Polar Sea Ice Monitoring Using HY-2A Scatterometer Measurements

Li, Mingming; Zhao, Chunjiang; Zhao, Yong; Wang, Zhixiong; Shi, Lijian

doi:10.3390/rs8080688

Cited by 21 publications

(9 citation statements)

References 27 publications

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“…Previous studies (Li et al, 2016;Lindell & Long, 2016a) have shown that the backscatter coefficient of the Kuband scatterometer is sensitive to sea ice types in winter. MYI has relatively more air voids than FYI (Tucker et al, 1992), giving it stronger volume scattering properties than FYI.…”

Section: Parameter Selectionmentioning

confidence: 99%

Arctic Sea Ice Type Classification by Combining CFOSCAT and AMSR‐2 Data

Zhao

Zhai

et al. 2022

Earth and Space Science

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Sea ice limits the exchange of heat, mass, momentum, and chemical composition between the ocean and atmosphere, which makes it an important factor in climate and weather systems (Sandven & Johannessen, 2006). In addition, the presence of polar sea ice can affect high-latitude ship traffic, fishing, and offshore operations. Firstyear ice (FYI) and multi-year ice (MYI) are the two most common ice types in Arctic (Z. Zhang et al., 2019). Generally, MYI refers to sea ice that has survived at least one summer melting season (World Meteorological Organization, 1970), and MYI has a higher albedo compared with FYI, which makes it have a greater impact on climate. In addition, MYI with a larger thickness poses a greater risk to sea vessels than FYI (Walker et al., 2006). Therefore, the discrimination of MYI is of great significance.Common polar sea ice observation methods include ship, aircraft, and satellite observations. Compared with ship and aircraft detection, satellite observation is more efficient, lower cost, and less susceptible to the influence of complex terrain and unstable climate. In particular, microwave remote sensing has the ability of penetrating clouds and working all day, which makes it an important means for polar sea ice observation.Passive microwave sensors monitor polar sea ice by measuring brightness temperature (Tb). The brightness temperature is affected by the physical properties of sea ice such as salinity and brine volume (Shokr & Agnew, 2013). FYI with high salinity has higher Tb than MYI with low salinity. Based on the Tb difference between MYI and FYI, many studies on MYI discrimination using passive microwave sensors have emerged. In 2012, Comiso (2012) derived the MYI concentration from 1978 to 2010 by using Tb obtained from passive microwave observations. In 2015, Hao and Su (2015) developed a modified NASA team algorithm to derive MYI concentration by adding the Advanced Microwave Scanning Radiometer-Earth Observing System sensor 6.9 GHz brightness temperature data and sea ice concentration. In addition to directly using the difference between MYI and FYI in passive microwave sensor parameters to perform MYI concentration retrieval, indirect use of sea ice motion products for MYI identification is also an important application of microwave radiometers (Fowler et al., 2004;Tschudi et al., 2010).

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Section: Parameter Selectionmentioning

confidence: 99%

Arctic Sea Ice Type Classification by Combining CFOSCAT and AMSR‐2 Data

Zhao

Zhai

et al. 2022

Earth and Space Science

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“…Driven by wind and ocean currents, the scatterometer-observed sea-ice margin has been observed to move as much as 50 km in one day [42]. A number of investigators have developed algorithms for discriminating between sea ice and open ocean by using only scatterometer data [36], [43]- [48]. Scatterometerderived ice margins provide an independent data source of evaluating the variability of ice coverage [49], [50] compared to radiometers.…”

Section: A Sea Ice Extent Mappingmentioning

confidence: 99%

“…Those using pencil-beam scatterometers have focused primarily on MY and FY ice classification [48], [62], [63], while others working with fan-beam systems discriminate additional ice types [4], [39], [43], [61], [64], [65]. Other researchers have employed additional sensors to aid in classification [66]- [69].…”

Section: Sea Ice Classificationmentioning

confidence: 99%

Polar Applications of Spaceborne Scatterometers

Long

2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Wind scatterometers were originally developed for observation of near-surface winds over the ocean. They retrieve wind indirectly by measuring the normalized radar cross section (σ o ) of the surface, and estimating the wind via a geophysical model function relating σ o to the vector wind. The σ o measurements have proven to be remarkably capable in studies of the polar regions where they can map snow cover; detect the freeze/thaw state of forest, tundra, and ice; map and classify sea ice; and track icebergs. Further, a long time series of scatterometer σ o observations is available to support climate studies. In addition to fundamental scientific research, scatterometer data are operationally used for sea-ice mapping to support navigation. Scatterometers are, thus, invaluable tools for monitoring the polar regions. In this paper, a brief review of some of the polar applications of spaceborne wind scatterometer data is provided. The paper considers both C-band and Ku-band scatterometers, and the relative merits of fan-beam and pencil-beam scatterometers in polar remote sensing are discussed.

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“…The validation results show that the OSCAT derived sea ice extent falls between the SSM/I 0% and 30% sea ice concentration. Li et al used Fisher's linear discriminant analysis and image processing technology to map the sea ice extent based on HY-2A/SCAT data, where the combination of five parameters including polarization ratio, HH and VV polarized measurement and their corresponding daily standard deviations were verified to identify sea ice and water effectively [33]. The results show the HY-2A/SCAT ice extent falls between SSMIS NASA Team 5% and 30% ice extent.…”

Section: Introductionmentioning

confidence: 99%

Sea Ice Monitoring with CFOSAT Scatterometer Measurements Using Random Forest Classifier

et al. 2021

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The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). This paper performs sea ice monitoring with the CSCAT backscatter measurements in polar areas. The CSCAT measurements have the characteristics of diverse incidence and azimuth angles and separation between open water and sea ice. Hence, five microwave feature parameters, which show different sensitivity to ice or water, are defined and derived from the CSCAT measurements firstly. Then the random forest classifier is selected for sea ice monitoring because of its high overall accuracy of 99.66% and 93.31% in the Arctic and Antarctic, respectively. The difference of features ranked by importance in different seasons and regions shows that the combination of these parameters is effective in discriminating sea ice from water under various conditions. The performance of the algorithm is validated against the sea ice edge data from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) on a global scale in a period from 1 January 2019 to 10 May 2021. The mean sea ice area differences between CSCAT and OSI SAF product in the Arctic and Antarctic are 0.2673 million km2 and −0.4446 million km2, respectively, and the sea ice area relative errors of CSCAT are less than 10% except for summer season in both poles. However, the overall sea ice area derived from CSCAT is lower than the OSI SAF sea ice area in summer. This may be because the CSCAT is trained by radiometer sea ice concentration data while the radiometer measurement of sea ice is significantly affected by melting in the summer season. In conclusion, this research verifies the capability of CSCAT in monitoring polar sea ice using a machine learning-aided random forest classifier. This presented work can give guidance to sea ice monitoring with radar backscatter measurements from other spaceborne scatterometers, particular for the recently launched FY-3E scatterometer (called WindRad).

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Polar Sea Ice Monitoring Using HY-2A Scatterometer Measurements

Cited by 21 publications

References 27 publications

Arctic Sea Ice Type Classification by Combining CFOSCAT and AMSR‐2 Data

Arctic Sea Ice Type Classification by Combining CFOSCAT and AMSR‐2 Data

Polar Applications of Spaceborne Scatterometers

Sea Ice Monitoring with CFOSAT Scatterometer Measurements Using Random Forest Classifier

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