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Land surface temperature (LST) is an important environmental variable for urban studies such as those focused on the urban heat island (UHI). Though satellite-derived LST could be a useful complement to traditional LST data sources, the spatial resolution of the thermal sensors limits the utility of remotely sensed thermal data. To balance the trade-off between the spatial and temporal resolutions of the current satellite-derived thermal data, thermal sharpening technology is developed. However, the existing thermal sharpening methods suffer from many limitations. In this study, a thermal sharpening technique, called Super-Resolution Thermal Sharpener (SRTS), is proposed which could enhance the spatial resolution of satellite-derived LST based on super-resolution mapping (SRM) and super-resolution reconstruction (SRR). This method overcomes the limitation of traditional thermal image sharpeners that require fine spatial resolution images for resolution enhancement. Furthermore, environmental studies such as UHI modelling typically use statistical methods which require the input variables to be independent, which means the input LST and other indices should be uncorrelated. The proposed SRTS does not rely on any surface index, ensuring that the derived LST is as independent as possible from the other variables that UHI modelling often requires. To validate the SRTS, its performance is compared against that of four popular thermal sharpeners: one called TsHARP (which is not an abbreviation), adjusted stratified stepwise regression method (Stepwise), pixel block intensity modulation (PBIM), and emissivity modulation (EM), and using two types of data: MODIS and Landsat imagery which can be the representatives of coarse and medium spatial resolution data sources, respectively. The advantage of using the combination of SRR and SRM was also verified by comparing the accuracy of SRTS with a sharpening process only based on SRM or SRR. The results show that the SRTS can enhance the spatial resolution of LST with a magnitude of accuracy that is equal or even superior to other thermal sharpeners, despite not requiring fine spatial resolution input. This shows the potential of SRTS for application in conditions where only limited meteorological data sources are available yet where fine spatial resolution LST is desirable.