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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, and urban heat islands. These studies require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensors can deliver TIR data at both high temporal and spatial resolution. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of TIR data, but rare of those can enhance both spatial and temporal details. This paper presents a new data fusion algorithm for producing Landsat-like LST data by blending daily MODIS and periodic Landsat TM datasets. The original Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was …