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.