Sea Ice Monitoring by Synthetic Aperture Radar

Dierking, Wolfgang

doi:10.5670/oceanog.2013.33

Cited by 107 publications

(95 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is especially useful for observing the polar regions, where SAR data are widely used for exploring sea ice concentration, extent, detection of leads, polynyas, ice floes and ice edge, and ice type identification and classification (Johannessen et al, 2007;Dierking, 2013). Monitoring of sea ice processes, i.e., ice edge variations and motion, is important for practical tasks such as ice navigation and for scientific studies.…”

Section: Introductionmentioning

confidence: 99%

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Zakhvatkina

Korosov²,

Muckenhuber³

et al. 2017

The Cryosphere

View full text Add to dashboard Cite

Abstract. Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

show abstract

Section: Introductionmentioning

confidence: 99%

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Zakhvatkina

Korosov²,

Muckenhuber³

et al. 2017

The Cryosphere

View full text Add to dashboard Cite

show abstract

“…For more information regarding SAR polarization and the influence of different surface scattering see Dierking W. [23] and Jackson et al [24]. In order to ensure similar sea ice conditions and allow for an unbiased comparison between SAR and altimetry, only images with a time lag less than about 3.5 h, with respect to the altimetry crossings, are used.…”

Section: Imaging Synthetic Aperture Radar (Sar) Datamentioning

confidence: 99%

Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions

et al. 2017

View full text Add to dashboard Cite

Open water areas surrounded by sea ice significantly influence the ocean-ice-atmosphere interaction and contribute to Arctic climate change. Satellite altimetry can detect these ice openings and enables one to estimate sea surface heights and further altimetry data derived products. This study introduces an innovative, unsupervised classification approach for detecting open water areas in the Greenland Sea based on high-frequency data from Envisat and SARAL. Altimetry radar echoes, also called waveforms, are analyzed regarding different surface conditions. Six waveform features are defined to cluster radar echoes into different groups indicating open water and sea ice waveforms. Therefore, the partitional clustering algorithm K-medoids and the memory-based classification method K-nearest neighbor are employed, yielding an internal misclassification error of about 2%. A quantitative comparison with several SAR images reveals a consistency rate of 76.9% for SARAL and 70.7% for Envisat. These numbers strongly depend on the quality of the SAR images and the time lag between the measurements of both techniques. For a few examples, a consistency rate of more than 90% and a true water detection rate of 94% can be demonstrated. The innovative classification procedure can be used to detect water areas with different spatial extents and can be applied to all available pulse-limited altimetry datasets.

show abstract

“…Hence, SAR is in general very useful for operational mapping tasks on regional and local spatial scales (Dierking, 2013). The disadvantage of SAR systems is that higher spatial resolutions are linked with a limited coverage between 10 and 500 km, compared, for example, to more than 1000 km for passive microwave radiometers.…”

Section: Introductionmentioning

confidence: 99%

“…Operational services charged with providing sea ice maps and forecasting ice conditions for marine transportation and offshore operations need near-real-time regular information about local and regional ice thickness distributions. The use of sensors providing high spatial resolutions on the order of 100 m or better for ice thickness retrieval, such as synthetic aperture radar (SAR), is an important topic of recent research (Dierking, 2013).…”

Section: Introductionmentioning

confidence: 99%

Retrieval of the thickness of undeformed sea ice from simulated C-band compact polarimetric SAR images

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper we introduce a parameter for the retrieval of the thickness of undeformed first-year sea ice that is specifically adapted to compact polarimetric (CP) synthetic aperture radar (SAR) images. The parameter is denoted as the "CP ratio". In model simulations we investigated the sensitivity of the CP ratio to the dielectric constant, ice thickness, ice surface roughness, and radar incidence angle. From the results of the simulations we deduced optimal sea ice conditions and radar incidence angles for the ice thickness retrieval. C-band SAR data acquired over the Labrador Sea in circular transmit and linear receive (CTLR) mode were generated from RADARSAT-2 quad-polarization images. In comparison with results from helicopter-borne measurements, we tested different empirical equations for the retrieval of ice thickness. An exponential fit between the CP ratio and ice thickness provides the most reliable results. Based on a validation using other compact polarimetric SAR images from the same region, we found a root mean square (rms) error of 8 cm and a maximum correlation coefficient of 0.94 for the retrieval procedure when applying it to level ice between 0.1 and 0.8 m thick.

show abstract

Sea Ice Monitoring by Synthetic Aperture Radar

Cited by 107 publications

References 2 publications

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions

Retrieval of the thickness of undeformed sea ice from simulated C-band compact polarimetric SAR images

Contact Info

Product

Resources

About