Year: 2025
Type: article
Abstract: Land surface temperature (LST) data are often required at coarser resolutions than the native satellite data for user applications. LST products from infrared sensors are clear-sky only, and thus, coarsening such data introduces a sampling uncertainty where the target domain is not fully sampled. In this manuscript, we calculate sampling uncertainty as a function of clear-sky fraction for 0.01° products re-gridded to 0.05° and 0.1°. We find that sampling uncertainty is dependent on both the underlying land cover (biome) and the solar geometry at the time of the observation. The largest sampling uncertainties are seen for mixed pixels (encompassing a variety of biomes) at 0.05° resolution (0.98 K) and for urban pixels at 0.1° resolution (2.5 K). The spatial sampling uncertainty methodology presented here is applicable to any infrared LST products provided at these resolutions (from a native resolution of 0.01°/~1 km), irrespective of retrieval algorithm or satellite, provided that the uncertainty due to noise can be removed.
Source: Remote Sensing
DOI: 10.3390/rs17203435
Authors: Claire E. Bulgin, Darren J. Ghent, Mike Perry
Institutions: National Centre for Earth Observation, University of Reading
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4415207398