Evaluating the use of QuikSCAT data for operational sea ice monitoring

Abreu, R. De; Wilson, Keith; Arkett, M.; Langlois, D.

doi:10.1109/igarss.2002.1026860

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…Comparison with the NT ice concentration showed that the ice edge extracted from the BYU ice mask corresponds to an average ice concentration between 40% and 60% during winter. Case studies with high-resolution RS data have shown, however, that this ice edge underestimates the ice extent, and misses important areas of thin ice and ice concentration below 70% [19] that could present a potential risk to maritime operations.…”

Section: B Ice-ocean Discrimination With Qs (Byu Algorithm)mentioning

confidence: 99%

“…To analyze the performance of QS ice-ocean discrimination, first case studies were done to evaluate the automatic Remund and Long [1] ice mapping algorithm developed at Brigham Young University and henceforth referred to as "BYU algorithm. De Abreu et al [19] showed that the retrieved BYU ice edge successfully maps heavy areas of pack ice with ice concentrations above 70%, but it detects neither thin ice below 15-cm thickness nor areas of low ice concentrations. Other algorithms that combine the active polarization ratio (APR) (see Section III for definition) of QuikScat L2B products and the SSM/I 19-GHz passive polarization ratio (PPR) have also been developed to detect new ice [20].…”

mentioning

confidence: 99%

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Automatic detection and validity of the sea-ice edge: an application of enhanced-resolution QuikScat/SeaWinds data

Haarpaintner

Tonboe

Long

et al. 2004

IEEE Trans. Geosci. Remote Sensing

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Sea-ice edge detection is an essential task at the different national ice services to secure navigation in ice-covered seas. Comparison between the Remund and Long ice mask image from enhanced-resolution QuikScat/SeaWinds (QS) products and the analyzed ice edge from high-resolution RADARSAT synthetic aperture radar has shown that the automatically determined QS ice mask underestimates the Arctic ice extent. QS data was statistically analyzed by colocating the data with ice charts around Greenland and with the National Aeronautics and Space Administration Team's Special Sensor Microwave/Imager (SSM/I) ice concentration algorithm over the whole Arctic region. All variables, i.e., the backscatter in vertical and horizontal polarization, the active polarization ratio (APR) and the daily standard deviation, are sensitive to ice types and are strongly correlated with ice concentration when the relationship is expressed in exponential form. Our study showed that the APR is especially suitable for ice-ocean separation, and a threshold of 0 02 was determined. An ice edge algorithm based on this APR threshold was developed using the other variables with conservative season-dependent thresholds to eliminate additional ocean noise. Also, the history of the ice cover is considered in order to detect single ice fields that are separated from the main Arctic pack ice. Validation with RADARSAT 1 and with the Advanced Very High Resolution Radiometer showed that the new algorithm successfully detects very low ice concentrations of about 10% during the entire year. The validity of the detected ice edge for near-real-time issues is also discussed in relation to the ice motion in the Marginal Ice Zone and the integration time necessary to produce the enhanced-resolution images. The new algorithm improves the automatic global ice edge resolution by a factor of two when compared to SSM/I products and could be used in both model initialization and data assimilation.

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Section: B Ice-ocean Discrimination With Qs (Byu Algorithm)mentioning

confidence: 99%

mentioning

confidence: 99%

Automatic detection and validity of the sea-ice edge: an application of enhanced-resolution QuikScat/SeaWinds data

Haarpaintner

Tonboe

Long

et al. 2004

IEEE Trans. Geosci. Remote Sensing

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“…Both sensors have been investigated for their use in providing sea ice type information in sea ice type segmentation algorithms. The QuikSCAT sensor has shown promising results for sea ice typing [5] [6], monitoring sea ice extent [7]- [9], sea ice edge detection [10] [11], sea ice melt detection [12] [13], and monitoring sea ice drift [14].…”

mentioning

confidence: 99%

Fusing AMSR-E and QuikSCAT Imagery for Improved Sea Ice Recognition

Clausi

Howell

2009

IEEE Trans. Geosci. Remote Sensing

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Abstract-The benefits of augmenting AMSR-E image data with QuikSCAT image data for supervised sea ice classification in the Western Arctic region are investigated. Experiments compared the performance of a maximum likelihood classifier when used with the AMSR-E only data set against using the combined data. The preferred number of bands to use for classification was examined, as well as whether principal components analysis can be used to reduce the dimensionality of the data. The reliability of training data over time was also investigated. Adding QuikSCAT often improves classifier accuracy in a statistically significant manner and never decreased it significantly when a sufficient number of bands are used. Combining these data sets is beneficial for sea ice mapping. Using all available bands is recommended, data fusion with principal components analysis does not offer any benefit for these data and training data from a specific date remains reliable within 30 days.

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“…Remund and Long (1999) developed an automatic ice-ocean discrimination algorithm using NSCAT data for the Arctic region. De Abreu et al (2002) found that the retrieved ice edge successfully maps pack ice areas having ice cover higher than 70% but it fails in case of the areas having thin ice or ice with low concentration. Automatic identification of seaice edge and its validation using enhanced resolution QuikSCAT data has been carried out by Haarpaintner et al (2004).…”

Section: Introductionmentioning

confidence: 97%

Spatio-Temporal Coherence Based Technique for Near-Real Time Sea-Ice Identification from Scatterometer Data

Oza

Singh

Vyas

et al. 2011

J Indian Soc Remote Sens

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The identification of sea-ice has frequently been cited as one of the most important tasks for deriving the sea-ice parameters and to avoid erroneous retrieval of wind vector over sea-ice infested oceans using space-borne scatterometer data. Discrimination between sea-ice and ocean is ambiguous under the high wind and/or thin/scattered ice conditions. The pre-launch technique developed for Oceansat-2, utilizes the dual-polarized QuikSCAT scatterometer data by using the spatio-temporal coherence properties of sea ice in addition to backscatter coefficient and the Active Polarization Ratio. Results were compared with the operational sea-ice products from National Snow and Ice Data Center. The threshold API value of −0.025 was found optimum for sea-ice and ocean discrimination. The overall sea-ice identification accuracy achieved was of the order of 95 per cent, ranging from 92.5% (during December in Southern Hemisphere) to 98% (during March in Northern Hemisphere). The applicability of the algorithm for both the Arctic as well as Antarctic makes it suitable for its operational use with the Oceansat-2 scatterometer data.

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Evaluating the use of QuikSCAT data for operational sea ice monitoring

Abstract: Near-real time QuikSCAT image products are evaluated for ice mapping from the perspective of the Canadian Ice Services Operational Environment.

Cited by 7 publications

References 3 publications

Automatic detection and validity of the sea-ice edge: an application of enhanced-resolution QuikScat/SeaWinds data

Automatic detection and validity of the sea-ice edge: an application of enhanced-resolution QuikScat/SeaWinds data

Fusing AMSR-E and QuikSCAT Imagery for Improved Sea Ice Recognition

Spatio-Temporal Coherence Based Technique for Near-Real Time Sea-Ice Identification from Scatterometer Data

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