Remotely sensed Land Surface Temperatures （LSTs） usually have low spatial resolutions. Downscaling is an effective technique to enhance the spatial resolutions. Current methods for downscaling remotely sensed LSTs were summarized. Using satellite data

we made an inter-comparison among three typical methods

including the Normalized Difference Vegetation Index （NDVI） method

the Pixel Block Intensity Modulation （PBIM） method

and the Linear Spectral Mixture Model （LSMM） method. We further designed an index

Co-Occurrence Root Mean Square Error （CO-RMSE）

for measuring the textural similarity in inter-comparisons. Results indicate that （1） the performance of the NDVI method is most affected by the season

followed by the PBIM method; （2） the performance of the LSMM method is most influenced by the spatial resolution; the NDVI method has an advantage over the PBIM method at high resolutions

while at low resolutions

the performance of the PBIM method is better than that of the NDVI method; （3） these three methods are suitable for areas with combination of vegetation and bare ground

areas with varied topography and albedo

and areas with distinct LST differences in different classes

respectively; （4） the NDVI method is the easiest to implement

while the LSMM method is the most difficult. Further analysis showed that scale factor is the key issue to the LST downscaling and it needs to be carefully selected regarding the season

spatial resolution

land cover

application and the operability.