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Sea ice concentration retrieval using spaceborne GNSS-R during the melting period
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Yue Wang1, Tao Xie1, 2, 3, 4, *, Jian Li1, 3, 4, Xuehong Zhang1, 3, 4, Shuying Bai1, 3, 4, Minghua Wang1, 3, 4
Haiyang Xuebao | 2024, 46(5) : 127 - 136
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Haiyang Xuebao | 2024, 46(5): 127-136
Sea ice concentration retrieval using spaceborne GNSS-R during the melting period
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Yue Wang1, Tao Xie1, 2, 3, 4, *, Jian Li1, 3, 4, Xuehong Zhang1, 3, 4, Shuying Bai1, 3, 4, Minghua Wang1, 3, 4
Affiliations
  • 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
Published: 2024-05-31 doi: 10.12284/hyxb2024026
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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
Yue Wang, Tao Xie, Jian Li, Xuehong Zhang, Shuying Bai, Minghua Wang. Sea ice concentration retrieval using spaceborne GNSS-R during the melting period[J]. Haiyang Xuebao, 2024 , 46 (5) : 127 -136 . DOI: 10.12284/hyxb2024026
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Article Info
doi: 10.12284/hyxb2024026
  • Receive Date：2023-05-30
  • Online Date：2025-11-26
  • Published：2024-05-31
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History
  • Received：2023-05-30
  • Revised：2023-11-21
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Affiliations
    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
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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