scholarly journals An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic

2021 ◽  
Vol 13 (18) ◽  
pp. 3783
Author(s):  
Valeria Selyuzhenok ◽  
Denis Demchev
Keyword(s):  
Sea Ice ◽  
Tracking Algorithm ◽  
The Arctic ◽  
Data Sets ◽  
Envisat Asar ◽  
Drift Field ◽  
Automated Method ◽  
Fast Ice ◽  
Ice Edge ◽  
Sar Imagery

For regional environmental studies it is important to know the location of the fast ice edge which affects the coastal processes in the Arctic. The aim of this study is to develop a new automated method for fast ice delineation from SAR imagery. The method is based on a fine resolution hybrid sea ice tracking algorithm utilizing advantages of feature tracking and cross-correlation approaches. The developed method consists of three main steps: drift field retrieval at sub-kilometer scale, selection of motionless features and edge delineation. The method was tested on a time series of C-band co-polarized (HH) ENVISAT ASAR and Sentinel-1 imagery in the Laptev and East Siberian Seas. The comparison of the retrieved edges with the operational ice charts produced by the Arctic and Antarctic Research Institute (Russia) showed a good agreement between the data sets with a mean distance between the edges of <15 km. Thanks to the high density of the ice drift product, the method allows for detailed fast ice edge delineation. In addition, large stamukhas with horizontal size of tens of kilometers can be detected. The proposed method can be applied for regional fast ice mapping and large stamukhas detection to aid coastal research. Additionally, the method can serve as a tool for operational sea ice mapping.

scholarly journals Interannual sea ice thickness variability in the Bay of Bothnia

2018 ◽  
Vol 12 (11) ◽  
pp. 3459-3476 ◽  
Author(s):  
Iina Ronkainen ◽  
Jonni Lehtiranta ◽  
Mikko Lensu ◽  
Eero Rinne ◽  
Jari Haapala ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Thickness Variability ◽  
Fast Ice ◽  
Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

scholarly journals Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

2015 ◽  
Vol 8 (10) ◽  
pp. 4025-4041 ◽  
Author(s):  
H.-J. Kang ◽  
J.-M. Yoo ◽  
M.-J. Jeong ◽  
Y.-I. Won
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Data Sets ◽  
High Latitudes ◽  
Ice Surface ◽  
Moderate Resolution ◽  
Microwave Sounding ◽  
Polar Oceans ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Surface Temperature

Abstract. Uncertainties in the satellite-derived surface skin temperature (SST) data in the polar oceans during two periods (16–24 April and 15–23 September) 2003–2014 were investigated and the three data sets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. The AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically warmer up to 1.65 K at the sea ice boundary and colder down to −2.04 K in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992–0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968–0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a less accurate GCM forecast over the seasonally varying frozen oceans than the microwave data. The three data sets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ~ 2.8 ± 1.9 K decade−1) in the northern high regions (70–80° N) as expected from the ice-albedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

scholarly journals Interpretation of Slar Imagery of Sea Ice in Nares Strait and the Arctic Ocean

1975 ◽  
Vol 15 (73) ◽  
pp. 193-213
Author(s):  
Moira Dunbar
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
North Pole ◽  
The Arctic ◽  
Canadian Forces ◽  
Nares Strait ◽  
The North ◽  
The Arctic Ocean ◽  
Ice Edge ◽  
Airborne Sensors

AbstractSLAR imagery of Nares Strait was obtained on three flights carried out in. January, March, and August of 1973 by Canadian Forces Maritime Proving and Evaluation Unit in an Argus aircraft equipped with a Motorola APS-94D SLAR; the March flight also covered two lines in the Arctic Ocean, from Alert 10 the North Pole and from the Pole down the long. 4ºE. meridian to the ice edge at about lat. 80º N. No observations on the ground were possible, but -some back-up was available on all flights from visual observations recorded in the air, and on the March flight from infrared line-scan and vertical photography.The interpretation of ice features from the SLAR imagery is discussed, and the conclusion reached that in spite of certain ambiguities the technique has great potential which will increase with improving resolution, Extent of coverage per distance flown and independence of light and cloud conditions make it unique among airborne sensors.

scholarly journals Characteristics of Altimetry Signatures Over Sea Ice (Abstract)

1987 ◽  
Vol 9 ◽  
pp. 240
Author(s):  
N.F. McIntyre ◽  
S.W. Laxon
Keyword(s):  
Sea Ice ◽  
Continental Margin ◽  
Small Proportion ◽  
The Arctic ◽  
Antarctic Sea Ice ◽  
Surface Measurements ◽  
Characteristic Sequences ◽  
Data Coverage ◽  
Ice Edge

We report characteristics of Seasat altimetry signatures recorded over Antarctic sea ice. Up to four discrete zones can at times be seen in characteristic sequences in the Weddell and Ross Seas, and elsewhere. They are substantially larger than those reported in the Arctic, covering up to 2500 km at the time of maximum ice extent in 1978. Transitions between them can be abrupt, with marked changes occurring in less than a few kilometres. Some zones were found to persist through the 3 month satellite lifetime; others exhibited intermittent variations. Repeat data coverage has enabled temporal as well as spatial patterns to be investigated. Interpretation of the geophysical cause of the patterns observed has been limited by available data. Some comparisons may be made with surface measurements of nadir back-scatter on first- and multi-year floes but these account for only a small proportion of the altimetry returns studied. Correlations with the NOAA Navy Ice Charts show significant disparities in the determination of the ice edge which may relate to the sensitivity of the altimeter to the presence of fresh ice or ice in very small quantities. Similar signatures can be found next to small coastal leads at the continental margin, an area known to be important for the growth of new ice.

scholarly journals Compact Polarimetry Response to Modeled Fast Sea Ice Thickness

2020 ◽  
Vol 12 (19) ◽  
pp. 3240
Author(s):  
Mohammed Dabboor ◽  
Mohammed Shokr
Keyword(s):  
Sea Ice ◽  
Degree Of Polarization ◽  
Ice Thickness ◽  
Operational Mode ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Bay Area ◽  
Fast Ice ◽  
Preliminary Study ◽  
Sar Imagery

Compact Polarimetric (CP) Synthetic Aperture Radar (SAR) is expected to gain more and more ground for Earth observation applications in the coming years. This comes in light of the recently launched RADARSAT Constellation Mission (RCM), which uniquely provides CP SAR imagery in operational mode. In this study, we present observations about the sensitivity of CP SAR imagery to thickness of thermodynamically-grown fast sea ice during early ice growth (September–December 2017) in the Resolute Bay area, Canadian Central Arctic. Fast ice is most suitable to use for this preliminary study since it exhibits only thermodynamic growth in absence of ice mobility and deformation. Results reveal that ice thickness up to 30 cm can be retrieved using several CP parameters from the tested set. This ice thickness corresponds to the thickness of young ice. We found the surface scattering mechanism to be dominant during the early ice growth, exposing an increasing tendency up to 30 cm thickness with a correlation coefficient with the thickness equal to 0.86. The degree of polarization was found to be the parameter with the highest correlation up to 0.95. While thickness retrieval within the same range is also possible using parameters from Full Polarimetric (FP) SAR parameters as shown in previous studies, the advantage of using CP SAR mode is the much larger swath coverage, which is an operational requirement.

scholarly journals Fast Ice Prediction System (FIPS) for land-fast sea ice at Prydz Bay, East Antarctica: an operational service for CHINARE

2020 ◽  
pp. 1-13
Author(s):  
Jiechen Zhao ◽  
Bin Cheng ◽  
Timo Vihma ◽  
Petra Heil ◽  
Fengming Hui ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ice Thickness ◽  
Cold Bias ◽  
Prediction System ◽  
Atmospheric Forcing ◽  
Ice Melt ◽  
Fast Ice ◽  
Sar Imagery ◽  
Operational Service ◽  
Snow And Ice

Abstract A Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/V Xuelong when optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station.

scholarly journals Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm

2018 ◽  
Author(s):  
Nils Hutter ◽  
Lorenzo Zampieri ◽  
Martin Losch
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Tracking Algorithm ◽  
The Arctic ◽  
Power Law Distribution ◽  
Data Set ◽  
Extensive Evaluation ◽  
Ice Drift ◽  
Arctic Sea ◽  
Sea Ice Drift

Abstract. Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This paper introduces two methods that detect and track LKFs in sea ice deformation data and establish an LKF data set for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). Both algorithms are available as open-source code and applicable to any gridded sea-ice drift and deformation data. The LKF detection algorithm classifies pixels with higher deformation rates compared to the immediate environment as LKF pixels, divides the binary LKF map into small segments, and re-connects multiple segments into individual LKFs based their distance and orientation relative to each other. The tracking algorithm uses sea-ice drift information to estimate a first guess of LKF distribution and identifies tracked features by the degree of overlap between detected features and the first guess. An optimization of the parameters of both algorithms is presented, as well as an extensive evaluation of both algorithms against hand-picked features in a reference data set. An LKF data set is derived from RGPS deformation data for the years from 1996 to 2008 that enables a comprehensive description of LKFs. LKF densities and LKF intersection angles derived from this data set agree well with previous estimates. Further, a power-law distribution of LKF length, an exponential distribution of LKF lifetimes, and a strong link to atmospheric drivers, here Arctic cyclones, is derived from the data set. Both algorithms are applied to output of a numerical sea-ice model to compare the LKF intersection angles in a high-resolution Arctic sea-ice simulation with the LKF data set.

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