Long-Term Pan-Arctic Evaluation of a Sentinel-1 SAR Sea Ice Extent Product and Insights into Model Integration
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| Publicado en: | Remote Sensing vol. 17, no. 18 (2025), p. 3166-3186 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| Resumen: | <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>A pan-Arctic sea ice extent product generated from over 85,000 Sentinel-1 images shows strong agreement with the AMSR2 sea ice concentration product and provides superior capability in depicting the marginal ice zone. <list-item> An Integrated Index is introduced to quantify sub-model contributions in the ensemble used for sea ice extent generation, revealing that three sub-models dominate the results. </list-item> What is the implication of the main finding? <list list-type="bullet"> <list-item> </list-item>The SAR-based sea ice extent product serves as reliable baseline data for both operational applications and scientific research. <list-item> The Integrated Index offers a methodological basis for optimizing integration strategies, with potential applications in future sea ice ensemble models. </list-item> Reliable sea ice extent (SIE) information is essential for Arctic navigation, climate research, and resource exploration. Synthetic Aperture Radar (SAR), with its all-weather, high-resolution capabilities, is well suited for SIE extraction. This study evaluates a pan-Arctic SIE product automatically generated from over 85,000 Sentinel-1 SAR images acquired between 2020 and 2023 using an integrated stacking U-Net framework. To validate its performance, all the SIE products are converted to sea ice concentration (SIC) and compared against the 3.125 km resolution Advanced Microwave Scanning Radiometer-2 (AMSR2) SIC products. The S1-derived SIC shows strong agreement with AMSR2 SIC, yielding a Pearson correlation of 0.99 and annual mean absolute differences between 5.93% and 7.85%. Case analyses demonstrate that the S1 products effectively capture small-scale ice features, such as floes, which are often missed by AMSR2. Furthermore, we introduce an Integrated Index to quantify the relative contribution of each sub-model within the integrated stacking U-Net framework. The analysis indicates that three sub-models provide the primary contribution to the ensemble, offering insights into improving integration efficiency and guiding the design of more scientifically grounded ensemble strategies. |
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| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs17183166 |
| Fuente: | Advanced Technologies & Aerospace Database |