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.
Accurate, timely and spatially detailed information on the snow cover distribution and on the snow pack properties is needed in various research and practical applications including numerical weather prediction, climate modeling, river runoff estimates and flood forecasts. Owing to the wide area coverage, high spatial resolution and short repeat cycle of observations satellites present one of the key components of the global snow and ice cover monitoring system. The Global Multisensor Automated Snow and Ice Mapping System (GMASI) has been developed at the request of NOAA National Weather Service (NWS) and NOAA National Ice Center (NIC) to facilitate NOAA operational monitoring of snow and ice cover and to provide information on snow and ice for use in NWP models. Since 2006 the system has been routinely generating daily snow and ice cover maps using combined observations in the visible/infrared and in the microwave from operational meteorological satellites. The output product provides continuous (gap free) characterization of the global snow and ice cover distribution at 4 km spatial resolution. The paper presents a basic description of the snow and ice mapping algorithms incorporated in the system as well as of the product generated with GMASI. It explains the approach used to validate the derived snow and ice maps and provides the results of their accuracy assessment.
Conference Committee Involvement (4)
Land Surface and Cryosphere Remote Sensing V
2 December 2024 | Kaohsiung, Taiwan
Land Surface and Cryosphere Remote Sensing IV
25 September 2018 | Honolulu, Hawaii, United States
Land Surface and Cryosphere Remote Sensing III
4 April 2016 | New Delhi, India
Disaster Forewarning Diagnostic Methods and Management
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