Abstract
ABSTRACTLand 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).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
