Introduction Land surface temperature (LST) is a key parameter that reflects the physical processes of land at local and global scales, and it plays an important role in the interaction between the surface and the atmosphere, as well as the process of water cycle and energy exchange [1–4]. It is of great significance to the studies of hydrology, ecology, environment, and biogeochemistry [5–7]. Satellites carrying thermal infrared (TIR) sensors can effectively measure LST at the local and global scales [8–11]. Landsat satellites carrying TIR sensors have been launched since the 1980s. Table1 summarizes the characteristics of the TIR sensors onboard the Landsat satellites. Except for the Thermal Infrared Sensor (TIRS), which is aboard the Landsat 8 satellite, other sensors (e.g., the Thematic Mapper (TM) and the Enhanced Thematic Mapper (ETM+)) have only one TIR band [12]. Though TIRS provides satellite measurements at its TIR bands, i.e., bands 10 (10.60–11.19 µm) and 11 (11.50–2.51 µm), the stray light phenomenon causes a severe problem in radiometric calibration in TIRS band 11 [13,14]. Thus, a single-channel algorithm is usually used to estimate LST from TIRS band 10 data [12,15–17]. Several single-channel algorithms, including the Qin single-channel algorithm [18,19], the Jimenez-Munoz and Sobrino (JMS) single-channel algorithm [20–22], and the radiative transfer equation (RTE)-based single-channel algorithm [23,24] have been proposed in other studies. The RTE-based single-channel algorithm in conjunction with atmospheric profiles has been proven to have high LST retrieval accuracy [25,26].

Table 1. Characteristics of thermal infrared (TIR) sensors onboard Landsat satellites.

Equatorial Number Repeat Spatial Sensor Platform Launch Date Decommission Crossing of TIR Spectral Range Cycle Resolution Time Bands TM Landsat 4 16 July 1982 30 June 2001 9:45 16 days 1 Band 6: 10.4–12.5 µm 120 m TM Landsat 5 1 March 1984 5 June 2013 9:45 16 days 1 Band 6: 10.4–12.5 µm 120 m ETM+ Landsat 7 15 April 1999 Operational 10:00 16 days 1 Band 6: 10.4–12.5 µm 60 m B10: 10.60-11.19 µm TIRS Landsat 8 11 February 2013 Operational 10:00 16 days 2 100 m B11: 11.50–12.51 µm

Atmospheric profiles, in combination with the radiative transfer model (RTM), are crucial for the compensation of the attenuation that is introduced by the atmosphere in the TIR region. With the rapid development of four-dimensional assimilation techniques, several reanalysis atmospheric profile products have been produced by assimilating different radiosonde, ground, and satellite measurements. In addition, some satellite-derived atmospheric and radio sounding profiles are available for the scientific community. The three types of atmospheric profiles have their advantages and disadvantages. A radio sounding profile can characterize atmospheric situations at specific latitudes and longitudes with a high precision. However, it is only available at limited sites and times. Satellite-derived profiles (e.g., the MODerate-resolution Imaging Spectroradiomete (MxD07) and the Atmospheric Infrared Sounder atmospheric profile product (AIRS)) are available at the satellite pixel scale with a relatively high spatial resolution (dozens of kilometers) but only at the satellite overpassing time. Furthermore, satellite-derived profiles may be missed because of the influence of clouds or the problem of the profile retrieval method. Reanalysis profiles are provided at low spatial resolution of several degrees every three or six hours [27]. Nevertheless, they have been provided with spatial continuity since the 1970s. The RTE-based single-channel algorithm, in conjunction with atmospheric profiles, has great potential to generate long-term time series LST products from Landsat TIR data. Several studies have been carried out to evaluate the performance of satellite-derived or reanalysis profiles in LST retrieval that use single-channel algorithms [28–30]. However, only two-to-four atmospheric profiles have been analyzed at limited test sites [31–33]. It is still necessary to perform an in-depth analysis and comprehensive comparison of reanalysis and satellite-derived atmospheric profiles in the single-channel LST retrieval algorithm. In this research, seven atmospheric profiles that were derived from multiple sources are compared: two satellite-derived profiles and five reanalysis profiles. Remote Sens. 2020, 12, 791 3 of 24

2. Materials and Methods

2.1. Data

2.1.1. Atmospheric Profiles Eight atmospheric profiles that were obtained from multiple sources were used in this study, including one radio sounding profile downloaded from the University of Wyoming (UWYO), two satellite-derived profiles (MxD07 and AIRS), and five reanalysis profiles (the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS ), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)). The UWYO profile is more representative of real atmospheric situations; therefore, it was used as reference data to evaluate the accuracies of the other seven profiles. Table2 lists the main characteristics of these atmospheric profiles.

Table 2. A summary of the main characteristics of diﬀerent atmospheric proﬁles.

Atmospheric Data Data Temporal Spatial Vertical Website Profile Source Periods Resolution Resolution Resolution http://weather.uwyo. Radio 1973 to UWYO Twice daily — varying edu/upperair/ sounding present sounding.html https://ladsweb. 2000 to 20 pressure MxD07 Twice daily 5 km 5 km modaps.eosdis.nasa. present × levels Satellite-derived gov/search/ profile 2002 to 24 pressure https://search. AIRS Twice daily 1 1 present ◦ × ◦ levels earthdata.nasa.gov/ 1979 to 37 pressure http://apps.ecmwf.int/ ECMWF 6 hourly 0.125 0.125 present ◦ × ◦ levels datasets/ 1980 to 42 pressure https: MERRA2 3 hourly 0.625 0.5 present ◦ × ◦ levels //disc.gsfc.nasa.gov Reanalysis NCEP 2007 to 31 pressure https://nomads.ncdc. 6 hourly 0.5 0.5 profile /GFS present ◦ × ◦ levels noaa.gov/data/gfsanl/ NCEP 1999 to 26 pressure http://rda.ucar.edu/ 6 hourly 1.0 1.0 /FNL present ◦ × ◦ levels datasets/ds083.2/ NCEP 1979 to 17 pressure http://rda.ucar.edu/ 6 hourly 2.5 2.5 /DOE present ◦ × ◦ levels datasets/ds091.0/

(1). Radio sounding profile The radio sounding profile (hereafter, the “UWYO profile”) collected by the globally distributed radiosonde stations that was used in this study is from the University of Wyoming and can be downloaded from the website shown in Table2. The UWYO profile is released twice per day at 0 and 12 UTC. It provides atmospheric pressure, altitude, temperature, relative humidity, dew point temperature, and other parameters. To ensure that the UWYO profile represents real atmospheric situations at the satellite overpassing time, the time difference between the UWYO profile and the satellite-derived profiles (i.e., MYD07 and AIRS) is limited to 1 h. This study used 17 UWYO radiosonde stations with longitudes ranging from 15◦ E to 30◦ E. Figure1 shows the geolocation of these 17 selected radiosonde stations. Table3 summarizes the detailed information about these radiosonde stations. RemoteRemote Sens. Sens.2020 2019, 12,, 79111, x FOR PEER REVIEW 5 of 426 of 24

Figure 1. Geographical location of 17 radiosonde stations.

Table 3. Summary of the detailed information about 17 radiosonde stations. ID Country Station Latitude Longitude Elevation (m) ID Country Station Latitude Longitude Elevation (m) 02365 Sweden Sundsvall-Harnosand 62.53◦ N 17.45◦ E 6 02365 Sweden Sundsvall-Harnosand 62.53° N 17.45° E 6 Visby Aervlogiska 02591 Sweden 57.65◦ N 18.35◦ E 47 02591 Sweden Visby AervlogiskaStn Stn 57.65° N 18.35° E 47 11035 Austria Wien 48.25◦ N 16.36◦ E 200 11035 Austria Wien 48.25° N 16.36° E 200 11747 Czech Republic Prostejov 49.45◦ N 17.13◦ E 216 11747 Czech Republic Prostejov 49.45° N 17.13° E 216 11952 Slovakia Poprad-Ganovce 49.03◦ N 20.31◦ E 706

1195212120 Slovakia Poland Poprad Leba-Ganovce 54.7549.03◦ N° N 17.5320.31◦ E° E 6 706 12374 Poland Legionowo 38.43 N 27.16 E 96 12120 Poland Leba 54.75◦ ° N 17.53◦ ° E 6 12425 Poland Wroclaw 51.13◦ N 16.98◦ E 116 12374 Poland Legionowo 38.43° N 27.16° E 96 12843 Hungary Budapest 47.43◦ N 19.18◦ E 139 1242512982 Poland Hungary SzegedWroclaw 46.2551.13◦ N° N 20.1016.98◦ E° E 83116 13275 Serbia Beograd 44.76◦ N 20.42◦ E 203 1284314240 Hungary Croatia ZagrebBudapest 45.8247.43◦ N° N 16.0319.18◦ E° E 246139 14430 Croatia Zadar 44.10◦ N 15.34◦ E 79 12982 Hungary BucurestiSzeged 46.25° N 20.10° E 83 15420 Romania 44.50 N 26.13 E 91 lnmh-Banesa ◦ ◦ 13275 Serbia Beograd 44.76° N 20.42° E 203 16320 Italy Brindisi 40.65◦ N 17.95◦ E 10 1424017064 Croatia Turkey IstanbulZagreb 40.9045.82◦ N° N 29.1516.03◦ E° E 17246 17220 Turkey Izmir 38.43◦ N 27.16◦ E 29 14430 Croatia Zadar 44.10° N 15.34° E 79

(2). MxD07 proﬁle Bucuresti 15420 Romania 44.50° N 26.13° E 91 lnmh-Banesa The Moderate-resolution Imaging Spectroradiometer (MODIS) instruments are aboard two National16320 Aeronautics Italy and Space AdministrationBrindisi (NASA) satellites,40.65° N i.e.,17.95 Terra° andE Aqua, which10 were sent into orbit on 18 December 1999 and 4 May 2002, respectively. The Terra satellite passes through 17064 Turkey Istanbul 40.90° N 29.15° E 17 the equator from north to south at approximately 10:30 am at local solar time. The Aqua satellite passes through17220 the equatorTurkey in the opposite directionIzmir from south to38.43 north° N at approximately27.16° E 1:30 p.m.29 [34]. The MODIS atmospheric proﬁle product (MOD07_L2 and MYD07_L2) can be obtained freely from the NASA’s Level-1 and Atmosphere Archive and Distribution System (LAADS) website Remote Sens. 2020, 12, 791 5 of 24

(https://ladsweb.modaps.eosdis.nasa.gov/). This product provides atmospheric parameters that contain temperature and moisture proﬁles, atmospheric stability, atmospheric water vapor, and total-ozone burden at a spatial resolution of 5 km. The temperature and water vapor proﬁles are produced at 20 vertical levels: 1000, 950, 920, 850, 780, 700, 620, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, and 5 hPa.

(3). AIRS profile AIRS is another sensor aboard the Aqua satellite. The AIRS L3 daily gridded standard retrieval product (AIRS_3STD) with a spatial resolution of 1 1 was used in this study. The temperature ◦ × ◦ profile is reported at 24 pressure levels: 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 15, 10, 7, 5, 3, 2, 1.5, and 1 hPa. A water vapor profile is only provided with the first 12 pressure levels [35].

(4). ECMWF profile The ECMWF Interim Reanalysis (ERA-Interim) is a reanalysis product of the atmosphere that is provided by ECMWF. The ECMWF profile with a spatial resolution of 0.125 0.125 was used in this ◦ × ◦ study. It gives geopotential height, pressure, temperature, and relative humidity from the surface up to 1 hPa at 37 vertical levels: 1000, 975, 950, 925, 900, 875, 850, 825, 800, 775, 750, 700, 650, 600, 550, 500, 450, 400, 350, 300, 250, 225, 200, 175, 150, 125, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, and 1 hPa [36]. It is noted that this profile was not released after 31 August 2019, and it has been replaced by ERA5 reanalysis. However, at the time of our study, the ERA5 profile was not released, so this study used ERA-interim instead of ERA5.

(5). MERRA2 proﬁle MERRA2 is a NASA atmospheric reanalysis that is generated using the Goddard Earth Observing System Model, Version 5 (GEOS-5) combined with its Atmospheric Data Assimilation System (ADAS), version 5.12.4 [37]. It provides 42 pressure levels at 1000, 975, 950, 925, 900, 875, 850, 825, 800, 775, 750, 725, 700, 650, 600, 550, 500, 450, 400, 350, 300, 250, 200, 150, 100, 70, 50, 40, 30, 20, 10, 7, 5, 4, 3, 2, 1, 0.7, 0.5, 0.4, 0.3, and 0.1 hPa every 6 h with a spatial resolution of 0.625 longitude 0.5 latitude. Temperature, ◦ × ◦ relative humidity, and geopotential height variables were extracted from the MERRA2 proﬁle.

(6). NCEP/GFS profile The GFS is a weather forecast model that is produced by the National Centers for Environmental Prediction (NCEP). The spatial resolution is 0.5 0.5 and 1 1 , and the temporal resolution is 6 h ◦ × ◦ ◦ × ◦ at 00, 06, 12, and 18 UTC daily [38]. It provides a total of 31 atmospheric levels at 1000, 975, 950, 925, 900, 850, 800, 750, 700, 650, 600, 550, 500, 450, 400, 350, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, and 1 hPa. The NCEP/GFS profile with a spatial resolution of 0.5 0.5 was used in this study. ◦ × ◦ (7). NCEP/FNL profile The NCEP/FNL profile is produced by the Global Data Assimilation System (GDAS). The data have been obtained at a spatial resolution of 1 1 every 6 h at 00, 06, 12, and 18 UTC from 1999 to ◦ × ◦ present [39]. The dataset has 26 vertical atmospheric layers, ranging from 1000 hPa to 10 hPa, i.e., 1000, 975, 950, 925, 900, 850, 800, 750, 700, 650, 600, 550, 500, 450, 400, 350, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hPa. It provides surface pressure, sea-level pressure, geopotential height, temperature, sea-surface temperature, and soil values. Temperature, relative humidity, and geopotential height variables were extracted from the NCEP/FNL profile. Remote Sens. 2020, 12, 791 6 of 24

(8). NCEP/DOE proﬁle The NCEP/DOE Reanalysis 2 (R2) data are generated by using an improved version of the NCEP Reanalysis I model. The improved model ﬁxed several errors and updated parameterizations of physical processes. The NCEP/DOE dataset has been available every 6 h with a spatial resolution of 2.5◦ latitude and longitude from 1979 to present. It provides temperature, relative humidity, and geopotential height data at 17 vertical pressure levels: 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hPa [40].

2.1.2. Landsat Data Landsat 8 is the eighth satellite of the U.S. Landsat program, which was launched on 11 February 2013. The Operational Land Imager (OLI) and TIRS are instruments that are carried by the Landsat 8 satellite, which collects images of the Earth with a 16-day repeat cycle [13]. The at-sensor radiance data can be freely downloaded from the NASA Earth data website (https://earthexplorer.usgs.gov). As mentioned in Section1, Landsat 8 TIRS provides TIR data at a spatial resolution of 100 m in two channels: channels 10 (10.60–11.19 µm) and 11 (11.50–12.51 µm). However, there is a large error in the absolute calibration accuracy of TIRS band 11 [14]. In this study, only TIR data in channel 10 were used to estimate LST. In addition to the at-sensor radiance data, the Landsat 8 OLI surface reflectance product was used in this study. It provides surface reflectance in nine bands with a spatial resolution of 30 m. The surface reflectance in Landsat 8 OLI red and near-infrared (NIR) bands was used to calculate the normalized difference vegetation index (NDVI) for the adjustment of surface emissivity.

2.1.3. ASTER GED Data Prior to retrieving the LST with the atmospheric profiles mentioned above, it is necessary to determine surface emissivity to correct for the emissivity effect. The Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (ASTER GED) is an emissivity product that was generated from all available clear-sky ASTER data between 2000 and 2008. It provides global static emissivity of the Earth’s land surface in five ASTER TIR bands, and can be downloaded from the NASA Earth data website (https://search.earthdata.nasa.gov/search). This product includes the mean emissivity and standard deviation for all five ASTER TIR bands, the mean and standard deviation of LST, ASTER global digital elevation model (GDEM), land–water masks, and mean and standard deviations of the NDVI, latitude, and longitude. It was released in two spatial resolutions (100 m and 1 km) and two output formats (Hierarchical Data Format Version 5 (HDF5) and binary) [41]. In this study, the surface emissivity was estimated at Landsat 8 TIRS band 10 by using ASTER GED data with a spatial resolution of 100 m.

2.1.4. In Situ LST Measurements In situ LST measurements that were collected at three study sites (i.e., Hailar, Urad Front Banner, and Wuhai) were used to validate the retrieved LST that used diﬀerent atmospheric proﬁles. The three sites are in Inner Mongolia, China. The land cover at these sites is dominated by relatively homogeneous grassland or desert. The three sites have a semiarid temperate continental climate that is characterized by a large diurnal variation in temperature and little precipitation. The geolocation of and measurement instruments deployed at these sites are shown in Figure2. Remote Sens. 2020, 12, 791 7 of 24 Remote Sens. 2019, 11, x FOR PEER REVIEW 8 of 26

Figure 2. Geolocation of three study sites for land surface temperature (LST) validation. Figure 2. Geolocation of three study sites for land surface temperature (LST) validation. SI-111 infrared radiometers were used to collect in situ measurements at the three study sites. SI-111 infrared radiometers were used to collect in situ measurements at the three study µ TIR radiancesites. TI inR theradiance range in of 8–14the rangem wasof 8 measured–14 μm was by measured an SI-111 radiometerby an SI-111 every radiometer minute. every Before the field experiment,minute. Before all SI-111 the field radiometers experiment, were all calibrated SI-111 radiometers by using a Nationalwere calibrated Physical by Laboratory using a (NPL) blackbodyNational with Physical an absolute Laboratory accuracy (NPL) of 0.1blackbody K and anwith eff ectivean absolute emissivity accuracy of 0.9998of ±0.1 inK theand laboratory. an ± An upturnedeffective SI-111 emissivity radiometer, of 0.9998 which in the lookslaboratory. toward An the upturned sky at aSI 53-111◦ zenith radiometer, angle, which was deployedlooks at each sitetoward to measure the sky atmospheric at a 53° zenith downwelling angle, wasradiance deployed [ 42at]. each Surface site emissivityto measure wasatmospheric measured by a Modeldownwelling 102F handheld radiance portable [42]. Fourier Surface transform emissivity infrared was measured (FT-IR) spectroradiometer by a Model 102F for hand grassheld samples in the fieldportable or a Fourier Nicolet transform iS50R FT-IR infrared spectrometer (FT-IR) spectroradiometer for sand samples for in grass the laboratory samples in [17 the]. field Inor situ a Nicolet LSTs were iS50R calculated FT-IR spectrometer based on for surface-leaving sand samples in and the atmospheric laboratory [17]. downwelling longwave radiation byIn using situ LSTs Equation were (1):calculated based on surface-leaving and atmospheric downwelling longwave radiation by using Equation (1): " ( ) # 1 L 1 ε Latm Ts = B− − − ↓ (1) ε −1 LL−−(1ε ) atm↓ TBs =  (1) where T is the LST, B is the Planck function, L is the surface-leavingε radiance (which was gauged by s  an SI-111 radiometer), ε is the surface effective emissivity at the SI-111 radiometer channel, and Latm where Ts is the LST, B is the Planck function, L is the surface-leaving radiance (which was ↓ is the atmospheric downwelling radiance. gauged by an SI-111 radiometer), ε is the surface effective emissivity at the SI-111 radiometer channel, and Latm↓ is the atmospheric downwelling radiance. 2.1.5. In Situ LST Uncertainty Prior2.1.5. to In the Situ validation LST Uncertainty of theretrieved LST, it is necessary to know about in situ LST uncertainty. The total inPrior situ LST to the uncertainty validation is o calculatedf the retrieved as the LST, root it sumis necessary square (RSS) to know of LST about uncertainties in situ LST that are associateduncertainty. with SI-111 The radiometertotal in situ calibration LST uncertainty error, spatialis calculated and temporal as the root variability sum square of LST (RSS) at eachof site, and surfaceLST uncertainties emissivity measurement that are associated error. with It is expressedSI-111 radiometer as: calibration error, spatial and temporal variability of LSTq at each site, and surface emissivity measurement error. It is expressed as: 2 2 2 2 ∆Ts(total) = ∆Ts(cal) + ∆Ts(spat) + ∆Ts(temp) + ∆Ts(emis) (2) 22 2 2 ∆T( total) = ∆ T( cal) +∆ T( spat) +∆ T( temp) +∆ T( emis) (2) where ∆Ts(total) is thes total in situ ss LST uncertainty, ∆Ts(cal) iss the LST uncertainty s that is associated with SI-111 radiometer calibration error, ∆Ts(spat) and ∆Ts(temp) are the LST uncertainties that are where ΔTs(total) is the total in situ LST uncertainty, ΔTs(cal) is the LST uncertainty that is associated with spatial and temporal variability of LST at each site, and ∆Ts(emis) is the LST uncertainty associated with SI-111 radiometer calibration error, ΔTs(spat) and ΔTs(temp) are the LST that is associateduncertainties with that surface are associated emissivity with measurementspatial and temporal error. variability of LST at each site, and Each SI-111 radiometer was calibrated by using an NPL blackbody in the laboratory on a one-year cycle. The calibration accuracy was approximately 0.2 K at the temperature range from 263 to 323 K. The spatial variability of LST at each site was evaluated with all available ASTER LST products under clear-sky conditions. The STD of ASTER LST within a 3 3 window centered at each site was × Remote Sens. 2019, 11, x FOR PEER REVIEW 9 of 26

ΔTs(emis) is the LST uncertainty that is associated with surface emissivity measurement error. Each SI-111 radiometer was calibrated by using an NPL blackbody in the laboratory on a one-year cycle. The calibration accuracy was approximately 0.2 K at the temperature range from 263 to 323 K. The spatial variability of LST at each site was evaluated with all available Remote Sens. 2020, 12, 791 8 of 24 ASTER LST products under clear-sky conditions. The STD of ASTER LST within a 3 × 3 window centered at each site was used to characterize the spatial variability of LST. The STD used toof characterizein situ LST measurements the spatial variability within 3 of mi LST.n centered The STD at of Landsat in situ LST8 TIRS measurements acquisition time within at 3each min centeredsite atwas Landsat used to 8 TIRSestimate acquisition the temporal time atvariability each site of was LST. used The to surface estimate emissivity the temporal measurement variability of LST.error The was surface assumed emissivity to be measurement 0.01, which error represents was assumed a typical to be uncertainty 0.01, which in represents field emissivity a typical uncertaintymeasurements in ﬁeld emissivity over barren measurements surfaces [43]. over barren surfaces [43]. The inThe situ in LST situ uncertainties LST uncertainties that were that caused were bycaused diﬀerent by different error sources error at sources the Hailar, at the Urad Hailar, Front Banner,Urad and Front Wuhai Banner, sites are and shown Wuhai in Figure sites 3are. For shown each site,in Figure the largest 3. For error each sources site, the were largest the spatial error variabilitysources of LSTwere and the error spatial in surfacevariability emissivity of LST measurement.and error in surface The total emissivity in situ LST measurement. uncertainty The was less thantotal 0.7 in Ksitu for LST all sites.uncertainty was less than 0.7 K for all sites.

Figure 3. In situ LST uncertainties that were caused by different error sources at Hailar, Urad Front Banner,Figure and Wuhai 3. In situ sites. LST uncertainties that were caused by different error sources at Hailar, Urad Front Banner, and Wuhai sites. 2.2. Methodology 2.2. Methodology 2.2.1. Single-Channel LST Retrieval Algorithm The2.2.1. total Single radiance-Channel that LST was Retrieval received Algorithm by a satellite TIR sensor contained three parts: (1) the upwellingThe radiance total ofradiance the atmosphere, that was (2)received the surface-emitted by a satellite radianceTIR sensor attenuated contained by three the atmosphere, parts: (1) and (3)the the upwelling downwelling radiance atmospheric of the atmosphere, thermal radiance (2) the that surface was reflected-emitted by radiance the surface attenuated and attenuated by the by theatmosphere, atmosphere and [44]. (3) The the RTE downwelli in the TIRng regionatmospheric is written thermal as radiance that was reflected by the surface and attenuated by the atmosphere [44]. The RTE in the TIR region is written as B (T ) = ε B (Ts)τ + (1 ε )L τ + L (3) i i i i i − i ad,i i au,i BT( )=ε BT ( ) τ +− (1 ετ ) L + L (3) i i i i s i i ad,, i i au i where Bi(Ti) is the at-sensor radiance in channel i, Ts is the LST, εi is the surface emissivity in channel i, Lau,i iswhere the atmospheric Bi(Ti) is the upwelling at-sensor radianceradiance in in channel channel i, i,L ad,iTs isis the the LST, atmospheric εi is the downwellingsurface emissivity radiance in in channelchannel i, and i, Lτau,ii is is the the atmospheric atmospheric transmittance upwelling radiance in channel in i. channel i, Lad,i is the atmospheric Thedownwelling radiance emitted radiance by in the channel surface i, can and be τi obtained is the atmospheric from the at-sensor transmittance radiance in bychannel correcting i. the atmosphericThe and radiance emissivity emitted effect: by the surface can be obtained from the at-sensor radiance by correcting the atmospheric and emissivity effect: Bi(Ti) (1 εi)Lad,iτi Lau,i B (T ) = − − − (4) i s BTi( i )−− (1ετi ) L ad,, i i − L au i BT()= εiτi (4) is ετ The LST can be then retrieved by reversing the Planckii function [45].

c2 Ts = (5) c1 λ ln 5 + 1 λ Bi(Ts) where c and c are radiation constants, and the value and unit of the two constants are 1.191 108 1 2 × W µm4 sr 1 m 2 and 1.439 104 µm K, respectively. · · − · − × · Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 26

The LST can be then retrieved by reversing the Planck function [45]. c T = 2 s  c1 (5) λ ln 5 +1 λ BTis()

where c1 and c2 are radiation constants, and the value and unit of the two constants are 1.191 × 108 W·μm4·sr−1·m-2 and 1.439 × 104 μm·K, respectively.

2.2.2. Calculation of atmospheric parameters Atmospheric parameters were crucial to retrieve LST from satellite TIR data by using Equations (4) and (5). The reanalysis profiles described in Section 2.1 were provided at different spatial and temporal resolutions. To obtain atmospheric parameters pixel-by-pixel in a satellite TIR image, three main procedures were used to perform the interpolation of the reanalysis profiles in elevation, space, and time, as follows [23,46]. Figure 4 shows the schematic diagram of the interpolation of the reanalysis profiles in space and time. (1) The reanalysis profile for each grid point at each prescribed UTC time was linearly interpolated in elevation according to surface elevation of each radiosonde station shown in RemoteTable Sens. 3.2020 The, 12 ,elevation 791 interpolated profile was completed with the MODerate resolution9 of 24 atmospheric TRANsmittance computer code (MODTRAN) built-in midlatitude summer 2.2.2.profile Calculation up to 100 of km. atmospheric parameters (2) The reanalysis profiles after elevation interpolation at each prescribed UTC time were furtherAtmospheric linearly parameters interpolated were in crucialspace by to retrievethe geographic LST from latitude satellite and TIR datalongitude by using of Equationsfour grid (4) andpoints (5). The closest reanalysis to each profiles radiosonde described station. in Section 2.1 were provided at different spatial and temporal resolutions.(3) The To r obtaineanalysis atmospheric profiles after parameters spatial interpolation pixel-by-pixel were in further a satellite linearly TIR image,interpolated three mainin procedurestime based were on usedtwo prescribed to perform UTC the interpolationtimes closest ofto theLandsa reanalysist acquisition profiles time. in elevation, space, and time, as follows [23,46]. Figure4 shows the schematic diagram of the interpolation of the reanalysis profiles in space and time.

FigureFigure 4. Schematic 4. Schematic diagram diagram of the of interpolationthe interpolation of the of reanalysis the reanalysis proﬁles profiles in space in space and time and (adapted time from(adapted Barsi et from al. [23 Barsi]): (x, et y), al. (x,[23] y):+ (x,1), y), (x (x,+ 1, y y+ +1),1), (x and+ 1, y (x ++ 1),1, and y) are (x + the 1, coordinatesy) are the coordinates of the four of grid cornersthe four surrounding grid corners a site. surrounding A pentagram a or site. circle A inpentagram red represents or circle the coordinate in red represents of a site, t0theis the satellitecoordinate overpassing of a site, time, t0 is and the t 1satelliteand t2 are overpassing the prescribed time, UTCand t time1 and of t the2 are reanalysis the prescribed proﬁles UTC before andtime after of satellite the reanalysis overpassing profiles time. before and after satellite overpassing time.

(1) The reanalysis profile for each grid point at each prescribed UTC time was linearly interpolated in elevation according to surface elevation of each radiosonde station shown in Table3. The elevation interpolated profile was completed with the MODerate resolution atmospheric TRANsmittance computer code (MODTRAN) built-in midlatitude summer profile up to 100 km. (2) The reanalysis profiles after elevation interpolation at each prescribed UTC time were further linearly interpolated in space by the geographic latitude and longitude of four grid points closest to each radiosonde station. (3) The reanalysis profiles after spatial interpolation were further linearly interpolated in time based on two prescribed UTC times closest to Landsat acquisition time. The UWYO, MYD07, and AIRS, and site-specific interpolated reanalysis profiles were imported into MODTRAN to estimate four atmospheric parameters, i.e., transmittance, upwelling radiance, downwelling radiance, and water vapor content.

2.2.3. Surface Emissivity Estimation Apart from the atmospheric parameters, surface emissivity is another key parameter with which to retrieve LST from satellite TIR data by using Equations (4) and (5). The emissivity of a pixel can be expressed as the weighted sum of the emissivity of vegetation and bare soil [47,48]:

ε = Pv ε + (1 Pv) ε (6) i vi − si Remote Sens. 2020, 12, 791 10 of 24

where εi is the emissivity of a pixel in channel i, εvi is the emissivity of the vegetation component in channel i, εsi is the emissivity of the soil component in channel i, and Pv is the fractional vegetation coverage whose value can be calculated from the NDVI by using Equation (7) [49]:

" #2 NDVI NDVImin Pv = − (7) NDVImax NDVI − min

Where NDVI can be calculated from surface reﬂectance in the red and NIR channels, and NDVImin and NDVImax represent the NDVI values that corresponded to full soil and vegetation, which were set to 0.05 and 0.85, respectively. In this research, the surface emissivity in the Landsat 8 TIR channel was estimated in terms of the ASTER GED product. Following the research that was conducted by Hulley et al. [41], the emissivity of the bare soil component can be estimated with the ASTER GED product as follows:

εi_ASTER Pv_ASTERεvi_ASTER εsi = − (8) _ASTER (1 P ) − v_ASTER where εi_ASTER is the emissivity of the ASTER GED product in channel i and εvi_ASTER is the emissivity of the vegetation component in channel i, whose value can be estimated from the mean emissivity of various types of vegetation in the ASTER spectral library by using the spectral response function of the ASTER channels. Furthermore, εsi_ASTER is the emissivity of the soil component in channel i and Pv_ASTER is the fractional vegetation coverage that can be estimated from the ASTER NDVI by using Equation (7). To estimate the emissivity of the bare soil component in TIRS channel 10, the emissivity of the TIRS and ASTER channels was ﬁtted to a linear relationship. The coeﬃcients in the linear regression relationship were obtained via multiple regression between soil emissivity in ASTER channels 13 and 14 and TIRS channel 10, which were calculated from several kinds of soil in the ASTER spectral library by using the spectral response function of the ASTER and TIRS channels. The linear regression equation is given as follows:

ε = 0.443 ε + 0.469 ε + 0.086 (9) s_TIRS10 × s_ASTER13 × s_ASTER14 where εs_TIRS10 is the emissivity of the bare soil component in TIRS channel 10, εs_ASTER13 is the emissivity of the bare soil component in ASTER channel 13, and εs_ASTER14 is the emissivity of the bare soil component in ASTER channel 14. Once the emissivity of the soil component in TIRS channel 10 is available, the emissivity of a pixel in this channel can be calculated as:

ε = P ε + (1 P ) ε (10) TIRS10 v_TIRS v_TIRS10 − v_TIRS s_TIRS10 where εTIRS10 is the emissivity of a pixel in TIRS channel 10, εv_TIRS10 is the emissivity of the vegetation component in TIRS channel 10, which is estimated by averaging the emissivity of various types of vegetation in the ASTER spectral library by using the spectral response function of Landsat 8 TIRS channel 10, and Pv_TIRS is the vegetation coverage that is estimated from NDVI values by using Equation (7). The NDVI value is extracted from reflectance in the red and NIR band from the Landsat 8 surface reflectance product. The flowchart of the estimation of surface emissivity in TIRS channel 10 is shown in Figure5. Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 26

function of Landsat 8 TIRS channel 10, and Pv_TIRS is the vegetation coverage that is estimated Remotefrom Sens. NDVI2020, 12 values, 791 by using Equation (7). The NDVI value is extracted from reflectance11 in of 24 the r

ASTER GED product

ASTER ASTER ASTER spectral ASTER spectral NDVI emissivity library (vegetation) response function

Equation(7)

ASTER emissivity P Equation(8) v of vegetation

Landsat 8 TIRS ASTER spectral ASTER spectral band10 spectral response function library (soil) response function

NIRband5 ASTER emissivities Landsat 8 TIRS band10 Landsat 8 OLI surface Landsat 8 NDVI of soil emissivities of soil reflectance product

Rband4

establish linear ASTER emissivity Equation(7) regression relationship of soil

regression ASTER spectral Landsat 8 TIRS band10 Equation(9) Pv coefficients library (vegetation) spectral response function

Landsat 8 TIRS band10 Landsat 8 TIRS band10 Equation(10) emissivity of soil emissivity of vegetation

Landsat 8 TIRS band10 emissivity

Figure 5. FlowchartFlowchart of of the the estimation estimation of of surface emissivity in TIRS channel 10.

2.2.4.2.2.4. Sensitivity Sensitivity Analysis Analysis of Single-Channelof Single-Channel Algorithm Algorithm TheThe uncertainty uncertainty of theof the single-channel single-channel algorithm algorithm was was analyzed analyzed in terms in terms of a sensitivityof a sensitivity analysis. Theanalysis. total uncertainty The total in uncertainty LST that is retrievedin LST that by theis retrieved single-channel by the algorithm single-channel is calculated algorithm as the is RSS of LSTcalculated uncertainties as the RSS that areof LST associated uncertainties with errors that are in the associated atmospheric with watererrors vapor in the and atmospheric temperature profile,water surface vapor emissivity,and temperature instrument profile, noise, surface and RTM. emissivity, It is expressed instrument as: noise, and RTM. It is expressed as: q 2 2 2 2 2 ∆Ts(total) = ∆Ts(W) + ∆Ts(Ta) + ∆Ts(emis) + ∆Ts(noise) + ∆Ts(RTM) (11) ∆ = ∆22 +∆ +∆ 2 +∆ 2 +∆ 2 Ts()()()()()()total Ts W Tsa T T s emis Ts noise Ts RTM (11) where ∆Ts(total) is the total uncertainty in LST that is retrieved by the single-channel algorithm; ∆Ts(W) and ∆Ts(Ta) are the LST uncertainties that are associated with errors in atmospheric water vaporwhere and Δ temperatureTs(total) is profile,the total respectively; uncertainty∆ Tins( emisLST) isthat the is LST retrieved uncertainty by thatthe issingle associated-channel with erroralgorithm; in surface Δ emissivity;Ts(W) and ∆ ΔTss(noiseTa) are) is the LSTLST uncertaintyuncertainties that that is associatedare associated with with instrument errors noise;in andatmospheric∆Ts(RTM) is water the LST vapor uncertainty and temperature that is associated profile, with respectively; error in RTM. ΔTs(emis) is the LST uncertaintyAtmospheric that eff ects is onassociated the LST that with are error retrieved in bysurface the single-channel emissivity; algorithmΔTs(noise) canis bethe simulated LST in twouncertainty steps. First, that airis temperatureassociated with is increased instrument by 1noi Kse; at each and atmosphericΔTs(RTM) is level.the LST Second, uncertainty the water vaporthat mixing is associated ratio is with increased error in by RTM. 10% at each atmospheric level. These variations represent typical uncertaintiesAtmospheric in the atmospheric effects on the profile LST [31that]. Theare retrieved surface emissivity by the single error-channel is assumed algorithm to be 0.01, can whichbe is asimulated typical uncertainty in two steps. in surface First, emissivityair temperature estimation is increased [50]. LST by uncertainty 1 K at each that atmospheric is associated level. with the RTM error of MODTRAN can be assumed to be 0.2 K. The instrument noise (NE∆T) is approximately 0.05 K in Landsat 8 TIRS band 10 [14]. Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 26

Second, the water vapor mixing ratio is increased by 10% at each atmospheric level. These variations represent typical uncertainties in the atmospheric profile [31]. The surface emissivity error is assumed to be 0.01, which is a typical uncertainty in surface emissivity estimation [50]. LST uncertainty that is associated with the RTM error of MODTRAN can be Remoteassumed Sens. 2020, 12to, 791be 0.2 K. The instrument noise (NEΔT) is approximately 0.05 K in Landsat 8 12TIRS of 24 band 10 [14]. To evaluate the uncertainty of the single-channel algorithm under different water vapor Tocontent evaluate (WVC) the uncertaintyconditions, ofsix the MODTRAN single-channel standard algorithm atmospheres under diwerefferent used water in the vapor sensitivity content (WVC)analysis. conditions, The results six MODTRAN are shown standardin Figure atmospheres6. For each atmosphere, were used the in thelargest sensitivity error source analysis. is The resultsthe uncertainty are shown in in atmospheric Figure6. For eachwater atmosphere, vapor and thetemperature largest error profile, source whereas is the uncertainty the surface in atmosphericemissivity water error vapor has and a temperaturerelatively small profile, influence whereas theon surfacethe LST emissivity that is errorretrieved has a relativelyby the smallsingle influence-channel on the algorithm. LST that isThe retrieved total LST by error the single-channel is less than 1 K algorithm. for an atmospheric The total LST WVC error smaller is less 2 than 1than K for 3 ang/cm atmospheric2. However, WVC it dramatically smaller than 3increases g/cm . However, to 2 K for it dramaticallya tropical atmosphere increases to with 2 K for an a 2 tropicalatmospheric atmosphere WVC with of an approximately atmospheric WVC4 g/cm of2. approximately 4 g/cm .

Figure 6. Uncertainties in LST retrieved by the single-channel algorithm due to diﬀerent error sources Figure 6. Uncertainties in LST retrieved by the single-channel algorithm due to different error under six MODTRAN standard atmosphere conditions—SAW: subarctic winter atmosphere, MLW: sources under six MODTRAN standard atmosphere conditions—SAW: subarctic winter midlatitude winter atmosphere, USS: 1976 US standard atmosphere, SAS: subarctic summer atmosphere, atmosphere, MLW: midlatitude winter atmosphere, USS: 1976 US standard atmosphere, SAS: MLS: midlatitude summer atmosphere, and TRO: tropical atmosphere. subarctic summer atmosphere, MLS: midlatitude summer atmosphere, and TRO: tropical 3. Resultsatmosphere.

3.1. Comparison3. Results of Vertical Distributions of Different Atmospheric Profiles To analyze the performances of different atmospheric profiles, the vertical distribution of air 3.1. Comparison of Vertical Distributions of Different Atmospheric Profiles temperature and dew point temperature that was extracted from the atmospheric profiles of 1 January 2018 at BucurestiTo analyze lnmh-Banesa the performan station,ces 30of Aprildifferent 2018 atmospheric at Istanbul station,profiles, and the 11vertical August distribution 2018 at Izmir of stationair is temperature displayed in and Figure dew7 .point These temperature profiles on that the was three extracted dates were from used the toatmospheric represent diprofilesfferent atmosphericof 1 January situations 2018 withat Bucuresti WVCs of lnmh 1.5,- 2.5,Banesa and 3.4station, g/cm 302. April 2018 at Istanbul station, and 11 ExceptAugust for 2018 the at MYD07 Izmir profile,station thereis displayed were small in Figure discrepancies 7. These between profiles theon verticalthe three distribution dates were of air temperatureused to represent extracted different from theatmospheric UWYO profile situations and with the other WVCs atmospheric of 1.5, 2.5, and profiles 3.4 g/cm below2. 50 km (approximately 1–1000 hPa). The differences between the air temperatures that were extracted from the UWYO and MYD07 profiles below 5 km (approximately 500–1000 hPa) ranged from 3 to 7 K. A relatively large discrepancy of 2–5 K between 10 and 30 km (approximately 10–300 hPa) could also be observed. Compared with air temperature, the discrepancies of the vertical distribution of dew point temperatures that were extracted from different atmospheric profiles were larger. Except for the AIRS profile, the differences between the dew point temperatures that were extracted from the UWYO profile and the other profiles from 10 to 50 km (approximately 1–300 hPa) were as much as 12 K. The largest difference for the AIRS profile was higher than 40 K. There were relatively large discrepancies of 5–8 K between the dew point temperatures that were extracted from the UWYO profile and the other profiles from 2 to 5 km (approximately 500–800 hPa). Remote Sens.Remote2020 Sens., 12 ,2019 791, 11, x FOR PEER REVIEW 14 of 26 13 of 24

Figure 7.FigureComparison 7. Comparison of vertical of vertical distribution distribution of of air air temperature—( temperature—(a), ( cc),), and and (e ()e—)—andand dew dew point temperature—(point temperatureb), (d), and—(b (),f)—values (d), and (f) that—values were that extracted were extracted from di fromfferent different atmospheric atmospheric profiles on 1 profiles on 1 January 2018 at Bucuresti lnmh-Banesa station, 30 April 2018 at Istanbul station, January 2018 at Bucuresti lnmh-Banesa station, 30 April 2018 at Istanbul station, and 11 August 2018 at and 11 August 2018 at Izmir station, respectively. Izmir station, respectively. Except for the MYD07 profile, there were small discrepancies between the vertical 3.2. Comparison of Atmospheric Parameters Calculated from Different Atmospheric Profiles distribution of air temperature extracted from the UWYO profile and the other atmospheric Theprofiles UWYO below radio 50 sounding km (approximately profile was used1–1000 as referencehPa). Thedata differences to evaluate between the accuracies the air of the other seventemperatures atmospheric that profileswere extracted at the 17 radiosondefrom the UWYO stations and shown MYD07 in Table profiles3. All below atmospheric 5 km profiles were input(approximately into MODTRAN 500–1000 5.2 hPa) to calculate ranged from four 3 atmospheric to 7 K. A relatively parameters: large discrepancy atmospheric of transmittance,2–5 K upwellingbetween radiance, 10 and downwelling 30 km (approximately radiance, 10 and–300 WVC.hPa) could also be observed. Compared with air temperature, the discrepancies of the vertical distribution of dew Figure8 shows the boxplots of the di fferences between atmospheric parameters that were point temperatures that were extracted from different atmospheric profiles were larger. calculated from the UWYO profile and the other seven atmospheric profiles. The corresponding Except for the AIRS profile, the differences between the dew point temperatures that were root-mean-squareextracted from error the (RMSE) UWYO profile values and of the the diotherfferences profiles between from 10atmospheric to 50 km (approximately parameters 1 that– were calculated300 fromhPa) were the UWYO as much profile as 12 K. and The the largest other difference seven atmospheric for the AIRS profiles profile arewas summarized higher than 40 in Table4. K. There were relatively large discrepancies of 5–8 K between the dew point temperatures that were extracted from the UWYO profile and the other profiles from 2 to 5 km (approximately 500–800 hPa). Remote Sens. 2019, 11, x FOR PEER REVIEW 15 of 26

3.2. Comparison of Atmospheric Parameters Calculated from Different Atmospheric Profiles The UWYO radio sounding profile was used as reference data to evaluate the accuracies of the other seven atmospheric profiles at the 17 radiosonde stations shown in Table 3. All atmospheric profiles were input into MODTRAN 5.2 to calculate four atmospheric parameters: atmospheric transmittance, upwelling radiance, downwelling radiance, and WVC. Figure 8 shows the boxplots of the differences between atmospheric parameters that were calculated from the UWYO profile and the other seven atmospheric profiles. The corresponding root-mean-square error (RMSE) values of the differences between atmospheric Remoteparameters Sens. 2020, 12 ,that 791 were calculated from the UWYO profile and the other seven atmospheric14 of 24 profiles are summarized in Table 4.

FigureFigure 8. Boxplots 8. Boxplots of the of di fftheerences differences between between atmospheric atmospheric parameters parameters that were that calculated were calculated from the UWYOfrom profile theand UWYO the other profile seven and atmospheric the other profiles: seven (a ) atmospheric transmittance,profiles: (a) (batmospheric) atmospheric upwellingtransmittance, radiance, (cb)) atmosphericatmospheric downwelling upwelling radiance, radiance and(c) atmospheric (d) atmospheric downwelling WVC. P1: MYD07,radiance P2: AIRS,and P3: ( ECMWF,d) atmospheric P4: MERRA2, WVC. P1: P5: MYD07, NCEP/GFS, P2: AIRS, P6: NCEP P3: ECMWF,/FNL, and P4: P7: MERRA2, NCEP/DOE. P5: NCEP/GFS, P6: NCEP/FNL, and P7: NCEP/DOE. Table 4. RMSE values of the differences between atmospheric parameters that were calculated from the UWYO profile and the other seven atmospheric profiles. Table 4. RMSE values of the differences between atmospheric parameters that were calculated from the UWYO profile andUpwelling the other seven atmosphericDownwelling profiles. Water Vapor Atmospheric Transmittance Radiance Radiance Content Profile 2Upwelling Downwelling2 Water2 vapor Atmospheric (W/m /sr/µm) (W/m /sr/µm) (g/cm ) Transmittance Radiance Radiance Content MYD07Profile 0.059 0.46 0.70 0.43 2 2 2 AIRS 0.031(W/m 0.27/sr/μm) (W/m 0.37/sr/μm) 0.21(g/cm ) ECMWFMYD07 0.0220.059 0.170.46 0.240.70 0.180.43 MERRA2AIRS 0.0240.031 0.190.27 0.280.37 0.190.21 NCEPECMWF/GFS 0.0190.022 0.150.17 0.220.24 0.140.18 NCEP/FNL 0.020 0.15 0.22 0.15 MERRA2 0.024 0.19 0.28 0.19 NCEP/DOE 0.039 0.31 0.46 0.27 NCEP/GFS 0.019 0.15 0.22 0.14 NCEP/FNL 0.020 0.15 0.22 0.15 ComparedNCEP/DOE with the UWYO0.039 profile, the worst accuracy0.31 of the estimated0.46 atmospheric0.27 parameters was obtained for the MYD07 profile, with RMSE values of approximately 0.06, 0.5 W/m2/sr/µm, 0.7 W/m2/sr/µm, and 0.4 g/cm2 for atmospheric transmittance, upwelling radiance, downwelling radiance, and WVC, respectively. The RMSE values of these parameters were higher than all reanalysis profiles. Even if the NCEP/DOE profile had the lowest spatial and vertical resolution, the accuracy of atmospheric parameters was the lowest of all reanalysis profiles. It is worth noting that its accuracy was still higher than the MYD07 profile. Though the spatial resolution of the MYD07 profile was the highest in all satellite-derived or reanalysis profiles, only 11 infrared bands (bands 25 and 27–36) were used for the retrieval of the MYD07 profile [51], which led to a low accuracy of air temperature and dew point temperature. There are 2378 spectral bands between 3.7 and 15 µm to retrieve the AIRS profile [35,52]. Therefore, the accuracy of atmospheric parameters that were estimated from the AIRS profile was better than that of atmospheric parameters that were obtained from the MYD07 profile. Remote Sens. 2019, 11, x FOR PEER REVIEW 16 of 26

Compared with the UWYO profile, the worst accuracy of the estimated atmospheric parameters was obtained for the MYD07 profile, with RMSE values of approximately 0.06, 0.5 W/m2/sr/μm, 0.7 W/m2/sr/μm, and 0.4 g/cm2 for atmospheric transmittance, upwelling radiance, downwelling radiance, and WVC, respectively. The RMSE values of these parameters were higher than all reanalysis profiles. Even if the NCEP/DOE profile had the lowest spatial and vertical resolution, the accuracy of atmospheric parameters was the lowest of all reanalysis profiles. It is worth noting that its accuracy was still higher than the MYD07 profile. Though the spatial resolution of the MYD07 profile was the highest in all satellite-derived or reanalysis profiles, only 11 infrared bands (bands 25 and 27–36) were used for the retrieval of the MYD07 profile [51], which led to a low accuracy of air temperature and dew point temperature. There are 2378 spectral bands between 3.7 and 15 μm to retrieve the AIRS profile [35,52]. Therefore, the accuracy of atmospheric parameters that were estimated from the AIRS profile was better than that of atmospheric parameters that were obtained from the MYD07 profile. Remote Sens. 2020, 12, 791 15 of 24 3.3. Comparison of Retrieved LST Using Different Atmospheric Profiles 3.3. ComparisonTo further of Retrieved evaluate LSTthe Usingaccuracy Diff oferent the Atmospheric atmospheric Profiles profiles, the retrieved LST that used theTo UWYO further evaluateprofile thewas accuracy used as of reference the atmospheric data to profiles, compare the retrievedthose using LST thatthe usedother the seven UWYO profileatmospheric was used asprofiles. reference Except data tofor compare the atmospheric those using parameters the other seven that atmospheric were calculated profiles. from Except fordifferent the atmospheric atmospheric parameters profiles, that the were other calculated parameters from in diEquationsfferent atmospheric (4) and (5) profiles,were the thesame other parametersfor all atmospheric in Equations profiles. (4) and Thus, (5) were the the differences same for allbetween atmospheric the retrieved profiles. LST Thus, that the used differences the betweenUWYO the profile retrieved and LST the that other used seven the UWYOatmospheric profile pro andfiles the were other caused seven atmospheric by the discrepancies profiles were causedbetween by the the discrepancies UWYO profile between and the the other UWYO seven profile atmospheric and the otherprofiles. seven atmospheric profiles. TheThe histograms histograms of theof the di ffdifferenceserences between between the the retrieved retrieved LST LST that that used used the the UWYO UWYO profile profile and theand other the seven other atmospheric seven atmospheric profiles are profiles shown are in Figureshown9 .in For Figure all profiles, 9. For the all LST profiles, di fferences the LST were locateddifferences in the rangewere betweenlocated in 1the and range 1 K inbetween most cases. -1 and Relatively 1 K in most large cases. LST diRelativelyfferences couldlarge LST also be − observeddifferences for the could MYD07, also AIRS,be observed and NCEP for /theDOE MYD07, profiles. AIRS, The RMSEand NCEP/DOE values were profiles. smaller The than RMSE 0.6 K for thevalues ECMWF, were MERRA2, smaller NCEP than /GFS,0.6 K and for NCEPthe ECMWF,/FNL profiles. MERRA2, The worst NCEP/GFS, accuracy and was NCEP/FNL obtained with theprofiles. MYD07 profileThe worst with accuracy an RMSE was value obtained of 1 K. with the MYD07 profile with an RMSE value of 1 K.

Figure 9. Cont. Remote Sens. 2019, 11, x FOR PEER REVIEW 17 of 26 RemoteRemote Sens. Sens.2020 2019, 12,, 79111, x FOR PEER REVIEW 17 of16 26 of 24

Figure 9. Histograms of the differences (Ts_other minus Ts_UWYO) between the retrieved LST that FigureFigure 9. Histograms9. Histograms of of the the di differencesfferences ( T(Ts_others_other minus TTs_UWYOs_UWYO) between) between the the retrieved retrieved LST LST that that used the UWYO profile and the other seven atmospheric profiles: (a) MYD07, (b) AIRS, (c) usedused the the UWYO UWYO profile profile and and the otherthe other seven seven atmospheric atmospheric profiles: profiles: (a) MYD07, (a) MYD07, (b) AIRS, (b) AIRS, (c) ECMWF, (c) ECMWF, (d) MERRA2, (e) NCEP/GFS, (f) NCEP/FNL, and (g) NCEP/DOE. (d)ECMWF, MERRA2, (d (e)) MERRA2, NCEP/GFS, (e) ( fNCEP/GFS,) NCEP/FNL, (f) and NCEP/FNL, (g) NCEP and/DOE. (g) NCEP/DOE.

TheThe relationships relationships between between LST LST di differencesfferences ( ∆ (ΔTsT=s =T s_otherTs_other- -T s_UWYOTs_UWYO) andand atmosphericatmospheric WVC The relationships between LST differences (ΔTs = Ts_other - Ts_UWYO) and atmospheric diWVCfferences differences (∆W = (ΔWWother = W- WotherUWYO - W)UWYO were) were further further examined examined for all for atmospheric all atmospheric profiles. profiles. Figure 10 WVC differences (ΔW = Wother - WUWYO) were further examined for all atmospheric profiles. Figure 10 displays the scatterplots of LST differences versus atmospheric WVC differences displaysFigure the 10 scatterplotsdisplays the of scatterplots LST differences of LST versus differences atmospheric versus WVC atmospheric differences WVC between differences the UWYO between the UWYO profile and the other seven atmospheric profiles. The coefficient2 of profilebetween and the the other UWYO seven profile atmospheric and the profiles. other Theseven coe ffiatmosphericcient of determination profiles. The (R )coefficient was higher of than 0.7determination for all the reanalysis (R2) was profiles. higher Thethan results 0.7 for indicated all the reanalysis that more thanprofiles. 70% ofThe the results variation indicated in ∆T could determination (R2) was higher than 0.7 for all the reanalysis profiles. The results indicateds bethat explained more than by 70%∆W forof the all ofvariation the reanalysis in ΔTs profiles.could be Relatively explained low by valuesΔW for of all R 2ofwere the reanalysis obtained for the that more than 70% of the variation in ΔTs could be explained by ΔW for all of the reanalysis profiles. Relatively low values of R2 were obtained for the MYD07 and AIRS profiles. The T MYD07profiles. and Relatively AIRS profiles. low Thevalues results of R showed2 were obtained that only for approximately the MYD07 50%and ofAIRS the variationprofiles. The in ∆ s wasresults explained showed by that∆W only. approximately 50% of the variation in ΔTs was explained by ΔW. results showed that only approximately 50% of the variation in ΔTs was explained by ΔW.

Figure 10. Cont. Remote Sens. 2019, 11, x FOR PEER REVIEW 18 of 26 Remote Sens. 2020, 12, 791 17 of 24

Figure 10. Scatterplot of LST differences (Ts_other minus Ts_UWYO) versus atmospheric water vapor Figure 10. Scatterplot of LST differences (Ts_other minus Ts_UWYO) versus atmospheric water content (WVC) differences (Wother minus WUWYO) between the UWYO profile and the other seven vapor content (WVC) differences (Wother minus WUWYO) between the UWYO profile and the atmospheric profiles: (a) MYD07, (b) AIRS, (c) ECMWF, (d) MERRA2, (e) NCEP/GFS, (f) NCEP/FNL, other seven atmospheric profiles: (a) MYD07, (b) AIRS, (c) ECMWF, (d) MERRA2, (e) and (g) NCEP/DOE. NCEP/GFS, (f) NCEP/FNL, and (g) NCEP/DOE. 3.4. LST Validation Using in Situ Measurements 3.4. LST Validation Using in Situ Measurements In situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sitesIn were situ used LST to measurements validate the retrieved that were LST collected that used at dithefferent Hailar, atmospheric Urad Front profiles. Banner, There and are no radiosondeWuhai sites stations were used available to validate at the threethe retrieved sites. Therefore, LST that the used UWYO different profile atmospheric was not used. profiles. Furthermore, thereThere wasare no a smaller radiosonde temporal stations gap available between theat the overpassing three sites. time Therefore, of Terra the/MODIS UWYO and profile Landsat was 8 /TIRS atnot the used. three Furthermore, sites. Therefore, there the was MOD07 a smaller profile, temporal rather thangap thebetween MYD07 the profile, overpassing was used. time of Terra/MODISThe scatterplots and Landsat of the retrieved8/TIRS at LST the thatthree used sites. di ffTherefore,erent atmospheric the MOD07 profiles profile, versus rather the than in situ LST atthe the MYD07 three sitesprofile, are was shown used. in Figure 11. Compared with the in situ LST, the accuracy of the retrieved LST thatThe usedscatterplots different of atmosphericthe retrieved profiles LST that was used approximately different atmospheric 1.0–1.5 K. Theprofiles worst ver accuracysus the of the retrievedin situ LST LST at was the obtainedthree sites for are the shown NCEP/ DOEin Figure profile, 11. with Compared a bias and with RMSE the in of approximatelysitu LST, the 0.7 − andaccuracy 1.5 K, of respectively. the retrieved These LST resultsthat used can different be attributed atmospheric to the lower profiles spatial was resolution approximately of 2.5 1.02.5– and ◦ × ◦ vertical1.5 K. The resolution worst accuracy of 17 levels of the for theretrieved NCEP /LSTDOE was profile. obtained Similar for RMSE the NCEP/DOE values of approximately profile, with 1.0 K werea bias achieved and RMSE for the of ECMWF, approximately MERRA2, −0.7 NCEP and/GFS, 1.5 andK, respectively. NCEP/FNL profiles. These results can be attributedThe bias to the and lower RMSE spatial of the diresolutionfferences of between 2.5° × 2.5° the retrievedand vertical LST resolution that used diofff 17erent levels atmospheric for profiles and the in situ LST at the three sites are summarized in Table5. The mean atmospheric WVC Remote Sens. 2019, 11, x FOR PEER REVIEW 19 of 26

the NCEP/DOE profile. Similar RMSE values of approximately 1.0 K were achieved for the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles. Remote Sens. 2020, 12, 791 18 of 24 The bias and RMSE of the differences between the retrieved LST that used different atmospheric profiles and the in situ LST at the three sites are summarized in Table 5. The for eachmean site is atmospheric also listed inWVC Table for5. each The RMSEsite is valuesalso listed for allin Table atmospheric 5. The RMSE profiles values at the for Urad all Front atmospheric profiles at the Urad Front Banner site were larger than those at the Hailar and Banner site were larger than those at the Hailar and Wuhai sites. Two possible reasons could explain Wuhai sites. Two possible reasons could explain this result. First, the uncertainty of the in situ this result. First, the uncertainty of the in situ LST in the Urad Front Banner site was relatively large, LST in the Urad Front Banner site was relatively large, which we can see from Figure 3. which weSecond, can see the from atmospheric Figure3 WVC. Second, of the the Urad atmospheric Front Banner WVC site ofwas the relatively Urad Front large, Banner reaching site was 2 relatively0.90 large, g / cm reaching2. As shown 0.90 gin/ Figurecm . As6, a shown larger total in Figure uncertainty6, a larger in LST total that uncertainty was retrieved in LSTby the that was retrievedsingle by the-channel single-channel algorithm algorithmwas obtained was at obtained a larger atmospheric at a larger atmospheric WVC. Compared WVC. with Compared the in with the in situsitu LST, LST, the the accuracies accuracies of of the the retrievedretrieved LSTLST that used the the ECMWF, ECMWF, MERRA2, MERRA2, NCEP/GFS, NCEP/GFS, and NCEP/FNLand profilesNCEP/FNL were profiles better were than better those than that those used that the used MOD07, the MOD07, AIRS,and AIRS, NCEP and /NCEP/DOEDOE profiles. profiles.

Remote Sens. 2019, 11, x FOR PEER REVIEW 20 of 26

Figure 11. Scatterplots of the retrieved LST that used diﬀerent atmospheric proﬁles versus the in situ Figure 11. Scatterplots of the retrieved LST that used different atmospheric profiles versus the in LSTsitu at theLSTHailar at the Hailar (S1), Urad (S1), FrontUrad Front Banner Banner (S2), (S2), and and Wuhai Wuhai (S3) (S3) sites: sites: (a) ( MOD07,a) MOD07, (b ()b AIRS,) AIRS, ( c()c) ECMWF, (d)ECMWF, MERRA2, (d () eMERRA2,) NCEP/GFS, (e) NCEP/GFS, (f) NCEP /(FNL,f) NCEP/FNL, and (g) NCEPand (g)/ DOE.NCEP/DOE. Remote Sens. 2020, 12, 791 19 of 24

Table 5. Bias and RMSE values of the differences (Ts_retrieved minus Ts_insitu) between the retrieved LST that used different atmospheric profiles and the in situ LST at the Hailar (S1), Urad Front Banner (S2), and Wuhai (S3) sites.

MOD07 AIRS ECMWF MERRA2 NCEP/GFS NCEP/FNL NCEP/DOE Mean WVC Site Num (g/cm2) Bias RMSE Bias RMSE Bias RMSE Bias RMSE Bias RMSE Bias RMSE Bias RMSE (K) (K) (K) (K) (K) (K) (K) (K) (K) (K) (K) (K) (K) (K) S1 3 0.40 0.11 0.35 0.64 0.86 0.03 0.30 0.03 0.32 0.02 0.30 0.01 0.29 0.08 0.35 − − − − S2 16 0.90 0.82 1.50 0.58 1.25 0.77 1.15 0.42 1.25 0.64 1.24 0.73 1.25 1.13 1.85 − − − − − − − S3 8 0.43 0.05 0.47 0.14 0.53 0.14 0.55 0.18 0.57 0.15 0.59 0.33 0.71 0.09 0.54 Remote Sens. 2020, 12, 791 20 of 24

4. Discussion In general, the vertical distributions of air temperature and dew point temperature that were extracted from the reanalysis profiles were more similar than those from the satellite-derived profiles (i.e., MYD07 and AIRS) when using the UWYO profile as reference data. As shown in Figure7, the difference between the satellite-derived profiles and the UWYO profile was approximately twice the difference between the reanalysis profiles and UWYO profile, regardless of the vertical distribution of air temperature or dew point temperature. The reason for this may be because the reanalysis profiles, which were the results of four-dimensional assimilation of meteorological data, including radiosonde, ground, and satellite measurements, were more representative of the real atmospheric situation than the satellite-derived profiles from satellite measurements from a single source. The accuracy of the atmospheric parameters that were estimated from the four reanalysis profiles (i.e., ECMWF,MERRA2, NCEP/GFS, and NCEP/FNL) was better than that of the atmospheric parameters that were obtained from the two satellite-derived profiles (i.e., MYD07 and AIRS). As shown in Figure8 and Table4, the RMSE values of the atmospheric parameters of these four reanalysis profiles were less than 0.025, 0.2 W/m2/sr/µm, 0.3 W/m2/sr/µm, and 0.2 g/cm2 for atmospheric transmittance, upwelling radiance, downwelling radiance, and WVC, respectively. The result could be explained by the reason that the satellite-derived profiles that were temporally and spatially closest to each radiosonde station were used in this study, whereas temporal and spatial interpolation was performed for the reanalysis profiles, which may better represent real atmospheric conditions at the radiosonde stations. When using the LST that was calculated by UWYO atmospheric profile as reference data to compare the other seven atmospheric profiles, the accuracy of reanalysis profiles were relatively good, and MYD07 had the worst accuracy. The reason for this may be that the atmospheric parameters (transmittance, upwelling radiance, and downwelling radiance) in the TIR region were dependent not only on the total amount of atmospheric WVC but also on the vertical distribution of water vapor and temperature profile. As shown in Figure7 and Table4, the discrepancy of the vertical distribution between the MYD07 profile and the UWYO profile was the largest. Furthermore, compared with the UWYO profile, the RMSE value of the atmospheric parameters that were calculated by the MYD07 profile was the largest. Despite the findings of this study, there are still some limitations. (1) Only five types of reanalysis atmospheric profile data were selected in this study, and more reanalysis atmospheric profiles such as the Japanese 55-year Reanalysis Data (JRA-55) were not included in the scope of the study. In addition, this article used ERA-Interim data, but now with the time resolution of the new ERA5 data is higher, which may improve the accuracy of the atmospheric profile. (2) The uncertainty analysis of the UWYO profile was not analyzed, and it was directly used as comparative data. (3) LST validation was only performed at three sites with limited land cover types, and the results may not have been widely representative.

5. Conclusions In this study, the performances of seven atmospheric profiles from different sources (MYD07, AIRS, ECMWF, MERRA2, NCEP/GFS, NCEP/FNL, and NCEP/DOE) were evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIRS data. The UWYO profile that was collected at 17 radiosonde stations was used as reference data to evaluate the accuracies of the seven atmospheric profiles. Furthermore, in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites were further used to validate the accuracies of the retrieved LST that used the seven atmospheric profiles. Some conclusions were drawn, as follows. (1) The vertical distributions of air temperature and dew point temperature profiles extracted from the reanalysis profiles (i.e., ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL) were more similar than those from the satellite-derived profiles (i.e., MYD07 and AIRS) when using the UWYO profile as reference data. The largest discrepancy of the vertical distribution of air temperature and dew point temperature profiles was observed between the MYD07 and UWYO profiles. Remote Sens. 2020, 12, 791 21 of 24

(2) Compared with the UWYO profile, the accuracies of atmospheric parameters for the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were better than those for the MYD07, AIRS, and NCEP/DOE profiles. The worst accuracy of atmospheric parameters was obtained for the MYD07 profile, with RMSE values of approximately 0.06, 0.5 W/m2/sr/µm, 0.7 W/m2/sr/µm, and 0.4 g/cm2 for atmospheric transmittance, upwelling radiance, downwelling radiance, and WVC, respectively. (3) Compared with the UWYO profile, the RMSE values (approximately 0.5 K) of the retrieved LST that used the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those (larger than 0.8 K) that used the MYD07, AIRS, and NCEP/DOE profiles. The worst accuracy of the retrieved LST was obtained for the MYD07 profile, with an RMSE value of approximately 1.0 K. (4) Compared with the in situ LST, the RMSE values of the retrieved LST that used the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The worse accuracy of the retrieved LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. In general, the performances of the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were better than those of the MYD07, AIRS, and NCEP/DOE profiles. Therefore, the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles had great potential to perform accurate atmospheric correction for the generation of long-term time series LST products from Landsat TIR data by using the single-channel algorithm.

Author Contributions: Conceptualization, S.-B.D.; Methodology, S.-B.D., and J.Y.; Software, J.Y.; Validation, J.Y., S.-B.D.; Formal analysis, J.Y.; Investigation, J.Y.; Resources, X.Z., M.G., P.L., and P.W.; Data curation, J.Y. and S.-B.D.; Writing—original draft preparation, J.Y.; Writing—review and editing, S.-B.D. and C.H.; Supervision, S.-B.D.; Funding acquisition, S.-B.D. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Key R&D Program of China, 2018YFB0504800 (2018YFB0504804), by the National Natural Science Foundation of China, grant numbers 41871275 and 41921001. Acknowledgments: The radio sounding profile used in this study was downloaded from the University of Wyoming website (http://weather.uwyo.edu/upperair/sounding.html). MxD07 profile data, AIRS profile data and ASTER GED product were downloaded from NASA’s Earth Observing System Data and Information System (EOSDIS) website (https://search.earthdata.nasa.gov/search). ECMWF profile was downloaded from the European Centre for Medium-Range Weather Forecasts website (http://apps.ecmwf.int/datasets/). MERRA2 profile was downloaded from the Goddard Earth Observing System Model (GEOS-5) combined with its Atmospheric Data Assimilation System (ADAS) website (https://disc.gsfc.nasa.gov). NCEP/GFS profile was downloaded from the National Centers for Environmental Prediction (NCEP) website (https://nomads.ncdc.noaa.gov/data/gfsanl/). NCEP/FNL profile was downloaded from the global data assimilation system (GDAS) website (http://rda.ucar.edu/ datasets/ds083.2/). NCEP/DOE profile was downloaded from the NCEP/DOE Reanalysis 2 (R2) Project website (http://rda.ucar.edu/datasets/ds091.0/). The at-sensor radiance data, TIR data and Landsat 8 OLI surface reflectance product were downloaded from the NASA Earth data website (https://earthexplorer.usgs.gov). Conflicts of Interest: The authors declare no conflict of interest.

References

1. Anderson, M.C.; Norman, J.M.; Diak, G.R.; Kustas, W.P.; Mecikalski, J.R. A two-source time-integrated model for estimating surface ﬂuxes using thermal infrared remote sensing. Remote Sens. Environ. 1997, 60, 195–216. [CrossRef] 2. Anderson, M.C.; Kustas, W.P.; Norman, J.M.; Hain, C.R.; Mecikalski, J.R.; Schultz, L.; González-Dugo, M.P.; Cammalleri, C.; d’Urso, G.; Pimstein, A.; et al. Mapping daily evapotranspiration at ﬁeld to continental scales using geostationary and polar orbiting satellite imagery. Hydrol. Earth Syst. Sci. 2011, 15, 223–239. [CrossRef] 3. Zhou, L.; Dickinson, R.E.; Tian, Y.; Jin, M.; Ogawa, K.; Yu, H.; Schmugge, T. A sensitivity study of climate and energy balance simulations with use of satellite-derived emissivity data over Northern Africa and the Arabian Peninsula. J. Geophys. Res Atmos. 2003, 108, 4795–4803. [CrossRef] 4. Duan, S.-B.; Li, Z.-L.; Wang, C.; Zhang, S.; Tang, B.-H.; Leng, P.; Gao, M.-F. Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product. Int. J. Remote Sens. 2019, 40, 1763–1778. [CrossRef] Remote Sens. 2020, 12, 791 22 of 24

5. Wan, Z.; Wang, P.; Li, X. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. Int. J. Remote Sens. 2004, 25, 61–72. [CrossRef] 6. Weng, Q. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS J. Photogramm. 2009, 64, 335–344. [CrossRef] 7. Anderson, M.C.; Norman, J.M.; Kustas, W.P.; Houborg, R.; Starks, P.J.; Agam, N. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sens. Environ. 2008, 112, 4227–4241. [CrossRef] 8. Prata, A.J.; Caselles, V.; Coll, C.; Sobrino, J.A.; OttlO, C. Thermal remote sensing of land surface temperature from satellites: Current status and future prospects. Remote Sens Reviews. 1995, 12, 175–224. [CrossRef] 9. Dash, P.; Göttsche, F.-M.; Olesen, F.-S.; Fischer, H. Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends. Int. J. Remote Sens. 2002, 23, 2563–2594. [CrossRef] 10. Duan, S.-B.; Li, Z.-L.; Tang, B.-H.; Wu, H.; Tang, R. Generation of a time-consistent land surface temperature product from MODIS data. Remote Sens. Environ. 2014, 140, 339–349. [CrossRef] 11. Duan, S.-B.; Li, Z.-L.; Leng, P. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data. Remote Sens. Environ. 2017, 195, 107–117. [CrossRef] 12. Malakar, N.K.; Hulley, G.C.; Hook, S.J.; Laraby, K.; Cook, M.; Schott, J.R. An operational land surface temperature product for Landsat thermal data: Methodology and validation. IEEE Trans. Geosci. Remote Sens. 2018, 56, 5717–5735. [CrossRef] 13. Barsi, J.A.; Schott, J.R.; Hook, S.J.; Raqueno, N.G.; Markham, B.L.; Radocinski, R.G. Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sens. 2014, 6, 11607–11626. [CrossRef] 14. Montanaro, M.; Levy, R.; Markham, B. On-orbit radiometric performance of the Landsat 8 thermal infrared sensor. Remote Sens. 2014, 6, 11753–11769. [CrossRef] 15. Isaya Ndossi, M.; Avdan, U. Application of open source coding technologies in the production of land surface temperature (LST) maps from Landsat: A PyQGIS plugin. Remote Sens. 2016, 8, 413. [CrossRef] 16. Wang, M.; Zhang, Z.; He, G.; Wang, G.; Long, T.; Peng, Y. An enhanced single-channel algorithm for retrieving land surface temperature from Landsat series data. J. Geophys. Res Atmos. 2016, 121, 11712–11722. [CrossRef] 17. Duan, S.-B.; Li, Z.-L.; Li, H.; Göttsche, F.-M.; Wu, H.; Zhao, W.; Leng, P.; Zhang, X.; Coll, C. Validation of Collection 6 MODIS land surface temperature product using in situ measurements. Remote Sens. Environ. 2019, 225, 16–29. [CrossRef] 18. Qin, Z.; Karnieli, A.; Berliner, P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int. J. Remote Sens. 2010, 22, 3719–3746. [CrossRef] 19. Wang, F.; Qin, Z.; Song, C.; Tu, L.; Karnieli, A.; Zhao, S. An improved mono-window algorithm for land surface temperature retrieval from landsat 8 thermal infrared sensor data. Remote Sens. 2015, 7, 4268–4289. [CrossRef] 20. Jiménez-Muñoz, J.C.; Sobrino, J.A. A generalized single-channel method for retrieving l surface temperature from remote sensing data. J. Geophys. Res Atmos. 2003, 109, 4688. [CrossRef] 21. Jiménez-Muñoz, J.C.; Cristóbal, J.; Sobrino, J.A.; Sòria, G.; Ninyerola, M.; Pons, X. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Trans. Geosci. Remote Sens. 2009, 47, 339–349. [CrossRef] 22. Cristdoi, J.; Jiménez-Muñoz, J.C.; Sobrino, J.A.; Ninyerola, M.; Pons, X. Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. J. Geophys. Res Atmos. 2009, 114, D08103. [CrossRef] 23. Barsi, J.A.; Barker, J.L.; Schott, J.R. An atmospheric correction parameter calculator for a single thermal band rarth-sensing instrument. IGARSS 2003 2003, 5, 3014–3016. [CrossRef] 24. Sobrino, J.A.; Jiménez-Muñoz, J.C.; Paolini, L. Land surface temperature retrieval from LANDSAT TM 5. Remote Sens. Environ. 2004, 90, 434–440. [CrossRef] 25. Yu, X.; Guo, X.; Wu, Z. Land surface temperature retrieval from Landsat 8 TIRS-Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sens. 2014, 6, 9829–9852. [CrossRef] Remote Sens. 2020, 12, 791 23 of 24

26. Windahl, E.; de Beurs, K. An intercomparison of Landsat land surface temperature retrieval methods under variable atmospheric conditions using in situ skin temperature. Int. J. Appl. Earth Obs. 2016, 51, 11–27. [CrossRef] 27. Wang, A.; Zeng, X. Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau. J. Geophys. Res Atmos. 2012, 117, D05102. [CrossRef] 28. Jiménez-Muñoz, J.C.; Sobrino, J.A.; Mattar, C.; Franch, B. Atmospheric correction of optical imagery from MODIS and reanalysis atmospheric products. Remote Sens. Environ. 2010, 114, 2195–2210. [CrossRef] 29. Sobrino, J.A.; Jiménez-Muñoz, J.C.; Mattar, C.; Sòria, G. Evaluation of Terra/MODIS atmospheric profiles product (MOD07) over the Iberian Peninsula: A comparison with radiosonde stations. Int. J. Digit. Earth. 2014, 8, 771–783. [CrossRef] 30. Meng, X.; Cheng, J. Evaluating eight global reanalysis products for atmospheric correction of thermal infrared sensor-Application to Landsat 8 TIRS10 data. Remote Sens. 2018, 10, 474. [CrossRef] 31. Coll, C.; Caselles, V.; Valor, E.; Nicl, R. Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data. Remote Sens. Environ. 2012, 117, 199–210. [CrossRef] 32. Li, H.; Liu, Q.; Du, Y.; Jiang, J.; Wang, H. Evaluation of the NCEP and MODIS atmospheric products for single channel land surface temperature retrieval with ground measurements: A case study of HJ-1B IRS data. IEEE J. STARS. 2013, 6, 1399–1408. [CrossRef] 33. PjstarPlanells, L.; Garc-Santos, V.; Caselles, V. Comparing different profiles to characterize the atmosphere for three MODIS TIR bands. Atmos. Res. 2015, 161, 108–115. [CrossRef] 34. Crosson, W.L.; Al-Hamdan, M.Z.; Hemmings, S.N.J.; Wade, G.M. A daily merged MODIS Aqua-Terra land surface temperature data set for the conterminous United States. Remote Sens. Environ. 2012, 119, 315–324. [CrossRef] 35. Aumann, H.H.; Chahine, M.T.; Gautier, C.; Goldberg, M.D.; Kalnay, E.; McMillin, L.M.; Revercomb, H.; Rosenkranz, P.W.; Smith, W.L.; Staelin, D.H.; et al. AIRS/AMSU/HSB on the Aqua mission: Design, science objective, data products, and processing systems. IEEE Trans. Geosci. Remote Sens. 2003, 41, 253–264. [CrossRef] 36. Fotsing Talla, C.; Njomo, D.; Cornet, C.; Dubuisson, P.; Akana Nguimdo, L. ECMWF atmospheric profiles in Maroua, Cameroon: Analysis and overview of the simulation of downward global solar radiation. Atmosphere 2018, 9, 44. [CrossRef] 37. Molod, A.; Takacs, L.; Suarez, M.; Bacmeister, J. Development of the GEOS-5 atmospheric general circulation model: Evolution from MERRA to MERRA2. Geosci. Model Dev. 2015, 8, 1339–1356. [CrossRef] 38. Zia ul Rehman, T.; Muhammad, A.; Muhammad, A.; Nasir, H.; Hamza, S.; Hasnain, A. Evaluation of NCEP-Products (NCEP-NCAR, NCEP-DOE, NCEP-FNL, NCEP-GFS) of solar radiation for Karachi, Pakistan. ISES 2018, 1–12. [CrossRef] 39. Eom, H.-S.; Myoung-Seok, S. Seasonal and diurnal variations of stability indices and environmental parameters using NCEP FNL data over East Asia. Asia Pac. J. Atmos. Sci. 2011, 47, 181–192. [CrossRef] 40. Lim, Y.-K.; Stefanova, L.B.; Chan, S.C.; Schubert, S.D.; Ochubert, S.J. High-resolution subtropical summer precipitation derived from dynamical downscaling of the NCEP/DOE reanalysis: How much small-scale information is added by a regional model? Clim. Dynam. 2010, 37, 1061–1080. [CrossRef] 41. Hulley, G.C.; Hook, S.J.; Abbott, E.; Malakar, N.; Islam, T.; Abrams, M. The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth’s emissivity at 100 meter spatial scale. Geophys. Res. Lett. 2015, 42, 7966–7976. [CrossRef] 42. Wang, C.; Duan, S.-B.; Zhang, X.; Wu, H.; Gao, M.-F.; Leng, P. An alternative split-window algorithm for retrieving land surface temperature from Visible Infrared Imaging Radiometer Suite data. Int. J. Remote Sens. 2019, 40, 1640–1654. [CrossRef] 43. Rubio, E.; Caselles, V.; Coll, C.; Valour, E.; Sospedra, F. Thermal-infrared emissivities of natural surfaces: Improvements on the experimental set-up and new measurements. Int. J. Remote Sens. 2003, 24, 5379–5390. [CrossRef] 44. Li, Z.-L.; Tang, B.-H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z.; Trigo, I.F.; Sobrino, J.A. Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ. 2013, 131, 14–37. [CrossRef] Remote Sens. 2020, 12, 791 24 of 24

45. Li, F.; Jackson, T.J.; Kustas, W.P.; Schmugge, T.J.; French, A.N.; Cosh, M.H.; Bindlish, R. Deriving land surface temperature from landsat 5 and 7 during SMEX02/SMACEX. Remote Sens. Environ. 2004, 92, 521–534. [CrossRef] 46. Rosas, J.; Houborg, R.; McCabe, M.F. Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems. Remote Sens. 2017, 9, 988. [CrossRef] 47. Sobrino, J.A.; Caselles, V.; Becker, F. Significance of the remotely sensed thermal infrared measurements obtained over a citrus orchard. ISPRS J. Photogramm. 1990, 44, 343–354. [CrossRef] 48. Valor, E.; Caselles, V. Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote Sens. Environ. 1996, 57, 167–184. [CrossRef] 49. Carlson, T.N.; Perry, E.M.; Schmugge, T.J. Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agr. Forest Meteorol. 1990, 52, 45–69. [CrossRef] 50. Li, Z.-L.; Wu, H.; Wang, N.; Qiu, S.; Sobrino, J.A.; Wan, Z.; Tang, B.-H.; Yan, G. Land surface emissivity retrieval from satellite data. Int. J. Remote Sens. 2013, 34, 3084–3127. [CrossRef] 51. Seemann, S.W.; Borbas, E.E.; Li, J.; Menzel, W.P.; Gumley, L.E. MODIS atmospheric profile retrieval algorithm theoretical basis document; Cooperative Institute for Meteorological Satellite Studies: University of Wisconsin-Madison, Madison, WI, USA, 2006. 52. Susskind, J.; Barnet, C.D.; Blaisdell, J.M. Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB under cloudy conditions. IEEE Trans. Geosci. Remote Sens. 2003, 41, 390–409. [CrossRef]

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).