Separability of sea ice types from wide swath C- and L-band synthetic aperture radar imagery acquired during the melt season

Casey, J Alec; Howell, Stephen E. L.; Tivy, Adrienne; Haas, Christian

doi:10.1016/j.rse.2015.12.021

Cited by 72 publications

(60 citation statements)

References 47 publications

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“…Thus, a comparison of the plots in the two studies would show that the histograms of FYI and OI for the σ 0 HH backscattering coefficient shown in Figure 5 agree with those presented for the test sites 3 and 4 in [22]. Different plots with increased discrimination capability between FYI and OI were shown in [22] for the test sites 1 and 2. This should be related to the fact that the test sites 1 and 2 were located in the radar far range with large radar incidence angles >38 • , while the test sites 3 and 4 were located in the radar near range with radar incidence angles close to those in our case study.…”

Section: Separability Estimationsupporting

confidence: 54%

“…It should be noted in Figure 5 that the H parameter for L-band SAR data covers a wide range of values, indicating the presence of one main scattering mechanism and secondary scattering mechanisms [20]. In [22], similar plots to those in Figure 5 were presented over four test sites, but for the backscattering coefficient only. Thus, a comparison of the plots in the two studies would show that the histograms of FYI and OI for the backscattering coefficient shown in Figure 5 agree with those presented for the test sites 3 and 4 in [22].…”

Section: Histogram Interpretationsupporting

confidence: 52%

“…The only parameter that falls in the third category of very good separability is the SPAN with K-S distance = 0.97. The , , and backscattering coefficients belong to the In [22], similar plots to those in Figure 5 were presented over four test sites, but for the σ 0 HH backscattering coefficient only. Thus, a comparison of the plots in the two studies would show that the histograms of FYI and OI for the σ 0 HH backscattering coefficient shown in Figure 5 agree with those presented for the test sites 3 and 4 in [22].…”

Section: Separability Estimationmentioning

confidence: 56%

“…In [22], similar plots to those in Figure 5 were presented over four test sites, but for the backscattering coefficient only. Thus, a comparison of the plots in the two studies would show that the histograms of FYI and OI for the backscattering coefficient shown in Figure 5 agree with those presented for the test sites 3 and 4 in [22]. Different plots with increased discrimination capability between FYI and OI were shown in [22] for the test sites 1 and 2.…”

Section: Histogram Interpretationmentioning

confidence: 57%

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Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data

et al. 2017

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Sea ice monitoring and classification is one of the main applications of Synthetic Aperture Radar (SAR) remote sensing. C-band SAR imagery is regarded as an optimal choice for sea ice applications; however, other SAR frequencies has not been extensively assessed. In this study, we evaluate the potential of fully polarimetric L-band SAR imagery for monitoring and classifying sea ice during dry winter conditions compared to fully polarimetric C-band SAR. Twelve polarimetric SAR parameters are derived using sets of C-and L-band SAR imagery and the capabilities of the derived parameters for the discrimination between First Year Ice (FYI) and Old Ice (OI), which is considered to be a mixture of Second Year Ice (SYI) and Multiyear Ice (MYI), are investigated. Feature vectors of effective C-and L-band polarimetric parameters are extracted and used for sea ice classification. Results indicate that C-band SAR provides high classification accuracy (98.99%) of FYI and OI in comparison to the obtained accuracy using L-band SAR (82.17% and 81.85%), as expected. However, L-band SAR was found to classify only the MYI floes as OI, while merging both FYI and SYI into one separate class. This comes in contrary to C-band SAR, which classifies as OI both MYI and SYI. This indicates a new potential for discriminating SYI from MYI by combining C-and L-band SAR in dry ice winter conditions.

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Section: Separability Estimationsupporting

confidence: 54%

Section: Histogram Interpretationsupporting

confidence: 52%

Section: Separability Estimationmentioning

confidence: 56%

Section: Histogram Interpretationmentioning

confidence: 57%

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Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data

et al. 2017

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“…This rapid change in sea ice forms part of a series of Arctic climate transitions, which include shifts in the atmospheric circulation with concomitant changes in air temperature and an increase in melt-water storage in the upper Arctic Ocean [4] [5] [6] [7]. The IPCC's fifth Assessment Report suggests that Arctic sea ice will disappear during summer of 2100 [8].…”

mentioning

confidence: 99%

A Method to Determine the Margins of High Sea Ice Concentration Using AMSR-E Passive Microwave Imagery

Zhang¹,

Liu²,

Zhang³

et al. 2017

GEP

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The margin of the sea ice with high sea ice concentration is a principal feature in microwave image and a hotspot in image recognition. A method for determining the margins is developed using the feature of dual-polarized brightness temperatures at 36.5 GHz and a new parameter (contrast ratio) is used in this paper. For the microwaves, the ratio of the horizontal-polarized emissivity to the vertical-polarized emissivity is approximately equal to the ratio between horizontal-polarized and vertical-polarized brightness temperatures of sea surface, which called as the dual-polarized emissivity ratio in this study. It is found that the dual-polarized emissivity ratio of sea ice with nearly 100% sea ice concentration in Arctic at 36.5 GHz band has a value ranged between 0.92 and 0.96, as shown by satellite-observed data in figure of horizontal-polarized brightness temperature versus vertical-polarized brightness temperature. From open water to sea ice covered area, the contrast-ratio can show the changing features of the dual-polarized brightness temperature at 36.5 GHz. The contrast ratio rapidly changes at the ice margins and its gradient appears an extreme value when the ratio changes around 0.92. This extreme value is examined by the ice concentration calculated by the MODIS data. And the results indicate that the threshold ratio coincides with the contour line of 96% sea ice concentration. So the parameter of contrast ratio could be used to determine the position of margins in microwave image.

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Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets

Sandven¹,

Spreen

Heygster

et al. 2023

Surv Geophys

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Sea ice monitoring by polar orbiting satellites has been developed over more than four decades and is today one of the most well-established applications of space observations. This article gives an overview of data product development from the first sensors to the state-of-the-art regarding retrieval methods, new products and operational data sets serving climate monitoring as well as daily operational services including ice charting and forecasting. Passive microwave data has the longest history and represents the backbone of global ice monitoring with already more than four decades of consistent observations of ice concentration and extent. Time series of passive microwave data is the primary climate data set to document the sea ice decline in the Arctic. Scatterometer data is a valuable supplement to the passive microwave data, in particular to retrieve ice displacement and distinguish between firstyear and multiyear ice. Radar and laser altimeter data has become the main method to estimate sea ice thickness and thereby fill a gap in the observation of sea ice as an essential climate variable. Data on ice thickness allows estimation of ice volume and masses as well as improvement of the ice forecasts. The use of different altimetric frequencies also makes it possible to measure the depth of the snow covering the ice. Synthetic Aperture Radar (SAR) has become the work horse in operational ice observation on regional scale because high-resolution radar images are delivered year-round in nearly all regions where national ice services produce ice charts. Synthetic Aperture Radar data are also important for sea ice research because the data can be used to observe a number of sea ice processes and phenomena, like ice type development and sea ice dynamics, and thereby contribute to new knowledge about sea ice. The use of sea ice data products in modelling and forecasting services as well as in ice navigation is discussed. Finally, the article describes future plans for new satellites and sensors to be used in sea ice observation.

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Separability of sea ice types from wide swath C- and L-band synthetic aperture radar imagery acquired during the melt season

Cited by 72 publications

References 47 publications

Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data

Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data

A Method to Determine the Margins of High Sea Ice Concentration Using AMSR-E Passive Microwave Imagery

Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets

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