scholarly journals Characterizing the Sea-Ice Floe Size Distribution in the Canada Basin from High-Resolution Optical Satellite Imagery

2021 ◽  
Author(s):  
Alexis Anne Denton ◽  
Mary-Louise Timmermans
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Size Distribution ◽  
Satellite Images ◽  
Threshold Value ◽  
Canada Basin ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Optical Satellite Images

Abstract. The sea-ice floe size distribution (FSD) characterizes the sea-ice response to atmosphere and ocean forcing and is important for understanding and modeling the evolving ice pack in a warming Arctic. FSDs are evaluated from 78 floe- segmented high-resolution (1-m) optical satellite images capturing a range of settings and sea-ice states during spring through fall from 1999 to 2014 in the Canada Basin. For any given image, the structure of the FSD is found to be sensitive to a classification threshold value (i.e., to specify an image pixel as being either water or ice) used in image segmentation, and an objective approach to minimize this sensitivity is presented. The FSDs are found to exhibit a single power-law regime between floe areas 50 m2 and 5 km2, characterized by exponents (slopes in log-log space) in the range −2.03 to −1.65. A distinct linear relationship between slopes and sea-ice concentrations is found, with steeper slopes (i.e., a larger proportion of smaller to larger floes) corresponding to lower sea-ice concentrations. Further, a seasonal variation in slopes is found for fixed sites in the Canada Basin that undergo a seasonal cycle in sea-ice concentration, while sites with extensive sea-ice cover year-round do not exhibit any seasonal change in FSD properties. Our results suggest that sea-ice concentration should be considered in any characterization of a time-varying FSD (for use in sea-ice models, for example).

scholarly journals Measuring sea-ice concentration and floe-size distribution by image processing

1993 ◽  
Vol 18 ◽  
pp. 33-38 ◽  
Author(s):  
Ken-Ichiro Muramoto ◽  
Kohki Matsuura ◽  
Tatsuo Endoh
Keyword(s):  
Image Processing ◽  
Sea Ice ◽  
Size Distribution ◽  
Video Camera ◽  
Sea Ice Concentration ◽  
Syowa Station ◽  
Video Recorder ◽  
Ice Concentration ◽  
On Line ◽  
Line Analysis

A technique for analyzing sea-ice concentration and floe-size distribution by means of image processing is proposed. The sea ice was photographed by video camera from the ship. The technique proposed for analyzing sea-ice images can be applied on line or off line. In on-line analysis, sea-ice images photographed by a video camera are digitized in real time. Ice concentration is calculated by summing ice pixels of each row of a digital image and ice shape can be obtained roughly by a composition of each row. In off-line analysis, sea-ice images recorded to a video recorder are processed. Both ice shape and ice concentration can be obtained accurately by analyzing predetermined square regions of an image. Although the off-line method requires more time to calculate, it is useful for detailed analysis of regional ice properties. Computations of ice concentration and floe-size distribution are performed using images obtained between Breid Bay and Syowa Station in 1988.

scholarly journals Arctic sea ice cover data from spaceborne SAR by deep learning

2020 ◽  
Author(s):  
Yi-Ran Wang ◽  
Xiao-Ming Li
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Sea Ice ◽  
Spatial Resolution ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Absolute Difference ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic changes in polar sea ice, high-resolution sea ice cover data are drawing increasing attention for polar navigation, environmental research, and offshore operations. In this paper, we focused on developing an approach for deriving a high-resolution sea ice cover product for the Arctic using Sentinel-1 (S1) dual-polarization (horizontal-horizontal, HH, and horizontal-vertical, HV) data in extra wide swath (EW) mode. The approach for discriminating sea ice from open water by synthetic aperture radar (SAR) data is based on a modified U-Net architecture, a deep learning network. By employing an integrated stacking model to combine multiple U-Net classifiers with diverse specializations, sea ice segmentation is achieved with superior accuracy over any individual classifier. We applied the proposed approach to over 28,000 S1 EW images acquired in 2019 to obtain sea ice cover products in a high spatial resolution of 400 m. By converting the S1-derived sea ice cover to concentration and then compared with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data, showing an average absolute difference of 5.55 % with seasonal fluctuations. A direct comparison with Interactive Multisensor Snow and Ice Mapping System (IMS) daily sea ice cover data achieves an average accuracy of 93.98 %. These results show that the developed S1-derived sea ice cover results are comparable to the AMSR and IMS data in terms of overall accuracy but superior to these data in presenting detailed sea ice cover information, particularly in the marginal ice zone (MIZ). Data are available at: https://doi.org/10.11922/sciencedb.00273 (Wang and Li, 2020).

scholarly journals Characterization of Moisture Sources for Austral Seas and Relationship with Sea Ice Concentration

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 627
Author(s):  
Michelle Simões Reboita ◽  
Raquel Nieto ◽  
Rosmeri P. da Rocha ◽  
Anita Drumond ◽  
Marta Vázquez ◽  
...  
Keyword(s):  
Sea Ice ◽  
Negative Temperature ◽  
Southern Annular Mode ◽  
Weddell Sea ◽  
Lagrangian Model ◽  
Sea Ice Concentration ◽  
Moisture Sources ◽  
Ice Concentration ◽  
Backward Analysis

In this study, the moisture sources acting over each sea (Weddell, King Haakon VII, East Antarctic, Amundsen-Bellingshausen, and Ross-Amundsen) of the Southern Ocean during 1980–2015 are identified with the FLEXPART Lagrangian model and by using two approaches: backward and forward analyses. Backward analysis provides the moisture sources (positive values of Evaporation minus Precipitation, E − P > 0), while forward analysis identifies the moisture sinks (E − P < 0). The most important moisture sources for the austral seas come from midlatitude storm tracks, reaching a maximum between austral winter and spring. The maximum in moisture sinks, in general, occurs in austral end-summer/autumn. There is a negative correlation (higher with 2-months lagged) between moisture sink and sea ice concentration (SIC), indicating that an increase in the moisture sink can be associated with the decrease in the SIC. This correlation is investigated by focusing on extremes (high and low) of the moisture sink over the Weddell Sea. Periods of high (low) moisture sinks show changes in the atmospheric circulation with a consequent positive (negative) temperature anomaly contributing to decreasing (increasing) the SIC over the Weddell Sea. This study also suggests possible relationships between the positive (negative) phase of the Southern Annular Mode with the increase (decrease) in the moisture that travels from the midlatitude sources to the Weddell Sea.

Sign in / Sign up

Export Citation Format

Share Document