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 Sensing

Meijun Jin¹  Junming Li⁴  Caili Wang³  Ruilan Shang²  Zhao-Liang Li⁵  Jose A. Sobrino⁵ 

[1] College of Architecture and Civil Engineering, Taiyuan University of Technology, Yingze Street 79, Taiyuan 030024, China; E-Mail:;Shanxi Administration of Surveying, Mapping and Geoinformation, Yingze Street 136, Taiyuan 030001, China; E-Mail:;Wanfang College of Science & Technology, Henan Polytechnic University, Qiancheng North Road 8, Vocational Education Park, Zhengzhou 451400, China; E-Mail:;Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Xudong Street 340, Wuhan 430077, China;College of Architecture and Civil Engineering, Taiyuan University of Technology, Yingze Street 79, Taiyuan 030024, China; E-Mail

关键词: Landsat-8;    land surface temperature (LST);    split-window algorithm (SWA);    land surface emissivity (LSE);   

DOI  :  10.3390/rs70404371

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.