Benefits of downscaled satellite-derived land surface temperature for permafrost modelling in the northern high latitudes

Northern high latitudes have experienced pronounced warming throughout the last decades, with particularly high temperatures during winter and spring. Due to Arctic Amplification, the Arctic region is warming four times faster than anywhere else. Permafrost, a crucial component of arctic ecosystems, is particularly sensitive to increasing air temperatures and changes in the snow regime. In the last decade, satellite-derived land surface temperature (LST) products combined with snow cover information and land cover data have been increasingly used for permafrost modelling. For example, the CryoGrid community model, a ground thermal model, is used within the frame of the ESA Permafrost Climate Change Initiative (CCI) project to produce permafrost extent maps on a hemispheric scale. These maps and permafrost modelling outputs are based on Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. A drawback is that MODIS LST products have only been available since 2001, which prevents differentiating multi-decadal climate trends from decadal-scale climate oscillations.To leverage the historic Advanced Very High-Resolution Radiometer (AVHRR) sensors series, a new pan-Arctic LST dataset has been developed using EUMETSAT’s AVHRR Fundamental Data Record (FDR). The pan-Arctic AVHRR LST product covers a period from 1981 to 2021 and has a spatial resolution of approximately 4 km. It incorporates snow cover information derived from fractional snow cover and snow water equivalent data, allowing for accurate emissivity and temperature retrievals over snow and ice. To obtain AVHRR LST data at a spatial resolution similar to the MODIS LST dataset (~ 1 km) and allow for intercomparison of the permafrost modelling outputs, the AVHRR pan-Arctic LST dataset is downscaled to a spatial resolution of 1 km. Recent advances in spatiotemporal fusion and super-resolution models offer new solutions to downscale thermal infrared (TIR) data, allowing obtaining LST data at a high spatial and temporal resolution. Guided super-resolution (SR) is another downscaling strategy that only relies on a low-resolution source and a high-resolution guide. It returns a high-resolution version of the source. In the case of the AVHRR LST downscaling, the guide comprises information derived from land cover, elevation models, and canopy height data. Downscaling results of the pan-Arctic LST dataset based on guided deep anisotropic diffusion for the region of the Yamal Peninsula (Siberia) and along the Alaska Highway in the Yukon (Canada) showed promising results. The downscaling methodology demonstrated its potential for capturing the complexities of typical permafrost landscapes.

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