Beschreibung: With current remote sensing technologies, it is not possible to directly measure the thermal state of the ground from spaceborne platforms. Here, we demonstrate that such limitations can be overcome by exploiting the combined information content of several remote sensing products in a data fusion approach. For this purpose, 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 (~25,000 km2) at 1 km 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 depths. 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 such schemes and give a feasibility assessment for both mountain and lowland permafrost regions.

Beschreibung: We present a summary of validation efforts of MODIS land surface temperature (MOD11A1, MYD11A1) using in-situ observations from the high-arctic sites Ny-Ålesund (79 °N) and Austfonna ice cap (80 °N) on Svalbard, as well as Samoylov Island in NE Siberia (72 °N). For all three sites, multi-year time series of outgoing and incoming long-wave radiation are available from which the skin temperature can be calculated. Our analysis is focused on long-term averages of all-sky temperatures which are required to determine trends of surface temperatures. At all sites, yearly averages computed from all available MODIS LST measurements are cold-biased by up to 3 °C, which is mainly caused by a significant cold-bias during the winter period. A closer analysis using in-situ observations of cloudiness reveals two main error sources. First, winter surface temperatures are systematically warmer for cloudy skies, so that the satellite predominantly samples “cold” clear-sky conditions. Secondly, the cloud detection algorithm fails to exclude a significant number of cloudy scenes, so that colder cloud top temperatures are contained in the surface temperature record. For the Austfonna ice cap, we estimate that the fraction of such cloud top temperatures could exceed 40%, which highlights the importance of this error source. Over the N Atlantic region, the number of MODIS LST retrievals varies by up to a factor of three, with highest numbers on the Greenland ice sheet and lowest numbers on Iceland the coastal regions of Norway. When assessing trends in land surface temperatures through remote sensing, three factors must be considered: a) trends in the “true” fraction of cloudy conditions, b) trends in the surface temperature for cloudy conditions, and c) trends in misidentified cloudy scenes and cloud top temperatures. We demonstrate that a simple gap-filling procedure using downscaled air temperatures from the ERA-interim reanalysis can significantly improve the agreement with in-situ measurements. Such a composite product has the potential to moderate the influence of factors a and b, but cloud top temperatures due to misidentified cloudy scenes are still contained.