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Sea Ice Monitoring in the European Arctic Seas Using a Multi-Sensor Approach

  • S. Sandven

Advances in satellite remote sensing of sea ice and icebergs in Arctic regions are described in case studies showing the benefits of using multi-sensor observations. It is demonstrated how Synthetic Aperture Radar (SAR) used in combination with optical images can improve discrimination of open water, nilas, young ice and three gradations of deformed first-year ice. The classification method is based on multi-sensor data fusion and neural network, where in situ observations were used for training of the algorithm. Synergetic use of scatterometer and passive microwave (PMW) data is well-established to estimate large scale ice motion, but in straits and marginal seas more detailed ice drift data are needed. In the Fram Strait SAR images from ENVISAT have been used to estimate ice drift and ice area flux since early 2004. It is demonstrated that SAR wideswath images can provide more accurate and higher-resolution ice drift vectors compared to scatterometer and PMW data. Methods for retrieval of thickness for thin ice are available using thermal infrared, passive microwave and SAR data, but these methods are research-oriented and not used in regular monitoring. Laser and radar altimeter measurements from satellites have shown promising capability to observe sea ice freeboard and thickness for ice thicker than about one meter. Such data used in combination with ice drift and ice types will provide new estimates of ice volume variability and fluxes. SAR and optical data have also been used to observe icebergs in the Barents Sea. The two data types are complementary and can improve iceberg detection if they are used in combination.

Keywords

Synthetic Aperture Radar Synthetic Aperture Radar Image Synthetic Aperture Radar Data Multisensor Approach Radar Altimeter Measurement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media B.V 2008

Authors and Affiliations

  • S. Sandven
    • 1
  1. 1.Nansen Environmental and Remote Sensing CentreNorway

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