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ORIGINAL RESEARCH article

Front. Mar. Sci.
Sec. Ocean Observation
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1464391

Evaluation and Fusion of Multi-Source Sea Ice Thickness Products with Limited In-situ Observations

Provisionally accepted
Tongtong Li Tongtong Li 1Yangjun Wang Yangjun Wang 2*Bin Wang Bin Wang 1Yan Li Yan Li 3Kefeng Liu Kefeng Liu 2Xi Chen Xi Chen 2Rui Sun Rui Sun 1
  • 1 Jiangsu Ocean Universiity, Lianyungang, China
  • 2 National University of Defense Technology, Nanjing, Liaoning Province, China
  • 3 Jiangsu Institute of Marine Resources Development, Lianyungang, China

The final, formatted version of the article will be published soon.

    Sea ice thickness (SIT) is a critical and sensitive parameter in the climate system, with its dynamic changes profoundly influencing global climate models, navigational routes, and the potential for Arctic resource development. Given the widespread application of current satellite remote sensing technology in monitoring SIT, significant uncertainties remain. This study first underscores the importance of in-situ observations as a direct measurement method for SIT. However, the limitations of in-situ data in terms of acquisition cost, spatiotemporal coverage continuity, and distribution uniformity significantly hinder the effective evaluation of multi-source SIT products. To address this, the study innovatively introduces the Triple Collocation (TC) method, which effectively mitigates the impact of errors from individual data sources on the overall evaluation results through a mutual validation mechanism among multiple satellite data sources. This allows for a scientific assessment of multi-source SIT products even in the context of scarce in-situ observations. The findings indicate that the TC method not only successfully resolves the challenges of multi-source data evaluation but also facilitates data integration among these products, significantly enhancing the overall accuracy and spatiotemporal consistency of SIT data.

    Keywords: sea ice thickness, Limited In-situ Observations, triple collocation, Multi-Source Data Evaluation, Multi-source data fusion

    Received: 14 Jul 2024; Accepted: 02 Oct 2024.

    Copyright: © 2024 Li, Wang, Wang, Li, Liu, Chen and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yangjun Wang, National University of Defense Technology, Nanjing, Liaoning Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.