Assessment of Arctic Sea Ice Classification Ability of Chinese HY-2B Dual-Band Radar Altimeter During Winter to Early Spring Conditions

Zhang, Xi; Zhu, Yixun; Zhang, Jie; Wang, Qimao; Shi, Lijian; Meng, Junmin; Fan, Chenqing; Liu, Meijie; Liu, Genwang; Bao, Meng

doi:10.1109/jstars.2021.3114228

Cited by 9 publications

(3 citation statements)

References 47 publications

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“…There has been a recent focus on lead detection from moderate resolution thermal infrared (IR) satellite imagers [9,[13][14][15][16]. Sea ice leads can also be detected using satellite passive and active microwave data [10,[17][18][19][20], with the advantage being that clouds are transparent or semi-transparent at microwave wavelengths. Ongoing work is investigating the application of an AI technique towards the detection of leads in microwave imagery [21].…”

Section: Introductionmentioning

confidence: 99%

A 20-Year Climatology of Sea Ice Leads Detected in Infrared Satellite Imagery Using a Convolutional Neural Network

et al. 2022

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Sea ice leads, or fractures account for a small proportion of the Arctic Ocean surface area, but play a critical role in the energy and moisture exchanges between the ocean and atmosphere. As the sea ice area and volume in the Arctic has declined over the past few decades, changes in sea ice leads have not been studied as extensively. A recently developed approach uses artificial intelligence (AI) and satellite thermal infrared window data to build a twenty-year archive of sea ice lead detects with Moderate Resolution Imaging Spectroradiometer (MODIS) and later, an archive from Visible Infrared Imaging Radiometer Suite (VIIRS). The results are now available and show significant improvement over previously published methods. The AI method results have higher detection rates and a high level detection agreement between MODIS and VIIRS. Analysis over the winter season from 2002–2003 through to the 2021–2022 archive reveals lead detections have a small decreasing trend in lead area that can be attributed to increasing cloud cover in the Arctic. This work reveals that leads are becoming increasingly difficult to detect rather than less likely to occur. Although the trend is small and on the same order of magnitude as the uncertainty, leads are likely increasing at a rate of 3700 km2 per year with a range of uncertainty of 3500 km2 after the impact of cloud cover changes are removed.

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Section: Introductionmentioning

confidence: 99%

A 20-Year Climatology of Sea Ice Leads Detected in Infrared Satellite Imagery Using a Convolutional Neural Network

et al. 2022

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“…As a result, the waveforms can be labeled as the corresponding categories including TI, FYI, MYI and SW. Considering the coarse spatial resolution (tens of kilometers), sea ice types show slight changes in the Arctic in three days in winter, and other remote sensors with the similar spatial resolution, such as scatterometers and altimeters, also do the same process [15,17,43,44].…”

mentioning

confidence: 99%

Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles

et al. 2021

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Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles.

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“…Due to the smooth surface of the ice lead (specular reflection), the difference in the leading edge between the envelope of the power spectrum and the track window (a rectangle window) is significant, which could lead to the OCOG tracker being selected and the SMLE tracker being suspended [26]. Therefore, compared with Dong et al and Zhang et al [24,25], the results of waveform classification in this paper are more robust and credible due to using the HY-2B altimeter L1B products, which contain data from both the OCOG tracker and SMLE tracker.…”

Section: Discussionmentioning

confidence: 99%

Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data

Zhong

Jiang

et al. 2023

Remote Sensing

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Sea ice thickness is one of the essential characteristics of sea ice. Sea ice lead detection is the key to sea ice thickness estimation from radar altimetry data. This research studies ten different surface type classification methods, including supervised learning, unsupervised learning, and threshold methods, being applied to the HY-2B radar altimeter data collected in October 2019 in the Arctic Ocean. The Sentinel-1 Synthetic Aperture Radar (SAR) images were used for training and validation of the classifiers. Compared with other classifiers, the supervised Bagging ensemble learning classifier showed excellent and robust performance with overall accuracy up to 95.69%. In order to assess the performance of the Bagging classifier in practical applications, lead fractions from January 2019 to March 2021 based on the HY-2B radar altimeter data were mapped using the trained Bagging classifier and compared to the CryoSat-2 L2I data product. The results of the lead fraction showed the monthly variability of ice lead, and the ice lead had a reasonable spatial distribution and was consistent with CryoSat-2 L2I data products. According to these results, the Bagging classifier can provide an essential reference for future studies of Arctic sea ice thickness and sea level estimation from HY-2B radar altimeter data.

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Assessment of Arctic Sea Ice Classification Ability of Chinese HY-2B Dual-Band Radar Altimeter During Winter to Early Spring Conditions

Cited by 9 publications

References 47 publications

A 20-Year Climatology of Sea Ice Leads Detected in Infrared Satellite Imagery Using a Convolutional Neural Network

A 20-Year Climatology of Sea Ice Leads Detected in Infrared Satellite Imagery Using a Convolutional Neural Network

Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles

Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data

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