• Journal of Ocean Engineering and Technology
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• v.25 no.2
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• pp.55-61
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• 2011

A field trial in an ice-covered sea is one of the most important tasks in the design of icebreaking ships and offshore structures. To correctly estimate the ice load and ice resistance of a ship's hull, it is essential to understand the material properties of sea ice during ice field trials and to use the proper experimental procedure for gathering effective ice data. The first Korean-made icebreaking research vessel, "ARAON," had her second sea ice trial in the Arctic Ocean during the summer season of 2010. This paper describes the test procedures used to obtain proper sea ice data, which provides the basic information for the ship's performance in an ice-covered sea and is used to estimate the correct ice load and ice resistance of the IBRV ARAON. The data gathered from the sea ice in the Chukchi Sea and Beaufort Sea during the Arctic voyage of the ARAON includes the temperature, density, and salinity of the sea ice, which was believed to be from two-year old ice floes. This paper analyses the gathered sea ice data in comparison with data from the first voyage of the ARAON during her Antarctic Sea ice trial.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.543-553
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• 2011

Sea ice concentration calculated from the AMSR-E onboard Aqua satellite by using NASA Team2 sea ice algorithm has proven to be very accurate over sea ice in Antarctic Ocean. When glacial ice such as icebergs and ice shelves are dominant in an AMSR-E footprint, the accuracy of the ice concentration calculated from NASA Team2 algorithm is not well maintained due to the different microwave characteristics of the glacial ice from sea ice. We extracted the concentrations of sea ice and glacial ice from two ENVISAT ASAR images of George V coast in southern Antarctica, and compared them with NASA Team2 sea ice concentration. The result showed that the NASA Team2 algorithm underestimates the concentration of glacial ice. To interpret the large deviation of estimation over glacial ice, we analyzed the characteristics of microwave radiation of the glacial ice in PR(polarization ratio), GR(spectral gradient ratio), $PR_R$(rotated PR), and ${\Delta}GR$ domain. We found that glacial ice occupies a unique region in the PR, GR, $PR_R$, and ${\Delta}GR$ domain different from other types of ice such as ice type A, B, and C, and open water. This implies that glacial ice can be added as a new category of ice to the AMSR-E NASA Team2 sea ice algorithm.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.535-542
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• 2011

We extracted a surface displacement map of Canisteo Peninsula and the surrounding area in West Antarctica by applying 4-pass DInSAR technique to two ERS-1/2 tandem pairs and analyzed the surface displacement of glaciers and sea ice. In the displacement map, glaciers showed fast motion pushing the adjoining land-fast sea ice which has the displacement in the same direction as the glacier. Cosgrove ice shelf showed large displacement pushing the adjoining land-fast sea ice as well. Some sea ice indicated the displacement that is opposite to the land-fast sea ice. This was because the type of the sea ice is drift ice that is affected by ocean current. Therefore, we could confirmed the boundary between land-fast sea ice and drift ice. It was difficult to distinguish ice shelf from ice sheet because they showed similarities both in brightness of the SAR images and in fringe rates of the interferograms. However, a boundary between fast-moving ice shelf and stable ice sheet was easily confirmed in the displacement map after the phase unwrapping process.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.88-90
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• 2013

최근 지구온난화의 영향으로 하절기 북극해의 얼음이 예상보다 빨리 녹고 있어 북극해항로의 상용화가 더욱 가속화될 전망이다. 지난해 여름 북극해빙의 크기는 1979년 인공위성관측 이래 최소치를 기록하였으며 또한 다년생 얼음구성비율도 낮아져 대부분이 1년생 얼음으로 대체됨으로써 선박의 운항기간도 점차 늘어나게 되었다.. 이러한 해빙의 가속화가 지속된다면 2030년경에는 북극해의 얼음이 완전히 녹을 것으로 예측되고 있다. 한편 러시아의 NSR 개방이후 비러시아 선박으로서는 2009년 독일 벨루가 선사 소속의 화물선 두 척이 NSR을 통과한 이래 지난 해에는 46척, 금년에는 그 수가 더욱 급격히 늘어나고 있어 앞으로 한.중.일의 NSR 선점경쟁이 더욱 치열해질 것으로 예상된다.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.507-520
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• 2007

Satellite passive microwave(PM) sensors have been observing polar sea ice concentration(SIC), ice temperature, and snow depth since 1970s. Among them SIC is playing an important role in the various studies as it is considered the first factor for the monitoring of global climate and environment changes. Verification and correction of PM SIC is essential for this purpose. In this study, we calculated SIC from KOMPSAT-1 EOC images obtained from Arctic sea ice edges from July to August 2005 and compared with SSM/I SIC calculated from NASA Team(NT) algorithm. When we have no consideration of sea ice types, EOC and SSM/I NT SIC showed low correlation coefficient of 0.574. This is because there are differences in spatial resolution and observing time between two sensors, and the temporal and spatial variation of sea ice was high in summer Arctic ice edge. For the verification of SSM/I NT SIC according to sea ice types, we divided sea ice into land-fast ice, pack ice, and drift ice from EOC images, and compared them with SSM/I NT SIC corresponding to each ice type. The concentration of land-fast ice between EOC and SSM/I SIC were calculated very similarly to each other with the mean difference of 0.38%. This is because the temporal and spatial variation of land-fast ice is small, and the snow condition on the ice surface is relatively dry. In case of pack ice, there were lots of ice ridge and new ice that are known to be underestimated by NT algorithm. SSM/I NT SIC were lower than EOC SIC by 19.63% in average. In drift ice, SSM/I NT SIC showed 20.17% higher than EOC SIC in average. The sea ice with high concentration could be included inside the wide IFOV of SSM/I because the drift ice was located near the edge of pack ice. It is also suggested that SSM/I NT SIC overestimated the drift ice covered by wet snow.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1245-1255
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• 2022

Terra Nova Bay polynya (TNBP) is a representative coastal polynya in East Antarctica, which is formed by strong katabatic winds. As the TNBP is one of the major sea ice factory in East Antarctica and has a great impact on regional ocean circulation and surrounding marine ecosystem, it is very important to analyze its area change and development characteristics. In this study, we detected the TNBP from synthetic aperture radar (SAR) and optical images obtained from April 2007 to April 2022 by visually analyzing the stripes caused by the Langmuir circulation effect and the boundary between the polynya and surrounding sea ice. Then, we analyzed the area change and development characteristics of the TNBP. The TNBP occurred frequently but in a small size during the Antarctic winter (April-July) when strong katabatic winds blow, whereas it developed in a large size in March and November when sea ice thickness is thin. The 12-hour mean wind speed before the satellite observations showed a correlation coefficient of 0.577 with the TNBP area. This represents that wind has a significant effect on the formation of TNBP, and that other environmental factors might also affect its development process. The direction of TNBP expansion was predominantly determined by the wind direction and was partially influenced by the local ocean current. The results of this study suggest that the influences of environmental factors related to wind, sea ice, ocean, and atmosphere should be analyzed in combination to identify the development characteristics of TNBP.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1193-1202
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• 2018

It is important to quantitatively analyze the energy budget for understanding of long-term climate change in Arctic. High-quality and long-term radiative parameters are needed to understand the energy budget. Since most of radiative flux components based on satellite are provide for a short period, several data must be used together. It is important to acquaint differences between data to link for conjunction with several data. In this study, we investigated the comparative analysis of Arctic radiative flux product such as CERES and GEWEX to provide basic information for data linkage and analysis of changes in Arctic climate. As a result, GEWEX was underestimated the radiative variables, and it difference between the two data was about $3{\sim}25W/m^2$. In addition, the difference in high-latitude and sea ice regions have increased. In case of comparing with monthly means, the other variables except for longwave downward flux represent high difference of $9.26{\sim}26.71W/m^2$ in spring-summer season. The results of this study can be used standard data for blending and selecting GEWEX and CERES radiative flux data due to recognition of characteristics according to ice-ocean area, season, and regions.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1261-1272
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• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

• Korean Journal of Soil Science and Fertilizer
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• v.28 no.2
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• pp.135-144
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• 1995

Understanding on nutrient solute movement during the course of freezing and thawing was attempted through laboratory and field obsevations. Small sectioned tubes with 5cm inner diameter, 0.2cm thick and 1cm long were connected to 30cm long soil columns for laboratory study. The columns were filled with soil, and treated with 20mmol/kg $KNO_3$ for upper 5cm. The upper end was set in the freezing section, and the lower end was set in the refrigerating section of a refrigerator. Temperature was controlled at $-7({\pm}1)^{\circ}C$ and $1.5({\pm}1)^{\circ}C$, respectively. After top 5cm soil was frozen, the columns were sectioned, and analyzed for $NO_3^-$, $NH_4^+$ and $K^+$. For field study, the 20cm inner diameter and lm long soil columns were installed in Chuncheon and Daegwanryung, where the altitude was 74m and 840m, respectively. The soils used were silt loam and clay loam. The top 20cm soils were treated with 50mmol/kg as $KNO_3$. The soil columns were taken during winter freezing and after thawing. By laboratiry study, upward movement of $NO_3^-$ and $K^+$ during the course of freezing was confirmed. The upward movement of $K^+$ was, however, one fifth to one tenth of $NO_3^-$. The upward movement of inorganic nitrogen as well as laboratory during the course of freezing, but large amount of nitrogen was lost from the profile after thawing in early spring. Leached nitrogen from the upper 20cm to lower part was 17 to 24 percents. The maximum depth of leaching during the experiment was 50cm for all soils. The net loss of inorganic nitrogen from the whole profile ranged 8.7 to 39.5 percents. The net loss was greater in Daegwanryung where temperature was lower and snowfall was larger than Chuncheon, and the loss was greater from the silt loam soil than clay loam soil of which percolation rate was small. The results implied that reasons for nitrogen loss during the winter might include surface washing by snow melt as well as leaching and denitrification.