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Retrieval of Land Surface Temperature from Landsat 8 OLI and TIRS: A Comparative Analysis Between Radiative Transfer Equation-Based Method and Split-Window Algorithm

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Abstract

The system of observation and capturing the earth resource features have been improving with the scientific revolution and technological development in remote sensing techniques. In comparison with the previous Landsat series, Landsat 8 OLI and TIRS (Operational Land Imager and Thermal Infrared Sensor) is the latest applications of thermal infrared sensor for the Landsat project offers two adjacent thermal bands that has a great advantage for retrieving land surface temperature. In this study, an effort was made to compare two different approaches of land surface temperature retrieval method from TIRS data including the radiative transfer equation (RTE) and the split-window algorithm (SWA). The objective of this study was to estimate land surface temperature from TIRS data of Landsat 8 using different techniques and compare with actual ground temperature for pre-monsoon, monsoon, and post-monsoon season to determine accurate technique and thermal band. In this regard, twelve ground stations such as New Delhi, Noida, Ghaziabad, Bulandshahr, Gurugram, Faridabad, Muradnagar, Safdarjung airport, Indira Gandhi international airport, Rajiv Chowk, Dadri, and Kirti Nagar were marked on Landsat 8 product with Path 146 and Row 40. Based on analysis, the result shows that the radiative transfer equation (RTE) using band 10 has highest accuracy with the lowest root mean square error (1.0334 ℃, 1.5189 ℃, and 1.4197 ℃, respectively for pre-monsoon, monsoon, and post-monsoon), while RTE using band 11 and split-window algorithm (SWA) using band 10 and 11 has lower accuracy with higher root mean square error (> 2.0 ℃ in all cases). Thus, it is recommended that for those methods LST retrieval using single band, band 10 using RTE has higher accuracy than band 11 and split-window algorithm.

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Fig. 1

(Source: Landsat 8 data user’s handbook, Department of the Interior U.S. Geological Survey 2016)

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Data Availability

The data that support the findings of this study are available from the corresponding author Sk Ajim Ali (skajimali@myau.ac.in / skajimali.saa@gmail.com) on reasonable request.

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Acknowledgements

The authors thankfully acknowledge four anonymous reviewers, the managing editor, and the editor-in-chief for their valuable time, productive comments, and suggestions during the review which helped in improving the overall quality of the manuscript.

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  1. Department of Geography, Aligarh Muslim University (AMU), Aligarh, UP, 202002, India

    Sk Ajim Ali, Farhana Parvin & Ateeque Ahmad

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SAA and FP prepared data, developed the methodology, analyzed, and wrote the original draft. AA critically reviewed and approved the final manuscript.

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Correspondence to Sk Ajim Ali.

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Ali, S.A., Parvin, F. & Ahmad, A. Retrieval of Land Surface Temperature from Landsat 8 OLI and TIRS: A Comparative Analysis Between Radiative Transfer Equation-Based Method and Split-Window Algorithm. Remote Sens Earth Syst Sci 6, 1–21 (2023). https://doi.org/10.1007/s41976-022-00079-0

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