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Remote Sens. 2015, 7(4), 4371-4390; doi:10.3390/rs70404371

A Practical Split-Window Algorithm for Retrieving Land Surface Temperature from Landsat-8 Data and a Case Study of an Urban Area in China

1
College of Architecture and Civil Engineering, Taiyuan University of Technology, Yingze Street 79, Taiyuan 030024, China
2
Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Xudong Street 340, Wuhan 430077, China
3
Shanxi Administration of Surveying, Mapping and Geoinformation, Yingze Street 136, Taiyuan 030001, China
4
Wanfang College of Science & Technology, Henan Polytechnic University, Qiancheng North Road 8, Vocational Education Park, Zhengzhou 451400, China
*
Author to whom correspondence should be addressed.
Academic Editors: Zhao-Liang Li, Jose A. Sobrino and Prasad S. Thenkabail
Received: 4 January 2015 / Revised: 26 February 2015 / Accepted: 30 March 2015 / Published: 14 April 2015
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
View Full-Text   |   Download PDF [27583 KB, uploaded 14 April 2015]   |  

Abstract

This paper proposes a practical split-window algorithm (SWA) for retrieving land surface temperature (LST) from Landsat-8 Thermal Infrared Sensor (TIRS) data. This SWA has a universal applicability and a set of parameters that can be applied when retrieving LSTs year-round. The atmospheric transmittance and the land surface emissivity (LSE), the essential SWA input parameters, of the Landsat-8 TIRS data are determined in this paper. We also analysed the error sensitivity of these SWA input parameters. The accuracy evaluation of the proposed SWA in this paper was conducted using the software MODTRAN 4.0. The root mean square error (RMSE) of the simulated LST using the mid-latitude summer atmospheric profile is 0.51 K, improving on the result of 0.93 K from Rozenstein (2014). Among the 90 simulated data points, the maximum absolute error is 0.99 °C, and the minimum absolute error is 0.02 °C. Under the Tropical model and 1976 US standard atmospheric conditions, the RMSE of the LST errors are 0.70 K and 0.63 K, respectively. The accuracy results indicate that the SWA provides an LST retrieval method that features not only high accuracy but also a certain universality. Additionally, the SWA was applied to retrieve the LST of an urban area using two Landsat-8 images. The SWA presented in this paper should promote the application of Landsat-8 data in the study of environmental evolution. View Full-Text
Keywords: Landsat-8; land surface temperature (LST); split-window algorithm (SWA); land surface emissivity (LSE) Landsat-8; land surface temperature (LST); split-window algorithm (SWA); land surface emissivity (LSE)
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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. (CC BY 4.0).

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Jin, M.; Li, J.; Wang, C.; Shang, R. 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. 2015, 7, 4371-4390.

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