Land surface temperature (LST) is widely used in a variety of applications, such as meteorology, climatology, and
ecology. Up to now, there are no all-weather LST products at high spatial resolution. In this study, we propose a method
to generate an all-weather LST product by merging MODIS and AMSR-E data. Two main processes are performed in
this method, including retrieving AMSR-E LST and downscaling AMSR-E LST to MODIS pixel resolution. After the
implement of these two processes, MODIS LSTs under clear-sky conditions and AMSR-E LSTs under cloudy conditions
are merged to generate an all-weather LST product. Results indicate that the merged LSTs filled up the missing data in
the original MODIS LSTs due to the effects of cloud when compared with the original MODIS LSTs.