A System for Automated Vision-Based Sea-Ice Concentration Detection and Floe-Size Distribution Indication From an Icebreaker

2017 ◽  
Author(s):  
Hans-Martin Heyn ◽  
Martin Knoche ◽  
Qin Zhang ◽  
Roger Skjetne
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Size Distribution ◽  
Panoramic Image ◽  
Edge Detector ◽  
Image System ◽  
Camera System ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Ice Conditions

This paper presents a ship-mounted multi-lens camera system for sea-ice monitoring and algorithms to automatically evaluate the sea-ice concentration and to indicate the floe-sizes in a radius of 100 meter around the vessel. During the SWEDARCTIC Arctic Ocean 2016 expedition, 11 camera lenses recorded the sea-ice conditions around the Swedish icebreaker Oden. As an example of the possible use of this image system, the images of six lenses are combined into one 360° panoramic image. To distinguish between water and sea-ice in the images, and thus to evaluate the sea-ice concentration around the vessel, a direct thresholding, the k-means, and a novel adaptive thresholding method are applied. Moreover, an edge detector gives the number of pixels that either form the boundary between sea-ice and water or are part of a visible ice fracture. The ratio between these edge pixels and the total number of pixels containing sea-ice gives an indication of the floe size distribution (FSD) in the image.

scholarly journals Measuring sea ice concentration in the Arctic Ocean using SMOS

2016 ◽  
Author(s):  
Carolina Gabarro ◽  
Antonio Turiel ◽  
Pedro Elosegui ◽  
Joaquim A. Pla-Resina ◽  
Marcos Portabella
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Radiative Properties ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Wide Range ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
Ice Conditions ◽  
Microwave Imager

Abstract. We present a new method to estimate sea ice concentration in the Arctic Ocean using brightness temperature observations from the Soil Moisture Ocean Salinity (SMOS) interferometric satellite. The method, which employs a Maximum Likelihood Estimator (MLE), exploits the marked difference in radiative properties between sea ice and seawater, in particular when observed over the wide range of satellite viewing angles afforded by SMOS. Observations at L-band frequencies such as those from SMOS (i.e., 1.4 GHz, or equivalently 21-cm wavelength) are advantageous to remote sensing of sea ice because the atmosphere is virtually transparent at that frequency. We find that sea ice concentration is well determined (correlations of about 0.75) as compared to estimates from other sensors such as the Special Sensor Microwave/Imager (SSM/I and SSMIS). We also find that the efficacy of the method decreases under thin sea ice conditions (ice thickness

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

scholarly journals Cryospheric Impacts of Soviet River Diversion Schemes

1984 ◽  
Vol 5 ◽  
pp. 61-68 ◽  
Author(s):  
T. Holt ◽  
P. M. Kelly ◽  
B. S. G. Cherry
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
River Discharge ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
The Impact

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.

scholarly journals Evolution of the Amundsen Sea Polynya, Antarctica, 2016–2021

2021 ◽  
Author(s):  
Grant J. Macdonald ◽  
Stephen F. Ackley ◽  
Alberto M. Mestas-Nuñez
Keyword(s):  
Sea Ice ◽  
Reanalysis Data ◽  
Sea Ice Concentration ◽  
Amundsen Sea ◽  
Annual Variations ◽  
Ice Concentration ◽  
Ice Conditions ◽  
Sar Imagery ◽  
Key Sites ◽  
Ice Production

Abstract. Polynyas are key sites of ice production during the winter and are important sites of biological activity and carbon sequestration during the summer. The Amundsen Sea Polynya (ASP) is the fourth largest Antarctic polynya, has recorded the highest primary productivity and lies in an embayment of key oceanographic significance. However, knowledge of its dynamics, and of sub-annual variations in its area and ice production, is limited. In this study we primarily utilize Sentinel-1 SAR imagery, sea ice concentration products and climate reanalysis data, along with bathymetric data, to analyze the ASP over the period November 2016–March 2021. Specifically, we analyze (i) qualitative changes in the ASP's characteristics and dynamics, and quantitative changes in (ii) summer polynya area, (iii) winter polynya area and ice production. From our analysis of SAR imagery we find that ice produced by the ASP becomes stuck in the vicinity of the polynya and sometimes flows back into the polynya, contributing to its closure and limiting further ice production. The polynya forms westward off a persistent chain of grounded icebergs that are located at the site of a bathymetric high. Grounded icebergs also influence the outflow of ice and facilitate the formation of a 'secondary polynya' at times. Additionally, unlike some polynyas, ice produced by the polynya flows westward after formation, along the coast and into the neighboring sea sector. During the summer and early winter, broader regional sea ice conditions can play an important role in the polynya. The polynya opens in all summers, but record-low sea ice conditions in 2016/17 cause it to become part of the open ocean. During the winter, an average of 78 % of ice production occurs in April–May and September–October, but large polynya events often associated with high winds can cause ice production throughout the winter. While passive microwave data or daily sea ice concentration products remain key for analyzing variations in polynya area and ice production, we find that the ability to directly observe and qualitatively analyze the polynya at a high temporal and spatial resolution with Sentinel-1 imagery provides important insights about the behavior of the polynya that are not possible with those datasets.

scholarly journals Impact of the Assimilation of Sea Ice Concentration Data on an Atmosphere-Ocean-Sea Ice Coupled Simulation of the Arctic Ocean Climate

SOLA ◽  
2011 ◽  
Vol 7 ◽  
pp. 37-40 ◽  
Author(s):  
Takahiro Toyoda ◽  
Toshiyuki Awaji ◽  
Nozomi Sugiura ◽  
Shuhei Masuda ◽  
Hiromichi Igarashi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
The Arctic ◽  
Coupled Simulation ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ocean Climate ◽  
Ice Concentration ◽  
The Arctic Ocean

Retrieval of Arctic sea ice freeboard from passive microwave data using deep neural network

2020 ◽  
Author(s):  
Junhwa Chi ◽  
Hyun-Cheol Kim ◽  
Sung Jae Lee
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Arctic Sea Ice ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Sea Ice Concentration ◽  
Ice Volume ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Microwave Sensors

<p>Changes in Arctic sea ice cover represent one of the most visible indicators of climate change. While changes in sea ice extent affect the albedo, changes in sea ice volume explain changes in the heat budget and the exchange of fresh water between ice and the ocean. Global climate simulations predict that Arctic sea ice will exhibit a more significant change in volume than extent. Satellite observations show a long-term negative trend in Arctic sea ice  during all seasons, particularly in summer. Sea ice volume has been estimated by ICESat and CryoSat-2 satellites, and then NASA’s second-generation spaceborne lidar mission, ICESat-2 has successfully been launched in 2018.  Although these sensors can measure sea ice freeboard precisely, long revisit cycles and narrow swaths are problematic for monitoring of the freeboard in the entire of Arctic ocean effectively. Passive microwave sensors are widely used in retrieval of sea ice concentration. Because of the capability of high temporal resolution and wider swaths, these sensors enable to produce daily sea ice concentration maps over the entire Arctic ocean. Brightness temperatures from passive microwave sensors are often used to estimate sea ice freeboard for first-year ice, but it is difficult to associate with physical characteristics related to sea ice height of multi-year ice. In machine learning community, deep learning has gained attention and notable success in addressing more complicated decision making using multiple hidden layers. In this study, we propose a deep learning based Arctic sea ice freeboard retrieval algorithm incorporating the brightness temperature data from the AMSR2 passive microwave data and sea ice freeboard data from the ICESat-2. The proposed retrieval algorithm enables to estimate daily freeboard for both first- and multi-year ice over the entire Arctic ocean. The estimated freeboard values from the AMSR2 are then quantitatively and qualitatively compared with other sea ice freeboard or thickness products.  </p>

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 The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation

2020 ◽  
Author(s):  
Clara Burgard ◽  
Dirk Notz ◽  
Leif T. Pedersen ◽  
Rasmus T. Tonboe
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Climate Model ◽  
System Model ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Brightness Temperatures ◽  
Climate Model Evaluation ◽  
Observational Uncertainty ◽  
Ice Concentration

Abstract. The observational uncertainty in sea-ice-concentration estimates from remotely-sensed passive-microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarisation from standard output of an Earth System Model. We evaluate ARC3O by simulating brightness temperatures based on three assimilation runs of the MPI Earth System Model (MPI-ESM) assimilated with three different sea-ice concentration products. We then compare these three sets of simulated brightness temperatures to brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that they differ up to 10 K in the period between October and June, depending on the region and the assimilation run. However, we show that these discrepancies between simulated and observed brightness temperature can be mainly attributed to the underlying observational uncertainty in sea-ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea-ice concentration but also by the melt-pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable to realistically translate the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization.

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