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Comparing different space-borne sensors and methods for the retrieval of land surface temperature

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Abstract

The importance of land surface temperature (LST) is increasingly recognized, and various methodologies have been proposed for the retrieval of LST using space-borne thermal infrared data. However, the selection of LST retrieval from Thermal Infrared Sensor (TIRS) of Landsat-8 based on different methods and the readily available MODIS LST products is still a challenging topic for local and global environmental studies. In this study, the potential of three different methods for retrieving LST using Landsat-8 TIRS data, including Radiative Transfer Equation (RTE), Single Channel (SC), and Split Window (SW) method in comparison with MODIS MOD11A1 LST product was evaluated. For accuracy assessment, 0 cm ground surface temperature (LSTGST) data was used. Our results almost showed same accuracy for RTEB10 with RMSE = 0.35 °C, followed by MODIS with RMSE = 0.36 °C, and SCB10 with RMSE = 0.38 °C. Secondly, SCmean (Mean of B10 and B11), and RTEmean (Mean of B10 and B11) generate nearly the same accuracy with RMSE = 0.53 °C, and RMSE = 0.54 °C, respectively. The other methods viz., SCB11, RTEB11, and SW method slightly showed lower accuracy with RMSE = 0.87 °C, RMSE = 0.88 °C, and RMSE = 0.91 °C, respectively. We found all the methods highly accurate and can be used successfully by climatologists, environmentalists, hydrologists, and urban planners concerning planning, and monitoring of the ever-increasing LST at local and global scale studies.

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References

  • Amir Siddique M, Dongyun L, Li P, Rasool U, Ullah Khan T, Javaid Aini Farooqi T, Wang L, Fan B, Rasool MA (2020) Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China. PeerJ 8:e9115–e9115. https://doi.org/10.7717/peerj.9115

    Article  Google Scholar 

  • Avdan U (2016) Jovanovska G (2016) algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. J Sens 2016:1–8

    Google Scholar 

  • Barsi JA, Barker JL, Schott JR (2003) An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument. In: IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477). Toulouse, France, 2003, pp. 3014-3016 vol.5, https://doi.org/10.1109/IGARSS.2003.1294665

  • Barsi JA, Schott JR, Palluconi FD, Hook SJ (2005) Validation of a web-based atmospheric correction tool for single thermal band instruments," Proc. SPIE 5882, Earth Observing Systems X, 58820E (22 August 2005). https://doi.org/10.1117/12.619990

  • Becker F, Li Z-L (1990) Temperature-independent spectral indices in thermal infrared bands. Remote Sens Environ 32(1):17–33

    Google Scholar 

  • Behrens CE (2009) Landsat and the data continuity mission. In, 2009. Congressional research service, Library of Congress,

  • Berk A, Anderson G, Acharya P, Chetwynd J, Bernstein L, Shettle E, Matthew M, Adler-Golden S (1999) MODTRAN4 user’s manual.1. Air Force Research Laboratory 1

  • Berk A, Conforti P, Kennett R, Perkins T, Hawes F, Van Den Bosch J (2014) MODTRAN® 6: A major upgrade of the MODTRAN® radiative transfer code. In: 2014 6th workshop on Hyperspectral image and signal processing: evolution in remote sensing (WHISPERS). IEEE, pp 1–4

  • Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252

    Google Scholar 

  • Chatterjee R, Singh N, Thapa S, Sharma D, Kumar D (2017) Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. Int J Appl Earth Obs Geoinf 58:264–277

    Google Scholar 

  • Coll C, Caselles V, Valor E, Niclòs R (2012a) Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data. Remote Sens Environ 117:199–210

    Google Scholar 

  • Coll C, Valor E, Galve JM, Mira M, Bisquert M, García-Santos V, Caselles E, Caselles V (2012b) Long-term accuracy assessment of land surface temperatures derived from the advanced along-track scanning radiometer. Remote Sens Environ 116:211–225

    Google Scholar 

  • Cristóbal J, Jiménez-Muñoz J, Sobrino J, Ninyerola M, Pons X (2009) Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. J Geophys Res: Atmospheres 114 (D8)

  • Cristóbal J, Jiménez-Muñoz J, Prakash A, Mattar C, Skoković D, Sobrino J (2018a) An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sens 10(3):431

    Google Scholar 

  • Cristóbal J, Jiménez-Muñoz JC, Prakash A, Mattar C, Skoković D, Sobrino JA (2018b) An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sens 10(3):431

    Google Scholar 

  • Du C, Ren H, Qin Q, Meng J, Zhao S (2015) A practical split-window algorithm for estimating land surface temperature from Landsat 8 data. Remote Sens 7(1):647–665

    Google Scholar 

  • Fu G, Shen Z, Zhang X, Shi P, Zhang Y, Wu J (2011) Estimating air temperature of an alpine meadow on the northern Tibetan plateau using MODIS land surface temperature. Acta Ecol Sin 31(1):8–13. https://doi.org/10.1016/j.chnaes.2010.11.002

    Article  Google Scholar 

  • García-Santos V, Cuxart J, Martínez-Villagrasa D, Jiménez MA, Simó G (2018) Comparison of three methods for estimating land surface temperature from landsat 8-tirs sensor data. Remote Sens 10(9):1450

    Google Scholar 

  • He G, Zhang Z, Jiao W, Long T, Peng Y, Wang G, Yin R, Wang W, Zhang X, Liu H (2018) Generation of ready to use (RTU) products over China based on Landsat series data. Big Earth Data 2(1):56–64

    Google Scholar 

  • Hulley G, Malakar N, Hughes T, Islam T, Hook S (2016) Moderate resolution imaging spectroradiometer (MODIS) MOD21 land surface temperature and emissivity algorithm theoretical basis document. Pasadena, CA: jet Propulsion Laboratory, National Aeronautics and space administration,

  • Irons JR, Dwyer JL, Barsi JA (2012) The next Landsat satellite: the Landsat data continuity mission. Remote Sens Environ 122:11–21

    Google Scholar 

  • Jang K, Kang S, Kim J, Lee CB, Kim T, Kim J, Hirata R, Saigusa N (2010) Mapping evapotranspiration using MODIS and MM5 four-dimensional data assimilation. Remote Sens Environ 114(3):657–673. https://doi.org/10.1016/j.rse.2009.11.010

    Article  Google Scholar 

  • Jiménez-Muñoz JC, Sobrino JA (2003) A generalized single-channel method for retrieving land surface temperature from remote sensing data. J Geophys Res: Atmos 108 (D22)

  • Jiménez-Muñoz JC, Sobrino JA (2008) Split-window coefficients for land surface temperature retrieval from low-resolution thermal infrared sensors. IEEE Geosci Remote Sens Lett 5(4):806–809. https://doi.org/10.1109/LGRS.2008.2001636

    Article  Google Scholar 

  • Jiménez-Muñoz JC, Sobrino JA (2010) A single-channel algorithm for land-surface temperature retrieval from ASTER data. IEEE Geosci Remote Sens Lett 7(1):176–179

    Google Scholar 

  • Jiménez-Muñoz JC, Cristóbal J, Sobrino JA, Sòria G, Ninyerola M, Pons X (2009) Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Trans Geosci Remote Sens 47(1):339–349

    Google Scholar 

  • Jiménez-Muñoz JC, Sobrino JA, Skoković D, Mattar C, Cristóbal J (2014) Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geosci Remote Sens Lett 11(10):1840–1843

    Google Scholar 

  • Jin M, Li J, Wang C, Shang R (2015) A practical split-window algorithm for retrieving land surface temperature from Landsat-8 data and a case study of an urban area in China. Remote Sens 7(4):4371–4390

    Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–472

    Google Scholar 

  • Kerr YH, Lagouarde JP, Imbernon J (1992) Accurate land surface temperature retrieval from AVHRR data with use of an improved split window algorithm. Remote Sens Environ 41(2–3):197–209

    Google Scholar 

  • Kustas W, Anderson M (2009) Advances in thermal infrared remote sensing for land surface modeling. Agric For Meteorol 149(12):2071–2081

    Google Scholar 

  • Li H, Sun D, Yu Y, Wang H, Liu Y, Liu Q, Du Y, Wang H, Cao B (2014) Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China. Remote Sens Environ 142:111–121

    Google Scholar 

  • Li Z-L, Wu H, Wang N, Qiu S, Sobrino JA, Wan Z, Tang B-H, Yan G (2013) Land surface emissivity retrieval from satellite data. Int J Remote Sens 34(9–10):3084–3127

    Google Scholar 

  • Mao K, Qin Z, Shi J, Gong P (2005) A practical split-window algorithm for retrieving land-surface temperature from MODIS data. Int J Remote Sens 26(15):3181–3204

    Google Scholar 

  • Markham BL, Storey JC, Williams DL, Irons JR (2004) Landsat sensor performance: history and current status. IEEE Trans Geosci Remote Sens 42(12):2691–2694

    Google Scholar 

  • Masiello G, Serio C (2013) Simultaneous physical retrieval of surface emissivity spectrum and atmospheric parameters from infrared atmospheric sounder interferometer spectral radiances. Appl Opt 52(11):2428–2446. https://doi.org/10.1364/AO.52.002428

    Article  Google Scholar 

  • Mattar C, Durán-Alarcón C, Jiménez-Muñoz JC, Santamaría-Artigas A, Olivera-Guerra L, Sobrino JA (2015) Global atmospheric profiles from reanalysis information (GAPRI): a new database for earth surface temperature retrieval. Int J Remote Sens 36(19–20):5045–5060

    Google Scholar 

  • Missions UL (2016) Using the USGS Landsat8 product. US Department of the Interior-US Geological Survey–NASA

  • Niclòs R, Galve JM, Valiente JA, Estrela MJ, Coll C (2011) Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data. Remote Sens Environ 115(8):2126–2140

    Google Scholar 

  • Oltra-Carrió R, Sobrino J, Franch B, Nerry F (2012) Land surface emissivity retrieval from airborne sensor over urban areas. Remote Sens Environ 123:298–305

    Google Scholar 

  • Reuter D, Richardson C, Pellerano F, Irons J, Allen R, Anderson M, Jhabvala M, Lunsford A, Montanaro M, Smith R (2015) The thermal infrared sensor (TIRS) on Landsat 8: design overview and pre-launch characterization. Remote Sens 7(1):1135–1153

    Google Scholar 

  • Rongali G, Keshari AK, Gosain AK, Khosa R (2018) Split-window algorithm for retrieval of land surface temperature using Landsat 8 thermal infrared data. J Geovisualiz Spatial Analys 2(2):14

    Google Scholar 

  • Roy DP, Wulder MA, Loveland TR, Woodcock C, Allen RG, Anderson MC, Helder D, Irons JR, Johnson DM, Kennedy R (2014) Landsat-8: science and product vision for terrestrial global change research. Remote Sens Environ 145:154–172

    Google Scholar 

  • Sadiq Khan M, Ullah S, Sun T, Rehman AU, Chen L (2020) Land-use/land-cover changes and its contribution to Urban Heat Island: a case study of Islamabad, Pakistan. Sustainability 12(9):3861

    Google Scholar 

  • Sobrino J, Raissouni N, Li Z-L (2001) A comparative study of land surface emissivity retrieval from NOAA data. Remote Sens Environ 75(2):256–266

    Google Scholar 

  • Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90(4):434–440

    Google Scholar 

  • Sobrino JA, Jiménez-Muñoz JC, Sòria G, Romaguera M, Guanter L, Moreno J, Plaza A, Martínez P (2008) Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Trans Geosci Remote Sens 46(2):316–327

    Google Scholar 

  • Song F, Zhu Q, Wu R, Jiang Y, Xiong A, Wang B, Zhu Y, Li Q (2007) Meteorological data set for building thermal environment analysis of China. Proceedings of the 10th international building performance simulation association conference and exhibition, Beijing, pp 9–16

    Google Scholar 

  • Sun D, Pinker RT (2003) Estimation of land surface temperature from a geostationary operational environmental satellite (GOES-8). J Geophys Res: Atmos 108 (D11)

  • Team RC (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  • Trigo IF, Monteiro IT, Olesen F, Kabsch E (2008) An assessment of remotely sensed land surface temperature. J Geophys Res: Atmospheres 113 (D17)

  • Urban M, Eberle J, Hüttich C, Schmullius C, Herold M (2013) Comparison of satellite-derived land surface temperature and air temperature from meteorological stations on the pan-arctic scale. Remote Sens 5(5):2348–2367. https://doi.org/10.3390/rs5052348

    Article  Google Scholar 

  • USGS (2016a) Department of the Interior U.S. Geological Survey, vol 8. 2.0 edn. USGS

  • USGS (2016b) Landsat-8 data users handbook version 2.0. EROS, Sioux Falls, South Dakota

  • Valor E, Caselles V (1996) Mapping land surface emissivity from NDVI: application to European, African, and south American areas. Remote Sens Environ 57(3):167–184. https://doi.org/10.1016/0034-4257(96)00039-9

    Article  Google Scholar 

  • Vlassova L, Perez-Cabello F, Nieto H, Martín P, Riaño D, de la Riva J (2014) Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sens 6(5):4345–4368

    Google Scholar 

  • Wan Z, Dozier J (1996) A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans Geosci Remote Sens 34(4):892–905

    Google Scholar 

  • Wang M, He G, Zhang Z, Wang G, Long T (2015) NDVI-based split-window algorithm for precipitable water vapour retrieval from Landsat-8 TIRS data over land area. Remote Sens Lett 6(11):904–913. https://doi.org/10.1080/2150704X.2015.1089363

    Article  Google Scholar 

  • Weng Q, Fu P (2014) Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data. Remote Sens Environ 140:267–278. https://doi.org/10.1016/j.rse.2013.09.002

    Article  Google Scholar 

  • Williams DL, Goward S, Arvidson T (2006) Landsat: Yesterday Today Tom 72(10):1171–1178

    Google Scholar 

  • Wulder MA, White JC, Loveland TR, Woodcock CE, Belward AS, Cohen WB, Fosnight EA, Shaw J, Masek JG, Roy DP (2016) The global Landsat archive: status, consolidation, and direction. Remote Sens Environ 185:271–283

    Google Scholar 

  • Xu Y, Gao X, Shen Y, Xu C, Shi Y, Giorgi a (2009) A daily temperature dataset over China and its application in validating a RCM simulation. Adv Atmos Sci 26 (4):763–772

  • Yang L, Cao Y, Zhu X, Zeng S, Yang G, He J, Yang X (2014) Land surface temperature retrieval for arid regions based on Landsat-8 TIRS data: a case study in Shihezi, Northwest China. J Arid Land 6(6):704–716

    Google Scholar 

  • Ying M, Zhang W, Yu H, Lu X, Feng J, Fan Y, Zhu Y, Chen D (2014) An overview of the China Meteorological Administration tropical cyclone database. J Atmos Ocean Technol 31(2):287–301

    Google Scholar 

  • Young NE, Anderson RS, Chignell SM, Vorster AG, Lawrence R, Evangelista PH (2017) A survival guide to Landsat preprocessing. Ecology 98(4):920–932. https://doi.org/10.1002/ecy.1730

    Article  Google Scholar 

  • Yu X, Guo X, Wu Z (2014) Land surface temperature retrieval from Landsat 8 TIRS—comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sens 6(10):9829–9852

    Google Scholar 

  • Zhang Z, He G (2013) Generation of Landsat surface temperature product for China, 2000–2010. Int J Remote Sens 34(20):7369–7375

    Google Scholar 

  • Zhou J, Li J, Zhang L, Hu D, Zhan W (2012) Intercomparison of methods for estimating land surface temperature from a Landsat-5 TM image in an arid region with low water vapour in the atmosphere. Int J Remote Sens 33(8):2582–2602

    Google Scholar 

  • Zhu W, Lu A, Jia S (2013) Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products. Remote Sens Environ 130:62–73. https://doi.org/10.1016/j.rse.2012.10.034

    Article  Google Scholar 

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Acknowledgments

We are thankful to the China Meteorological Department for providing the ground stations temperature data for selected dates, the US Geological Survey (USGS) for providing level 1 and level 2 Landsat-8 imagery and MODIS products and to the team of GIS & Space Applications in Geosciences (G-SAG) laboratory at the NCE in Geology, University of Peshawar with the partnership of Shaheed Benazir Bhutto University, National Center of GIS and Space Applications for helping in processing of remote sensing datasets, analysis, and final writing up.

Funding

“This research is part of a master’s degree thesis of the first author and it was funded by the Chinese Government Scholarship (CSC Number: 2018SLJ021190).

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Authors and Affiliations

  1. College of Forestry, Beijing Forestry University, Beijing, 100083, China

    Arif UR Rehman & Qijing Liu

  2. Department of Forestry, Shaheed Benazir Bhutto University Sheringal, Dir, 18050, Pakistan

    Sami Ullah

  3. GIS & Space Applications in Geosciences (G-SAG) laboratory at the NCE in Geology, University of Peshawar, National Center of GIS and Space Applications, Peshawar, Pakistan

    Sami Ullah

  4. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China

    Muhammad Sadiq Khan

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  1. Arif UR Rehman
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Contributions

Arif UR Rehman is the lead author and was involved in the overall processing, analysis, and writing. Sami Ullah supported the processing of Landsat imagery and developing R codes. Muhammad Sadiq Khan contributed to the implementation of the methodological approach. The first draft of the manuscript was prepared by Arif UR Rehman and was further improved by Sami Ullah, Muhammad Sadiq Khan, and finally by Qijing Liu.

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Correspondence to Qijing Liu.

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Rehman, A.U., Ullah, S., Liu, Q. et al. Comparing different space-borne sensors and methods for the retrieval of land surface temperature. Earth Sci Inform 14, 985–995 (2021). https://doi.org/10.1007/s12145-021-00578-6

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