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Permafrost is a key element of the terrestrial cryosphere which makes mappingand monitoring of its state variables an imperative task. We present amodeling scheme based on remotely sensed land surface temperatures andreanalysis products from which mean annual ground temperatures (MAGT) can bederived at a spatial resolution of 1 km at continental scales. The approachexplicitly accounts for the uncertainty due to unknown input parameters andtheir spatial variability at subgrid scale by delivering a range of MAGTs foreach grid cell. This is achieved by a simple equilibrium model with only fewinput parameters which for each grid cell allows scanning the range ofpossible results by running many realizations with different parameters. Theapproach is applied to the unglacierized land areas in the North Atlanticregion, an area of more than 5 million km<sup>2</sup> ranging from the UralMountains in the east to the Canadian Archipelago in the west. A comparisonto in situ temperature measurements in 143 boreholes suggests a modelaccuracy better than 2.5 °C, with 139 considered boreholes withinthis margin. The statistical approach with a large number of realizationsfacilitates estimating the probability of permafrost occurrence within a gridcell so that each grid cell can be classified as continuous, discontinuousand sporadic permafrost. At its southern margin in Scandinavia and Russia,the transition zone between permafrost and permafrost-free areas extends overseveral hundred km width with gradually decreasing permafrost probabilities.The study exemplifies the unexploited potential of remotely sensed data setsin permafrost mapping if they are employed in multi-sensor multi-source datafusion approaches

A ground temperature map of the North Atlantic permafrost region based on remote sensing and reanalysis data