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Land surface temperature (LST) plays an important role in many fields. However, thermal bands in prevailing sensors that are onboard satellites have limited spatial resolutions, which seriously impede their potential applications. Many approaches that aim to downscale thermal imageries to finer spatial resolution levels have been developed in recent years. This paper managed to construct a Generalized Theoretical Framework from an Assimilation Perspective for them with semiempirical regression and modulation integration techniques. Based on three hierarchical sharpening levels, which include digital number, radiance, and surface temperature, many of them can be brought into such a unified framework as derivatives. Two typical land cover patterns were chosen as case study areas to evaluate the capabilities of various kernels to represent the LST distribution. The results demonstrate that there are great discrepancies among those kernels. The single-band kernels are dependent on different land cover types, while the band-derivative kernels perform better in most circumstances when portraying the LST variations. In addition, the simulated imageries that were resampled by scaling up the original thermal bands with an aggregation technique were utilized to validate a localization approach of temperature vegetation dryness index (TVDI). The results indicate that the TVDI has satisfactory effects when depicting slight LST variations due to soil anomalies. More intercomparisons between the approach presented here and other different methods, including artificial neural network and Gram-Schmidt techniques, were made thoroughly, coupling with the Moderate Resolution Imaging Spectroradiometer and Advanced Spaceborne Thermal Emission Reflection Radiometer data. Consequently, the generalized framework opens up the foreground for sharpening thermal images with high efficiency over a solid theoretical foundation.