Land Surface Temperature（ LST） is a significant surface biophysical variable. This parameter is also important in vari-ous fields such as urban thermal environment

agricultural monitoring

surface radiation

and energy balance. Data from Landsat satellites are vital remote sensing data for LST retrieval since the 1980 s. The present Landsat 8 Thermal Infrared Sensor（ TIRS）imagery provides a new data source for LST retrieval. Landsat 8 TIRS is improved compared with the Landsat 6 Thematic Mapper.Landsat 8 data are extensively applied

so the mono-window algorithm should be updated with new sensor characteristics. Therefore

we aim to explore an adaptive method with more reliable accuracy to retrieve LST using Landsat 8 TIRS data.In this paper

a relation model（ TIRS10

SC） was established between LST and several parameters

namely

brightness temperature

mean atmospheric temperature

atmospheric transmittance

and land surface emissivity. The model was based on the radiative transfer equation and characteristics of Landsat 8 TIRS10. The LSTs of the study area were retrieved by initially deriving the atmospheric transmittance from MODIS data and MODTRAN simulation results. Then

the mean atmospheric temperature was obtained using empirical formulas

and land surface emissivity was retrieved from the Landsat 8 OLI data using image classification-based method. Finally

the LSTs of the study area were retrieved from the processed data. The algorithm and retrieval results were assessed by simulated and measured data. Meanwhile

the sensitivity of variables in themono-window algorithm was analyzed.Results show that the mono-window algorithm can perform well for Landsat8 TIRS data for LST retrieval. The LSTs of different land-cover types in study area varied. The LSTs of bare soil and cements were evidently higher than those of the vegetated areas.The LST of the former varied between 24. 12 ℃ and 32. 25 ℃

whereas that of latter ranged from 10. 72 ℃ to 19. 79 ℃. Furthermore

compared with the measured data

the average error and correlation coefficient of retrieved LSTs were 0. 83 ℃ and 0. 805

respectively. The accuracy of the algorithm was also assessed using simulated data

which showed that the error in the LST data in the majority of cases ranged between 0. 2 ℃ and 0. 3 ℃. The retrieval results agree with the assessed temperature data. Results from the analysis of the sensitivities of land surface emissivity

atmospheric water vapor content

and average temperature showed that the TIRS10

S

C algorithm can obtain more reliable results with higher sensitivities for the former two para meters and lower sensitivity for the latter one. The proposed algorithm can be applied for the fast retrieval of LST using Landsat 8 TIRS data.