A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data

Korosov, Anton; Rampal, Pierre

doi:10.3390/rs9030258

Cited by 50 publications

(45 citation statements)

References 30 publications

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“…This important region of the world ocean will not be well monitored in terms of currents by existing and planned satellite missions. Ice concentration is the 5 only parameter that is well monitored near the ice edge, with difficulties in recovering ice thickness and ice drift (Korosov and Rampal, 2017). The very rich dynamics across the ice edge offer great opportunities for Doppler-based measurements.…”

Section: Challenges and Opportunities Over Sea Icementioning

confidence: 99%

Measuring currents, ice drift, and waves from space: the Sea Surface KInematics Multiscale monitoring (SKIM) concept

Ardhuin¹,

Aksenov²,

Benetazzo³

et al. 2017

Preprint

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Abstract. We propose a new satellite mission that uses a near-nadir Ka-band Doppler radar to measure surface currents, ice drift and ocean waves at spatial scales of 40 km and more, with snapshots at least every day for latitudes 75 to 82, and every few days otherwise. The use of incidence angles at 6 and 12 degrees allows a measurement of the directional wave spectrum which yields accurate corrections of the wave-induced bias in the current measurements. The instrument principle, algorithm for current velocity and mission performance are presented here. The proposed instrument can reveal features on tropical 5 ocean and marginal ice zone dynamics that are inaccessible to other measurement systems, as well as a global monitoring of the ocean mesoscale that surpasses the capability of today's nadir altimeters. Measuring ocean wave properties facilitates many applications, from wave-current interactions and air-sea fluxes to the transport and convergence of marine plastic debris and assessment of marine and coastal hazards. 1Ocean Sci. Discuss., https://doi

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Section: Challenges and Opportunities Over Sea Icementioning

confidence: 99%

Measuring currents, ice drift, and waves from space: the Sea Surface KInematics Multiscale monitoring (SKIM) concept

Ardhuin¹,

Aksenov²,

Benetazzo³

et al. 2017

Preprint

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“…We also plan to test the benefits of assimilation of SAR-derived ice drift (Korosov and Rampal, 2017) on the system.…”

Section: Discussionmentioning

confidence: 99%

Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Williams¹,

Korosov²,

Rampal³

et al. 2019

Preprint

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The neXtSIM-F forecast system consists of a stand-alone sea ice model, neXtSIM, forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation. It was tested for the northern winter of 2018 -2019 with different data being assimilated and was found to perform well. Despite drift not being assimilated in our system, we obtain quite good agreement between observations, comparing well to more sophisticated coupled ice-ocean forecast systems. The RMSE in drift speed is around 3 km/day for the first three days, climbing to about 4 km/day for the 5 next day or two; computing the RMSE in the total drift adds about 1 km/day to the error in speed. The drift bias remains close to zero over the whole period from Nov 2018 -Apr 2019. The neXtSIM-F forecast system assimilates OSISAF sea ice concentration products (both SSMI and AMSR2) and SMOS sea ice thickness by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. This greatly improved the agreement of these quantities with observations for the first 3 -4 days of the forecast.(Svalbard) after getting into trouble with sea ice. The crew had to be rescued by the Norwegian Coast Guard icebreaker K.V.Svalbard, who then had to drain 300kL of diesel from the damaged vessel. 2 Thus sea ice forecasting is becoming increasingly important. As well as search and rescue/accident prevention, other applications are optimized ship (icebreaker) routing based on forecasts (Kaleschke et al., 2016) and support of research activities -e.g. Schweiger and Zhang (2015) give an example of scheduling of high-resolution SAR images in order to follow the drift 5 of some ice-mass balance (IMB) buoys by using the PIOMAS/MIZMAS forecast from the University of Washington. The planned year-long drift of the Polarstern from September 2019 (part of the MOSAIC project) will also rely heavily on sea ice and weather forecasts.The sea ice forecast system neXtSIM-F is based on a stand-alone version of the sea ice model neXtSIM (Rampal et al., 2016. The dynamical core of this model is based on the Maxwell-Elasto-Brittle (MEB) rheology as developed for sea ice and 10 originally presented in Dansereau et al. (2016), which showed its capabilities at reproducing the main spatial characteristics of sea ice mechanics and deformation: strain localization and scaling (Marsan et al., 2004;Rampal et al., 2008; Stern and Lindsay, 2009). With the implementation of this rheology in neXtSIM (along with accompanying thermodynamics and general model infrastructure: Rampal et al., 2019), MEB was able to be assessed over longer simulations, and it was found that it could also reproduce the observed temporal deformation scalings, in addition to the spatial ones. In particular, the results show strong 15 multifractality, meaning that higher deformations are more localised in space and more intermittent in time than smaller ones.These properties have strong implications for things like distribution and size of lead op...

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“…The landmask has a resolution of 250 m. The absolute location accuracy of the medium-resolution EWS product is not specified in the User's Manual. For the IWS mode it is 7 m. Korosov and Rampal [20] found that the accuracy of georeferencing was about 5 pixels (i.e., 200 m) for the EWS medium resolution product.…”

Section: Datamentioning

confidence: 99%

Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images

Soldal

Dierking

Korosov³

et al. 2019

Remote Sensing

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Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8-22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency-polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization.

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A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data

Cited by 50 publications

References 30 publications

Measuring currents, ice drift, and waves from space: the Sea Surface KInematics Multiscale monitoring (SKIM) concept

Measuring currents, ice drift, and waves from space: the Sea Surface KInematics Multiscale monitoring (SKIM) concept

Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images

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