Sea ice concentration retrieval using spaceborne GNSS-R during the melting period

Wang Yue; Xie Tao; Li Jian; Zhang Xuehong; Bai Shuying; Wang Minghua

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Wang Yue，Xie Tao，Li Jian, et al. Sea ice concentration retrieval using spaceborne GNSS-R during the melting period[J]. Haiyang Xuebao，2024, 46(x)：1–10

Citation:

Wang Yue，Xie Tao，Li Jian, et al. Sea ice concentration retrieval using spaceborne GNSS-R during the melting period[J]. Haiyang Xuebao，2024, 46(x)：1–10

Citation:

Wang Yue，Xie Tao，Li Jian, et al. Sea ice concentration retrieval using spaceborne GNSS-R during the melting period[J]. Haiyang Xuebao，2024, 46(x)：1–10

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Sea ice concentration retrieval using spaceborne GNSS-R during the melting period

Wang Yue^1
,,
Xie Tao^{1, 2, 3, 4
,
,},
Li Jian^{1, 3, 4},
Zhang Xuehong^{1, 3, 4},
Bai Shuying^{1, 3, 4},
Wang Minghua^{1, 3, 4}

1.
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and Technology Center, 266200, Qingdao, Shangdong Province, China
3.
Technology Innovation Center for Integration Applications in Remote Sensing and Navigation, Ministry of Natural Resources, Nanjing 210044, China
4.
Jiangsu Province Engineering Research Center of Collaborative Navigation/Positioning and Smart Application, Nanjing 210044, China

Received Date: 2023-05-30
Rev Recd Date: 2023-11-21

Available Online: 2024-03-14

Abstract

Abstract

In this paper, a high spatial-temporal resolution sea ice concentration estimation method for the Arctic melting season is proposed, aiming to improve the overestimation of sea ice concentration in seawater by the Global Navigation Satellite System-Reflectometry (GNSS-R). The method utilizes machine learning algorithms to extract feature parameters from the Delay Doppler Maps (DDM) obtained through GNSS-R and combines them with sea surface temperature data to establish a LightGBM model. The inversion results are then subjected to correlation analysis and evaluation against reference sea ice concentration values. The model's performance is compared with the sea ice concentration product from OSI SAF, demonstrating good consistency, with correlation coefficient, mean absolute error, and root mean square error being 0.965, 0.061, and 0.090, respectively. This approach enables high-precision estimation of sea ice concentration in the Arctic marginal ice zone.
- GNSS-R,
- DDM,
- melting season,
- sea ice concentration,
- LightGBM,
- Arctic

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References(25)

References

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