Primary objective: Increasing the understanding of use of satellite remote sensing data over sea ice covered areas by means of sophisticated validation and evaluation experiments in the Fram Strait region.
Secondary objective: Obtain an improved satellite remote sensing dataset of the sea ice thickness and sea ice drift distribution in Fram Strait. The new datasets will be both longer and have a higher temporal resolution than previous records. Special attention will be given to establish reliable error margins for the satellite observations and to validate satellite observations with in situ data from different seasons and over the duration of multiple years. Finally, an improved Fram Strait sea ice volume export estimate will be obtained, which (1) is essential to understand the current dramatic sea ice change and (2) can influence the local and global ocean circulation.
The project will improve our understanding of the sea ice thickness and sea ice drift distribution in the Fram Strait region, which has a high impact on the local Arctic and global climate.
the influence on the ocean dense water formation and thereby the global thermohaline circulation.
ice drift) and new (CryoSat-2 ice freeboard/thickness, SAR ice drift, SMOS thin ice thickness) sea ice remote sensing datasets will be compared and evaluated against available in situ measurements obtained from ship, ice station, and helicopter surveys. When necessary, available remote sensing methods will be improved (ICESat, CryoSat-2) or newly developed (sea ice drift tracking from polarimetric SAR) specifically for the very dynamic and complex sea ice pack situation in Fram Strait.
As a final outcome longer and better validated time series of sea ice thickness, drift, and volume export in Fram Strait will be obtained and combined with other sea ice mass balance observations from the grander Arctic region.
Korosov A, Rampal P. A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data. Remote Sensing. 2017;9(3).