Estimation of Arctic land-fast ice cover based on dual-polarized Sentinel-1 SAR imagery

Karvonen, Juha

doi:10.5194/tc-12-2595-2018

Cited by 13 publications

(16 citation statements)

References 35 publications

(39 reference statements)

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“…In order to further improve the quality of the FIPS service, our attention will focus on improving remote-sensing data to identify properties of the fast ice cover (Karvonen, 2018). We also plan to apply multi-source reanalysis products to improve the representation of precipitation during the initial simulation preceding the start of FIPS prediction.…”

Section: Discussionmentioning

confidence: 99%

“…The thickness of fast ice and the overlying snow are the most important parameters for R/V Xuelong and snowcats operations. Satellite remote-sensing observations can yield accurate information on fast ice extent (Hui and others, 2017; Karvonen, 2018). However, there are major problems with the retrieval of ice thickness from satellite observations (Spreen and others, 2008; Laxon and others, 2013).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…This would require accurate tracking of the ice fields if they are moving. Multitemporal filtering is easier to be applied to static SI areas, such as LFI [32]. However, this would also require a significantly higher temporal resolution than we have with the currently available L-band data set.…”

Section: Discussionmentioning

confidence: 99%

“…LFI class (white) was not included in our classification as it is difficult to distinguish from other classes. However, it can be rather reliably identified based on temporal cross correlation of multitemporal SAR imagery [32] if there is a dense enough temporal SAR cover over the study area. We again used equivalent prior probabilities (weights) for the ice classes.…”

Section: Si Typementioning

confidence: 99%

On Suitability of ALOS-2/PALSAR-2 Dual-Polarized SAR Data for Arctic Sea Ice Parameter Estimation

Karvonen

Rinne

Sallila

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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In this article, ALOS-2/PALSAR-2 dual-polarized [horizontal-transmit-horizontal-receive and horizontal-transmitvertical-receive (HH/HV)] ScanSAR mode L-band synthetic aperture radar (SAR) imagery over an Arctic study area was evaluated for their suitability for operational sea ice (SI) monitoring. The L-band SAR data are studied for the estimation of different SI parameters: SI concentration, SI thickness, SI type, and SI drift. Also, some comparisons with nearly coincident C-band data over the same study area have been made. The results indicate that the L-band SAR data from ALOS-2/PALSAR-2 are very useful for estimating the studied SI parameters and equally good or better than using the conventional operational dual-polarized C-band SAR satellite data.

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“…The duration of landfast ice in the entire Arctic decreased at a rate of −0.06 ± 0.03 weeks/yr. A new algorithm based on high resolution satellite data will provide us with more accurate knowledge on landfast ice extent and duration in the future [56].…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variations in the Extent and Thickness of Arctic Landfast Ice

Zhao

et al. 2019

Remote Sensing

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Analyses of landfast ice in Arctic coastal areas provide a comprehensive understanding of the variations in Arctic sea ice and generate data for studies on the utilization of the Arctic passages. Based on our analysis, Arctic landfast ice mainly appears in January–June and is distributed within the narrow straits of the Canadian Archipelago (nearly 40%), the coastal areas of the East Siberian Sea, the Laptev Sea, and the Kara Sea. From 1976–2018, the landfast ice extent gradually decreased at an average rate of −1.1 ± 0.5 × 104 km2/yr (10.5% per decade), while the rate of decrease for entire Arctic sea ice was −6.0 ± 2.4 × 104 km2/yr (5.2% per decade). The annual maximum extent reached 2.3 × 106 km2 in the early 1980s, and by 2018, the maximum extent decreased by 0.6 × 106 km2, which is an area approximately equivalent the Laptev Sea. The mean duration of Arctic landfast ice is 44 weeks, which has gradually been reduced at a rate of −0.06 ± 0.03 weeks/yr. Regional landfast ice extent decreases in 16 of the 17 subregions except for the Bering Sea, making it the only subregion where both the extent and duration increases. The maximum mean landfast ice thickness appears in the northern Canadian Archipelago (>2.5 m), with the highest increasing trend (0.1 m/yr). In the Northeast Passage, the mean landfast ice thickness is 1.57 m, with a slight decreasing trend of −1.2 cm/yr, which is smaller than that for entire Arctic sea ice (−5.1 cm/yr). The smaller decreasing trend in the landfast ice extent and thickness suggests that the well-known Arctic sea ice decline largely occurred in the pack ice zone, while the larger relative extent loss indicates a faster ice free future in the landfast ice zone.

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Estimation of Arctic land-fast ice cover based on dual-polarized Sentinel-1 SAR imagery

Cited by 13 publications

References 35 publications

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

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

On Suitability of ALOS-2/PALSAR-2 Dual-Polarized SAR Data for Arctic Sea Ice Parameter Estimation

Spatial and Temporal Variations in the Extent and Thickness of Arctic Landfast Ice

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