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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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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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  2. Sami Ullah
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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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