Barents Sea seasonal ice zone features and processes from ERS 1 synthetic aperture radar: Seasonal Ice Zone Experiment 1992

Sandven, Stein; Johannessen, Ola M.; Miles, Martin W.; Pettersson, Lasse H.; Kloster, K.

doi:10.1029/1998jc900050

Cited by 39 publications

(16 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, since the variation in backscatter values within one ice type is too large or, in other words, the ranges of radar return intensities for different ice types overlap, one cannot base an analysis solely on the backscatter or higher moments of the backscatter [26]. Findings in the literature [27], therefore, suggest the use of additional image features for SAR-based ice-type classification, which take into account higher order textural features.…”

Section: Introductionmentioning

confidence: 95%

A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images

Ressel

Frost

Lehner

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

106

View full text Add to dashboard Cite

We examine the performance of an automated sea ice classification algorithm based on TerraSAR-X ScanSAR data. In the first step of our process chain, gray-level co-occurrence matrix(GLCM)-based texture features are extracted from the image. In the second step, these data are fed into an artificial neural network to classify each pixel. Performance of our implementation is examined by utilizing a time series of ScanSAR images in the Western Barents Sea, acquired in spring 2013. The network is trained on the initial image of the time series and then applied to subsequent images. We obtain a reasonable classification accuracy of at least 70% depending on the choice of our ice-type regime, when the incidence angle range of the training data matches that of the classified image. Computational cost of our approach is sufficiently moderate to consider this classification procedure a promising step toward operational, near-realtime ice charting.Index Terms-Earth and atmospheric sciences, pattern analysis, remote sensing, texture.

show abstract

Section: Introductionmentioning

confidence: 95%

A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images

Ressel

Frost

Lehner

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

106

View full text Add to dashboard Cite

show abstract

“…Satellite and airborne SAR systems are used for a variety of tasks regarding retrieval of information about the state of the sea ice cover (e.g., Sandven et al, 1999). Examples are monitoring of ice drift and deformation, ice type classifica- Occasionally, it is difficult to achieve the optimal balance between the spatial res- The typical ice type classification is mainly based on ice thickness and macroscopic properties that are easy to identify visually (e.g., from the bridge of a ship).…”

Section: Retrieval Of Geophysical Information About Sea Ice Conditionmentioning

confidence: 99%

Sea Ice Monitoring by Synthetic Aperture Radar

Dierking¹

2013

oceanog

104

View full text Add to dashboard Cite

“…Note that the Svalbard sea ice barrier formed over the deep channel north of the Great Bank. In contrast, Sandven et al 's [1999] results from observations in March 1992 along ship tracks, crossing over an area in the Svalbard Bank region, showed that sea ice formed over shallow water locations but not over deep water. This contrast in the formation pattern of sea ice can be observed by QSCAT over an extensive sea surface in this region.…”

Section: Observation Of the Svalbard Sea Ice Barriermentioning

confidence: 80%

“…Thus bathymetry, through its control of the water distribution, and ice‐ocean‐atmosphere interactions can significantly impact ice formation at the sea surface. Indeed, Sandven et al [1999] observed that sea ice formation was related to bathymetric features through their influence on Arctic and Atlantic water masses.…”

Section: Introductionmentioning

confidence: 99%

Rapid formation of a sea ice barrier east of Svalbard

Nghiem¹,

Woert²,

Neumann³

2005

J. Geophys. Res.

View full text Add to dashboard Cite

[1] Daily SeaWinds scatterometer images acquired by the QuikSCAT satellite show an elongated sea ice feature that formed very rapidly ($1-2 days) in November 2001 east of Svalbard over the Barents Sea. This sea ice structure, called ''the Svalbard sea ice barrier,'' spanning approximately 10°in longitude and 2°in latitude, restricts the sea route and poses a significant navigation hazard. The secret of its formation appears to lie in the bottom of the sea: A comparison between bathymetry from the International Bathymetric Chart of the Arctic Ocean data and the pattern of sea ice formation from scatterometer data reveals that the sea ice barrier conforms well with and stretches above a deep elongated channel connecting the Franz Josef-Victoria Trough to the Hinlopen Basin between Svalbard and Franz Josef Land. Historic hydrographic data from this area indicate that this sea channel contains cold Arctic water less than 50 m below the surface. Strong and persistent cold northerly winds force strong heat loss from this shallow surface layer, leading to the rapid formation of the sea ice barrier. Heat transfer rates estimated from European Centre for Medium-Range Weather Forecasts temperature and wind data over this region suggest that the surface water along the deep channel can be rapidly cooled to the freezing point. Scatterometer results in [1999][2000][2001][2002][2003] show that sea ice forms in this area between October and December. Understanding the ice formation mechanisms helps to select appropriate locations for deployment of buoys measuring wind and air-sea temperature profile and to facilitate ice monitoring, modeling, and forecasting.

show abstract

Barents Sea seasonal ice zone features and processes from ERS 1 synthetic aperture radar: Seasonal Ice Zone Experiment 1992

Abstract: Abstract. Sea ice features and processes in the Barents

Cited by 39 publications

References 25 publications

A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images

A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images

Sea Ice Monitoring by Synthetic Aperture Radar

Rapid formation of a sea ice barrier east of Svalbard

Contact Info

Product

Resources

About