Spatial downscaling of land surface temperature in combination with TVDI and elevation.

Land surface temperatures (LSTs) at high-spatial resolution are significant for hydrological, meteorological, and ecological studies. An alternative method to obtain LSTs at a high-spatial resolution is to downscale LSTs from a coarse resolution to a finer resolution. In this article, we propose a new algorithm based on temperature and vegetation dryness index in combination with elevation correction to downscale Moderate Resolution Imaging Spectroradiometer LST data from 1000 m to 90 m. The performance of the algorithm was assessed using Advanced Spaceborne Thermal Emission and Reflection Radiometer LST data as a reference LST data set. The results show that the proposed algorithm is superior to the temperature sharpening algorithm, which is widely used nowadays. The root mean square error and mean absolute error values of downscaled LSTs decrease from 3.1 K (2.4 K) to 2.8 K (2.3 K). [ABSTRACT FROM AUTHOR]