ESA CLIM4cities - Land Surface Temperature Downscaled from Sentinel-3 SLSTR Level-2 LST products - Copenhagen, Aarhus, Aalborg and Odense, 2020-2023

The present dataset provides land surface temperatures (LST) of 300 m resolution for the Danish functional urban areas of Copenhagen, Aarhus, Aalborg, and Odense between 2020 and 2023 (including). These were obtained from coarse Sentinel-3 SLSTR Level-2 LST products, with a resolution of 1000 m, regridded onto a regular grid and upsampled using scale-invariance-based downscaling models applied on pure spatial data and spectral indexes (derived from regridded Sentinel-3 SYN Level-2 products, with a resolution of 300 m). Such predictors corresponded to coastal distance, imperviousness density (IMD), tree cover density (TCD), fractional vegetation cover (FVC) and normalised difference water index (NDWI). The spatial predictors, which were originally even finer than the ones of the SYN products, were reprojected to the grid of the latter. A total of 112 late-morning timestamps, for which the cloud cover fraction in the area of interest did not surpass 5 %, were processed. The obtainted downscaling models may be regarded as a refinement of the well-known TsHARP and DisTrad architectures by considering a larger number of predictors. A downscaling model was obtained for each timestamp: training and inference was done by firstly reprojecting a copy of the fine predictors to LST's coarse grid, training a base linear regression model with the coarse predictors and target, applying the base model on the fine predictors (therefore, assuming scale-invariance) and correcting the fine prediction by adding to it the finely interpolated residual of the coarse prediction. The dataset was produced within the CLIM4cities project funded by the European Space Agency (ESA Contract No. 4000143628/24/I-DT, AI Trustworthy Applications for Climate).