1. To clearly demonstrate the effectiveness of the proposed U-Net + Multi-textRG approach, it is necessary to include a quantitative comparison table showing the performance of the baseline U-Net model. While the paper reports an overall accuracy (OA) of 84.9% for the proposed approach, validated using Landsat-8 and Sentinel-2 optical data, no equivalent performance metrics (e.g., OA, false positive rate, false negative rate) are provided for the baseline model. Including these results is required to have a first assessment of improvement of this approach upon the baseline model and to clarify the contribution of the Multi-textRG algorithm.

2. The authors validate their U-Net + Multi-textRG method using optical imagery from Landsat-8 and Sentinel-2, but there are still some concerns about how accurate and reliable the created labels are for evaluating a SAR-based classification model. The ground truth is based on QA snow/ice signs from optical data, with further refinement using MNDWI and manual visual interpretation. While these steps show a careful attempt to improve the label quality, they also introduce some uncertainty, for example, it’s not clear how consistent the manual corrections were, how the MNDWI threshold was selected, or how cloud-related misclassifications were handled across scenes. It’s important to note that optical and SAR sensors are sensitive to fundamentally different surface properties, and the QA-based snow/ice signs, despite being algorithmically generated, aren’t always reliable, especially under thin or patchy cloud cover. Even with visual correction, the final labels may still be subjective and difficult to reproduce. To further strengthen the assessment, it would be worth providing results on an SAR-based dataset such as ExtremeEarth, which includes pixel-level, high-quality annotations directly derived from Sentinel-1 images. If not, this limitation of the work should be clearly discussed and the claims of the paper modified to tone down.

3. The authors applied the Multi-textRG algorithm as a separate, post-processing procedure following the primary segmentation by the U-Net model. This approach is computationally inefficient, but more importantly, it appears that Multi-textRG refinement is non-learnable and end-to-end trainability is not supported, which defeats the purpose of deep learning-based approaches. The approach also introduces additional overhead and therefore may be less suitable for operational or  large-scale applications. Current studies have also suggested that texture information (e.g., GLCM texture features) can be more effectively utilized when incorporated into the model during training. For instance, the article "Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery" explains how the application of GLCM-based texture information during training can be utilized to improve the classification of sea ice using a Dual-Branch U-Net (DBU-Net).

4. The paper lacks a quantitative comparison with more recent state-of-the-art sea ice segmentation models or any other recognized baseline to make it suitable for publication at a high-end journal. It would be helpful to include comparisons with deep learning-based approaches such as DeepLabv3, which captures multiscale textural information through atrous spatial pyramid pooling and has showed improvement on benchmarks such as AutoICE. Including such comparisons would help clearly demonstrate the effectiveness of the proposed method relative to baseline models that support end-to-end training and are more easily replaceable and adaptable in operational or large-scale settings.

5. While the use of GLCM features here is well grounded in earlier work, the specific selection of Sum Average and Contrast features, together with chosen window sizes, normalization intervals, and nonlinear HV/HH ratio transformation functions, appears to be based primarily on internal experimentation using the J-M distance metric. These choices are not justified through ablation or sensitivity analysis, and their individual contribution or robustness is hard to assess, particularly when used on other SAR observations. Ideally, further experiments in larger and more polar regions are also necessary to evaluate the robustness of the proposed algorithm as stated in this paper, but at the very least, more ablation can shot the value of the work.