A Geostatistics‐Based Tool to Characterize Spatio‐Temporal Patterns of Remotely Sensed Land Surface Temperature Fields Over the Contiguous United States.

Surface fluxes and states can recur and remain consistent across various spatial and temporal scales, forming space‐time patterns. Quantifying and understanding the observed patterns is desirable, as they provide information about the dynamics of the processes involved. This study introduces the empirical spatio‐temporal covariance function and a corresponding parametric covariance function as tools to identify and characterize spatio‐temporal patterns in remotely sensed fields. The method is demonstrated using 2 km hourly GOES‐16/17 land surface temperature (LST) data over the Contiguous United States by splitting the area into 1.0° × 1.0° domains. The summer day‐time LST ESTCFs for 2018 to 2022 are derived for each domain, and a parametric covariance model is fitted. Clustering analysis is applied to detect areas with similar spatio‐temporal LST patterns. Six main zones within CONUS are identified and characterized based on their variance and temporal and spatial characteristic length scales (i.e., scales for which the temperature variations are temporally and spatially related), respectively: (a) Eastern plains with 3 K2, ∼6 hr, and 0.15°, (b) Gulf of California with 60 K2, ∼8 hr, and 0.34°, (c) mountains and coasts transition 1 with 16 K2, ∼11 hr, and 0.25°, (d) central US, Midwest, and South cities with 5.5 K2, ∼8 hr, and ∼0.2°, (e) mountains and coasts transition 2 with ∼10 K2, ∼8 hr, and 0.2°, and (f) largest mountains and coastlines with ∼19 K2, ∼13 hr, and 0.3°. The tools introduced provide a pathway to formally identify and summarize the spatio‐temporal patterns observed in remotely sensed fields and relate those to more complex processes within the Soil‐Vegetation‐Atmosphere System. Plain Language Summary: Specific processes on Earth's surface, like heat and water fluxes and air movement, often exhibit coherent patterns across space and time. Figuring out where these patterns occur can be helpful as they can aid to explain how the underlying physical processes work. This research introduces a new tool to find and describe these patterns and applies it to temperature data derived from satellites. The United States is divided into smaller areas, and the developed tool is used to analyze the land temperature data within those areas; then, regions with similar temperature patterns are identified. These results help to determine that certain landscape features, like coastlines, mountains, and cities, influence how temperature patterns behave in space, time and both. These tools help us to recognize and describe the patterns we see in satellite observations and ultimately, connect them to more complicated processes happening in the Soil‐Vegetation‐Atmosphere System. Key Points: Space‐time covariance can summarize the space‐time structure of time‐varying spatial fields of surface fluxes and statesThe observed space‐time covariance of land surface temperature (LST) reveals variability in its characteristic length and time scales over the United StatesClustering of the fitted LST space‐time covariance parameters reveals the role of environmental characteristics in the observed patterns [ABSTRACT FROM AUTHOR]