The thermal infrared data of the Landsat series satellite are the main data for the monitoring of the Earth’s surface temperature change. The Thermal Infrared Sensor（TIRS） of Landsat 8

a new generation of Landsat series satellites

contributes to this important mission. The TIRS data from Landsat 8 have been widely utilized since its launch on February 11

2013. Several algorithms for the derivation of land surface temperature（LST） from TIRS data have also been developed successively. Studies on TIRS-data calibration accuracy and the precision of related LST inversion algorithms have been conducted. This paper presents a brief summary of the changes in TIRS-data calibration parameters and analyzes the suitability and effectiveness of current LST inversion algorithms based on the time point between current calibration parameters and LST inversion algorithms. In particular

the paper focuses on the suitability of two recently proposed split-window algorithms for current TIRS data. Users and researchers can therefore acquire further understanding of the algorithms and properly apply them to Landsat 8 TIRS data. In general

owing to the influence of significant stray light coming from outside TIRS’ field of view

the calibration accuracy of TIRS data still cannot meet design goals

and the uncertainty of TIRS band 11 is twice as large as that of TIRS band10 at this stage. Moreover

the root mean square error of both TIRS band 10 and band 11 is much higher than that of Landsat 7 Enhanced Thematic Mapper Plus（ETM+） band 6. Therefore

before the problem of out-of-field stray light can be completely resolved

employing a split-window algorithm to retrieve LST using both TIRS band 10 and band 11 should be avoided as this may cause a large amount of uncertainty in the results. Users should work with TIRS band 10 data as a single spectral band

similar to Landsat 5 Thematic Mapper（TM） band6 or Landsat 7 ETM+ band 6

given the large amount of uncertainty in the band 11 values. Users may use the single-channel（SC） algorithm of Jiménez–Mu?oz and Sobrino to retrieve LST using TIRS band 10 when the atmospheric water vapor content is less than 3 g/cm2.