ALBERT

Description: Permafrost is a key element of the terrestrial cryosphere which makes mapping and monitoring of its state variables an imperative task. We present a modeling scheme based on remotely sensed land surface temperatures and reanalysis products from which mean annual ground temperatures (MAGT) can be derived at a spatial resolution of 1 km on continental scale. The approach explicitly accounts for the uncertainty due to unknown input parameters and their spatial variability at subgrid scale by delivering a range of MAGTs for each grid cell. This is achieved by a simple equilibrium model with only few input parameters which for each grid cell allows scanning the range of possible results by running many realizations with different parameters. The approach is applied to the unglacierized land areas in the North Atlantic region, an area of more than 5 million km2 ranging from the Ural mountains in the East to the Canadian Archipelago in the West. A comparison to in-situ temperature measurements in 143 boreholes suggests a model accuracy better than 2.5 °C, with 139 considered boreholes within this margin. The statistical approach with a large number of realizations facilitates estimating the probability of permafrost occurrence within a grid cell so that each grid cell can be classified as continuous, discontinuous and sporadic permafrost. At its southern margin in Scandinavia and Russia, the transition zone between permafrost and permafrost-free areas extends over several hundred km width with gradually decreasing permafrost probabilities. The study exemplifies the unexploited potential of remotely sensed data sets in permafrost mapping if they are employed in multi-sensor multi-source data fusion approaches.

Description: Permafrost is a key element of the terrestrial cryosphere which makes mapping and monitoring of its state variables an imperative task. We present a modeling scheme based on remotely sensed land surface temperatures and reanalysis products from which mean annual ground temperatures (MAGT) can be derived at a spatial resolution of 1 km at continental scales. The approach explicitly accounts for the uncertainty due to unknown input parameters and their spatial variability at subgrid scale by delivering a range of MAGTs for each grid cell. This is achieved by a simple equilibrium model with only few input parameters which for each grid cell allows scanning the range of possible results by running many realizations with different parameters. The approach is applied to the unglacierized land areas in the North Atlantic region, an area of more than 5 million km2 ranging from the Ural Mountains in the east to the Canadian Archipelago in the west. A comparison to in situ temperature measurements in 143 boreholes suggests a model accuracy better than 2.5 °C, with 139 considered boreholes within this margin. The statistical approach with a large number of realizations facilitates estimating the probability of permafrost occurrence within a grid cell so that each grid cell can be classified as continuous, discontinuous and sporadic permafrost. At its southern margin in Scandinavia and Russia, the transition zone between permafrost and permafrost-free areas extends over several hundred km width with gradually decreasing permafrost probabilities. The study exemplifies the unexploited potential of remotely sensed data sets in permafrost mapping if they are employed in multi-sensor multi-source data fusion approaches.

Description: With current remote sensing technologies, it is not possible to directly infer the thermal state of the ground from spaceborne platforms. We demonstrate that such limitations can be overcome by combining the information content of several remote sensing products in a data fusion approach: time series of remotely sensed land surface temperature, as well as snow cover and snow water equivalent, are employed to force ground thermal models which deliver ground temperatures and thaw depths. First, we present a semi-empirical model approach based on remotely sensed land surface temperatures and reanalysis products from which mean annual ground temperatures (MAGT) can be estimated at a spatial resolution of 1 km at continental scales. The approach is tested for the unglacierized land areas in the North Atlantic region, an area of more than 5 million km2. The results are compared to in-situ temperature measurements in more than 100 boreholes from which the accuracy of the scheme is estimated to approximately 2.5 °C. Furthermore, we explore transient modeling of ground temperatures driven by remotely sensed land surface temperature, snow cover and snow water equivalent. The permafrost model CryoGrid 2 is applied to the Lena River Delta in NE Siberia ( 25000 km2) at 1km spatial and weekly time resolution for the period 2000–2014. A comparison to in-situ measurements suggests a possible accuracy of around 1 °C for annual average ground temperatures, and around 0.1 m for thaw depth. However, information on subsurface stratigraphies including the distribution of ground ice is required to achieve this accuracy which is currently not available from remote sensing products alone. Finally, we discuss the potential and limitations of schemes using a combination of remote sensing data and thermal permafrost models to assess the thermal state of the ground, and give a feasibility assessment for both mountain and lowland permafrost regions.