scholarly journals Assessing polarimetric SAR sea-ice classifications using consecutive day images

2015 ◽  
Vol 56 (69) ◽  
pp. 285-294 ◽  
Author(s):  
M.-A.N. Moen ◽  
S.N. Anfinsen ◽  
A.P. Doulgeris ◽  
A.H.H. Renner ◽  
S. Gerland
Keyword(s):  
Sea Ice ◽  
Distance Measure ◽  
Expert Knowledge ◽  
Incidence Angle ◽  
Automatic Segmentation ◽  
Arctic Sea Ice ◽  
Distance Measures ◽  
In Situ Data ◽  
Arctic Sea ◽  
Class Labelling

AbstractThis paper investigates automatic segmentation and classification of C-band, polarimetric synthetic aperture radar (SAR) satellite images of Arctic sea ice under freezing conditions prior to melt. The objective is to investigate the robustness of the results obtained under slightly varying environmental conditions and different viewing geometries. Initially, three geographically overlapping SAR images from consecutive days are incidence-angle corrected and segmented into unknown classes. The segmentation is performed by an unsupervised mixture-of-Gaussian segmentation algorithm utilizing six features extracted from the polarimetric data. After segmentation, the segments are contextually smoothed. One segmented image is manually labelled based on in situ data and expert knowledge. Using this scene as reference, we consider two strategies for class labelling of the other scenes. The first manually labels the classes based on visual inspection of the reference; the second utilizes various statistical distance measures to automatically assign each unknown class to the statistically nearest reference class. These two scenes are also classified pixel-wise by a supervised classification algorithm based on the reference data. Poor classification results are obtained when the incidence angle is very different from the reference scene. Similar viewing geometries reveal good classification and labelling results, the latter regardless of the distance measure used.

scholarly journals The role of cyclone activity in snow accumulation on Arctic sea ice

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
M. A. Webster ◽  
C. Parker ◽  
L. Boisvert ◽  
R. Kwok
Keyword(s):  
Sea Ice ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Cyclone Activity ◽  
In Situ Data ◽  
Arctic Sea ◽  
Spatio Temporal

AbstractIdentifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming.

Incidence Angle Dependence of HH-Polarized C- and L-Band Wintertime Backscatter Over Arctic Sea Ice

2018 ◽  
Vol 56 (11) ◽  
pp. 6686-6698 ◽  
Author(s):  
Mallik S. Mahmud ◽  
Torsten Geldsetzer ◽  
Stephen E. L. Howell ◽  
John J. Yackel ◽  
Vishnu Nandan ◽  
...  
Keyword(s):  
Sea Ice ◽  
Incidence Angle ◽  
Arctic Sea Ice ◽  
Angle Dependence ◽  
Arctic Sea ◽  
L Band

scholarly journals Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles

2021 ◽  
Vol 14 (1) ◽  
pp. 91
Author(s):  
Meijie Liu ◽  
Ran Yan ◽  
Jie Zhang ◽  
Ying Xu ◽  
Ping Chen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Water ◽  
Incidence Angle ◽  
Arctic Sea Ice ◽  
Detection Methods ◽  
The Arctic ◽  
Support Vector ◽  
Remote Sensors ◽  
Arctic Sea ◽  
Small Incidence

Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

Eastern-Western Arctic Sea Ice Analysis: 1988

1988 ◽  
Author(s):  
NAVAL POLAR OCEANOGRAPHY CENTER WASHINGTON DC
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Western Arctic
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