Sea Ice

Arctic sea ice provides important services to people, including coastal communities and industry, as well as marine wildlife. In many regions of the Arctic, operations on or near sea ice are increasingly limited by a shorter ice season, reduced ice extent, more prolific ice hazards, reduced stability of shorefast ice, and rougher, less trafficable ice surface conditions. We are exploring the use of Synthetic Aperture Radar Interferometry (InSAR) as a viable tool to address the recognized need for cost effective approaches to provide stakeholder-relevant data on key constraints for sea ice use, in particular pack ice drift, landfast ice stability and morphology.

Depending on the temporal baseline between satellite passes, InSAR has the capability to quantify sea ice drift as well as detect small-scale landfast ice displacements, which are linked to important coastal hazards, including the formation of cracks, ungrounding of ice pressure ridges, catastrophic breakout and complete removal of landfast ice. While InSAR has shown promise in a few key studies, it has yet to be thoroughly validated and its potential remains largely underutilized in sea ice science.

Results from recent studies demonstrate the potential use of InSAR for providing key information to stakeholders operating on or near sea ice:

Using a large set of Sentinel-1 interferograms, we categorized the 2017 coastal arctic sea ice into different ice regimes based on stability including bottomfast, landlocked, grounded, ungrounded and drifting ice.

During a campaign near Utqiaġvik, Alaska, in 2015, InSAR-derived drift speeds and landfast ice deformation were validated using ground based radar / TanDEM-X and GPS/TerraSAR-X respectively. An inverse model has been developed to derive deformation rates and patterns of landfast ice, correctly identifying deformation modes and proxies for the associated relative internal elastic stress around Northstar Island ice road, Alaska. The derived potential for fractures corresponds well with large-scale sea ice patterns and local in-situ observations.

The utility of InSAR to quantify sea ice roughness has also been explored using TanDEM-X. A DEM constructed from the interferometric phase correlated well with a high-resolution Structure from Motion DEM and showed great promise for the routing of optimal navigation paths across landfast ice.

There is great utility and potential demand for SAR products to support stakeholders near sea ice and the recent and expected increase in SAR satellites will likely increase the potential use of these techniques in operational settings. 

In order to improve ship routing in polar waters, we present a software processor to retrieve high resolution sea ice motion fields from spaceborne Synthetic Aperture Radar (SAR) image sequences fully automatically.

Sea ice is almost continually in motion. Within hours, wind and ocean currents can cause significant changes within the sea ice. When the ice is pulled apart by winds or currents from opposite directions, the ice fractures, and open water leads appear. When ice is strongly pushed together by converging wind and currents, the ice sheet will break and either pile up randomly one piece over another, forming a thick, uneven surface, or be forced upwards, creating high walls called ridges. Such obstacles are difficult or impossible even for icebreakers to overcome.

SAR satellites such as TerraSAR-X or Sentinel-1 are well suitable to map different structures in the sea ice. Due to their near-polar orbit, spatially and temporally near coincident acquisitions in high latitudes are possible on a daily basis.  

The core of the presented software processor for sea ice motion retrieval is the well-known phase correlation technique, executed within a hierarchical motion estimation framework presented in our previous work. The output of the processor is a vector field indicating the sea ice displacement, which can be converted into sea ice velocity. Now, we investigate the accuracy of the retrieved displacement.

Our test deals with a series of TerraSAR-X ScanSAR mode images acquired over drift buoys that are located in arctic waters, as well as with collocated Sentinel-1 acquisitions for comparison. We monitored the buoys during July 2017 and January 2018. In the winter sequences, an ice concentration of >90 % is predominant, while the summer acquisitions capture an ice concentration of 50 % - 80 %. Altogether, the accuracy of motion vectors estimated from TerraSAR-X image pairs amounts to 30 m (1σ-error). The motion field has a resolution of 150 m x 150 m, which gives a very detailed look into the local sea ice motion, detecting small variations.

The presented processor is intended to be part of the operational data processing chain at DLR Ground Station Network sites. In ongoing work, we implement parallel processing in order to reduce computing time so vessels in ice infested waters can receive information on local sea ice motion in near real-time.

Shipping and commercial activities in the Arctic are inevitably increasing as a results of the climate change, opening of the Northern sea route and oil exploitation. As a consequence, demand for better operational sea ice forecast in the coastal areas becomes crucial both for the climate/marine researchers and for the commercial users.

A new general circulation sea ice model neXtSIM (Rampal et al, 2016) has been developed at NERSC on the basis of the Maxwell elasto-brittle rheology framework (Dansereau et al, 2016) in order to simulate realistic sea ice conditions for regions as large as the whole Arctic and over time scales up to several years. The model equations are discretized with the finite-element method and a pure Lagrangian scheme has been implemented to preserve the discontinuities (highly localized features like cracks, leads and ridges) simulated by the model. Sea ice deformation simulated with this model spontaneously localizes along linear-like faults separating essentially undamaged ice plates/floes. The new sea ice model accounts for the local level of mechanically-induced damage of sea ice. Initialization of sea ice damage using observations would likely increase the model skill in terms of both sea ice drift and deformation forecasting. Satellite derived ice deformation can be used to estimate the local damage of the ice. The main idea is that ice damage in a model using the elasto-brittle rheology could be initialized with a higher value at locations where the sea ice cover has deformed during the previous couple of days.

Sea ice drift is derived from sequences of Sentinel-1 SAR images using the combined feature tracking and pattern matching algorithm (Korosov and Rampal, 2017) at unprecedented 5 km spatial resolution. For the first time drift is derived not on a regular grid but on a Lagrangian mesh of the neXtSIM model. Anisotropic filtering is applied to sea ice drift (Bouillon & Rampal, 2015) for suppressing small scale noise. Deformation is derived from the spatial derivatives of the sea ice drift at high spatial resolution. The temporal scale of ice deformation (duration of ice drift integration) is varied for identification of persistent deformation zones. Ice damage is calculated as a function of sea ice deformation.

The neXtSIM model is initialized from the satellite derived sea ice concentration, thickness and sea ice damage and simulates sea ice thermodynamic and kinematic processes. Improvements of the model skills for 5 days forecasts are estimated by comparison with satellite observations. Preliminary results show that accounting for sea ice damage in the model initialization may significantly improve the skill of neXtSIM not only to predict ice damage, but also to improve prediction of sea ice drift and ice floe size distribution.