Abstract: Land surface temperature (LST) is one of the important biophysical variables affecting the exchange of water and energy between land-surface and atmosphere, and it is significant to retrieve LST accurately. The mono-window algorithm is more applied in Landsat TM 6 data, including Jiménez-Muñoz mono-window algorithm (JM_SC) and Qin Zhihao mono-window algorithm (Q_SC). There are a lot of changes for Landsat8 thermal infrared sensor (TIRS) compared with Landsat TM6. Thus a mono-window algorithm for Landsat 8 data (TIRS10_SC) was proposed first, and then some comparison and analysis of three mono-window algorithms were conducted in this paper. The results show that:(1) The TIRS10_SC algorithm is closely integrated with the characteristics of Landsat8 TIRS sensor and it performs well to retrieve LST of different land-cover types based on the retrieval of atmospheric transmittance and land-surface emissivity. (2) Through the comparative analysis, it is found that the retrieval accuracy with Q_SC and TIRS10_SC is higher than JM_SC algorithm. (3) The retrieval results of homogeneous underlying surfaces such as bare soil land and cement surfaces are more accurate than vegetated surfaces. For bare soil land and cement surfaces, the average error of TIRS10_SC and Q_SC algorithm is 0.60℃, and that of JM_SC is 1.01℃; for vegetated surfaces, the average error of TIRS10_SC and Q_SC algorithm is 1.48℃, and that of JM_SC is 1.26℃.In order to improve the LST retrieval accuracy of vegetated surfaces in urban areas, the land-surface emissivity characteristics of vegetated surfaces need to be quantified more accurately.