On the classification of melt season first-year and multi-year sea ice in the Beaufort Sea using Radarsat-2 data

Warner, Kerri; Iacozza, John; Scharien, Randall K.; Barber, David G.

doi:10.1080/01431161.2012.760855

Cited by 11 publications

(11 citation statements)

References 36 publications

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“…Newer studies include examination of backscatter signatures of multiyear sea ice with ship-based scatterometer (Isleifson et al, 2009) and investigation of the use of a supplementary frequency of either X or K u band in addition to C band in late-summer sea ice classification with an airborne scatterometer (Brath et al, 2013). Satellite-based studies include separation of MYI and FYI by dual polarisation intensity data from RADARSAT-2 (Warner et al, 2013), classification potential of polarimetric features from RADARSAT-2 (Gill et al, 2013) and investigations of melt pond fraction retrieval from co-polarisation ratio data acquired by RADARSAT-2 (Scharien et al, 2012(Scharien et al, , 2014. Separating different sea ice types during summer melt is still a challenge.…”

Section: A S Fors Et Al: Late-summer Sea Ice Segmentation With Mulmentioning

confidence: 99%

Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band

Fors¹,

Brekke²,

Doulgeris³

et al. 2016

The Cryosphere

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Abstract. In this study, we investigate the potential of sea ice segmentation by C-and X-band multi-polarisation synthetic aperture radar (SAR) features during late summer. Five highresolution satellite SAR scenes were recorded in the Fram Strait covering iceberg-fast first-year and old sea ice during a week with air temperatures varying around 0 • C. Sea ice thickness, surface roughness and aerial photographs were collected during a helicopter flight at the site. Six polarimetric SAR features were extracted for each of the scenes. The ability of the individual SAR features to discriminate between sea ice types and their temporal consistency were examined. All SAR features were found to add value to sea ice type discrimination. Relative kurtosis, geometric brightness, cross-polarisation ratio and co-polarisation correlation angle were found to be temporally consistent in the investigated period, while co-polarisation ratio and co-polarisation correlation magnitude were found to be temporally inconsistent. An automatic feature-based segmentation algorithm was tested both for a full SAR feature set and for a reduced SAR feature set limited to temporally consistent features. In C band, the algorithm produced a good late-summer sea ice segmentation, separating the scenes into segments that could be associated with different sea ice types in the next step. The X-band performance was slightly poorer. Excluding temporally inconsistent SAR features improved the segmentation in one of the X-band scenes.

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Section: A S Fors Et Al: Late-summer Sea Ice Segmentation With Mulmentioning

confidence: 99%

Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band

Fors¹,

Brekke²,

Doulgeris³

et al. 2016

The Cryosphere

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“…Accurate representations of snow density, albedo, and storage and refreezing of liquid water in the snowpack, as inputs to snow models, are required for consistent results (Essery et al, 2013). Inversion or assimilation schemes that focus on C-band backscatter in the Canadian Arctic may encounter error, as in situ conditions may not be as they appear in ice charts and satellite imagery (e.g., Barber et al, 2009;Warner et al, 2013).…”

mentioning

confidence: 99%

“…An expected increase in the rate of both early and late season precipitation and melt events in the Arctic will add complexity to both snow thermodynamic modeling and interpretation of microwave remote sensing data, as multiple snow and ice conditions can lead to similar backscatter results (Barber et al, 2009;Warner et al, 2013;Gill and Yackel, 2012;Gill et al, 2014;Fuller et al, 2014). In such cases, a snow thermodynamic model may be used for comparison and inversion of important snow properties (e.g., snow water equivalent (SWE), grain size) for a given backscatter response.…”

mentioning

confidence: 99%

“…The Canadian Ice Service (CIS) integrates, analyses, and interprets many data sources to produce weekly regional charts estimating properties such as ice type, thickness, and concentration; however, these may contain inaccuracies (e.g., Barber et al, 2009;Warner et al, 2013). The simulation of snow physical properties relevant to backscatter can lend insight to the actual cause of the microwave response, and is necessary given the vast scale of the Canadian Arctic, which has relatively few in situ climate or snow physical observations.…”

mentioning

confidence: 99%

“…Changes to the composition of sea ice in the Arctic system affect the accuracy of geophysical and thermodynamic properties, which are required for management strategies (Barber, 2005;Warner et al, 2013). An expected increase in the rate of both early and late season precipitation and melt events in the Arctic will add complexity to both snow thermodynamic modeling and interpretation of microwave remote sensing data, as multiple snow and ice conditions can lead to similar backscatter results (Barber et al, 2009;Warner et al, 2013;Gill and Yackel, 2012;Gill et al, 2014;Fuller et al, 2014).…”

mentioning

confidence: 99%

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Comparison of a coupled snow thermodynamic and radiative transfer model with in situ active microwave signatures of snow-covered smooth first-year sea ice

et al. 2015

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Abstract. Within the context of developing data inversion and assimilation techniques for C-band backscatter over sea ice, snow physical models may be used to drive backscatter models for comparison and optimization with satellite observations. Such modeling has the potential to enhance understanding of snow on sea-ice properties required for unambiguous interpretation of active microwave imagery. An end-to-end modeling suite is introduced, incorporating regional reanalysis data (NARR), a snow model (SNTHERM89.rev4), and a multilayer snow and ice active microwave backscatter model (MSIB). This modeling suite is assessed against measured snow on sea-ice geophysical properties and against measured active microwave backscatter. NARR data were input to the SNTHERM snow thermodynamic model in order to drive the MSIB model for comparison to detailed geophysical measurements and surface-based observations of C-band backscatter of snow on first-year sea ice. The NARR variables were correlated to available in situ measurements with the exception of long-wave incoming radiation and relative humidity, which impacted SNTHERM simulations of snow temperature. SNTHERM snow grain size and density were comparable to observations. The first assessment of the forward assimilation technique developed in this work required the application of in situ salinity profiles to one SNTHERM snow profile, which resulted in simulated backscatter close to that driven by in situ snow properties. In other test cases, the simulated backscatter remained 4-6 dB below observed for higher incidence angles and when compared to an average simulated backscatter of in situ endmember snow covers. Development of C-band inversion and assimilation schemes employing SNTHERM89.rev4 should consider sensitivity of the model to bias in incoming longwave radiation, the effects of brine, and the inability of SNTHERM89.Rev4 to simulate water accumulation and refreezing at the bottom and mid-layers of the snowpack. These impact thermodynamic response, brine wicking and volume processes, snow dielectrics, and thus microwave backscatter from snow on first-year sea ice.

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Sea Ice in a Climate Change Context

Lund-Hansen

Søgaard

Sorrell

et al. 2020

Arctic Sea Ice Ecology

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On the classification of melt season first-year and multi-year sea ice in the Beaufort Sea using Radarsat-2 data

Cited by 11 publications

References 36 publications

Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band

Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band

Comparison of a coupled snow thermodynamic and radiative transfer model with in situ active microwave signatures of snow-covered smooth first-year sea ice

Sea Ice in a Climate Change Context

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