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Land surface temperature (LST) retrieved from Landsat thermal infrared bands has been proved to have the most suitable spatial resolution for urban thermal environment studies, i.e., 60 m for Enhanced Thematic Mapper Plus (ETM+) and 120 m for Thematic Mapper (TM). However, its long revisit cycle (or low temporal resolution) coupled with cloud contamination has largely limited its application in urban environments. This letter presents a spatiotemporal image fusion model to produce high spatiotemporal resolution LST data, by combining the high spatial resolution of Landsat images and the frequent coverage of Moderate Resolution Imaging Spectroradiometer (MODIS) images. Taking into consideration light reflection and refraction among ground objects and the continuity of LST in the temperature space in urban areas, a spatiotemporal image fusion model based on bilateral filtering has been proposed. The main contribution of this model is that it accounts for the warming and cooling effect of ground objects in urban areas and establishes a new weight function to account for the effect of neighboring pixels. The proposed method is tested using four pairs of LST from Landsat ETM+ and MODIS on February 15, March 19, October 13, and November 14 in 2002, covering the center of Beijing, and the results show that our method is capable of generating dense time-series LST data by combining the strengths of the MODIS and Landsat images. Our method is also compared with a state-of-the-art method, and the better performance of our system in generating high spatiotemporal resolution LST is demonstrated.

High spatiotemporal resolution satellite imagery is useful for natural resource management and monitoring for land-use and land-cover change and ecosystem dynamics. However, acquisitions from a single satellite can be limited, due to trade-offs in either spatial or temporal resolution. The spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM (ESTARFM) were developed to produce new images with high spatial and high temporal resolution using images from multiple sources. Nonetheless, there were some shortcomings in these models, especially for the procedure of searching spectrally similar neighbor pixels in the models. In order to improve these modelsâ?? capacity and accuracy, we developed a modified version of ESTARFM (mESTARFM) and tested the performance of two approaches (ESTARFM and mESTARFM) in three study areas located in Canada and China at different time intervals. The results show that mESTARFM improved the accuracy of the simulated reflectance at fine resolution to some extent.