Abstract
The ultimate objective of this work is to improve characterization of the ice cover distribution in the polar areas, to improve sea ice mapping and to develop a new automated real-time high spatial resolution multi-sensor ice extent and ice edge product for use in operational applications. Despite a large number of currently available automated satellite-based sea ice extent datasets, analysts at the National Ice Center tend to rely on original satellite imagery (provided by satellite optical, passive microwave and active microwave sensors) mainly because the automated products derived from satellite optical data have gaps in the area coverage due to clouds and darkness, passive microwave products have poor spatial resolution, automated ice identifications based on radar data are not quite reliable due to a considerable difficulty in discriminating between the ice cover and rough ice-free ocean surface due to winds. We have developed a multisensor algorithm that first extracts maximum information on the sea ice cover from imaging instruments VIIRS and MODIS, including regions covered by thin, semitransparent clouds, then supplements the output by the microwave measurements and finally aggregates the results into a cloud gap free daily product. This ability to identify ice cover underneath thin clouds, which is usually masked out by traditional cloud detection algorithms, allows for expansion of the effective coverage of the sea ice maps and thus more accurate and detailed delineation of the ice edge. We have also developed a web-based monitoring system that allows comparison of our daily ice extent product with the several other independent operational daily products.
