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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. 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.
The SAR-based sea ice extent product serves as reliable baseline data for both operational applications and scientific research. The Integrated Index offers a methodological basis for optimizing integration strategies, with potential applications in future sea ice ensemble models. 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.
Details
; Xiao-Ming, Li 2
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [email protected] (H.Y.); [email protected] (Q.G.); [email protected] (Y.R.), University of Chinese Academy of Sciences, Beijing 100049, China
2 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [email protected] (H.Y.); [email protected] (Q.G.); [email protected] (Y.R.)
3 State Key Laboratory of Spatial Information System and Integrated Application, Beijing Institute of Satellite Information Engineering, Beijing 100095, China; [email protected]