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CHU Xiangyv, WANG Zhixiong, MIAO Jingjun, et al. Assessment of arctic sea ice extent and type retrieval using Chinese multisource spaceborne scatterometers[J]. Journal of Radars, in press. doi: 10.12000/JR26052
Citation: CHU Xiangyv, WANG Zhixiong, MIAO Jingjun, et al. Assessment of arctic sea ice extent and type retrieval using Chinese multisource spaceborne scatterometers[J]. Journal of Radars, in press. doi: 10.12000/JR26052
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Assessment of Arctic Sea Ice Extent and Type Retrieval Using Chinese Multisource Spaceborne Scatterometers

DOI: 10.12000/JR26052 CSTR: 32380.14.JR26052
  • 1.

    School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing, 210044, China

  • 2.

    Information Engineering University, Zhengzhou, 450002, China

  • 3.

    State Key Laboratory of Satellite Ocean Environment Dynamics, National Satellite Ocean Application Service, Beijing 100081, China

Funds:  National Key Research and Development Program of China (2022YFC2807003, 2024YFB3908000)
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    Abstract
  • Sea ice is an important indicator of global climate change, and its accurate monitoring is essential for climate research, polar navigation, and marine resource management. Spaceborne microwave scatterometers, with their all-weather, day-and-night, and wide-swath observation capabilities, are critical remote sensing tools for monitoring polar sea ice. In this study, scatterometer observations from three Chinese satellites (HY-2B, CFOSAT, and FY-3E) were used to develop Arctic sea ice extent detection and classification models for first-year and multiyear ice using a support vector machine. All models were created using a unified projection grid, shared sample labels, and a consistent classification framework. Daily sea ice products were generated using observations from March 2022 to February 2023. The scatterometers' performance differences were systematically evaluated by comparing them to the Ocean and Sea Ice Satellite Application Facility (OSI SAF), the National Snow and Ice Data Center (NSIDC), Moderate Resolution Imaging Spectroradiometer sea ice extent products, and synthetic aperture radar imagery. The findings show that the FY-3E dual-band approach performed best for ice-water discrimination, with annual mean overall accuracy and Kappa coefficient values of 99.11% and 97.39%, respectively. The results for CFOSAT, FY-3E Ku-band, and FY-3E C-band were all comparable and outperformed HY-2B. The FY-3E dual-band approach achieved the highest accuracy for sea ice type classification during the nonmelting period; using the OSI SAF sea ice type product as a reference, the mean overall accuracy and Kappa coefficient were 97.40% and 92.42%, respectively. Further cross-validation with the NSIDC sea ice age product revealed that the FY-3E dual-band approach performed the best, with an overall accuracy of 87.26% and a Kappa coefficient of 69.65%. CFOSAT, HY-2B, and FY-3E Ku-band all performed well at distinguishing sea ice types, whereas FY-3E C-band alone produced relatively low accuracy. Although different reference datasets affected the absolute accuracy of sea ice type classification, the relative performance of different methods remained consistent, indicating that the FY-3E dual-band results reflect genuine classification rather than merely fitting to the OSI SAF training labels. Overall, the FY-3E dual-band approach showed greater stability and consistency in annual sea ice extent detection and nonmelting-period sea ice type classification, highlighting the complementary benefits of dual-frequency scatterometer observations for polar sea ice monitoring. This study provides a reference for the operational application of Chinese scatterometers and multiband joint sea ice retrieval.

     

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    • References(37)
  • [1]
    谢涛, 赵立. 海冰密集度卫星遥感反演研究进展[J]. 海洋科学进展, 2022, 40(3): 351–366. doi: 10.12362/j.issn.1671-6647.20220209001.

    XIE Tao and ZHAO Li. Advances in sea ice concentration retrieval based on satellite remote sensing[J]. Advances in Marine Science, 2022, 40(3): 351–366. doi: 10.12362/j.issn.1671-6647.20220209001.
    [2]
    MOON T A, OVEREEM I, DRUCKENMILLER M, et al. The expanding footprint of rapid Arctic change[J]. Earth’s Future, 2019, 7(3): 212–218. doi: 10.1029/2018EF001088.
    [3]
    SANDVEN S, SPREEN G, HEYGSTER G, et al. Sea ice remote sensing-recent developments in methods and climate data sets[J]. Surveys in Geophysics, 2023, 44(5): 1653–1689. doi: 10.1007/s10712-023-09781-0.
    [4]
    LIGHT B, PEROVICH D K, WEBSTER M A, et al. Optical properties of melting first-year Arctic sea ice[J]. Journal of Geophysical Research: Oceans, 2015, 120(11): 7657–7675. doi: 10.1002/2015JC011163.
    [5]
    HALL D K, COMISO J C, DIGIROLAMO N E, et al. Variability in the surface temperature and melt extent of the Greenland ice sheet from MODIS[J]. Geophysical Research Letters, 2013, 40(10): 2114–2120. doi: 10.1002/grl.50240.
    [6]
    COMISO J C, CAVALIERI D J, and MARKUS T. Sea ice concentration, ice temperature, and snow depth using AMSR-E data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(2): 243–252. doi: 10.1109/TGRS.2002.808317.
    [7]
    KHALEGHIAN S, ULLAH H, KRÆMER T, et al. Sea ice classification of SAR imagery based on convolution neural networks[J]. Remote Sensing, 2021, 13(9): 1734. doi: 10.3390/rs13091734.
    [8]
    TILLING R L, RIDOUT A, and SHEPHERD A. Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data[J]. Advances in Space Research, 2018, 62(6): 1203–1225. doi: 10.1016/j.asr.2017.10.051.
    [9]
    LONG D G. Polar applications of spaceborne scatterometers[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(5): 2307–2320. doi: 10.1109/JSTARS.2016.2629418.
    [10]
    YUEH S H, KWOK R, LOU S H, et al. Sea ice identification using dual-polarized Ku-band scatterometer data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 560–569. doi: 10.1109/36.581968.
    [11]
    REMUND Q P and LONG D G. Automated Antarctic ice edge detection using NSCAT data[C]. IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing-A Scientific Vision for Sustainable Development, Singapore, Singapore, 1997: 1841–1843. doi: 10.1109/IGARSS.1997.609098.
    [12]
    REMUND Q P and LONG D G. Sea ice extent mapping using Ku band scatterometer data[J]. Journal of Geophysical Research: Oceans, 1999, 104(C5): 11515–11527. doi: 10.1029/98JC02373.
    [13]
    OTOSAKA I, RIVAS M B, and STOFFELEN A. Bayesian sea ice detection with the ERS scatterometer and sea ice backscatter model at C-band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2248–2254. doi: 10.1109/TGRS.2017.2777670.
    [14]
    RIVAS M B and STOFFELEN A. New Bayesian algorithm for sea ice detection with QuikSCAT[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 1894–1901. doi: 10.1109/TGRS.2010.2101608.
    [15]
    RIVAS M B, VERSPEEK J, VERHOEF A, et al. Bayesian sea ice detection with the advanced scatterometer ASCAT[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(7): 2649–2657. doi: 10.1109/TGRS.2011.2182356.
    [16]
    SWAN A M and LONG D G. Multiyear Arctic sea ice classification using QuikSCAT[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(9): 3317–3326. doi: 10.1109/TGRS.2012.2184123.
    [17]
    LINDELL D B and LONG D G. Multiyear Arctic sea ice classification using OSCAT and QuikSCAT[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 167–175. doi: 10.1109/TGRS.2015.2452215.
    [18]
    LINDELL D B and Long D G. Multiyear Arctic ice classification using ASCAT and SSMIS[J]. Remote Sensing, 2016, 8(4): 294. doi: 10.3390/rs8040294.
    [19]
    ZHANG Zhilun, YU Yining, LI Xinqing, et al. Arctic sea ice classification using microwave scatterometer and radiometer data during 2002–2017[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8): 5319–5328. doi: 10.1109/TGRS.2019.2898872.
    [20]
    ZHANG Zhilun, YU Yining, SHOKR M, et al. Intercomparison of Arctic sea ice backscatter and ice type classification using Ku-band and C-band scatterometers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4301718. doi: 10.1109/TGRS.2021.3099835.
    [21]
    ZHAI Xiaochun, WANG Zhixiong, ZHENG Zhaojun, et al. Sea ice monitoring with CFOSAT scatterometer measurements using random forest classifier[J]. Remote Sensing, 2021, 13(22): 4686. doi: 10.3390/rs13224686.
    [22]
    ZHAI Xiaochun, XU Rui, WANG Zhixiong, et al. Classification of arctic sea ice type in CFOSAT scatterometer measurements using a random forest classifier[J]. Remote Sensing, 2023, 15(5): 1310. doi: 10.3390/rs15051310.
    [23]
    LI Zhen, VERHOEF A, and STOFFELEN A. Bayesian sea ice detection algorithm for CFOSAT[J]. Remote Sensing, 2022, 14(15): 3569. doi: 10.3390/rs14153569.
    [24]
    ZENG Tao, SHI Lijian, SHI Yingni, et al. Polar sea ice monitoring using HY-2B satellite scatterometer and scanning microwave radiometer measurements[J]. Remote Sensing, 2024, 16(13): 2486. doi: 10.3390/rs16132486.
    [25]
    ZHAI Xiaochun, TIAN Shengrong, YE Yufang, et al. First results of Antarctic sea ice classification using spaceborne dual-frequency scatterometer FY-3E WindRAD[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 2000105. doi: 10.1109/LGRS.2023.3339720.
    [26]
    XU Rui, ZHAO Chaofang, ARNDT S, et al. Dual-frequency radar observations of snowmelt processes on Antarctic perennial sea ice by CFOSCAT and ASCAT[J]. The Cryosphere, 2024, 18(12): 5769–5788. doi: 10.5194/tc-18-5769-2024.
    [27]
    LIN Wenming, DONG Xiaolong, PORTABELLA M, et al. A perspective on the performance of the CFOSAT rotating fan-beam scatterometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 627–639. doi: 10.1109/TGRS.2018.2858852.
    [28]
    National Snow and Ice Data Center. Sea ice concentrations from nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave data, version 2[R]. NSIDC-0051, 2025. doi: 10.5067/MPYG15WAA4WX.
    [29]
    AABOE S, DOWN E J, and EASTWOOD S. Product user manual for the global sea-ice edge and type product[R]. GBL SIED OSI-402-d and GBL SITY OSI-403-d, 2021.
    [30]
    National Snow and Ice Data Center. EASE-grid sea ice age, version 4[R]. NSIDC-0611, 2024. doi: 10.5067/UTAV7490FEPB.
    [31]
    National Snow and Ice Data Center. MODIS/aqua sea ice extent daily L3 global 1km EASE-grid day, version 61[R]. MYD29P1D, 2026. doi: 10.5067/MODIS/MYD29P1D.061.
    [32]
    SNYDER J P. Map Projections: A Working Manual[M]. Washington, DC, USA: U.S. Government Printing Office, 1987: 157–163.
    [33]
    BUGAYEVSKIY L M and SNYDER J. Map Projections: A Reference Manual[M]. London, UK: Taylor & Francis, 1995: 109–122. doi: 10.1201/b16431.
    [34]
    NGHIEM S V and BERTOIA C. Study of multi-polarization C-band backscatter signatures for Arctic sea ice mapping with future satellite SAR[J]. Canadian Journal of Remote Sensing, 2001, 27(5): 387–402. doi: 10.1080/07038992.2001.10854882.
    [35]
    CHAPELLE O, HAFFNER P, and VAPNIK V N. Support vector machines for histogram-based image classification[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 1055–1064. doi: 10.1109/72.788646.
    [36]
    LI Xiaoming, SUN Yan, and ZHANG Qiang. Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(4): 3040–3053. doi: 10.1109/TGRS.2020.3007789.
    [37]
    XU Changjing, WANG Zhixiong, ZHAI Xiaochun, et al. SVM-based sea ice extent retrieval using multisource scatterometer measurements[J]. Remote Sensing, 2023, 15(6): 1630. doi: 10.3390/rs15061630.
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