Version 1
: Received: 24 September 2020 / Approved: 27 September 2020 / Online: 27 September 2020 (04:59:36 CEST)
How to cite:
Salakkham, E.; Piyatadsananon, P. The Optimum Method for Urban Land Surface Temperature Estimation. Preprints2020, 2020090664. https://doi.org/10.20944/preprints202009.0664.v1
Salakkham, E.; Piyatadsananon, P. The Optimum Method for Urban Land Surface Temperature Estimation. Preprints 2020, 2020090664. https://doi.org/10.20944/preprints202009.0664.v1
Salakkham, E.; Piyatadsananon, P. The Optimum Method for Urban Land Surface Temperature Estimation. Preprints2020, 2020090664. https://doi.org/10.20944/preprints202009.0664.v1
APA Style
Salakkham, E., & Piyatadsananon, P. (2020). The Optimum Method for Urban Land Surface Temperature Estimation. Preprints. https://doi.org/10.20944/preprints202009.0664.v1
Chicago/Turabian Style
Salakkham, E. and Pantip Piyatadsananon. 2020 "The Optimum Method for Urban Land Surface Temperature Estimation" Preprints. https://doi.org/10.20944/preprints202009.0664.v1
Abstract
Land Surface Temperature (LST) estimation has been studied for several purposes, while the optimal method of estimating the LST has not been criticized yet. This research explores the optimum method in Land Surface Temperature (LST) estimation using LANDSAT-8 imagery data. Four different LST retrieval approaches, the Radiative Transfer Equation-based method (RTE), the Improved Mono-Window method (IMW), the Generalized Single-Channel method (GSC), and the Split-Window algorithm (SW), were calculated to present the LSTs over Buriram Town Municipality, Thailand. The calculated LSTs from these four methods were compared with the ground-based temperature data, taken on the same date and time of the employed LANDSAT-8 images. For this reason, the optimum method of the LST calculation was justified by considering the lowest normalized root means square error (NRMSE) values. As a result, the SW algorithm presents an optimum method in LST estimation. Regarding the SW, this algorithm requires not only the atmospheric profiles during satellite acquisition but also the retrieval of several coefficients. Besides, the LST retrieval method based on the SW algorithm is sensitive to water vapor content and coefficients. Although the SW algorithm is an optimum method explored in this study, it is emphasized that the adjustable values of coefficient response to the atmospheric state may be recommended. With these conditions, the SW algorithm can generate the land-surface temperature over the mixed land-use and land cover on the LANDSAT-8 images.
Keywords
radiative transfer equation; improved mono-window; generalized single-channel; split-window; LANDSAT-8; urban land surface temperature
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
