A comprehensive global analysis of the spatiotemporal variability of Land Surface Temperature

Land surface temperature (LST) is a crucial state variable determining the interactions between the land surface and the atmosphere (i.e., energy, water, and carbon fluxes). Accordingly, several hydrological quantities, such as soil moisture content, vegetation water stress, gross primary production, and crop yield, correlate strongly with it. Thus, LST constitutes a critical variable in understanding the physics of multiple land surface processes. Decades of global satellite remotely sensed fields are now available, creating an unprecedented opportunity to understand better the LST spatiotemporal variability by diagnosing its spatial and temporal persistence, deriving spatial and temporal correlation lengths, identifying areas with similar spatiotemporal patterns, and determining the physical factors influencing this variability from regional to global scales. This presentation will address this gap in understanding by comprehensively analyzing the spatiotemporal variability of LST globally. Preliminary work regarding this topic has been performed using the

As part of our evaluation, we will first derive the Empirical Spatio-Temporal Covariance Functions (ESTCFs) for the global ~5x5 km Copernicus LST hourly product. A 1x1-arcdegree moving window will be defined over the globe to compute the ESTCFs, and an hourly time step between 2010 and 2022 will be used for the analysis. The analysis will focus exclusively on the daytime of summer months because spatial heterogeneity of LST will play the most significant role in summertime (e.g., daytime summer convection). To summarize the obtained ESTCFs, a parametric spatiotemporal covariance function model will be fit to each 1x1-arcdegree ESTCF. From this parametric fit, we will evaluate the persistence of the patterns, analyze the spatial and temporal correlation lengths, and evaluate the space-time interaction displayed for different locations. Additionally, clustering analysis will be applied directly to the derived parametric covariance functions to identify functionally similar areas. Finally, we will compare the derived empirical covariance functions to well-known factors spatiotemporal influencing LST variabilities such as land cover, surface thermal properties, topography, incoming solar radiation, and meteorological conditions.