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Arctic Sea Ice Thickness Retrieval from FY-3F GNSS-R Data Using an Ensemble Learning Approach.

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Title: Arctic Sea Ice Thickness Retrieval from FY-3F GNSS-R Data Using an Ensemble Learning Approach.
Authors: He, Qiu1 (AUTHOR) heqiu@dlmu.edu.cn, Zhang, Duling1 (AUTHOR), Li, Ying1 (AUTHOR), Wang, Kai1 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p2043. 23p.
Subjects: Ensemble learning, Satellite-based remote sensing, Artificial satellites, Machine learning, Glaciology, Global Positioning System
Geographic Terms: Arctic regions
Abstract: Highlights: What are the main findings? An ensemble stacking model using ET, LR, XGBR, and GBR for retrieving SIT from FY-3F GNSS-R data. Monthly SIT results throughout December 2024 to June 2025 in the Arctic region north. What are the implications of the main findings? The feasibility of FY-3F for sea ice thickness retrieval is demonstrated for the first time, confirming its potential to serve as an additional satellite asset for cryospheric observations. A comparative assessment of stacking models constructed from RF, DT, KNN, SVM, ET, GBR, and XGBR, together with an analysis of their performance across different sea ice thickness intervals and the robust monthly retrievals, provides methodological support for the development of operational sea ice products. Global Navigation Satellite System Reflectometry (GNSS-R), with its all-weather observation capability and low-cost advantage, provides an innovative solution for dynamic sea ice monitoring. In this paper, multi-dimensional features, including the GNSS-R Normalised Integrated Delay Waveform (N-IDW), the scattering coefficient and incidence angle derived from FY-3F satellite data, and the Delay Doppler Map (DDM) bistatic radar cross-section coefficient, are jointly used as model inputs. Experimental results show that this method successfully integrates FY-3F satellite data for sea ice thickness (SIT) retrieval, confirming the viability of employing FY-3F GNSS-R data for this purpose. An assessment of different algorithms in terms of their retrieval performance is conducted—covering RF, DT, KNN, SVM, ET, GBR, XGBR, and LR—and uses these eight models as base learners to construct different stacking models. After comparison, the ensemble stacking model using ET, LR, XGBR, and GBR as base models achieves the best retrieval performance. The MSE of this model for sea ice thickness retrieval reaches 0.0112 m, the RMSE reaches 0.1026 m and the correlation coefficient reaches 0.8876. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Arctic Sea Ice Thickness Retrieval from FY-3F GNSS-R Data Using an Ensemble Learning Approach.
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  Data: <searchLink fieldCode="AR" term="%22He%2C+Qiu%22">He, Qiu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> heqiu@dlmu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Duling%22">Zhang, Duling</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Ying%22">Li, Ying</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Kai%22">Wang, Kai</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p2043. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Satellite-based+remote+sensing%22">Satellite-based remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellites%22">Artificial satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Glaciology%22">Glaciology</searchLink><br /><searchLink fieldCode="DE" term="%22Global+Positioning+System%22">Global Positioning System</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Arctic+regions%22">Arctic regions</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Highlights: What are the main findings? An ensemble stacking model using ET, LR, XGBR, and GBR for retrieving SIT from FY-3F GNSS-R data. Monthly SIT results throughout December 2024 to June 2025 in the Arctic region north. What are the implications of the main findings? The feasibility of FY-3F for sea ice thickness retrieval is demonstrated for the first time, confirming its potential to serve as an additional satellite asset for cryospheric observations. A comparative assessment of stacking models constructed from RF, DT, KNN, SVM, ET, GBR, and XGBR, together with an analysis of their performance across different sea ice thickness intervals and the robust monthly retrievals, provides methodological support for the development of operational sea ice products. Global Navigation Satellite System Reflectometry (GNSS-R), with its all-weather observation capability and low-cost advantage, provides an innovative solution for dynamic sea ice monitoring. In this paper, multi-dimensional features, including the GNSS-R Normalised Integrated Delay Waveform (N-IDW), the scattering coefficient and incidence angle derived from FY-3F satellite data, and the Delay Doppler Map (DDM) bistatic radar cross-section coefficient, are jointly used as model inputs. Experimental results show that this method successfully integrates FY-3F satellite data for sea ice thickness (SIT) retrieval, confirming the viability of employing FY-3F GNSS-R data for this purpose. An assessment of different algorithms in terms of their retrieval performance is conducted—covering RF, DT, KNN, SVM, ET, GBR, XGBR, and LR—and uses these eight models as base learners to construct different stacking models. After comparison, the ensemble stacking model using ET, LR, XGBR, and GBR as base models achieves the best retrieval performance. The MSE of this model for sea ice thickness retrieval reaches 0.0112 m, the RMSE reaches 0.1026 m and the correlation coefficient reaches 0.8876. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18122043
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        Text: English
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        PageCount: 23
        StartPage: 2043
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      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: Satellite-based remote sensing
        Type: general
      – SubjectFull: Artificial satellites
        Type: general
      – SubjectFull: Machine learning
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      – SubjectFull: Glaciology
        Type: general
      – SubjectFull: Global Positioning System
        Type: general
      – SubjectFull: Arctic regions
        Type: general
    Titles:
      – TitleFull: Arctic Sea Ice Thickness Retrieval from FY-3F GNSS-R Data Using an Ensemble Learning Approach.
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              Text: Jun2026
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