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

Automated summary

This work explores how the diurnal and annual cycles of Earth affect land surface temperature variations and proposes a method for quantifying the dynamics of annual and diurnal temperature simultaneously. The method is validated using data from continental Europe, demonstrating the impact of climate and surface conditions on the temperature profiles and showing an excellent agreement between satellite-derived and in-situ data.