SAR Algorithm for Sea Ice Concentration—Evaluation for the Baltic Sea

Berg, Anders; Eriksson, Leif E. B.

doi:10.1109/lgrs.2012.2186280

Cited by 18 publications

(13 citation statements)

References 14 publications

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“…To the authors' knowledge, no method has so far been presented in literature addressing automatic LF retrievals from SAR. Existing sea ice classification methods (Berg and Eriksson, 2012;Karvonen, 2012Karvonen, , 2014Leigh et al, 2014;Liu et al, 2015) could potentially be adapted and tested for this purpose. However, the task of identifying such narrow elongated features as leads is different from sea ice classification.…”

Section: Introductionmentioning

confidence: 99%

Error assessment of satellite-derived lead fraction in the Arctic

Ivanova¹,

Rampal²,

Bouillon³

2016

The Cryosphere

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Abstract. Leads within consolidated sea ice control heat exchange between the ocean and the atmosphere during winter, thus constituting an important climate parameter. These narrow elongated features occur when sea ice is fracturing under the action of wind and currents, reducing the local mechanical strength of the ice cover, which in turn impact the sea ice drift pattern. This creates a high demand for a highquality lead fraction (LF) data set for sea ice model evaluation, initialization, and for the assimilation of such data in regional models. In this context, an available LF data set retrieved from satellite passive microwave observations (Advanced Microwave Scanning Radiometer -Earth Observing System, AMSR-E) is of great value, which has been providing pan-Arctic light-and cloud-independent daily coverage since 2002. In this study errors in this data set are quantified using accurate LF estimates retrieved from Synthetic Aperture Radar (SAR) images employing a threshold technique. A consistent overestimation of LF by a factor of 2-4 is found in the AMSR-E LF product. It is shown that a simple adjustment of the upper tie point used in the method to estimate the LF can reduce the pixel-wise error by a factor of 2 on average. Applying such an adjustment to the full data set may thus significantly increase the quality and value of the original data set.

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Section: Introductionmentioning

confidence: 99%

Error assessment of satellite-derived lead fraction in the Arctic

Ivanova¹,

Rampal²,

Bouillon³

2016

The Cryosphere

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“…High-resolution SIC estimates based on SAR backscatter and SAR texture measures were presented earlier (e.g., in [9][10][11][12][13][14][15][16][17]). However, these methods have different performances in different conditions.…”

Section: Discussionmentioning

confidence: 99%

“…So far, SAR imagery with fine spatial resolution, large spatial coverage, and the capability to acquire data through cloud cover, has not been widely used for the SIC estimation. Some studies on using single-band SAR for the SIC estimation have, however, been conducted [9][10][11][12][13][14][15][16][17]. Automated sea ice classification schemes-which implicitly include SIC or an open-water class-based on single-band SAR texture have been proposed [18][19][20][21][22][23][24].…”

Section: Sea Ice Concentrationmentioning

confidence: 99%

“…In low-to-moderate wind conditions, backscattering from open-water is typically strong at co-polarized channels (HH or VV), but low at cross-polarized channels, thereby allowing improvement in open water detection [29,30]. Better SIC estimation results can be achieved by using a combination of HH and HV (or VV and VH) channels, radar incidence angle information, and texture features (contextual information), such as auto-correlation (AC) [10,11,31]. Additionally, the co-polarization ratio (VV/HH) can be used to improve open-water detection and sea ice classification based on the SAR backscattering magnitude alone [32].…”

Section: Sea Ice Concentrationmentioning

confidence: 99%

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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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“…Several approaches have been proposed in the literature for classification of SAR sea ice imagery into ice and open water classes, and to estimate sea ice concentration from SAR imagery (Karvonen et al, 2005;Berg and Eriksson, 2012;Karvonen, 2012Karvonen, , 2014Leigh et al, 2014). Berg and Eriksson (2012) used image backscatter autocorrelation as input to a neural network to separate ice and open water in SAR imagery.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration

Scott¹,

Ashouri

Buehner

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TIn this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors.

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SAR Algorithm for Sea Ice Concentration—Evaluation for the Baltic Sea

Cited by 18 publications

References 14 publications

Error assessment of satellite-derived lead fraction in the Arctic

Error assessment of satellite-derived lead fraction in the Arctic

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

Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration

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