The paper of Attiah et al. „Lake Surface Temperature Dataset in the North Slave Region Retrieved from Landsat Satellite Series - 1984 to 2021“ brings an interesting overview of long-term dataset in the high latitude regions where the historic (but also recent) in situ observations are scarce. It analyses more than 500 lakes which also allow to analyse spatial variability of the temperature across the whole region.

I appreciate the detailed description of the methodology including data quality control. As not being a remote sensing expert I can’t judge on every detail of the process. Therefore, I stress that this issue is hopefully reviewed by other reviewers.

I would suggest including more details on the validation of the dataset against the in situ observations. This is to me essential for the reader/user as this basically can tell us something about how reliable the Landsat-derived results are. As I understand, the observations are made manually but some of them with use of automatic loggers. You demonstrated that the individual datapoints are well represented, but are the statistical parameters derived from in situ and remotely sensed datasets comparable in terms of open water season average LST for example?

I think it is essential to distinguish between water LST and temperature of a frozen/snow covered lake. I think it would be worth including this information in the dataset. This would also allow the user to estimate changes in lake ice cover duration, date of freezing and melting.

You stated that the aim of your work is to distribute the data among the public/authorities. I don’t think that any of these people would be able to process or use the data in its present form. I think that it would be great if you can also provide the reader/user with processed dataset of LST average values (for example) – in a table form. My suggestion would be to generate: annual average LST for open water season, length of the open water, melt date, freezing date.

Dear Authors,

Thank you for creating “Lake Surface Temperature Dataset in the North Slave Region Retrieved from Landsat Satellite Series”. Without a doubt, it will be useful for both scientific and applied research in the country with the largest number of lakes on the planet. Its relevance increases in the conditions of global and especially regional (high latitudes) warming.

I do not presume to judge the technical side of the remote analysis of the surface temperature of lake water, since I am not an expert in this field. I hope that some experts will give you more valuable recommendations. As a geographer, I am worried that you approached the formation of the database and its primary analysis formally, without taking into account the geographical differentiation of the natural essence of the lakes themselves, which largely determines the temperature state and temperature distribution over the surface (and not only) of the lakes.

Firstly, this refers to the typification of lakes according to the origin of their bottoms. Despite quite probable differences in morphometry, lake bottoms of the same origin have common similar patterns in the distribution of depths and, consequently, temperature characteristics, including the surface temperature of lake waters. The genetic grouping of lakes will make it possible to better understand the features of the temperature regime of lakes and their changes in space and time.

Secondly, the temperature regime of lakes is largely influenced by whether they are transit for runoff or not (without outflow). Grouping lakes on this basis will make your database even more valuable for science and practice.

It is quite obvious that the temperature regime of lakes is influenced by a much larger set of factors. However, the above groupings will take a big step forward in effectively using the database you have created.