An algorithm to detect sea ice leads by using AMSR-E passive microwave imagery

Röhrs, Johannes; Kaleschke, Lars

doi:10.5194/tc-6-343-2012

Cited by 72 publications

(83 citation statements)

References 25 publications

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“…This is a widely used method to identify leads, and usually the pulse peakiness parameter or the Maximum are used (Peacock and Laxon, 2004;Giles et al, 2007;Röhrs et al, 2012). We selected an equal number of waveforms from each surface class and set the threshold by maximizing the number of correct classified waveforms from this selection.…”

Section: Rule-based Thresholdmentioning

confidence: 99%

Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice

Zygmuntowska¹,

Khvorostovsky²,

Helm

et al. 2013

The Cryosphere

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Abstract. Sea ice thickness is one of the most sensitive variables in the Arctic climate system. In order to quantify changes in sea ice thickness, CryoSat-2 was launched in 2010 carrying a Ku-band radar altimeter (SIRAL) designed to measure sea ice freeboard with a few centimeters accuracy. The instrument uses the synthetic aperture radar technique providing signals with a resolution of about 300 m along track. In this study, airborne Ku-band radar altimeter data over different sea ice types have been analyzed. A set of parameters has been defined to characterize the differences in strength and width of the returned power waveforms. With a Bayesian-based method, it is possible to classify about 80 % of the waveforms from three parameters: maximum of the returned power waveform, the trailing edge width and pulse peakiness. Furthermore, the maximum of the power waveform can be used to reduce the number of false detections of leads, compared to the widely used pulse peakiness parameter. For the pulse peakiness the false classification rate is 12.6 % while for the power maximum it is reduced to 6.5 %. The ability to distinguish between different ice types and leads allows us to improve the freeboard retrieval and the conversion from freeboard into sea ice thickness, where surface type dependent values for the sea ice density and snow load can be used.

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Section: Rule-based Thresholdmentioning

confidence: 99%

Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice

Zygmuntowska¹,

Khvorostovsky²,

Helm

et al. 2013

The Cryosphere

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“…The maximum power classifier [7] has the simplest implementation of all the classification methods used within this study. It relies on the application of a threshold to the maximum power of multi-looked altimetry waveforms.…”

Section: Maximum Power Classifier (Max)mentioning

confidence: 99%

“…Connor et al [6] also used the pulse peakiness parameter with Envisat data to find leads in order to make estimates of the sea-ice freeboard. Röhrs and Kaleschke [7] used a maximum power threshold on Cryosat-2 (CS-2) SAR altimeter waveforms to identify leads. Zygmuntowska et al [8] developed a supervised classification method based on Bayesian classification [9] applied to ASIRAS (airborne version of the SIRAL instrument on-board CS-2) waveform shape parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The aim is to find the best existing CS-2 SAR altimeter lead classification algorithm that provides low false classification. The four classification methods under investigation are (1) the waveform maximum power as introduced, e.g., by Röhrs and Kaleschke [7]; (2) the multi-parameter classification method primarily based on pulse peakiness developed by Ricker et al [16]; (3) a method based on the stack peakiness presented by Passaro et al [10]; and (4) the unsupervised classification method published by Müller et al [11]. This is the first study that compares different classification approaches using exactly the same input data and the same very high resolution (<1 m) ground truth.…”

Section: Introductionmentioning

confidence: 99%

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Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data

et al. 2018

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Abstract:In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as lead classification; and (2) dedicated retracking algorithms to extract the ranges from the radar echoes. This study focuses on the first point and aims at identifying the best available lead classification method for Cryosat-2 SAR data. Four different altimeter lead classification methods are compared and assessed with respect to very high resolution airborne imagery. These methods are the maximum power classifier; multi-parameter classification method primarily based on pulse peakiness; multi-observation analysis of stack peakiness; and an unsupervised classification method. The unsupervised classification method with 25 clusters consistently performs best with an overall accuracy of 97%. Furthermore, this method does not require any knowledge of specific ice characteristics within the study area and is therefore the recommended lead detection algorithm for Cryosat-2 SAR in polar oceans.

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“…However, developments of satellite-based information on sea ice leads (Röhrs & Kaleschke, 2012;Willmes & Heinemann, 2015) and novel sea ice model approaches Wang et al, 2016) show promising results and will presumably advance our predictive capabilities considerably, in the future. Higher-resolution products are not available and challenged by cloud contamination issues, retrieval methodologies, and spatial coverage.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric Response to Kilometer‐Scale Changes in Sea Ice Concentration Within the Marginal Ice Zone

Batrak

Müller

2018

Geophysical Research Letters

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Sea ice kilometer‐scale characteristics are not explicitly represented in weather and climate model systems. However, due to the large differences of the surface temperature between sea ice and ocean, sea ice‐free areas (leads, polynyas, etc.) can have a strong impact on the surface heat fluxes and, in turn, on atmospheric dynamics. In the present study we analyze the atmospheric response to changes in the sea ice characteristics in the marginal ice zone of the Fram Strait and Barents Sea. A kilometer‐scale convection permitting atmospheric modeling system is used. The model sensitivity experiment is idealized, since there is no complete information on sea ice on these scales available. Instead, we are merging information from the passive microwave satellite product of sea ice concentration with a sea ice lead product based on thermal infrared sensors. An important constraint is that the sea ice‐free areas are introduced locally, but on scales of 100 km the sea ice coverage is kept constant for both the experiments. The sensitivity of air‐sea fluxes, atmospheric temperature, and winds is analyzed between the model experiments. In general, the standard deviation of the changes in surface air temperature and winds is, on large scales, about 2 K and 2 m/s, respectively. Those values are in the same order as the mean 12‐hr forecast error of advanced weather forecast models in this region.

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An algorithm to detect sea ice leads by using AMSR-E passive microwave imagery

Cited by 72 publications

References 25 publications

Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice

Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice

Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data

Atmospheric Response to Kilometer‐Scale Changes in Sea Ice Concentration Within the Marginal Ice Zone

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