Image DNV. Ice concentration (blue) generated using E.U. Copernicus Marine Service Information; DOI:10.48670/moi-00132 and AIS data of vessels traffic (green).
Image, based on Sentinel-1, are provided under COPERNICUS by the European Union and ESA, all rights reserved. Processed by e-GEOS within the Arctic Navigation with COSMO-SkyMed - ARNACOSKY - a joint research project of e-GEOS and the italian Hydrograpic Institute (IIM) of the Italian Navy, within the IT Navy High North Program.
Product Description
Ice concentration is typically measured using remote sensing techniques, including satellite imagery and aerial and ship surveys. Remote sensing methods involve capturing images of the water surface and using algorithms to distinguish between ice and open water based on the reflective properties of the surface.
The spatial resolution of ice concentration provided by satellite observations varies depending on the sensor and the specific dataset being used. Different satellites have different capabilities and sensors that capture data at varying levels of detail. It's important to note that while higher spatial resolution can provide more detailed information about ice concentration, it might also come with limitations in terms of coverage and frequency of observations. Additionally, different sensors and datasets might have varying degrees of accuracy and precision in estimating ice concentration, which can be affected by factors like the presence of clouds, snow cover, and surface roughness.
Data providers also often combine data from multiple sensors and sources to get a comprehensive understanding of ice concentration patterns. The choice of dataset depends on the specific objectives and the trade-offs between spatial resolution, coverage, and accuracy.
Satellite data is often used to create ice concentration charts, providing at the same time information about the percentage of open water in an area and ice as well. These charts help mariners identify ice leads; the information on ice concentration can be derived from multiple satellite sources -including SAR, optical imagery, and passive microwave data – however under certain constraint (i.e. severe cloud coverage) SAR data can be used as stand-alone to generate the ice concentration chart.
These satellite-based methods and technologies are essential for monitoring and navigating through ice-covered waters, enhancing safety for ships, research vessels, and other activities in polar regions. Various organizations, including national meteorological agencies and space agencies, provide satellite imagery and data to support ice detection and ice navigation.
Further, it must be take into account that data coming from commercial service provider – MDA Space, e-GEOS, DNV and other – can be exploited as well to support the ice detection and navigation.
Product Specifications
BUSINESS PROCESS | SD, SC, SCE, IN, SO |
DESCRIPTION | Ice concentration refers to the extent to which a given area of water is covered by ice. It is typically expressed as a percentage, representing the proportion of the water surface that is covered by ice at a specific point in time. |
EO INFORMATION OF INTEREST | Sea ice concentration (SIC). |
MAIN PROCESS STEPS | Various EO sensors and techniques are employed to monitor ice concentration. The calibration of sea ice fraction retrieval from SAR involves the use of observations from an open ocean area, and another presumed to be completely ice-covered. Various empirical schemes are employed to derive sea ice concentration, incorporating diverse assumptions and corrections to accommodate distinct surface conditions and the influence of snow and rain. To mitigate the impact of weather interference on ice concentration retrievals, a predetermined threshold in ice concentration is frequently applied. It is important to underline that the processing steps to generate the ice concentration maps might change depending on the sensors/input data; as an example, using AMSR-E data, the sea ice concentration maps can be retrieved compensating for atmospheric correction. Further, along the coast a land spillover correction is generally applied to refine the product. In the case of other sensors, calibration, as well as atmospheric compensation, stands out as the pivotal and pertinent processing steps essential for generating the ice concentration product. |
INPUT DATA SOURCE | SAR data and microwave radiometer. Harmonized data products also exist blending multiple sensors. e.g., SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data. |
SPATIAL RESOLUTION AND COVERAGE | 1 to 6 km Baltic and Arctic |
ACCURACY / CONSTRAINS | The uncertainties in the retrievals are largest in summer, because melt ponds are difficult to distinguish from open water and generally the techniques cannot see thin ice (which does not mask the microwave emission of the underlying ocean) or ice concentrations greater than 90% (owing to spatial and temporal variability in the overlying snow characteristics). Overall, an uncertainty of one million square kilometres (20% error in summer and 7% error in winter) might be expected for a measurement of total Arctic ice area (Metoffice, 2023). All SIC products have some limitations and uncertainties (Spreen & Kern, 2017), but overall, the trends agree well (Comiso et al., 2017). |
LIMITATIONS | The observations have a spatial coverage limitation/'hole' north of 87 degrees, where satellites are unable to take measurements, because of the nature of their orbits. However, this limitation can be overcome using - in case of SAR -extended range incidence angle, allowing to increase the coverage capability. The interpretation of data can indeed be challenging, especially when handled by personnel without extensive experience in the field. |
TEMPORAL RESOLUTION/ | Data is available from 1979 to ongoing at a variety of spatial and temporal resolution. Various products with various frequency, including hourly, daily mean. |
UPDATE FREQUENCY | Various products with various frequency, including daily, monthly, yearly, twice yearly |
DELIVERY / OUTPUT FORMAT | NetCDF-3, NetCDF-4 |
ACCESSIBILITY | Information products are available from Met office, DMI, MetNO, Copernicus Marine Service and Artic hub. and commercial providers. |
Business Process Challenges
Ship Design (SD) Challenges
- SD-1 Environmental Conditions
- SD-2 Defining Ice Class for Vessels
- SD-3 Vessel Concept, dimensions, and design
Ship Construction (SC) Challenges
- SC-3 Planning of Sea Trials Outside of Ice Season
- SC-4 Avoiding Ice During Sea Trials
- SC-5 Planning of Sea Ice Trials
- SC-6 Finding Suitable Ice During Sea Trials
- SC-7 Ship Operation in Ice During Sea Trials
Ship Certification (SCE) Challenges
- SCE-3 Risk Assessment for Operations in Ice
- SCE-4 Strategic Planning using Polaris
- SCE-6 Monitoring Sea Ice Conditions During Voyage
Insurance (IN) Challenges
Ship Operation (SO) Challenges
- SO-1 Navigating Through Ice
- SO-3 Navigating Along (or just inside) the Ice Edge
- SO-8 Strategic Planning
- SO-9 Risk Analysis According to POLARIS
- SO-10 Search & Rescue Operations
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