Land surface temperature (LST) is a key 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, and urban heat islands. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between these parameters. The Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was initially designed to predict surface reflectance and is based on the assumption that MODIS and Landsat surface reflectance are highly consistent over homogeneous areas. However, the ESTARFM method prediction results degrade somewhat when the method is used for heterogeneous fine-grained landscapes. This research extended the ESTARFM model from reflectivity range to thermal infrared for estimation of daily temperature at 90 m resolution combined MODIS and ASTER. The implementation of ESTARFM requires input of the search window size, selection of spectrally similar pixels, determination of the weight of similar pixels, and computation of the correction coefficient and temporal weight. The calculation of weights for spectrally similar pixels involves weighing the contribution of neighboring pixels to the computation of a central pixel. Using a local moving window, neighboring spectrally similar pixels were included for the computation of the LST corresponding to a central pixel with the temporal weights of the two dates. In this study, we used multiple bands, i.e., red, NIR, and LST bands, as the input variables and generated high spatial-temporal resolution land surface temperature, combining temporal change information from multi-temporal MODIS with high-resolution spatial resolution from ASTER.