The manuscript describes the methodology and the dataset of sea ice deformation and rotation derived from S1 and RCN-based sea ice drift. It is very well written and provides all the necessary information for users of the new dataset. Aspects of deformation computation and uncertainty estimates are covered in good detail. Artefacts appearing on ice deformation maps due to the coarse resolution of SAR data used for ice drift retrieval are well highlighted.

It is an excellent work, and I have only two minor comments on the manuscript's text. However, I have a major comment on the provided data. I recommend updating the dataset before the manuscript can be published, and I provide detailed instructions on how it can be done efficiently. 

Minor comments on the text

Line 61. The phrase "combination of S1 and RCM SAR" reads as ice drift was derived from pairs containing S1 and RCM images. However, that was not the case. S1 was paired with S1 and RCM with RCM. Please rephrase for clarity.

Line 265: 'will BE assessed'

Major comment on the dataset

The dataset's structure is such that start/end coordinates of all three nodes of an element are provided for each deformation value.  Therefore, the coordinates are duplicated for two neighbour elements. Therefore, the same coordinates are provided (duplicated) as many times as a node is shared by neighbour elements. On average, the dataset size is three -- four times larger than it should be. Moreover, this structure is not convenient to use for plotting and analysis.

For illustration purposes, let's consider two elements (triangles) with four nodes (two nodes are shared). Then, the current data structure can be presented as:

element_index = [1, 2]

longitudes_corner1 = [lon1, lon2]

longitudes_corner2 = [lon2, lon4]

longitudes_corner3 = [lon3, lon3]

Here the number with lon inside the longitude vector indicates the node index.

It is clear that values of lon2 and lon3 are stored in the dataset twice. The same happens with all start / end longitudes and latitudes and with indices of elements.

I propose to use a more efficient data structure instead:

longitudes = [lon1, lon2, lon3, lon4]

triangulation = [[1, 2, 3], [2, 3, 4]]

Longitudes is a vector with unique values of coordinates for each node.

Triangulation is a 3 x N array with indices of corners for each element.

Transformation of data from the current structure to the new structure can be efficiently performed in a few lines of code. Here I provide just a very brief snippet using Python:

# stack indices of all nodes for one image pair

ids = np.hstack([ids1, ids2, ids3]) 

The manuscript is concerned with a dataset of sea ice and rotation rates (SIDRR) based on Sentinel-1 and RadarSAT SAR imagery is presented. The method used to build the dataset is very well described, along with a budget of the errors the estimated parameters can be affected.

This is an excellent work, the product it describes waits only to be exploited by the sea ice community.

I do not have specific comments, so I recommend publication in its present form.

General comments:

This study presents a Sea Ice Deformation and Rotation Rates (SIDRR) dataset, derived from sea ice motion vectors obtained through the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS). The dataset spans from 2017 to 2023 and covers the pan-Arctic region, using Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 (S1) and RADARSAT Constellation Mission (RCM) satellites. The paper offers valuable insights into the processing methods and analysis of sea ice deformations across multiple spatial and temporal scales, and serves as an important contribution to sea ice dynamics research.

The overall structure of the manuscript is sound, and the scientific value of the dataset is well articulated. The data processing methods are thorough, and the analysis of uncertainties is a crucial and valuable aspect of the study. That said, there are several points where the paper could be improved. Below are specific suggestions and observations.

Specific comments:

1.    Line 40: The introduction of the RADARSAT Geophysical Processing System (RGPS) dataset lacks a clear transition and purpose. To improve clarity, it would be beneficial to emphasize why the RGPS dataset is insufficient for certain studies, particularly those requiring higher spatial and temporal resolution to capture fine-scale sea ice deformation. This would provide a stronger foundation for introducing the new dataset developed in the paper.

2.    Line 74: You should explicitly mention the spatial resolution of both products. The temporal resolution differences between S1 and RCM are also crucial for the analysis. S1 might have less frequent passes (especially due to the polar orbit), whereas RCM can provide higher temporal resolution with more frequent observations.

3.    Line 81: Explain the rationale for the 30% overlap to help the reader understand its importance. You could clarify whether this overlap increases the confidence in feature tracking or provides redundancy to account for data gaps.

4.    Line 86: Define what is meant by "raw" data here. Is the goal to maintain the dataset's flexibility so that users can choose their own post-processing methods, or is there another reason to keep the data in this format?

5.    Line 201: "Validation of the SIDRR features is complicated by 1. the multi-scale nature of sea ice deformations...", the first point regarding the multi-scale nature could benefit from additional explanation. What exactly about the multi-scale nature complicates validation? Adding a sentence to explain how multi-scale deformations make it difficult to compare or validate the data would be helpful.