Landsat surface temperature (LST) is an important physical quantity for global climate change monitoring. Over the past decades, several LST products have been produced by satellite thermal infrared (TIR) bands or land surface models (LSMs). Recent research has increased the spatio-temporal resolution of LST products to 2 km, hourly based on Geostationary Operational Environmental Satellites (GOES)-R Advanced Baseline Imager (ABI) LST data. The spatial resolution of 2 km, however, is insufficient for monitoring at the regional scale. This paper investigates the feasibility of applying spatio-temporal fusion to generate reliable 100 m, hourly LST data based on fusion of the newly released 2 km, hourly GOES-16 ABI LST and 100 m Landsat LST data. The most accurate fusion method was identified through a comparison between several popular methods. Furthermore, a comprehensive comparison was performed between fusion (with Landsat LST) involving satellite-derived LST (i.e., GOES) and model-derived LSMs (i.e., European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis v .5 (ERA5)-Land). The spatial and temporal adaptive reflectance fusion model (STARFM) method was demonstrated to be an appropriate method to generate 100 m, hourly data, which produced an average root mean square error (RMSE) of 2.640 K, mean absolute error (MAE) of 2.159 K and average coefficient of determination ( R 2 ) of 0.982 referring to the in situ time-series. Furthermore, inheriting the advantages of direct observation, and the fusion of Landsat and GOES for the generation of 100 m, hourly LST produced greater accuracy compared to the fusion of Landsat and ERA5-Land LST in the experiments. The generated 100 m, hourly LST can provide important diurnal data with fine spatial resolution for various monitoring applications.