Sea ice mapping algorithm for QuikSCAT and Seawinds

Remund, Q.P.; Long, D.G.

doi:10.1109/igarss.1998.692424

Cited by 16 publications

(19 citation statements)

References 6 publications

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“…The launches of QuikScat and Seawinds will extend the Ku-band data set and strengthen our capabilities for extracting polar snow and ice characteristics and monitoring climate change impact. Although the QuikScat and Seawinds scatterometers differ in design from NSCAT, NSCAT algorithms and analysis techniques can be extended to these sensors, e.g., [53]. …”

Section: Discussionmentioning

confidence: 99%

Cryosphere applications of NSCAT data

Long

Drinkwater

1999

IEEE Trans. Geosci. Remote Sensing

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Abstract-Though designed to measure vector winds over the ocean, new imaging techniques facilitate the use of NASA scatterometer data (NSCAT) in cryosphere studies. NSCAT provides data of unprecedented coverage, resolution, and quality which, when coupled with the scatterometer image reconstruction with filtering (SIRF) algorithm, enables images of at resolutions approaching 8 km over stationary targets. Such images are useful in ice mapping and classification, and multidecadal studies are possible by comparison with Seasat Scatterometer (SASS) data. The utility of NSCAT data in polar ice studies is illustrated through a review of two cryosphere applications of NSCAT data: 1) sea-ice mapping and tracking and 2) ice-sheet change in Greenland and Antarctica. The wide swath and frequent revisit, coupled with incidence and azimuth angle diversity makes NSCAT data very effective in mapping the extent of sea-ice. In Greenland, snow and ice "facies" are clearly delineated on the basis of the seasonally dependent radar backscattering cross section, due to sensitivity of radar backscatter to diagenetic changes occurring at and beneath the surface. Comparison of NSCAT and SASS data enables study of change in Greenland between 1978 and 1996.

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Section: Discussionmentioning

confidence: 99%

Cryosphere applications of NSCAT data

Long

Drinkwater

1999

IEEE Trans. Geosci. Remote Sensing

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“…These images are created for Vpol as seen in Figure 2.4(a) as well as for Hpol. Using Vpol, Hpol, and other SIR images, diurnal ice extent products are made by the Remund-Long (RL) algorithm [12,13]. The RL algorithm uses an iterative maximum likelihood classifier as well as image processing techniques to produce daily sea ice extent maps.…”

Section: Low and High Resolution Productsmentioning

confidence: 99%

Mitigation of Sea Ice Contamination in QuikSCAT Wind Retrieval

Hullinger

Long

2014

IEEE Trans. Geosci. Remote Sensing

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Satellite borne radar scatterometers provide frequent estimates of near surface wind vectors over the Earth's oceans. However in the polar oceans, the presence of sea ice in or near the measurement footprint can adversely affect scatterometer measurements resulting in inaccurate wind estimates. Currently, such ice contamination is mitigated by discarding measurements within 50 km of detected sea ice. This approach is imperfect and causes loss of coverage. This thesis presents a new algorithm which detects ice-contaminated measurements based on a metric called the Ice Contribution Ratio (ICR) which measures the spatial ice contribution for each measurement. The ICR calculation is made for each measurement using a spatial ice probability map which is determined using Bayesian probability theory. Determined by simulation, the ICR processing thresholds the ICR for each measurement depending on local wind, ice backscatter, and cross-track location. ICR processing retrieves winds at a distance of 22.5 km from the ice edge on average, while ensuring wind accuracy. Retrieved wind distributions using ICR processing more closely resembles uncontaminated wind distributions than winds retrieved using previous methods. The algorithm is applied to QuikSCAT in this thesis but could be applied to other scatterometers such as the Oceansat-2 scatterometer.

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“…In 1997, Remund and Long developed an automatic sea ice and open water discrimination algorithm using Ku-Band data derived from NASA Scatterometer (NSCAT) [7,8]. Their principles were later adopted by the French Institute of Research for the Exploitation of the Sea (IFREMER) active/passive microwave sensors derived sea ice products.…”

Section: Introductionmentioning

confidence: 99%

Polar Sea Ice Monitoring Using HY-2A Scatterometer Measurements

Zhao

et al. 2016

Remote Sensing

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Abstract:A sea ice detection algorithm based on Fisher's linear discriminant analysis is developed to segment sea ice and open water for the Ku-band scatterometer onboard the China's Hai Yang 2A Satellite (HY-2A/SCAT). Residual classification errors are reduced through image erosion/dilation techniques and sea ice growth/retreat constraint methods. The arctic sea-ice-type classification is estimated via a time-dependent threshold derived from the annual backscatter trends based on previous HY-2A/SCAT derived sea ice extent. The extent and edge of the sea ice obtained in this study is compared with the Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data and the Sentinel-1 SAR imagery for verification, respectively. Meanwhile, the classified sea ice type is compared with a multi-sensor sea ice type product based on data from the Advanced Scatterometer (ASCAT) and SSMIS. Results show that HY-2A/SCAT is powerful in providing sea ice extent and type information, while differences in the sensitivities of active/passive products are found. In addition, HY-2A/SCAT derived sea ice products are also proved to be valuable complements for existing polar sea ice data products.

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Sea ice mapping algorithm for QuikSCAT and Seawinds

Cited by 16 publications

References 6 publications

Cryosphere applications of NSCAT data

Cryosphere applications of NSCAT data

Mitigation of Sea Ice Contamination in QuikSCAT Wind Retrieval

Polar Sea Ice Monitoring Using HY-2A Scatterometer Measurements

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