Satellite-derived quantification of the diurnal and annual dynamics of land surface temperature

The diurnal and annual cycles of Earth drive the land surface temperature (LST) variations in space time but their combined effects are conveyed differently from place to as a function climate, local weather conditions, characteristics. Fitting model or LST cycle (DTC ATC, respectively) satellite data yields several parameters that summarize thermal dynamics each pixel. These distinct for reveal how ATC DTC differ between pixels. Currently, due lack with high spatial temporal resolution, it is impossible characterize both at kilometer sub-kilometer scale. This work addresses this problem presents method simultaneously quantifying proposed based on Annual Cycle Parameters (ACP) retrieved geostationary every 30 min 00:00 23:30 uses statistical downscaling enhance coarse resolution. We apply our continental Europe period 2009–2013 validate using independent ACP in-situ LST. Our results climate conditions influence shape profiles over illustrate first excellent agreement satellite-derived derived Even though limited by coverage data, provides novel way describing Earth's response solar heating. • Joint description Excellent (ACP). Downscaling multi-target random forest regression. Climate profiles. zone distinct.