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

• Published in: Remote sensing (Basel, Switzerland) Vol. 7; no. 4; pp. 4371 - 4390
• Main Authors: Jin, Meijun, Li, Junming, Wang, Caili, Shang, Ruilan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2015
• Subjects: Accuracy; Algorithms; Atmospherics; Computer simulation; Emissivity; Environmental studies; Errors; land surface emissivity (LSE); Land surface temperature; land surface temperature (LST); Landsat; Landsat-8; Mathematical models; Remote sensing; split-window algorithm (SWA); Urban areas
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs70404371

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 degree C, and the minimum absolute error is 0.02 degree 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.