Ice thickness estimation using SAR data and ice thickness history

Karvonen, Juha; Similä, M.; Heiler, I.

doi:10.1109/igarss.2003.1293683

Cited by 24 publications

(26 citation statements)

References 3 publications

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“…In this final phase of the ice thickness estimation, the segment-scale remapped HIGHTSI ice thickness is used as an input, and it is refined by using the pixel-wise SAR features to get the final local ice thickness estimates. The basic assumption here is, like for the Baltic Sea ice thickness charts (Karvonen et al, 2003), that the local SAR backscattering and its texture are functions of the ice thickness. In general, it is assumed here that the backscattering and the texture at a local scale are stronger for older and thicker ice, which has gone through more deformation events, conditioned by the background ice thickness based on the remapped feature and HIGHTSI distributions.…”

Section: Sar-and Model-based Ice Thicknessmentioning

confidence: 99%

A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

et al. 2012

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Abstract. An analysis of ice thickness distribution is a challenge, particularly in a seasonal sea ice zone with a strongly dynamic ice motion field, such as the Gulf of St. Lawrence off Canada. We present a novel automated method for ice concentration and thickness analysis combining modeling of sea ice thermodynamics and detection of ice motion on the basis of space-borne Synthetic Aperture Radar (SAR) data. Thermodynamic evolution of sea ice thickness in the Gulf of St. Lawrence was simulated for two winters, 2002-2003 and 2008-2009. The basin-scale ice thickness was controlled by atmospheric forcing, but the spatial distribution of ice thickness and concentration could not be explained by thermodynamics only. SAR data were applied to detect ice motion and ice surface structure during these two winters. The SAR analysis is based on estimation of ice motion between SAR image pairs and analysis of the local SAR texture statistics. Including SAR data analysis brought a significant added value to the results based on thermodynamics only. Our novel method combining the thermodynamic modeling and SAR yielded results that well match with the distribution of observations based on airborne Electromagnetic Induction (EM) method. Compared to the present operational method of producing ice charts for the Gulf of St. Lawrence, which is based on visual interpretation of SAR data, the new method reveals much more detailed and physically based information on spatial distribution of ice thickness. The algorithms can be run automatically, and the final products can then be used by ice analysts for operational ice service. The method is globally applicable to all seas where SAR data are available.

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Section: Sar-and Model-based Ice Thicknessmentioning

confidence: 99%

A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

et al. 2012

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“…For the Baltic Sea, an operational level-ice thickness chart is provided daily. It combines the SIT information from the Finnish Ice Service (FIS) ice chart (mainly manual interpretation) and C-band SAR imagery [51,52]. The SAR images are used to spatially produce a more accurate level-ice thickness chart (around 500 m resolution) than that given by the FIS ice chart (resolution ≈10 km).…”

Section: Sea Ice Thicknessmentioning

confidence: 99%

Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data

et al. 2017

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Abstract:We estimate two essential sea ice parameters-namely, sea ice concentration (SIC) and sea ice thickness (SIT)-for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometer. We compare the SIC and SIT estimation results with in-situ measurements conducted in the study area and estimates based on independent EO data from near-infrared/optical instruments. These comparisons suggest that the SAR-based discrimination between sea ice and open-water works well, and areas of thinner and thicker ice can be distinguished. A larger comprehensive training dataset is needed to set up an operational algorithm for the estimation of SIC and SIT.

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“…A detailed description can be found at the CIS website [18]. Digitized ice charts have also been used as input to the SAR classification in [19].…”

Section: A Sea-ice Types and Chartmentioning

confidence: 99%

SAR Sea-Ice Image Analysis Based on Iterative Region Growing Using Semantics

Clausi

2007

IEEE Trans. Geosci. Remote Sensing

100

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Abstract-Synthetic aperture radar (SAR) has been intensively used for sea-ice monitoring in polar regions. A computer-assisted analysis of SAR sea-ice imagery is extremely difficult due to numerous imaging parameters and environmental factors. This paper presents a system which, with some limited information provided, is able to perform an automated segmentation and classification for the SAR sea-ice imagery. In the system, both the segmentation and classification processes are based on a Markov random-field theory and are formulated in a joint manner under the Bayesian framework. Solutions to the formulation are obtained by a region-growing technique which keeps refining the segmentation and producing semantic class labels at the same time in an iterative manner. The algorithm is a general-segmentation approach named iterative region growing using semantics, which, in this paper, is dedicated to the problem of classifying the operational SAR sea-ice imagery provided by the Canadian Ice Service (CIS). The classified image results have been validated by the CIS personnel, and the resulting classifications are quite successful using the same algorithm applied to diverse data sets.Index Terms-Expert system, image segmentation, Markov random field (MRF), region growing, sea ice, synthetic aperture radar (SAR).

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Ice thickness estimation using SAR data and ice thickness history

Cited by 24 publications

References 3 publications

A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data

SAR Sea-Ice Image Analysis Based on Iterative Region Growing Using Semantics

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