Intercomparison of Operational Land Surface Temperature Products Derived From MSG-SEVIRI and Terra/Aqua-MODIS Data Si-Bo Duan and Zhao-Liang Li

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1 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 8, AUGUST Intercomparison of Operational Land Surface Temperature Products Derived From MSG-SEVIRI and Terra/Aqua-MODIS Data Si-Bo Duan and Zhao-Liang Li Abstract Accuracy assessment of land surface temperature (LST) products is critical to facilitate their use in various studies. As an alternative method for assessing the accuracy of LST products, a new satellite LST product is compared with a heritage LST product to validate and determine the uncertainties in the satellite-derived LST approach. In this study, we propose a method for the intercomparison of the Meteosat second generation-spinning enhanced visible and infrared imager (MSG- SEVIRI) and Terra/Aqua-moderate resolution imaging spectroradiometer (MODIS) LST products. The intercomparison was performed by verifying the collocation in space, temporal concurrence, viewing geometry alignment, and spatial homogeneity between the two LST products. The discrepancies between the SEVIRI and MODIS LST products were investigated over different seasons, times of day, and surface types. SEVIRI LST values are generally higher than MODIS LST values, with positive biases during daytime (approximately 2 4 K) and nighttime (approximately 1 2 K). Significant variability of the daytime LST discrepancies with season, time of day, and surface type is observed. Compared with the daytime LST discrepancies, the nighttime LST discrepancies are less dependent on season, time of day, and surface type. Index Terms Intercomparison, land surface temperature (LST), Meteosat second generation-spinning enhanced visible and infrared imager (MSG-SEVIRI), moderate resolution imaging spectroradiometer (MODIS). I. INTRODUCTION A S A KEY variable in the physical processes of land surface energy and water balance at regional and global scales, land surface temperature (LST) is widely used in a range of hydrological, meteorological, ecological, and climatological applications [1] [6]. Satellite remote sensing provides Manuscript received December 19, 2014; revised February 09, 2015; accepted June 01, Date of publication June 17, 2015; date of current version September 12, This work was supported by the National Natural Science Foundation of China under Grant (Corresponding author: Zhao-Liang Li.) S.-B. Duan is with the Key Laboratory of Agri-Informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing , China ( duansibo@ caas.cn). Z.-L. Li is with the Key Laboratory of Agri-Informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing , China, and also with the Laboratoire des Sciences de l Ingènieur, de l Informatique et de l Imagerie, Université de Strasbourg, Centre National de la Recherche Scientifique, Illkirch 67412, France ( lizhaoliang@caas.cn). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /JSTARS a unique way to derive LST over extended regions [7] [9]. Several satellite-derived LST products are available for the scientific community, including Meteosat second generationspinning enhanced visible and infrared imager (MSG- SEVIRI), Terra/Aqua-moderate resolution imaging spectroradiometer (MODIS), Terra-advanced spaceborne thermal emission and reflection radiometer (ASTER), environmental satellite-advanced along-track scanning radiometer (ENVISAT- AATSR), national oceanic and atmospheric administrationadvanced very high resolution radiometer (NOAA-AVHRR), and Suomi-national polar-orbiting partnership-visible infrared imaging radiometer suite (S-NPP-VIIRS). Assessing the accuracy of these LST products will help to improve their retrieval algorithms and facilitates the use of these LST products in various studies [10], [11]. Three different methods have been widely used to validate and determine the uncertainties in satellite-derived LST products, including temperature-based [12] [23], radiance-based [24] [28], and intercomparison [22], [23], [29], [30] methods. These approaches are complementary and provide different levels of information about the accuracy of the satellite-derived LST products. Detailed descriptions of these methods can be found in [31], [32]. The intercomparison method compares a satellite-derived LST product with a well-validated LST product. This method can provide useful information about spatial patterns in the LST discrepancies over a wide range of surface types and viewing geometries. Trigo et al. [33] compared the operational MSG-SEVIRI LST product with the Terra/Aqua-MODIS LST product over three areas during six 7-day periods. Sun et al. [34] compared the results from GOES-derived LST with MODIS LST data over the continental United States. The LST differences during daytime were found to be related to anisotropy in the viewing geometry, as well as surface properties. Gao et al. [35] compared MSG2-SEVIRI-derived and Terra-MODIS LST data during seven clear-sky days, investigating the discrepancies in the two approaches in terms of view zenith angle (VZA) differences and land cover type. Qian et al. [36] compared the MSG1-SEVIRI-derived LST and emissivity with the Terra/Aqua-MODIS LST and emissivity products over the Iberian Peninsula and Egypt/Middle East during nine clear-sky days, but did not analyze the sources contributing to the LST differences between the two products. Frey et al. [37] compared the NOAA-AVHRR LST product from DLR with the Terra/Aqua-MODIS LST product. They found that the LST IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.

2 4164 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 8, AUGUST 2015 TABLE I SELECTED SENSOR CHARACTERISTICS OF MODIS (BANDS 31 AND 32) AND SEVIRI (BANDS 9 AND 10) Fig. 1. Land cover types of the study area generated from MSG-SEVIRI land cover type product ( differences between these two products showed both a diurnal and an annual pattern, with the AVHRR LST higher (lower) than the MODIS LST at high (low) LST values. Even with these previous studies associated with the intercomparison method, further analyses of the variability of the discrepancies between two intercompared LST products with various factors are still necessary. Specifically, this study primarily focuses on the investigation of the discrepancies between the operational SEVIRI and MODIS LST products over different seasons, times of day, and surface types. This paper is organized as follows: Section II introduces the study area and data used, Section III describes the method for the intercomparison of the operational SEVIRI and MODIS LST products, Section IV presents the results and discussion, and Section V concludes. II. STUDY AREA AND DATA A. Study Area The study area extends from 20 Wto20 E longitude and 0 Nto20 N latitude. The VZA of SEVIRI in the study area is thus approximately less than 30. According to the MSG- SEVIRI land cover type product ( this area is mainly characterized by evergreen broadleaf forest (EBF; IGBP class 2), woody savannas (WDS; IGBP class 8), savannas (SVN; IGBP class 9), and barren or sparsely vegetated land (BSV; IGBP class 16). Fig. 1 shows the MSG-SEVIRI land cover type map for the study area. B. MODIS LST Product MODIS is a key instrument aboard the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra and Aqua satellites. Terra and Aqua were launched on December 18, 1999 and May 4, 2002, respectively. The Terra overpass time is approximately 10:30 A.M. local solar time in descending mode and 10:30 P.M. local solar time in ascending mode, while the Aqua overpass time is approximately 1:30 P.M. local solar time in ascending mode and 1:30 A.M. local solar time in descending mode. The selected sensor characteristics of MODISareshowninTableI. The MODIS LST product at the 1-km pixel scale is derived from the brightness temperature in bands 31 and 32 using a generalized split-window algorithm [38]. The spectral response functions of Terra/Aqua-MODIS in bands 31 and 32 are shown in Fig. 2. The daily level 3 MODIS LST products MOD11A1 and MYD11A1(collection-5) were used in this study. MOD11A1 and MYD11A1 are tile-based and gridded in the sinusoidal projection Fig. 2. Spectral response functions of Terra/Aqua-MODIS (bands 31 and 32) and MSG-SEVIRI (bands 9 and 10). at 1-km spatial resolution. Both products were downloaded from the Reverb website ( for January, April, July, and October The MODIS Reprojection Tool was used to reproject, mosaic, and resample the MOD11A1 and MYD11A1 products. The Science Data Set layers LST, observation time (local solar time), VZA, and quality control (QC) were extracted from the MOD11A1 and MYD11A1 products. Only the pixels flagged as the highest quality (i.e., QC =0) were used. C. MSG-SEVIRI LST Product SEVIRI is the main sensor onboard MSG, and it observes the full disk of the Earth with a temporal resolution of 15 min and a spatial sampling distance of 3 km at the subsatellite point. The selected sensor characteristics of MSG-SEVIRI are shown in Table I. The MSG-SEVIRI LST product generated by the Land Surface Analysis of the Satellite Application Facility (LSA SAF) at the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) was used. The LSA SAF LST product is derived from the brightness temperature in MSG-SEVIRI bands 9 and 10 using a generalized split-window algorithm [33], [39] similar to the algorithm proposed by Wan and Dozier [38]. The spectral response functions of MSG- SEVIRI in bands 9 and 10 are shown in Fig. 2. We downloaded the LSA SAF LST product for January, April, July, and October

3 DUAN AND LI et al.: INTERCOMPARISON OF OPERATIONAL LST PRODUCTS from the LSA SAF website ( The source codes of the routine RWHDF5 available at the LSA SAF website were used to convert the LSA SAF LST product in HDF5 format into binary files. Only the pixels flagged as good quality (i.e., QC = 10014, 10142, and 14238) were used. Three auxiliary data, including latitude, longitude, and land cover type, were also downloaded from the LSA SAF website. In addition, VZA data were calculated using the SEVIRI Preprocessing Toolbox. D. DEM Data To select the collocated pixel pairs over relatively flat areas, we downloaded the global digital elevation model (DEM) data GTOPO30 from the U.S. Geological Survey website ( The data cover the full extent of the longitudes from 180 W to 180 E and latitudes from 90 Nto90 S. The spatial resolution is 30 arc-seconds (approximately 1 km). The horizontal coordinate system is decimal degrees of latitude, and the longitude is referenced to WGS84. The vertical units represent elevation in meters above mean sea level. In the DEM data, ocean areas are masked as no data and are assigned a value of Four tiles (W020S10, E020S10, W020N40, and E020N40) covered the study area. The GTOPO30 data were aggregated to the SEVIRI pixel scale in terms of longitude and latitude. III. METHODOLOGY The basic premise of intercomparison is that two sensors should make identical measurements when they observe the same location, the same time, and viewing geometry. Because these idealized conditions never occur in reality, several thresholds are used to collocate the measurements of these two sensors. Fig. 3 shows the flowchart of the intercomparison between SEVIRI and MODIS LST products. The intercomparison of these two LST products involves four major steps to verify their: 1) collocation in space; 2) temporal concurrence; 3) viewing geometry alignment; and 4) spatial homogeneity. A. Collocation in Space The first step is to identify the spatially collocated pixel pairs by longitude and latitude of the MODIS and SEVIRI LST products. The MODIS LST data are aggregated to the SEVIRI pixel scale by averaging all MODIS pixels within the instantaneous field of view (IFOV) of a SEVIRI pixel. To minimize the effects of cloud contamination, a SEVIRI pixel is selected only if all MODIS pixels within the IFOV of the SEVIRI pixel are flagged as the highest quality. To speed up the spatial collocation, a look up table between MODIS and SEVIRI pixels is established in terms of longitude and latitude. Once the look up table is established, each MODIS pixel can be quickly matched to its corresponding SEVIRI pixel. B. Temporal Concurrence The next step is to check whether the spatially collocated pixels identified in the first step are concurrent in time. SEVIRI Fig. 3. Flowchart of the intercomparison between SEVIRI and MODIS LST products. Δt is the temporal difference between SEVIRI and MODIS LST products, θ S and θ M are the VZAs of SEVIRI and MODIS, respectively, σ LST is the STD of all MODIS LST values within a filter window size of 5 5 SEVIRI pixels centered at a collocated pixel pairs, and σ DEM is the STD of all GOTOP30 DEM values within a filter window size of 5 5 SEVIRI pixels centered at a collocated pixel pairs. observes the full disk of the Earth from east to west and south to north with a nominal repeat cycle of 15 min (actually 12 min for imaging and 3 min for retrace and onboard calibration). The UTC time recorded in the file name of the LSA SAF LST product is the scan starting time. The actual observation time of each SEVIRI pixel is calculated in terms of the scan starting time, scan line number, and actual imaging time. The MODIS observation time is extracted from the MOD11A1 and MYD11A1 products. The time difference for each collocated pixel pair is calculated, and only the collocated pixel pairs with time difference less than a predetermined threshold of 5 min are considered to be concurrent [37]. A reasonable upper limit for the temporal threshold is half of the SEVIRI repeat cycle; otherwise, one MODIS pixel would match SEVIRI pixels in two consecutive images [23], [36]. C. Viewing Geometry Alignment The third step is to check whether the pixels identified in the previous two steps as spatially and temporally collocated are aligned in viewing geometry. This means that the collocated pixel pairs view the surface at similar line of sight through the

4 4166 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 8, AUGUST 2015 atmosphere. The relative difference in the secant of the VZA between MODIS and SEVIRI is expressed as cos (VZA SEV IRI ) cos (VZA MODIS ) 1 < max _VZA (1) where VZA SEVIRI is the SEVIRI VZA, VZA MODIS is the MODIS VZA, and max_vzais the predetermined threshold of the relative difference. Only collocated pixel pairs with a relative difference less than the predetermined threshold are considered to be aligned in their viewing geometry. In this study, the angular threshold is set to 0.01, which corresponds to a maximum difference of 1% in atmospheric path length [40]. Another aspect of viewing geometry alignment is relative azimuth angle (RAA). Similar VZA assures similar atmospheric path length; additional requirement of similar RAA assures similar line of sight. Because the VZA of SEVIRI over the study area is approximately less than 30 and only relatively flat and homogeneous pixels are selected, the effects of RAA are considered to be small. Consequently, RAA is not considered in this study. D. Spatial Homogeneity The last step is to select the collocated pixel pairs over relatively flat and homogeneous areas. To reduce uncertainty in the comparison due to spatial and temporal variations, only pixels that exhibit a certain degree of homogeneity are chosen. The selection criteria for the spatial homogeneity filter used in the comparison are as follows. 1) All MODIS pixels within a filter window size of 5 5 SEVIRI pixels centered at a collocated pixel pairs are labeled the highest quality. 2) The standard deviation (STD) of all MODIS LST values within a filter window size of 5 5 SEVIRI pixels centered at a collocated pixel pairs is less than 1 K [37]. 3) The STD of all GOTOP30 DEM values within a filter window size of 5 5 SEVIRI pixels centered at a collocated pixel pairs is less than 50 m [33]. IV. RESULTS AND DISCUSSION A. Variability of LST Discrepancies by Season To investigate the variability of LST discrepancies by season, we selected SEVIRI and MODIS LST products in January, April, July, and October 2010 to represent four seasons of the year. Fig. 4 shows the comparison of SEVIRI versus MODIS LST products during daytime and nighttime over 4 months. The coefficients of determination (R 2 ) during daytime (nighttime) are 0.98 (0.96), 0.96 (0.82), 0.97 (0.84), and 0.97 (0.81) for January, April, July, and October, respectively. The bias, STD, root-mean-square error (RMSE) values of the differences between the two LST products during daytime and nighttime in January, April, July, and October over the entire study area are summarized in Table II. Significant variability in daytime LST discrepancies by season can be found, with larger bias (3.10 K) and STD (1.66 K) values obtained in July and smaller bias (2.04 K) and STD (0.90 K) values Fig. 4. Scatterplots of SEVIRI versus MODIS LST products during daytime and nighttime in: (a) January, (b) April, (c) July, and (d) October over the entire study area. TABLE II BIAS, STD,AND RMSE VALUES OF THE DIFFERENCES BETWEEN SEVIRI AND MODIS LST PRODUCTS DURING DAYTIME AND NIGHTTIME IN JANUARY, APRIL, JULY, AND OCTOBER seen in January. The bias may be due to the effects of surface emissivity. Emissivity errors can cause a larger bias in July, when the LST values are higher. The larger STD could be due to larger spatial variability of LST in July, when the LST contrasts between dry bare ground/grass versus vegetation canopies are likely to be more pronounced [33]. Another reason for the larger spatial variability of LST in July is likely an effect of increased atmospheric variability, e.g., warm and moist atmospheric conditions during the monsoon season in the Sahel region. Compared with the daytime LST discrepancies, the nighttime LST discrepancies are less dependent on season. The statistics at night for the four seasons of the year are similar, with bias < 1.2 K, STD < 1.5 K, and RMSE < 1.9 K. Fig. 5 displays the histograms of differences between SEVIRI and MODIS LST products during daytime and nighttime in January, April, July, and October. A bimodal distribution of the nighttime LST differences in January can be seen in Fig. 5(a). By carefully analyzing the nighttime LST differences in January, we found that: 1) MSG LSTs are generally higher than MODIS LSTs when MSG LSTs are less than approximately 290 K, which mostly occurs in the Sahel region and 2) MSG LSTs are generally lower than MODIS LSTs when

5 DUAN AND LI et al.: INTERCOMPARISON OF OPERATIONAL LST PRODUCTS 4167 Fig. 5. Histograms of the differences between SEVIRI and MODIS LST products during daytime and nighttime in: (a) January, (b) April, (c) July, and (d) October over the entire study area. Fig. 6. Scatterplots of SEVIRI versus MODIS LST products for: (a) Terra daytime, (b) Terra nighttime, (c) Aqua daytime, and (d) Aqua nighttime in July. MSG LSTs are greater than approximately 290 K, which mostly occurs in the tropical rainforest. The maximum daytime differences (LST SEVIRI LST MODIS ) range from 4.85 K in January to 8.15 K in July, whereas those at night vary from 4.55 K in October to 5.52 K in January. The minimum daytime LST differences range from 1.94 K in July to 0.79 K in October, whereas those at night vary from 3.35 K in January to 2.20 K in October. The SEVIRI LST values are generally higher than the MODIS LST values, with positive biases during daytime (approximately 2 4 K) and nighttime (approximately 1 2 K). These results are in agreement with the results reported by Ermida et al. [23] and Trigo et al. [33]. B. Variability of LST Discrepancies With Time of Day To analyze the variability of LST discrepancies with time of day, we calculated the differences between SEVIRI and MODIS LST products for Terra daytime, Terra nighttime, Aqua daytime, and Aqua nighttime in July. These four times of day correspond to the overpass times of Terra descending (10:30 A.M. local solar time), Terra ascending (10:30 P.M. local solar time), Aqua ascending (1:30 P.M. local solar time), and Aqua descending (1:30 A.M. local solar time), respectively. The R 2 values are 0.97, 0.83, 0.94, and 0.80 for Terra daytime, Terra nighttime, Aqua daytime, and Aqua nighttime, respectively. The LST values during the day are generally higher than those at night. The daytime LST values range from approximately 300 to 330 K, whereas the nighttime LST values range from approximately 280 to 310 K (Fig. 6). The bias values during daytime are larger than those during the night. The larger bias during the day could be mainly caused by the effects of surface emissivity and viewing geometry. There are no significant discrepancies between the bias values (approximately 0.7 K) during nighttime. Because of Fig. 7. Histograms of the differences between SEVIRI and MODIS LST products for: (a) daytime and (b) nighttime in July. lower dependency on differential surface heating/cooling, LSTs during nighttime are in the best position for the assessment of possible biases between algorithms/sensors [33]. The STD values during the day are relatively larger than those during nighttime. These results are mainly caused by the spatial variability of LST, which is usually more significant during the day than at night due to the effects of structural shading, evaporative cooling, and surface air temperature differences [31]. Another important reason for relatively larger spatial variability of LST during daytime is differential surface heating over different surface covers, e.g., trees and grass/soil background. Fig. 7 shows the histograms of the differences between SEVIRI and MODIS LST products for daytime and nighttime in July. The LST daytime differences (LST SEVIRI LST MODIS ) range from approximately 2 K to 8 K, whereas those during nighttime range from approximately 3 Kto5K. C. Variability of LST Discrepancies With Surface Type As mentioned, the study area is mainly dominated by EBF, WDS, SVN, and BSV. These four surface types were used to analyze the variability of LST discrepancies with surface type. Fig. 8 shows the comparison of SEVIRI versus MODIS LST

6 4168 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 8, AUGUST 2015 Fig. 8. Scatterplots of SEVIRI versus MODIS LST products during daytime and nighttime in July for: (a) EBF, (b) WDS, (c) SVN, and (d) BSV. TABLE III BIAS, STD,AND RMSE VALUES OF THE DIFFERENCES BETWEEN SEVIRI AND MODIS LST PRODUCTS DURING DAYTIME AND NIGHTTIME IN JULY FOR EBF, WDS, SVN, AND BSV Fig. 9. Histograms of the differences between SEVIRI and MODIS LST products during both daytime and nighttime in July for: (a) EBF, (b) WDS, (c) SVN, and (d) BSV. for EBF, WDS, SVN, and BSV. The maximum of the LST differences (LST SEVIRI LST MODIS ) during daytime (nighttime) ranges from 4.23 K (0.92 K) for EBF to 8.31 K (4.76 K) for BSV. The minimum of the LST differences during the day ranges from 1.62 KforBSVto 0.63 K for SVN, whereas those during the night vary from 3.05 K for WDS to 2.30 K for EBF. products during daytime and nighttime in July for these four surface types. The R 2 values during daytime (nighttime) are 0.71 (0.65), 0.85 (0.79), 0.97 (0.80), and 0.95 (0.80) for EBF, WDS, SVN, and BSV, respectively. The bias, STD, and RMSE values of the differences between these two LST products during daytime and nighttime in July for EBF, WDS, SVN, and BSV are shown in Table III. The surface types have significant effects on the LST discrepancies during daytime, with a smaller bias (0.90 K) seen for EBF and a larger bias (3.35 K) observed for BSV. These results can be explained by the fact that EBF is usually spatially extensive and thus homogeneous in terms of structure, whereas sparsely vegetated land is often structured and therefore exhibits strong viewing and illumination effects [41] [43]. Furthermore, emissivity of bare soil is often not well reproduced by satellite products and therefore emissivity effects are expected to be higher for sparsely vegetated land. Compared with the bias during the day, the bias during nighttime is relatively smaller, which probably indicates the effects of different emissivities used in the SEVIRI and MODIS LST retrieval algorithms [44] [46]. Fig. 9 displays the histograms of the differences between the two LST products during both daytime and nighttime in July V. CONCLUSION This study proposes a method for the intercomparison of operational SEVIRI and MODIS LST products. The collocated pixel pairs are selected through spatial, temporal, and angular consistencies between the two LST products, and discrepancies were analyzed in terms of different factors, including season, time of day, and surface type. Generally, SEVIRI LST values are higher than MODIS LST values. A significantly seasonal variability in the daytime LST discrepancies is found. The largest bias (3.10 K) and STD (1.66 K) are obtained in July, whereas the smallest bias (2.04 K) and STD (0.90 K) are seen in January. Compared with daytime LST discrepancies, nighttime LST discrepancies are less dependent on season, with a bias less than 1.2 K and a STD less than 1.5 K. The bias and STD values of LST discrepancies during the day are generally greater than those during the night. Moreover, the bias (3.91 K) for Aqua daytime is greater than that (2.50 K) for Terra daytime. However, the bias values for Terra and Aqua at night are nearly equal, with a bias of approximately 0.7 K. Therefore, the nighttime LSTs are probably the best choice for the evaluation of the possible biases between two compared LST products. Surface types have significant impacts on the daytime LST biases. The smallest bias (0.90 K) is obtained for EBF, whereas the largest bias (3.35 K) is achieved for BSV. Compared with

7 DUAN AND LI et al.: INTERCOMPARISON OF OPERATIONAL LST PRODUCTS 4169 the bias during the day, the bias during nighttime is relatively smaller, which probably indicates the effects of different emissivities used in the SEVIRI and MODIS LST retrieval algorithms. This study analyzes the magnitude of LST discrepancies in terms of various factors. These discrepancies may be attributed to different sources, e.g., sensor calibration, atmospheric correction, the emissivity effect, and cloud masking. Further analysis in the future is necessary to identify the exact causes of these errors. 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8 4170 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 8, AUGUST 2015 [35] C. Gao, X. Jiang, H. Wu, B. Tang, Z. Li, and Z.-L. Li, Comparison of land surface temperature from MSG-2/SEVIRI and Terra/MODIS, J. Appl. Remote Sens., vol. 6, no. 1, pp , 2012, doi: /1.JRS [36] Y. Qian, Z.-L. Li, and F. Nerry, Evaluation of land surface temperature and emissivities retrieved from MSG/SEVIRI data with MODIS land surface temperature and emissivity products, Int. J. Remote Sens., vol. 34, pp , [37] C. M. Frey, C. Kuenzer, and S. Dech, Quantitative comparison of the operational NOAA-AVHRR LST product of DLR and the MODIS LST product, Int. J. Remote Sens., vol. 32, pp , [38] Z. Wan and J. Dozier, A generalized split-window algorithm for retrieving land-surface temperature from space, IEEE Trans. Geosci. Remote Sens., vol. 34, no. 4, pp , Jul [39] S. C. Freitas, I. F. Trigo, J. M. Bioucas-Dias, and F.-M. Göttsche, Quantifying the uncertainty of land surface temperature retrievals from SEVIRI/Meteosat, IEEE Trans. Geosci. Remote Sens., vol. 48, no. 1, pp , Jan [40] T. J. Hewison et al., GSICS inter-calibration of infrared channels of geostationary imagers using Metop/IASI, IEEE Trans. Geosci. Remote Sens., vol. 51, no. 3, pp , Mar [41] Z.-L. Li et al., Land surface emissivity retrieval from satellite data, Int. J. Remote Sens., vol. 34, no. 9 10, pp , [42] H. Ren, S. Liang, G. Yan, and J. Cheng, Empirical algorithms to map global broadband emissivities over vegetated surfaces, IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp , May [43] H. Ren, G. Yan, L. Chen, and Z.-L. Li, Angular effect of MODIS emissivity products and its application to the split-window algorithm, ISPRS J. Photogramm., vol. 66, pp , [44] L. F. Peres and C. C. DaCamara, Emissivity maps to retrieve landsurface temperature from MSG/SEVIRI, IEEE Trans. Geosci. Remote Sens., vol. 43, no. 8, pp , Aug [45] I. F. Trigo, L. F. Peres, C. C. DaCamara, and S. C. Freitas, Thermal land surface emissivity retrieved from SEVIRI/Meteosat, IEEE Trans. Geosci. Remote Sens., vol. 46, no. 2, pp , Feb [46] W. Snyder, Z. Wan, Y. Zhang, and Y.-Z. Feng, Classification-based emissivity for land surface temperature measurement from space, Int. J. Remote Sens., vol. 19, pp Si-Bo Duan received the Ph.D. degree in cartography and geographical information system from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China, in He is currently a Postdoctoral Researcher with the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences. His research interests include the retrieval and validation of land surface temperature. Zhao-Liang Li received the Ph.D. degree in remote sensing from Louis Pasteur University (currently called University of Strasbourg), Strasbourg, France, in Since 1992, he has been a Research Scientist with CNRS, Illkirch, France. He joined the Institute of Agricultural Resources and Regional Planning in He has participated in many national and international projects such as NASA-funded MODIS, EC-funded program EAGLE, and ESA funded programs SPECTRA. He has authored more than 150 papers in international refereed journals. His research interests include thermal infrared radiometry, parameterization of land surface processes at large scale, and the assimilation of satellite data to land surface models.