Sea ice extent mapping using Ku band scatterometer data

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

doi:10.1029/98jc02373

Cited by 86 publications

(65 citation statements)

References 15 publications

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“…Then, the HY-2A/SCAT sea ice extent is compared with different ice extents at various SSMIS ice concentration levels. Past research has indicated that the 15% ice concentration level is consistent with sea ice extent derived in other studies [32], while SSMIS 30% ice concentration contour are more consistent with the NSCAT derived sea ice extent [10]. In addition, the 15% and 30% SSMIS concentration contours were compared with the QuikSCAT derived ice extent [3,33].…”

Section: Ssmis Sea Ice Concentration Comparisonmentioning

confidence: 94%

“…Results show that / is low for sea ice region and relatively higher for open water. However, wind-induced surface roughness can potentially cause open water with low polarization ratio close to sea ice [10]. In order to overcome this problem, the dual-polarized measurements ( , ) and their daily standard deviations ( , ) are combined for monitoring sea ice.…”

Section: Hy-2a Sea Ice Mapping Parametersmentioning

confidence: 99%

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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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Section: Ssmis Sea Ice Concentration Comparisonmentioning

confidence: 94%

Section: Hy-2a Sea Ice Mapping Parametersmentioning

confidence: 99%

Polar Sea Ice Monitoring Using HY-2A Scatterometer Measurements

Zhao

et al. 2016

Remote Sensing

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“…http://climate.rutgers.edu/snowc over/ Scatterometer Ice Extent Products Sea ice extent products derived from active microwave scatterometer data are available for both NSCAT and QuikSCAT in the Arctic and the Antarctic, provided in the appropriate polar stereographic grids on a near-real-time basis [155].…”

Section: Globicementioning

confidence: 99%

Open Access Data in Polar and Cryospheric Remote Sensing

et al. 2014

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This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use.

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“…The ice extent mapping algorithm used in this study is described in [18]. The technique uses the NSCAT polarization ratio , as well as the NSCAT v-pol incidence angle dependence of ( ) to discriminate between sea ice and ocean pixels.…”

Section: Ice Maskingmentioning

confidence: 99%

An iterative approach to multisensor sea ice classification

Remund

Long

Drinkwater

2000

IEEE Trans. Geosci. Remote Sensing

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Abstract-Characterizing the variability in sea ice in the polar regions is fundamental to an understanding of global climate and the geophysical processes governing climate changes. Sea ice can be grouped into a number of general classes with different characteristics. Multisensor data from NSCAT, ERS-2, and SSM/I are reconstructed into enhanced resolution imagery for use in ice-type classification. The resulting twelve-dimensional data set is linearly transformed through principal component analysis to reduce data dimensionality and noise levels. An iterative statistical data segmentation algorithm is developed using maximum likelihood (ML) and maximum a posteriori (MAP) techniques. For a given ice type, the conditional probability distributions of observed vectors are assumed to be Gaussian. The cluster centroids, covariance matrices, and a priori distributions are estimated from the classification of a previous temporal image set. An initial classification is produced using centroid training data and a weighted nearestneighbor classifier. Though validation is limited, the algorithm results in an ice classification that is judged to be superior to a conventional -means approach.

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Sea ice extent mapping using Ku band scatterometer data

Cited by 86 publications

References 15 publications

Polar Sea Ice Monitoring Using HY-2A Scatterometer Measurements

Polar Sea Ice Monitoring Using HY-2A Scatterometer Measurements

Open Access Data in Polar and Cryospheric Remote Sensing

An iterative approach to multisensor sea ice classification

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