Abstract

Land surface temperature (LST) is a key variable used for studies of the water cycles and energy budgets of land-atmosphere interfaces. The Chinese Gaofen-5 (GF5) satellite, with an onboard visual and infrared multispectral imager (VIMS), is the only satellite that can capture the earth’s thermal infrared information for use in the national high-resolution earth observation project of China; it can observe the earth surface at a high spatial resolution of 40 m in four thermal infrared channels and two mid-infrared channels. This article selects the optimum spectral channel combination for reducing the aerosol effect on LST retrieval with the aid of simulated data, and a new four-channel LST retrieval method from GF5 infrared data under heavy dust aerosol during nighttime is proposed. The results show that the channel combination of channels 7, 8, 9, and 10 (denoted as CC1) performed better than the combination of channels 7, 8, 11, and 12 (denoted as CC2). The root mean square errors (RMSEs) between the actual and estimated LST were 0.28 K for the CC1 group with an aerosol optical thickness (AOD) of 0.1 and 1.94 K for the CC1 group with an AOD of 1.0. The RMSEs for CC2 were 0.28 K for the group with an AOD of 0.1 and 2.54 K for the other group with an AOD of 1.0. Moreover, an error analysis for the proposed method was performed in terms of the noise equivalent temperature difference (NEΔT), the uncertainties of land surface emissivity (LSE), water vapor content (WVC) and AOD. The results show that the LST errors caused by an LSE uncertainty of 0.01, a NEΔT of 0.2 K, a WVC uncertainty of 20%, an AOD uncertainty of 0.1 were 0.31 ∼ 1.01 K, 0.4 ∼ 2.0 K, within 0.6 K, and within 0.3 K for CC1 and 0.32 ∼ 3.08 K, 0.4 ∼ 1.7 K, within 0.7 K, and within 0.3 K for CC2, respectively.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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    [Crossref]

2019 (8)

X. P. Zheng, Z.-L. Li, F. Nerry, and X. Zhang, “A new thermal infrared channel configuration for accurate land surface temperature retrieval from satellite data,” Remote Sens. Environ. 231, 111216 (2019).
[Crossref]

M. R. Saradjian and Y. Jouybari-Moghaddam, “Land Surface Emissivity and temperature retrieval from Landsat-8 satellite data using Support Vector Regression and weighted least squares approach,” Remote Sens. Lett. 10(5), 439–448 (2019).
[Crossref]

W. Zhao, S.-B. Duan, A. N. Li, and G. F. Yin, “A practical method for reducing terrain effect on land surface temperature using random forest regression,” Remote Sens. Environ. 221, 635–649 (2019).
[Crossref]

J. L. He, W. Zhao, A. N. Li, F. P. Wen, and D. J. Yu, “The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas,” Int. J. Remote Sens. 40(5-6), 1808–1827 (2019).
[Crossref]

S.-B. Duan, Z.-L. Li, H. Li, F. M. Göttsche, H. Wu, W. Zhao, P. Leng, X. Zhang, and C. Coll, “Validation of Collection 6 MODIS land surface temperature product using in situ measurements,” Remote Sens. Environ. 225, 16–29 (2019).
[Crossref]

Y. Y. Chen, S.-B. Duan, J. Labed, and Z.-L. Li, “Development of a split-window algorithm for estimating sea surface temperature from the Chinese Gaofen-5 data,” Int. J. Remote Sens. 40(5-6), 1621–1639 (2019).
[Crossref]

S. S. Miao, “Gaofen 5 and Gaofen 6 Satellites Put into Operation,” Aerospace China 20(1), 60 (2019).

B. Luo, P. J. Minnett, C. Gentemann, and G. Szczodrak, “Improving satellite retrieved night-time infrared sea surface temperatures in aerosol contaminated regions,” Remote Sens. Environ. 223, 8–20 (2019).
[Crossref]

2018 (5)

X. W. Fan, G. Z. Nie, H. Wu, and B.-H. Tang, “Estimation of land surface temperature from three thermal infrared channels of MODIS data for dust aerosol skies,” Opt. Express 26(4), 4148–4165 (2018).
[Crossref]

C. X. Zhang, C. Liu, Y. Wang, F. Q. Si, H. J. Zhou, M. J. Zhao, W. J. Su, W. Q. Zhang, K. L. Chan, and X. Liu, “Preflight evaluation of the performance of the Chinese environmental trace gas monitoring instrument (EMI) by spectral analyses of nitrogen dioxide,” IEEE Trans. Geosci. Electron. 56(6), 3323–3332 (2018).
[Crossref]

B.-H. Tang, “Nonlinear split-window algorithms for estimating land and sea surface temperatures from simulated chinese gaofen-5 satellite data,” IEEE Trans. Geosci. Electron. 56(11), 6280–6289 (2018).
[Crossref]

H. Z. Ren, X. Ye, R. Y. Liu, J. J. Dong, and Q. M. Qin, “Improving land surface temperature and emissivity retrieval from the Chinese gaofen-5 satellite using a hybrid algorithm,” IEEE Trans. Geosci. Electron. 56(2), 1080–1090 (2018).
[Crossref]

P. Sismanidis, B. Bechtel, I. Keramitsoglou, and C. T. Kiranoudis, “Mapping the spatiotemporal dynamics of Europe’s land surface temperatures,” IEEE Geosci. Remote Sensing Lett. 15(2), 202–206 (2018).
[Crossref]

2017 (6)

X. Y. Zhang, C. G. Wang, H. Zhao, and Z. H. Lu, “Retrievals of all-weather daytime land surface temperature from FengYun-2D data,” Opt. Express 25(22), 27210–27224 (2017).
[Crossref]

S.-B. Duan, Z.-L. Li, J. Cheng, and P. Leng, “Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces,” ISPRS-J. Photogramm. Remote Sens. 126, 1–10 (2017).
[Crossref]

E. Y. Zhao, C. X. Gao, X. G. Jiang, and Z. X. Liu, “Land surface temperature retrieval from AMSR-E passive microwave data,” Opt. Express 25(20), A940–A952 (2017).
[Crossref]

X.-J. Han, S.-B. Duan, and Z.-L. Zhao, “Atmospheric correction for retrieving ground brightness temperature at commonly-used passive microwave frequencies,” Opt. Express 25(4), A36–A57 (2017).
[Crossref]

Y. Y. Chen, S.-B. Duan, H. Z. Ren, J. Labed, and Z.-L. Li, “Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data,” Remote Sens. 9(2), 161 (2017).
[Crossref]

X. Ye, H. Z. Ren, R. Y. Liu, Q. M. Qin, Y. Liu, and J. J. Dong, “Land surface temperature estimate from chinese gaofen-5 satellite data using split-window algorithm,” IEEE Trans. Geosci. Electron. 55(10), 5877–5888 (2017).
[Crossref]

2016 (1)

2014 (4)

D. Stroppiana, M. Antoninetti, and P. A. Brivio, “Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation,” Eur. J. Remote Sens. 47(1), 133–152 (2014).
[Crossref]

S.-B. Duan, Z.-L. Li, B.-H. Tang, H. Wu, and R. L. Tang, “Generation of a time-consistent land surface temperature product from MODIS data,” Remote Sens. Environ. 140, 339–349 (2014).
[Crossref]

A. Berk, P. Conforti, R. Kennett, T. Perkins, and J. V. D. Bosch, “MODTRAN6: a major upgrade of the MODTRAN radiative transfer code,” Proc. SPIE 2309, 90880H (2014).
[Crossref]

E. Y. Zhao, Y. G. Qian, C. X. Gao, H. Y. Huo, X. G. Jiang, and X. S. Kong, “Land Surface Temperature Retrieval Using Airborne Hyperspectral Scanner Daytime Mid-Infrared Data,” Remote Sens. 6(12), 12667–12685 (2014).
[Crossref]

2013 (4)

C. X. Gao, B.-H. Tang, H. Wu, X. G. Jiang, and Z.-L. Li, “A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data,” Int. J. Remote Sens. 34(12), 4182–4199 (2013).
[Crossref]

Z.-L. Li, H. Wu, N. Wang, S. Qiu, J. A. Sobrino, Z. M. Wan, B.-H. Tang, and G. J. Yan, “Land surface emissivity retrieval from satellite data,” Int. J. Remote Sens. 34(9-10), 3084–3127 (2013).
[Crossref]

Z.-L. Li, B.-H. Tang, H. Wu, H. Z. Ren, G. J. Yan, Z. M. Wan, I. F. Trigo, and J. A. Sobrino, “Satellite-derived land surface temperature: Current status and perspectives,” Remote Sens. Environ. 131, 14–37 (2013).
[Crossref]

X. Y. Zhang and L. L. Li, “A method to estimate land surface temperature from Meteosat Second Generation data using multi-temporal data,” Opt. Express 21(26), 31907 (2013).
[Crossref]

2011 (1)

A. B. Ruescas, M. Arbelo, J. A. Sobrino, and C. Mattar, “Examining the Effects of Dust Aerosols on Satellite Sea Surface Temperatures in the Mediterranean Sea Using the Medspiration Matchup Database,” J. Atmos. Oceanic Technol. 28(5), 684–697 (2011).
[Crossref]

2010 (3)

D. Carrer, J.-L. Roujean, O. Hautecoeur, and T. Elias, “Daily estimates of aerosol optical thickness over land surface based on a directional and temporal analysis of SEVIRI MSG visible observations,” J. Geophys. Res. 115(D10), D10208 (2010).
[Crossref]

J. Cheng, S. L. Liang, J. D. Wang, and X. W. Li, “A stepwise refining algorithm of temperature and emissivity separation for hyperspectral thermal infrared data,” IEEE Trans. Geosci. Electron. 48(3), 1588–1597 (2010).
[Crossref]

M. Neteler, “Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data,” Remote Sens. 2(1), 333–351 (2010).
[Crossref]

2009 (1)

Q. H. Weng, “Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends,” ISPRS-J. Photogramm. Remote Sens. 64(4), 335–344 (2009).
[Crossref]

2008 (5)

M. Anderson, J. Norman, W. Kustas, R. Houborg, P. Starks, and N. Agam, “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. 112(12), 4227–4241 (2008).
[Crossref]

B. De Paepe, A. Ignatov, S. Dewitte, and A. Ipe, “Aerosol retrieval over ocean from SEVIRI for the use in GERB Earth's radiation budget analyses,” Remote Sens. Environ. 112(5), 2455–2468 (2008).
[Crossref]

Z. M. Wan, “New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products,” Remote Sens. Environ. 112(1), 59–74 (2008).
[Crossref]

D. Liu, Z. E. Wang, Z. Y. Liu, D. Winker, and C. Trepte, “A height resolved global view of dust aerosols from the first year CALIPSO lidar measurements,” J. Geophys. Res. 113(D16), D16214 (2008).
[Crossref]

B.-H. Tang, Y. Y. Bi, Z.-L. Li, and J. Xia, “Generalized split-window algorithm for estimate of land surface temperature from Chinese geostationary FengYun meteorological satellite (FY-2C) data,” Sensors 8(2), 933–951 (2008).
[Crossref]

2005 (1)

R. A. Kahn, B. J. Gaitley, J. V. Martonchik, D. J. Diner, K. A. Crean, and B. Holben, “Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations,” J. Geophys. Res. 110(D10), D10S04 (2005).
[Crossref]

2001 (1)

J. P. Diaz, M. Arbelo, F. J. Expósito, G. Podestá, J. M. Prospero, and R. Evans, “Relationship between errors in AVHRR-derived sea surface temperature and the TOMS aerosol index,” Geophys. Res. Lett. 28(10), 1989–1992 (2001).
[Crossref]

2000 (1)

J. A. Sobrino and N. Raissouni, “Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco,” Int. J. Remote Sens. 21(2), 353–366 (2000).
[Crossref]

1998 (2)

M. Hess, P. Koepke, and I. Schult, “Optical properties of aerosols and clouds: The software package OPAC,” Bull. Am. Meteorol. Soc. 79(5), 831–844 (1998).
[Crossref]

P. Russell, P. Hignett, J. Livingston, B. Schmid, A. Chien, R. Bergstrom, P. Durkee, P. Hobbs, T. Bates, and P. Quinn, “Radiative flux changes by aerosols from North America, Europe, and Africa over the Atlantic Ocean: Measurements and calculations from TARFOX and ACE-2,” J. Aerosol. Sci. 29(98), S255–S256 (1998).
[Crossref]

1994 (1)

M. Legrand, C. N’doume, and I. Jankowiak, “Satellite-derived climatology of the Saharan aerosol,” Proc. SPIE 2309, 127–135 (1994).
[Crossref]

1993 (1)

Y. J. Kaufman, “Aerosol optical thickness and atmospheric path radiance,” J. Geophys. Res. 98(D2), 2677–2692 (1993).
[Crossref]

Agam, N.

M. Anderson, J. Norman, W. Kustas, R. Houborg, P. Starks, and N. Agam, “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. 112(12), 4227–4241 (2008).
[Crossref]

Anderson, M.

M. Anderson, J. Norman, W. Kustas, R. Houborg, P. Starks, and N. Agam, “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. 112(12), 4227–4241 (2008).
[Crossref]

Antoninetti, M.

D. Stroppiana, M. Antoninetti, and P. A. Brivio, “Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation,” Eur. J. Remote Sens. 47(1), 133–152 (2014).
[Crossref]

Arbelo, M.

A. B. Ruescas, M. Arbelo, J. A. Sobrino, and C. Mattar, “Examining the Effects of Dust Aerosols on Satellite Sea Surface Temperatures in the Mediterranean Sea Using the Medspiration Matchup Database,” J. Atmos. Oceanic Technol. 28(5), 684–697 (2011).
[Crossref]

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D. Carrer, J.-L. Roujean, O. Hautecoeur, and T. Elias, “Daily estimates of aerosol optical thickness over land surface based on a directional and temporal analysis of SEVIRI MSG visible observations,” J. Geophys. Res. 115(D10), D10208 (2010).
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J. Guang, Y. Xue, J.-L. Roujean, D. Carrer, X. Ceamanos, C. Li, L. L. Mei, X. He, J. Liu, and H. Xu, “Comparison of two methods for aerosol optical depth retrieval over North Africa from MSG/SEVIRI data,” in Proceedings of IEEE Geoscience and Remote Sensing Symposium (IEEE2014), pp. 335–338.

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J. Guang, Y. Xue, J.-L. Roujean, D. Carrer, X. Ceamanos, C. Li, L. L. Mei, X. He, J. Liu, and H. Xu, “Comparison of two methods for aerosol optical depth retrieval over North Africa from MSG/SEVIRI data,” in Proceedings of IEEE Geoscience and Remote Sensing Symposium (IEEE2014), pp. 335–338.

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Y. Y. Chen, S.-B. Duan, H. Z. Ren, J. Labed, and Z.-L. Li, “Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data,” Remote Sens. 9(2), 161 (2017).
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S.-B. Duan, Z.-L. Li, J. Cheng, and P. Leng, “Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces,” ISPRS-J. Photogramm. Remote Sens. 126, 1–10 (2017).
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J. Cheng, S. L. Liang, J. D. Wang, and X. W. Li, “A stepwise refining algorithm of temperature and emissivity separation for hyperspectral thermal infrared data,” IEEE Trans. Geosci. Electron. 48(3), 1588–1597 (2010).
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S.-B. Duan, Z.-L. Li, H. Li, F. M. Göttsche, H. Wu, W. Zhao, P. Leng, X. Zhang, and C. Coll, “Validation of Collection 6 MODIS land surface temperature product using in situ measurements,” Remote Sens. Environ. 225, 16–29 (2019).
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R. A. Kahn, B. J. Gaitley, J. V. Martonchik, D. J. Diner, K. A. Crean, and B. Holben, “Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations,” J. Geophys. Res. 110(D10), D10S04 (2005).
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Duan, S.-B.

W. Zhao, S.-B. Duan, A. N. Li, and G. F. Yin, “A practical method for reducing terrain effect on land surface temperature using random forest regression,” Remote Sens. Environ. 221, 635–649 (2019).
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S.-B. Duan, Z.-L. Li, H. Li, F. M. Göttsche, H. Wu, W. Zhao, P. Leng, X. Zhang, and C. Coll, “Validation of Collection 6 MODIS land surface temperature product using in situ measurements,” Remote Sens. Environ. 225, 16–29 (2019).
[Crossref]

Y. Y. Chen, S.-B. Duan, J. Labed, and Z.-L. Li, “Development of a split-window algorithm for estimating sea surface temperature from the Chinese Gaofen-5 data,” Int. J. Remote Sens. 40(5-6), 1621–1639 (2019).
[Crossref]

S.-B. Duan, Z.-L. Li, J. Cheng, and P. Leng, “Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces,” ISPRS-J. Photogramm. Remote Sens. 126, 1–10 (2017).
[Crossref]

Y. Y. Chen, S.-B. Duan, H. Z. Ren, J. Labed, and Z.-L. Li, “Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data,” Remote Sens. 9(2), 161 (2017).
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X.-J. Han, S.-B. Duan, and Z.-L. Zhao, “Atmospheric correction for retrieving ground brightness temperature at commonly-used passive microwave frequencies,” Opt. Express 25(4), A36–A57 (2017).
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S.-B. Duan, Z.-L. Li, B.-H. Tang, H. Wu, and R. L. Tang, “Generation of a time-consistent land surface temperature product from MODIS data,” Remote Sens. Environ. 140, 339–349 (2014).
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P. Russell, P. Hignett, J. Livingston, B. Schmid, A. Chien, R. Bergstrom, P. Durkee, P. Hobbs, T. Bates, and P. Quinn, “Radiative flux changes by aerosols from North America, Europe, and Africa over the Atlantic Ocean: Measurements and calculations from TARFOX and ACE-2,” J. Aerosol. Sci. 29(98), S255–S256 (1998).
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D. Carrer, J.-L. Roujean, O. Hautecoeur, and T. Elias, “Daily estimates of aerosol optical thickness over land surface based on a directional and temporal analysis of SEVIRI MSG visible observations,” J. Geophys. Res. 115(D10), D10208 (2010).
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J. P. Diaz, M. Arbelo, F. J. Expósito, G. Podestá, J. M. Prospero, and R. Evans, “Relationship between errors in AVHRR-derived sea surface temperature and the TOMS aerosol index,” Geophys. Res. Lett. 28(10), 1989–1992 (2001).
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J. P. Diaz, M. Arbelo, F. J. Expósito, G. Podestá, J. M. Prospero, and R. Evans, “Relationship between errors in AVHRR-derived sea surface temperature and the TOMS aerosol index,” Geophys. Res. Lett. 28(10), 1989–1992 (2001).
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Fan, X. W.

Gaitley, B. J.

R. A. Kahn, B. J. Gaitley, J. V. Martonchik, D. J. Diner, K. A. Crean, and B. Holben, “Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations,” J. Geophys. Res. 110(D10), D10S04 (2005).
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E. Y. Zhao, C. X. Gao, X. G. Jiang, and Z. X. Liu, “Land surface temperature retrieval from AMSR-E passive microwave data,” Opt. Express 25(20), A940–A952 (2017).
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E. Y. Zhao, Y. G. Qian, C. X. Gao, H. Y. Huo, X. G. Jiang, and X. S. Kong, “Land Surface Temperature Retrieval Using Airborne Hyperspectral Scanner Daytime Mid-Infrared Data,” Remote Sens. 6(12), 12667–12685 (2014).
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C. X. Gao, B.-H. Tang, H. Wu, X. G. Jiang, and Z.-L. Li, “A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data,” Int. J. Remote Sens. 34(12), 4182–4199 (2013).
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[Crossref]

Guang, J.

J. Guang, Y. Xue, J.-L. Roujean, D. Carrer, X. Ceamanos, C. Li, L. L. Mei, X. He, J. Liu, and H. Xu, “Comparison of two methods for aerosol optical depth retrieval over North Africa from MSG/SEVIRI data,” in Proceedings of IEEE Geoscience and Remote Sensing Symposium (IEEE2014), pp. 335–338.

Han, X.-J.

Hautecoeur, O.

D. Carrer, J.-L. Roujean, O. Hautecoeur, and T. Elias, “Daily estimates of aerosol optical thickness over land surface based on a directional and temporal analysis of SEVIRI MSG visible observations,” J. Geophys. Res. 115(D10), D10208 (2010).
[Crossref]

He, J. L.

J. L. He, W. Zhao, A. N. Li, F. P. Wen, and D. J. Yu, “The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas,” Int. J. Remote Sens. 40(5-6), 1808–1827 (2019).
[Crossref]

He, X.

J. Guang, Y. Xue, J.-L. Roujean, D. Carrer, X. Ceamanos, C. Li, L. L. Mei, X. He, J. Liu, and H. Xu, “Comparison of two methods for aerosol optical depth retrieval over North Africa from MSG/SEVIRI data,” in Proceedings of IEEE Geoscience and Remote Sensing Symposium (IEEE2014), pp. 335–338.

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M. Hess, P. Koepke, and I. Schult, “Optical properties of aerosols and clouds: The software package OPAC,” Bull. Am. Meteorol. Soc. 79(5), 831–844 (1998).
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P. Russell, P. Hignett, J. Livingston, B. Schmid, A. Chien, R. Bergstrom, P. Durkee, P. Hobbs, T. Bates, and P. Quinn, “Radiative flux changes by aerosols from North America, Europe, and Africa over the Atlantic Ocean: Measurements and calculations from TARFOX and ACE-2,” J. Aerosol. Sci. 29(98), S255–S256 (1998).
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Hobbs, P.

P. Russell, P. Hignett, J. Livingston, B. Schmid, A. Chien, R. Bergstrom, P. Durkee, P. Hobbs, T. Bates, and P. Quinn, “Radiative flux changes by aerosols from North America, Europe, and Africa over the Atlantic Ocean: Measurements and calculations from TARFOX and ACE-2,” J. Aerosol. Sci. 29(98), S255–S256 (1998).
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Holben, B.

R. A. Kahn, B. J. Gaitley, J. V. Martonchik, D. J. Diner, K. A. Crean, and B. Holben, “Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations,” J. Geophys. Res. 110(D10), D10S04 (2005).
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M. Anderson, J. Norman, W. Kustas, R. Houborg, P. Starks, and N. Agam, “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. 112(12), 4227–4241 (2008).
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E. Y. Zhao, Y. G. Qian, C. X. Gao, H. Y. Huo, X. G. Jiang, and X. S. Kong, “Land Surface Temperature Retrieval Using Airborne Hyperspectral Scanner Daytime Mid-Infrared Data,” Remote Sens. 6(12), 12667–12685 (2014).
[Crossref]

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B. De Paepe, A. Ignatov, S. Dewitte, and A. Ipe, “Aerosol retrieval over ocean from SEVIRI for the use in GERB Earth's radiation budget analyses,” Remote Sens. Environ. 112(5), 2455–2468 (2008).
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Ipe, A.

B. De Paepe, A. Ignatov, S. Dewitte, and A. Ipe, “Aerosol retrieval over ocean from SEVIRI for the use in GERB Earth's radiation budget analyses,” Remote Sens. Environ. 112(5), 2455–2468 (2008).
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M. Legrand, C. N’doume, and I. Jankowiak, “Satellite-derived climatology of the Saharan aerosol,” Proc. SPIE 2309, 127–135 (1994).
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Jiang, X. G.

E. Y. Zhao, C. X. Gao, X. G. Jiang, and Z. X. Liu, “Land surface temperature retrieval from AMSR-E passive microwave data,” Opt. Express 25(20), A940–A952 (2017).
[Crossref]

E. Y. Zhao, Y. G. Qian, C. X. Gao, H. Y. Huo, X. G. Jiang, and X. S. Kong, “Land Surface Temperature Retrieval Using Airborne Hyperspectral Scanner Daytime Mid-Infrared Data,” Remote Sens. 6(12), 12667–12685 (2014).
[Crossref]

C. X. Gao, B.-H. Tang, H. Wu, X. G. Jiang, and Z.-L. Li, “A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data,” Int. J. Remote Sens. 34(12), 4182–4199 (2013).
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R. A. Kahn, B. J. Gaitley, J. V. Martonchik, D. J. Diner, K. A. Crean, and B. Holben, “Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations,” J. Geophys. Res. 110(D10), D10S04 (2005).
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A. Berk, P. Conforti, R. Kennett, T. Perkins, and J. V. D. Bosch, “MODTRAN6: a major upgrade of the MODTRAN radiative transfer code,” Proc. SPIE 2309, 90880H (2014).
[Crossref]

Keramitsoglou, I.

P. Sismanidis, B. Bechtel, I. Keramitsoglou, and C. T. Kiranoudis, “Mapping the spatiotemporal dynamics of Europe’s land surface temperatures,” IEEE Geosci. Remote Sensing Lett. 15(2), 202–206 (2018).
[Crossref]

Kiranoudis, C. T.

P. Sismanidis, B. Bechtel, I. Keramitsoglou, and C. T. Kiranoudis, “Mapping the spatiotemporal dynamics of Europe’s land surface temperatures,” IEEE Geosci. Remote Sensing Lett. 15(2), 202–206 (2018).
[Crossref]

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M. Hess, P. Koepke, and I. Schult, “Optical properties of aerosols and clouds: The software package OPAC,” Bull. Am. Meteorol. Soc. 79(5), 831–844 (1998).
[Crossref]

Kong, X. S.

E. Y. Zhao, Y. G. Qian, C. X. Gao, H. Y. Huo, X. G. Jiang, and X. S. Kong, “Land Surface Temperature Retrieval Using Airborne Hyperspectral Scanner Daytime Mid-Infrared Data,” Remote Sens. 6(12), 12667–12685 (2014).
[Crossref]

Kustas, W.

M. Anderson, J. Norman, W. Kustas, R. Houborg, P. Starks, and N. Agam, “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. 112(12), 4227–4241 (2008).
[Crossref]

Labed, J.

Y. Y. Chen, S.-B. Duan, J. Labed, and Z.-L. Li, “Development of a split-window algorithm for estimating sea surface temperature from the Chinese Gaofen-5 data,” Int. J. Remote Sens. 40(5-6), 1621–1639 (2019).
[Crossref]

Y. Y. Chen, S.-B. Duan, H. Z. Ren, J. Labed, and Z.-L. Li, “Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data,” Remote Sens. 9(2), 161 (2017).
[Crossref]

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M. Legrand, C. N’doume, and I. Jankowiak, “Satellite-derived climatology of the Saharan aerosol,” Proc. SPIE 2309, 127–135 (1994).
[Crossref]

Leng, P.

S.-B. Duan, Z.-L. Li, H. Li, F. M. Göttsche, H. Wu, W. Zhao, P. Leng, X. Zhang, and C. Coll, “Validation of Collection 6 MODIS land surface temperature product using in situ measurements,” Remote Sens. Environ. 225, 16–29 (2019).
[Crossref]

S.-B. Duan, Z.-L. Li, J. Cheng, and P. Leng, “Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces,” ISPRS-J. Photogramm. Remote Sens. 126, 1–10 (2017).
[Crossref]

Li, A. N.

W. Zhao, S.-B. Duan, A. N. Li, and G. F. Yin, “A practical method for reducing terrain effect on land surface temperature using random forest regression,” Remote Sens. Environ. 221, 635–649 (2019).
[Crossref]

J. L. He, W. Zhao, A. N. Li, F. P. Wen, and D. J. Yu, “The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas,” Int. J. Remote Sens. 40(5-6), 1808–1827 (2019).
[Crossref]

Li, C.

J. Guang, Y. Xue, J.-L. Roujean, D. Carrer, X. Ceamanos, C. Li, L. L. Mei, X. He, J. Liu, and H. Xu, “Comparison of two methods for aerosol optical depth retrieval over North Africa from MSG/SEVIRI data,” in Proceedings of IEEE Geoscience and Remote Sensing Symposium (IEEE2014), pp. 335–338.

Li, C.-R.

Li, H.

S.-B. Duan, Z.-L. Li, H. Li, F. M. Göttsche, H. Wu, W. Zhao, P. Leng, X. Zhang, and C. Coll, “Validation of Collection 6 MODIS land surface temperature product using in situ measurements,” Remote Sens. Environ. 225, 16–29 (2019).
[Crossref]

Y. Yang, H. Li, Y. Du, B. Cao, Q. Liu, L. Sun, J. Zhu, and F. Mo, “A Temperature and Emissivity Separation Algortihm for Chinese Gaofen-5 Satelltie Data,” in Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IEEE2018), pp. 2543–2546.

Li, L. L.

Li, X. W.

J. Cheng, S. L. Liang, J. D. Wang, and X. W. Li, “A stepwise refining algorithm of temperature and emissivity separation for hyperspectral thermal infrared data,” IEEE Trans. Geosci. Electron. 48(3), 1588–1597 (2010).
[Crossref]

Li, Z.-L.

Y. Y. Chen, S.-B. Duan, J. Labed, and Z.-L. Li, “Development of a split-window algorithm for estimating sea surface temperature from the Chinese Gaofen-5 data,” Int. J. Remote Sens. 40(5-6), 1621–1639 (2019).
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X. P. Zheng, Z.-L. Li, F. Nerry, and X. Zhang, “A new thermal infrared channel configuration for accurate land surface temperature retrieval from satellite data,” Remote Sens. Environ. 231, 111216 (2019).
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S.-B. Duan, Z.-L. Li, H. Li, F. M. Göttsche, H. Wu, W. Zhao, P. Leng, X. Zhang, and C. Coll, “Validation of Collection 6 MODIS land surface temperature product using in situ measurements,” Remote Sens. Environ. 225, 16–29 (2019).
[Crossref]

S.-B. Duan, Z.-L. Li, J. Cheng, and P. Leng, “Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces,” ISPRS-J. Photogramm. Remote Sens. 126, 1–10 (2017).
[Crossref]

Y. Y. Chen, S.-B. Duan, H. Z. Ren, J. Labed, and Z.-L. Li, “Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data,” Remote Sens. 9(2), 161 (2017).
[Crossref]

S.-B. Duan, Z.-L. Li, B.-H. Tang, H. Wu, and R. L. Tang, “Generation of a time-consistent land surface temperature product from MODIS data,” Remote Sens. Environ. 140, 339–349 (2014).
[Crossref]

Z.-L. Li, H. Wu, N. Wang, S. Qiu, J. A. Sobrino, Z. M. Wan, B.-H. Tang, and G. J. Yan, “Land surface emissivity retrieval from satellite data,” Int. J. Remote Sens. 34(9-10), 3084–3127 (2013).
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Z.-L. Li, B.-H. Tang, H. Wu, H. Z. Ren, G. J. Yan, Z. M. Wan, I. F. Trigo, and J. A. Sobrino, “Satellite-derived land surface temperature: Current status and perspectives,” Remote Sens. Environ. 131, 14–37 (2013).
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C. X. Gao, B.-H. Tang, H. Wu, X. G. Jiang, and Z.-L. Li, “A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data,” Int. J. Remote Sens. 34(12), 4182–4199 (2013).
[Crossref]

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Figures (8)

Fig. 1.
Fig. 1. The values of A0, A1, A2, A3, A4, A5 in Eq. (10) versus WVC. (a) for value of A0; (b) for value of A1; (c) for value of A2; (d) for value of A3; (e) for value of A4; and (f) for value of A5
Fig. 2.
Fig. 2. Plot of the atmospheric water vapor content as function of atmospheric temperature
Fig. 3.
Fig. 3. Coefficients for the sub-ranges of dry and wet atmospheres. (a) for CC1, WVC: 0∼1.5 g/cm2, AOD: 0.1; (b) for CC1, WVC: 0∼1.5 g/cm2, AOD: 1.0; (c) for CC1, WVC: 4∼5.5 g/cm2, AOD: 0.1; (d) for CC1, WVC: 4∼5.5 g/cm2, AOD: 1.0; (e) for CC2, WVC: 0∼1.5 g/cm2, AOD: 0.1; (f) for CC2, WVC: 0∼1.5 g/cm2, AOD: 1.0; (g) for CC2, WVC: 4∼5.5 g/cm2, AOD: 0.1; and (h) for CC2, WVC: 4∼5.5 g/cm2, AOD: 1.0.
Fig. 4.
Fig. 4. Histogram of the difference between the actual and estimated Ts for the sub-range with VZA being 0°, and WVC from 1.0 g/cm2 to 2.5 g/cm2. (a) for CC1, AOD: 0.1; (b) for CC1, AOD: 1.0; (c) for CC2, AOD: 0.1; and (d) for CC2, AOD: 1.0
Fig. 5.
Fig. 5. RMSEs between the actual and estimated Ts are given as functions of AOD for different sub-ranges of WVC for CC1and CC2.
Fig. 6.
Fig. 6. LST retrieval error caused by NEΔT. (a) for CC1, AOD: 0.1; (b) for CC1, AOD: 1.0; (c) for CC2, AOD: 0.1; and (d) for CC2, AOD: 1.0.
Fig. 7.
Fig. 7. Histogram of the difference between the estimated LSTWVC obtained from atmospheres with no WVC errors and estimated LSTΔWVC retrieved from atmospheres with 20% uncertainty of WVC for the dry and wet atmospheres. (a) for CC1, AOD = 0.1, WVC∈[0.0, 1.5] g/cm2; (b) for CC2, AOD = 0.1, WVC∈[0.0, 1.5] g/cm2; (c) for CC1, AOD = 1.0, WVC∈[0.0, 1.5] g/cm2; (d) for CC2, AOD = 1.0, WVC∈[0.0, 1.5] g/cm2; (e) for CC1, AOD = 0.1, WVC∈[4.0, 5.5] g/cm2; (f) for CC2, AOD = 0.1, WVC∈[4.0, 5.5] g/cm2; (g) for CC1, AOD = 1.0, WVC∈[4.0, 5.5] g/cm2; and (h) for CC2, AOD = 1.0, WVC∈[4.0, 5.5] g/cm2.
Fig. 8.
Fig. 8. Histogram of the LST error due to the uncertainty of AOD for the dry and wet atmospheres. (a) for CC1, original AOD = 0.1, WVC∈[0.0, 1.5] g/cm2; (b) for CC2, original AOD = 0.1, WVC∈[0.0, 1.5] g/cm2; (c) for CC1, original AOD = 0.9, WVC∈[0.0, 1.5] g/cm2; (d) for CC2, original AOD = 0.9, WVC∈[0.0, 1.5] g/cm2; (e) for CC1, original AOD = 0.1, WVC∈[4.0, 5.5] g/cm2; (f) for CC2, original AOD = 0.1, WVC∈[4.0, 5.5] g/cm2; (g) for CC1, original AOD = 0.9, WVC∈[4.0, 5.5] g/cm2; and (h) for CC2, original AOD = 0.9, WVC∈[4.0, 5.5] g/cm2.

Tables (2)

Tables Icon

Table 1. Effect of the emissivity uncertainty (0.01) on LST retrieval for CC1

Tables Icon

Table 2. Effect of the emissivity uncertainty (0.01) on LST retrieval for CC2

Equations (18)

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B i ( T i ) = τ i [ ε i B i ( T s ) + ( 1 ε i ) ( R a t m _ i + R a t m _ i s ) + ρ b i R i s ] + R a t m _ i + R a t m _ i s
B i ( T i ) = τ i [ ε i B i ( T s ) + ( 1 ε i ) R a t m _ i ] + R a t m _ i
R a t m _ i  = (1 -  τ i ) B i ( T a )
B i ( T i ) = τ i B i ( T i ) + ( 1 τ i ) B i ( T a )
B i ( T ) = B i ( T 0 ) + B i T ( T T 0 )
T i = T i τ i 1 τ i τ i T a
T s = T i + a 1 ( T i T j ) + a 2 ( 1 ε ) + a 3 Δ ε + a 4 W ( 1 ε ) + a 5 W Δ ε + a 0
T s = T 1 τ 1 + a 1 ( T 1 τ 1 T 2 τ 2 ) + [ a 1 1 τ 2 τ 2 a 1 1 τ 1 τ 1 1 τ 1 τ 1 ] T a + a 2 ( 1 ε T ) + a 3 Δ ε T + a 4 W ( 1 ε T ) + a 5 W Δ ε T + a 0
T s = T 3 τ 3 + b 1 ( T 3 τ 3 T 4 τ 4 ) + [ b 1 1 τ 4 τ 4 b 1 1 τ 3 τ 3 1 τ 3 τ 3 ] T a + b 2 ( 1 ε M ) + b 3 Δ ε M + b 4 W ( 1 ε M ) + b 5 W Δ ε M + b 0
T s = A 1 T 1 + A 2 T 2 + A 3 T 3 + A 4 T 4 + A 5 [ a 2 ( 1 ε T ) + a 3 Δ ε T + a 4 W ( 1 ε T ) + a 5 W Δ ε T ] + A 6 [ b 2 ( 1 ε M ) + b 3 Δ ε M + b 4 W ( 1 ε M ) + b 5 W Δ ε M ] + A 0
A 1 = ( 1 + a 1 ) M 2 ( M 2 M 1 ) τ 1
A 2 = a 1 M 2 ( M 2 M 1 ) τ 2
A 3 = ( 1 + b 1 ) M 1 ( M 2 M 1 ) τ 3
A 4 = b 1 M 1 ( M 2 M 1 ) τ 4
A 5 = M 2 M 2 M 1
A 6 = M 1 M 2 M 1 = 1 A 5
A 0 = a 0 M 2 b 0 M 1 M 2 M 1
T s = c 1 T T 1 + c 2 T T 2 + c 3 T M 1 + c 4 T M 2 + c 5 ε T + c 6 Δ ε T + c 7 ε M + c 8 Δ ε M + c 0

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