The manuscript is a valuable contribution to the delineation of lake ice/water cover using SAR imagery. Although coverage is limited to the ACP, the algorithm shows promise for application to broader areas across the northern hemisphere. The data provides advantages over optical imagery as expected of active microwave. The comparison to DW is a provides suitable validation for the ice fraction product.

There are some minor comments that would be good to address. Overall, the manuscript quality is very high but there a few key points that should be addressed.

There is clear indication that this product could be extended to be an operational product. Was there a reason that other operational products were not compared? For example, the CCI lakes lake ice cover product is available at roughly a 1km resolution and covers some of the lakes in the study area. A comparison to the CCI product would be beneficial due to the similarity between methods, both use a random forest algorithm to classify ice cover.

Another question for the authors relates to the choice of texture as a variable for the classifier. The citation provided was conducted for sea ice, however, to the reviewers knowledge no formal exploration of texture has been done for lake ice. Did the authors conduct any investigation into texture values for lake ice? For example, does the texture provide any context for heterogenous surfaces during freeze-up? break-up? Was an investigation done into the temporal evolution of the texture pattern?

There is no variable importance analysis provided - was this conducted? It would be of interest to users to see how the valuable the different variables were in the random forest classifier. The classifier used both SAR parameters and temperature variables, how does the model rate these? The concern here being that the classifier is being driven by temperature rather than SAR/EO data which is the original goal.

This work introduces a new product to support ice cover research in the north. It also presents a novel method that can be used in other regions to monitor ice cover fraction. This will be highly beneficial for those working in northern areas. The writing is clear and well-structured, and the data is easily accessible and well-labelled for the most part. I have only a few minor suggestions to improve clarity for the reader or user. 

‘small water bodies’, to me, refers to lakes, rather than rivers – so the validation sites being rivers seemed to come out of the blue while reading. The authors should consider clarifying in the abstract that they are referring to lakes and rivers as small water bodies.

While I understood the product had 2 bands of data, it wasn’t clear to me that each image did not cover the entire study area, so some clarification on that could be added to the text.

Overall model performance shows many pixels in the moderate-to-large error category (section starting around line 304). Given the data limitations, I agree that this is still a very useful product. The per-pixel quality product, however, could use some clarification. The text lists the values as percentages, but the product loads with a scale of 0 – 153.8. What is the link between uncertainty and the RRMSE values in the quality file? Perhaps even just in the .md file, some explanation of what exactly the RRMSE in the tif are in terms of uncertainty would be helpful.  

Also, can the user be given some cautions to watch for regarding reasons for high RRMSE? e.g., some of the larger errors occur in these regions (it did not appear to me to be particularly spatially based, from a brief review of the data product), or on this size or type of water body, etc.? or is there no discernable set of reasons? With the understanding that this is a data paper and not the venue for a deep exploration of the reasons, a brief comment or two to help the user would be beneficial.

A few minor things to note:

Figure 1: The map should include a panel with an overview of the site’s location for context. Even just an outline of Alaska would help to see where it is situated.   

Line 193: The authors explain that ascending and descending are processed separately, but don’t mention why. For clarity, it might be helpful to mention here for those less familiar with radar and the orbital times. (I fully agree with the methods used and the separate processing; this is just a clarification.)

Figure 2: b) Constructing the dataset using the 4 types of input data is clear. In panel C, then, the data set goes through the RF model, and it appears that two of the original datasets are then used again to generate the ice cover maps.  The text makes it clear that the RF model was applied to S1 to generate the 10m map. Perhaps the authors could make panel C clearer for workflow, but this might just be a matter of my interpretation.

Line 344: “This discrepancy may result from a mismatch between the observed freeze-up phase and the phase captured by remote sensing,” can the authors clarify what they mean here?