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Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE
Guijun Yang
Beijing Academy of Agriculture and Forestry Sciences
Qihao Weng
South China Normal University
Ruiliang Pu
University of South Florida, [email protected]
Feng Gao
Chinese Academy of Sciences
Chenhong Sun
Beijing Academy of Agriculture and Forestry Sciences
See next page for additional authors

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Scholar Commons Citation
Yang, Guijun; Weng, Qihao; Pu, Ruiliang; Gao, Feng; Sun, Chenhong; Li, Hua; and Zhao, Chunjiang, "Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE" (2016). School of Geosciences Faculty and Staff Publications. 1092. https://scholarcommons.usf.edu/geo_facpub/1092
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Authors Guijun Yang, Qihao Weng, Ruiliang Pu, Feng Gao, Chenhong Sun, Hua Li, and Chunjiang Zhao
This article is available at Scholar Commons: https://scholarcommons.usf.edu/geo_facpub/1092

remote sensing
Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE
Guijun Yang 1,*, Qihao Weng 2,3, Ruiliang Pu 4, Feng Gao 5, Chenhong Sun 1, Hua Li 6 and Chunjiang Zhao 1
Received: 12 November 2015; Accepted: 14 January 2016; Published: 21 January 2016 Academic Editors: Dale A. Quattrochi, Richard Gloaguen and Prasad S. Thenkabail
1 Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; [email protected] (C.S.); [email protected] (C.Z.)
2 School of Geography, South China Normal University, Guangzhou, Guangdong 510631, China; [email protected]
3 Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA
4 School of Geosciences, University of South Florida, Tampa, FL 33620, USA; [email protected] 5 Hydrology and Remote Sensing Laboratory, Agricultural Research Service, US Department of Agriculture,
Beltsville, MD 20705, USA; [email protected] 6 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences, Beijing 100010, China; [email protected] * Correspondence: [email protected]; Tel.: +86-10-51-503-647; Fax: +86-10-51-503-750
Abstract: Land surface temperature (LST) is an important parameter that is highly responsive to surface energy fluxes and has become valuable to many disciplines. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between them. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of thermal infrared (TIR) data or LST, but rarely both. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. The aim of this study is to conduct a comprehensive evaluation of the ESTARFM algorithm for generating ASTER-like daily LST by three approaches: simulated data, ground measurements and remote sensing products, respectively. The datasets of LST ground measurements, MODIS, and ASTER images were collected in an arid region of Northwest China during the first thematic HiWATER-Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012 from May to September. Firstly, the results of the simulation test indicated that ESTARFM could accurately predict background with temperature variations, even coordinating with small ground objects and linear ground objects. Secondly, four temporal ASTER and MODIS data fusion LSTs (i.e., predicted ASTER-like LST products) were highly consistent with ASTER LST products. Here, the four correlation coefficients were greater than 0.92, root mean square error (RMSE) reached about 2 K and mean absolute error (MAE) ranged from 1.32 K to 1.73 K. Finally, the results of the ground measurement validation indicated that the overall accuracy was high (R2 = 0.92, RMSE = 0.77 K), and the ESTARFM algorithm is a highly recommended method to assemble time series images at ASTER spatial resolution and MODIS temporal resolution due to LST estimation error less than 1 K. However, the ESTARFM method is also limited in predicting LST changes that have not been recorded in MODIS and/or ASTER pixels.
Keywords: ESTARFM; ASTER; MODIS; land surface temperature; evaluation

Remote Sens. 2016, 8, 75; doi:10.3390/rs8010075


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1. Introduction
Land-surface temperature (LST) is a key parameter of the physics of land-surface processes at the regional and global scales, and it includes combined effects of all surface–atmosphere interactions and energy fluxes [1–7]. Due to the strong heterogeneity of land surface characteristics such as vegetation, soil, water, and topography [8,9], LST changes rapidly over both spatial and temporal scales [10–12]. In practice, the spatial and temporal resolution requirements of satellite-derived surface temperature data for agricultural applications are estimated to be about 40 m and 1 day (revisit time), respectively [13]. Therefore, high-resolution LST measurements obtained with remote sensing approaches are highly desired. However, acquiring satellite images with high temporal and spatial resolutions remains extremely difficult due to tradeoffs between both resolutions. For example, the temporal frequency of the Moderate Resolution Imaging Spectrometer (MODIS, low-spatial/high-temporal resolution sensor) with a 1-km spatial resolution is greater than one visit per day [14,15]. In contrast, the temporal frequency of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, high-spatial/low-temporal resolution sensor) with a 90-m spatial resolution is greater than 15 days [16,17].
To bridge the gap between the low spatial resolution of available thermal data and the high spatial resolution required over agricultural areas, one may disaggregate low-spatial-resolution thermal data at high-temporal frequencies. Existing techniques have been reported in interdisciplinary literature, including image/data fusion [18–28], spatial sharpening [15,29–33], downscaling and disaggregation [3,10,17,34–36] and their comparisons [37–40]. Various methods of LST downscaling can be broadly grouped into physical and statistical categories. For statistical methods, a relationship between a vegetation index/variable (e.g., the Normalized Difference Vegetation Index, NDVI) and radiometric surface temperature can be addressed by a linear or nonlinear function. Due to the fact that vegetation indices (VIs) are often available at a finer pixel resolution than LST, there is a potential to make use of the VI–LST relationship to derive LST at the VI pixel resolution [17]. Physical downscaling uses modulation methods, which take a thermal pixel as a block and distribute its LST or thermal radiance into finer pixels corresponding to its shorter wavebands. However, the isothermal assumption that underpins various modulation methods may cause some errors, especially in vegetated areas that are composed of a mixture of different temperature components [10]. To our knowledge, many disaggregation methods do not yield ideal results in areas where vegetation cover is mixed with other land cover types, especially those areas covered with mixture of bare soil, water, and impervious components [3]. In fact, many bare lands, lakes or rivers, and villages or towns are irregular in shape and have variable sizes and distributions in agricultural fields, which are typical features of agricultural landscapes in China [41,42]. Although thermal downscaling methods can produce LST data with a relatively high spatial resolution in the order of 10–100 m, they do not simultaneously increase the temporal resolution of the sensor.
The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [18] is perhaps the most widely used data fusion algorithm for Landsat and MODIS imagery [19–28,37,39,40]. It is one of the few data fusion methods that result in synthetic Landsat-like surface reflectance [21,40,43]. The basic assumption underlying the method is that the surface reflectance on a predicted date can be estimated as a weighted sum of spectrally similar neighborhood information from both Landsat and MODIS reflectances on observation dates (close to the predicted date). In fact, STARFM integrates daily information from MODIS with periodic Landsat data to interpolate surface reflectance at the Landsat resolution of 30 m on a daily basis [19,20,24]. STARFM relies on temporal information from pure, homogeneous patches of land cover at the MODIS pixel scale. Simulations and predictions based on actual Landsat and MODIS images show that STARFM can accurately predict reflectance if these coarse-resolution homogeneous pixels exist [18]. However, the prediction results degrade somewhat when used on heterogeneous fine-grained landscapes, including small-scale agriculture [18,19]. Zhu et al. (2010) [20] developed an enhanced STARFM (ESTARFM) approach for application in a heterogeneous area by introducing a conversion coefficient and a spectral unmixing theory to the fusion model.

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However, as an essential part of the important work before using ESTARFM algorithm for generating high spatial-temporal LST products, an evaluation should be conducted over heterogeneous areas. The ESTARFM algorithm requires at least two pairs of fine- and coarse-resolution images acquired at the same date and a set of coarse-resolution images for desired prediction dates. Therefore, it should be evaluated how the selection of imagery pairs affects the performance of the data fusion algorithm.
Although ESTARFM was originally designed to fuse shortwave reflectance data from MODIS and Landsat to create daily reflectance, it is also greatly beneficial for the prediction of high spatial-temporal resolution thermal maps. Recently, Weng et al. (2014) [24] improved the STARFM method for predicting thermal radiance and LST data by considering the annual temperature cycle (ATC) over a heterogeneous urban area. This technique, the Spatio-temporal Adaptive Data Fusion Algorithm (SADFAT), blends Landsat and MODIS data to generate synthetic Landsat-like daily surface thermal data. They found that the prediction accuracy for the whole study area ranged from 1.3 K to 2 K [24]. Yang et al. (2015) [28] used ground measured temperature to evaluate the ESTARFM with a few temporal images. Therefore, based on our knowledge, most disaggregation methods for remotely sensed surface temperature were tested with only simulated data, or remote sensing products, or ground measurements. Therefore, these methods need a comprehensive evaluation and validation from three levels: simulation, remote sensing data, and ground observation.
The objective of this study was to evaluate the performance of the ESTARFM algorithm in retrieving LSTs using simulated data and coordination with ground measurements, MODIS, and ASTER data. All ground measurements and remote sensing data were collected in an arid region of Northwest China during the first thematic Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012 [44], as a part of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) [45]. Following a brief introduction to the ground LST measurements collected in the HiWATER experiment, the satellite data used in this study is presented. The theoretical basis and application of the ESTARFM algorithm method are discussed in Section 3. The evaluation results from simulation, ground measurements and ASTER LST products are presented in Section 4. Finally, the discussion and conclusions of this study are summarized in Sections 5 and 6 respectively.
2. Experimental Data
2.1. HiWATER-MUSOEXE Experiment
The HiWATER program was designed as a comprehensive ecohydrologic experiment and was based on the diverse needs of the interdisciplinary areas of the research plan and existing observation infrastructures in the Heihe River Basin [45]. The coordinate range of the study area is between 97.1˝E–102.0˝E and 37.7˝N–42.7˝N. The first thematic experiment launched in HiWATER was the HiWATER-MUSOEXE, which involved a flux observation matrix in the middle reach of a natural oasis area of the Heihe River Basin between May and September 2012 [44]. The HiWATER-MUSOEXE consisted of two nested matrices: one large experimental area (30 ˆ 30 km) and one core experimental area (5.5 ˆ 5.5 km) in Figure 1. The vegetation coverage was 49.23% of the total experimental area and consisted of maize (83.06%) and a small amount of shelter forests and shrubs. The non-vegetated area coverage was 50.08%, which was comprised of Gobi desert (71.59%) and a small amount of towns and roads. The water coverage was 0.69%. The goal of the HiWATER-MUSOEXE was to study the spatial-temporal variations in evapotranspiration (ET), the effects of advection in the oasis-desert ecosystem (30 ˆ 30 km), heterogeneity of ET in the irrigated oasis (5.5 ˆ 5.5 km), and ET acquisition at a pixel scale. A detailed description of the HiWATER-MUSOEXE could be found in Xu et al. (2013) [44].

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radiometers recorded intensive 1-min observations from 8:00 to 18:00 each day. The atmospheric downwelling radiance in the HiWATER-MUSOEXE experimental area was consistent with that observed by the SI-111 radiometer at the WATERNET-44 station.
The radiometric temperatures measured by all 39 (12 AWS + 27 WATERNET) SI-111 radiometers were corrected for emissivity and downward sky irradiance effects. Let Tr be the radiometric temperature measured by a radiometer, the true land surface temperature Ts is given by:

BpTsq “ rBpTrq ´ p1 ´ εqLskys{ε


where B is the Planck function weighted for the spectral response function of the SI-111 radiometer; ε is the surface emissivity of the SI-111 channel; and Lsky is the downward sky irradiance divided by π.
The emissivities of the stations were determined using the vegetation cover method [46]. This method requires the vegetation and background emissivities to be known. During the field experiment, the emissivities of bare soil and cropland were measured with an infrared spectroradiometer (ABB BOMEM® MR304) and a diffuse golden plate, and these values were used to obtain the radiometric data of the samples and the corresponding atmospheric downward radiance. The spectral resolution of the MR304 is 1 cm´1. The emissivity spectra in the range of 8–14 µm were retrieved using the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm, which has been proven to be an effective algorithm with a high accuracy for temperature and emissivity retrieval [47]. The Fractional Vegetation Cover (FVC) was measured using a photographic method [48] at a nadir view. Up until this point, all the processed SI-111 measured LST data in HiWATER-MUSOEXE [49].

2.3. ASTER and MODIS Data
To validate the ESTARFM method for downscaling satellite-retrieved LST data, this study used data from MODIS and ASTER sensors onboard NASA’s TERRA satellite. ASTER was designed to collect data for geological and environmental applications and to provide three spectral bands in the visible near-infrared (VNIR, 0.5–0.9 µm), six bands in the shortwave infrared (SWIR, 1.6–2.5 µm), and five bands in the thermal infrared (TIR, 8–12 µm) regions, with 15-, 30-, and 90-m ground resolutions, respectively [16]. In this study, six ASTER images were acquired from July to September 2012. Table 1 presents a list of all dates and the overpass times of the ASTER data. The land surface temperature and emissivities were derived from the ASTER data using the temperature emissivity separation (TES) algorithm [47], combined with the Water Vapor Scaling (WVS) atmospheric correction method [7,50] which can decrease the uncertainty of the TES algorithm by the parameterization of the sensor view angle and total column water vapor [12,51]. Since the ASTER LST products were generated and evaluated by Wang et al. (2015) [7], here, we just used the LST products without discussing the details of the retrieval method. For a more detailed description of the ASTER LST products, please refer to Wang et al. (2015) [7].

Table 1. A list of dates and statistics results of the ASTER and MODIS LSTs.

Date Case (Day/Month/Year)


10 July 2012


2 August 2012


18 August 2012


27 August 2012

5 3 September 2012

6 12 September 2012

Maximum LST (K)


323.36 323.80 311.72 320.98 312.92 312.38

325.76 327.63 314.32 323.99 316.37 315.28

Minimum LST (K)


301.76 302.96 298.82 301.44 296.82 293.97

299.61 301.50 297.34 299.27 295.08 291.95

Mean LST (K)


309.36 310.47 303.43 308.76 302.76 300.93

310.67 311.92 304.29 309.87 303.36 301.45















MODIS is a multispectral imager onboard the Terra and Aqua satellites of NASA’s EOS, and it provides daytime and nighttime imaging capability of any point on the Earth’s surface every 1–2 days, with a spatial resolution of ~1 km at nadir and 5 km at higher off-nadir viewing angles at the scan

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Remote Sens. 2016, 8, 75
edge [14]. The LST is retrieved with the new refined generalized split window (GSW) or the day/night
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September 2012 (Figure 2). Each MODIS dataset, including MOD03_L1A, MOD07_L2, MOD09GQ, and ManOdDM1O1D_L121_dLa2tdasaetatsse, tws,awsacsoclolellcetcetdedfrfroommtthhee LLaannddPPrrooccesessesseDs DistirsitbruibteudteAdctAivcetiAvrechAivrcehCiveneteCrenter (LP D(LAPADCA)AoCf )thoef tUhe.SU. G.S.eGoleoogloicgaiclaSl uSruvrveeyy(U(USSGGSS)).. TThheeMMOODD030_3L_1LA1Agegoleooclaoticoantipornodpuroctdcuocnttacionns tains geodegteiocdceotiocrdcoinoradteinsa, tgerso, ugrnodunedlevelaetviaotnio, na,nadndsastaetleliltlietezeznenitihthaannddaazziimmuutthh aanngglleessffoorreeaachchMMOODDISI1S-1-km pixel,kwmhpiicxhelw, wehreichusweedreinusveidsuinalvinsutearlpinrteetraptrioetnastiotonsatsosiasstswistitwhitthhtehaecaccucurarateteccoorreeggiistrattiioonnbbeetwtweeenen the
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[34]. Finally, both the ASTER and MODIS data were registered to the same coordinate system and both rtehseamApSTleEdRatatnhde MsaOmDe sISpadtiaatlarweseorluetrioengi(s9t0ermed). tTohtehleansadmcoevceoromrdaipnaintethseysHteiWmAaTnEdRr-eMsaUmSOplEeXdEat the sameesxppaertiimalernetsaol luarteioanw(9as0 mac)q.uTirheedlafrnodmcothveer3m0-amp irnestohluetHioniWgAloTbEaRl -lManUd ScOovEeXrE(GexLpCe)ridmaetansteat,l area was aGclqoubierLeadnfdr3o0m, wthheich30i-nmclurdeseoslmutoiroenthgalonbsaelvleannldancdovceorve(Gr tLyCpe)sd(actualstievta,tGedlolbaneLd,aanrdti3fi0c,iawl hsuicrhfacinesc,ludes morebtahraenlasnedv,ewnaltaenrdbocdoivees,rwtyeptleasnd(c,ushltriuvbatleadndlas,nadn,darfotirfiecstisa)l [s5u4r].faGcleosb,ebLaarnedl3a0ndda, twasaettesrwbeorde iuesse,dwteotland,
shrubclalasnsidfys,thaenldanfdorceosvtesr) [in5t4o].thGreloebtyeLpeasnidn3c0ondsaidtaesreintsg wthercehuarsaecdtetroistciclassosfiflyantdhesulrafnacdeccooverraignetointhree
typesthine sctoundsyidareerain. g the characteristics of land surface coverage in the study area.

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3. Methodology 3. Methodology
3.1. Theoretical Basis of the ESTARFM 3.1. Theoretical Basis of the ESTARFM
The ESTARFM algorithm described by Zhu et al. (2010) [20] was applied in the current study, TwhheichESisTdAeRscFriMbedalbgyoariltinhemardmeosdcerilbinedEqbuyatZiohnu(2e)tbaell.ow(2.0T1h0e)a[l2g0o]riwthams iaspbpasleieddoninthtehaescsuumrrpetniot nstudy, whichthiast dbeosthcrAibSeTdEbRyaandlinMeOarDmISoidmealgienryEqoubsaetriovne t(h2e) bsaemloewr.eTflhecetaanlgceorainthdmLSiTs,bbaisaesdedobnythaecoanssstuamntption that berortohr.ATShTisEeRrraorndis McaOusDedISbiymthaegecrhyaroabctseerrisvteicsthoef saapmixeelr,eaflnedctiasnscyestaenmdatLicSoTv,ebriashseodrt bteymapocoranlstant error.inTtehrvisales.rrTohreriesfocraeu, stheids ebryrotrhceanchbearcaaclctuerlaistetidcsfoor feaachpipxiexle,l ainndtheisimsyasgteeimf aatbiacseovAeSrTEshRo-MrtOteDmISporal intervals. Therefore, this error can be calculated for each pixel in the image if a base ASTER-MODIS synchronization acquired image pair is available. These errors can then be applied to the MODIS imagery of a prediction date to obtain a corresponding ASTER-like prediction image.

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Here, the predicted fine-resolution ASTER-like LST is directly calculated from two pairs of fineand coarse-resolution images at ta and tc, and the coarse resolution image at tb is only used to calculate the temporal weight at ta and tc.


Fbpxw{2, yw{2, tb, LSTq “ rTab ˆ Fpxw{2, yw{2, ta, LSTq ` Tcb ˆ Fpxw{2, yw{2, tc, LSTqs



Tab ˆ pCpxi, yi, tb, LSTq ´ Cpxi, yi, ta, LSTqq


` Wi ˆ Hi ˆ

` Tcb ˆ pCpxi, yi, tb, LSTq ´ Cpxi, yi, tc, LSTqq
























˘ ˇ

Tkb “

ˇ ´ˇ


ˇ¯ , pk “ a, cq


ř 1{ ˇˇřiw“1 řwj“1 C `xi, yj, tk˘ ´ řiw“1 řwj“1 C `xi, yj, tb˘ˇˇ


where Fb is the final predicted fine-resolution LST at the prediction time tb; F and C denote the fine-resolution reflectance and coarse resolution LST, respectively; ta and tc denote the acquisition date for one pair of fine-resolution and coarse-resolution images, respectively; w is the search window size; N is the number of similar pixels including the central “prediction” pixel within the search window; (xi, yi) is the location of the ith similar pixel; Wi is the weight of ith similar pixel; Hi is the conversion coefficient of the ith similar pixel; and Tab and Tcb denote the temporal weight at ta and tc as Equation (3), respectively, which can be calculated according to the change in magnitude detected by the resampled coarse-resolution LST between the time (ta or tc) and prediction time tb.
In order to get high spatial-temporal LST from ASTER and MODIS data based on Equation (2), the implementation of ESTARFM consists of three steps. Firstly, a moving window is applied to the ASTER imagery to identify similar neighboring pixels. Secondly, a weight is assigned to each similar neighbor based on: (a) the differences between surface reflectances of ASTER-MODIS image pair and between LSTs of ASTER-MODIS image pair; (b) the temporal difference of the pixel’s value in both MODIS images; and (c) the spatial Euclidean distance between the neighbor and the central pixel. The final step consists of calculating the surface LST of the central pixel. For a more detailed description of the ESTARFM algorithm, please refer to Gao et al. (2006) [18] and Zhu et al. (2010) [20].

3.2. Evaluation Schemes for Prediction of ASTER-Like LST
In this study, six temporal ASTER LST products already acquired also differed with respect to time intervals. The shortest interval was six days (27 August 2012–3 September 2012), and the longest was 22 days (10 July 2012–2 August 2012). Hence, two schemes for the estimation of 90-m LST were designed (Figure 2). Scheme 1 was a segment-based prediction made using ASTER data acquired on different dates (Figure 2). That is that the two adjacent temporal ASTER/MODIS data were used to estimate the 90-m LST on intermediate dates. In Scheme 1, the ASTER LST products of different dates were input successively to estimate the 90-m LST with ESTARFM. Because the ASTER LST products had different time intervals, the different temporal ASTER LST data were used as the input with different frequencies; the smallest frequency was 1 (10 July, 2 August, 12 September 2012) and the largest was 4 (27 August 2012). Finally, the 11 temporal LSTs at a 90-m resolution were estimated successively. In Scheme 2 (Figure 2), the two temporal ASTER/MODIS data, 10 July 2012 and 12 September 2012, were used for the direct estimation of the 11 temporal LSTs at the 90-m resolution.
The 90 m LST estimated by ESTARFM was validated with three experiments. (1) The simulation data of typical ground objects (water surface, vegetation) were chosen for the validation by changing their LST, shape, and size. With reference to the simulation tests conducted by Gao et al. (2006) [18] and Zhu et al. (2010) [20], we tested four cases: varying temperature/reflectance, varying shapes, predicting small objects, and predicting linear objects. In detail, a series of 198 ˆ 198 pixel fine-resolution images were first simulated by assigning each pixel a positive value ranging from 290 to 320 or 0 to 1 to denote the temperature or reflectance of each pixel, respectively. Coarse-resolution images were produced by scaling-up the fine-resolution images (i.e., each cluster of 11 ˆ 11 neighboring pixels in

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the fine-resolution image was aggregated to create a pixel in a coarse-resolution image). The spatial

resoluRtemiootne sSeonsf. 2fi01n6e, 8-, a75nd coarse-resolution simulated images were identical to those of ASTER and

MODIS. Specifically, three pairs of fine- and coarse-resolution images acquired on the same date were simulnaetiegdh.boTrihnegnp,ixtheles ifinrtshteafninde-lraessot lputaiiorns iamnadgethweascaogagrrseeg-aretesdotloutciroenateima paigxeeloinf athceoasresce-ornesdolpuatiiornwere
image). The spatial resolutions of fine- and coarse-resolution simulated images were identical to those used otof ApSrTedERictanthdeMfiOnDe-IrSe.sSopleuctiifoicnalilmy, athgreeeopf athires osef cfionne-dapnadicr,oaarnsde-rtheseoplurteiodnicitmedagaens dacrqeuairleidmoangtehsewere compsaarmede dtaoteawsseersessitmheulaactecdu.rTahceyno, tfhtehfeirsnteawndallagsot priatihrsma;n(d2t)hUe csoinargset-hreessocluhteiomneismiangeFoigf uthrees2e,cothnde four tempoparairl wAeSrTeEuRseLdStTo pprroeddiuctcttshe(2fiAneu-rgeusostlu, 1ti8onAiumgaugset,o2f7thAeusgecuosntdanpdair3, Sanepdttehme bpererd2i0c1te2d) wanedrerecahl osen for thiemvaagleisdwateiorencofmLpSaTreedsttoimasasteesds tbhye EacScTuAraRcyFMof;t(h3e)nTehweaLlgSoTriethstmim; (a2t)eUdsbinygEthSeTAscRheFmMeswinasFivgaulriedated

with 2th, tehLe SfoTurmtemaspuorreaml AenSTtsERofLtShTeprraoddiuocmtse(t2erAeuqgusipt,p1e8dAautguthste, 2W7AATuEguRsNt aEnTds3taSteipotnesmabnerd2A01W2)Ss in

HiWAwTeEreRc-hMosUenSOfoEr XthEe.vHaleidrea,titohneonf oLrSmT aelsitzimedatedrrboyr EinSdTeAxR(FNME; I()3)isThueseLdSTtoesetximparetesds bthyeErSeTlAatRivFMe LSTs’
deviawtioasnvbaeltidwaeteedn wthiethEtShTeALRSTFMmeeasstuimreamteedntLs SoTfsthaendragdrioomunetdermeeqausipupreemd eant tthsewWhiAleTvEaRrNyiEnTgstthateionnusmber of lanadndcoAvWerStsyipneHs iaWndATthEeR-sMizUe SoOf EseXaEr.cHhewrei,ntdhoewn.orImt caalinzebdeedrreosrcrinibdeedx a(Ns EEIq) uisautisoend (t4o).express the
relative LSTs’ deviation between the ESTARFM estimated LSTs and ground measurements while

varying the number of land cMovaexrptσyqp´esσand the size of search window. It can be described as

Equation (4).

NEI “ Maxpσq ´ Minpσq , σ “ LSTGround ´ LSTESTARFM


where σ is the LST predictNedEIer=rors bMyatxh(eσ)E−STσARF,Mσ =coLmSTparing−toLSgTround measurements, M(a4)xpσq is

Max(σ) − Min(σ)



the maximum of σ, and Minpσq is the minimum of σ.

where σ is the LST predicted errors by the ESTARFM comparing to ground measurements,

4. ReMsualxts(σ) is the maximum of σ , and Min(σ) is the minimum of σ .

4.1. E4v.aRlueastuilotns of ESTARFM by Simulation vwaliuthatiVonaroyf iEnSgTATeRmFMpebryatSuimreulation
The ESTARFM algorithm was tested with simulated temperature and reflectance data to help 4.1.1. Test with Varying Temperature understand its effectiveness and limitations. In this simple case, there were only two objects (e.g., wateTrhaenEdSvTeAgReFtaMtioanlg)o(rFiitghumrew3a)s. tTehsteedshwapitehssoimf wulaatteerdatnemd pveergaetutarteioanndwreerfelefictxaendce, adnadtawtoe haeslspumed that tuhnedwerastteanr dboitds yef(fceicrtcivlee)nhesasdanadcolinmsittaantitonsus.rIfnactehirsesflimecptalenccaesoe,ft0h.e0r5e awnedreaocnolynsttwaontotbejmectpse(rea.gtu., re of 290 Kwoavteerratnhde voebgseetravtiionng) p(Feigriuorde,3w). hTehreesahsatphees voef gweattaetrioanndrevfleegcettaatniocne wwearsesfeixteads, 0a.n1d, 0w.2e,aasnsudm0e.4d, and that the water body (circle) had a constant surface reflectance of 0.05 and a constant temperature of the co2r9r0esKpoovnedritnhge toebmseprveirnagtupreeriwoda,swsheet raesas30th0eKve(gFeigtautiroen3rae)f,le3c1ta0nKce(wFiagsusreet 3asb)0,.1a,n0d.23, 2an0dK0.(4F,iagnudre 3c). The MthOeDcoIrSr-elsikpeon9d9i0n-gmtesmppateiraaltureresowluastisoent atesm30p0eKra(tFuigreuriem3aag),e3s1(0FKig(uFrigeu3rde–3fb))w, aenrde3a2g0gKre(gFaigteudref3rco)m. the ASTETRh-elikMeO9D0-ImS-lsikpeat9i9a0l -rmesospluattiiaolnriemsoalugteison(Ftiegmupreer3aatu–cre). iUmsaignegs t(hFeigEuSreTA3dR–Ff)Mwaelrgeoargitghrmegaatnedd dfraotma from Figurteh3eaA,cS–Tf,EwR-elikeest9im0-amtesdpathtiealfirneseo-rluestioolnutiimoangteesm(Fpiegruarteu3rea–icm).aUgsein(FgigthuereES3TbA). RWFMhenalgthoeritshumrroanudnding spatiadlatianforormmaFtigiounreo3fa,fic–nfe, -wreseosltuimtiaotneddtahteafiwnea-rsesuosleudti,ona tnemeaprelryateuxreacimt magaet(cFhig(uFrieg3ubr)e. W3gh)encothueld be retriesvuerdroautndainfignesprarteiasloilnuftoiromnaitnionthoisf fsiinme-prelseocluatsioen. Zdahtua wetaaslu. s(e2d0,1a0)ne[a2r0l]y ceoxnacdtumctaetcdha(Fsiigmuriela3rgs)tudy for docowunldscbaelirnegtrileavnedd asut arffainceerrreeflseoclutatinonceindtahtias fsriommpleMcOasDe.IZShrueseot laul.t(i2o0n10t)o[L20a]ncdosnadtuEctTedMa+srimesiolalrution usingstaundeynfhoarndcoewdnsspcaatliinalgalnadndtesmurpfaocrealraedfleacptatinvcee rdeflateactfaronmce MfuOsiDonISmreosdoelul.tiTonhistoexLaamndpslaet rEeTvMea+ls the signifirecsaonlucetioonf audsidnigtiaonneanl hfiannecerdessoplauttiiaolnansdpatetmiapl oinrafol ramdaapttiiovne frreoflmectmanucletifsuosuiorncemroedmelo.tTehsiesnesxianmgpdleata to reveals the significance of additional fine resolution spatial information from multisource remote downssecnasliinnggdLaStaTt.o downscaling LST.




Figure 3. Cont.