Land surface temperature (LST) is a crucial parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, urban heat islands, fire detection and earthquake thermal precursors. There is a shortage of daily high spatial land surface temperature data for using in high spatial and temporal resolution environmental process monitoring. Due to the technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result one sensor doesn’t provide both high spatial resolution and high temporal resolution. The 16-day revisit cycle of ASTER leads to a disadvantage in studying the global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors’ ability to quantify biophysical processes in heterogeneous landscapes. The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. Since ASTER and MODIS sensors are onboard a platform (Terra or Aqua), therefore, this study has used an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm to blend ASTER and MODIS LST product. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from ASTER can be blended for applications that require high resolution in both time and space. The MODIS daily 1-km LST product and the 16-day repeat cycle ASTER 90-m LST product are used to produce a synthetic “daily” LST product at ASTER spatial resolution. The LST products of ASTER and MODIS sensors were fused for a part of Tehran city and finally, a virtual image was obtained with a spatial resolution equal to that of the ASTER sensor and a temporal resolution equal to that of the MODIS sensor. The results show that the accuracy of ESTARFM algorithm is better than the accuracy of the STARFM algorithm in the studied area—with an average difference of 1.77 Kelvin from the real observation data. The STARFM algorithm couldn’t preserve the spatial details in the predicted virtual image as well as two other algorithms. The results showed that the algorithm can produce high-resolution temporal synthetic ASTER data that were similar to the actual observations with a high correlation coefficient (r) of 0.87 between synthetic imageries and the actual observations.