Dear authors, 

In general, the article made a pleasant impression, is well written and fully represents the methodology and results of the study. 

In this kind of research, the most important challenge is to create training samples. Based on open FDB data and to improve classification accuracy, it is worth conducting field validation, but on the scale of even regions, this task is very difficult and time-consuming. On the other hand, the frequency of FDB data updates is important. The classifier's accuracy may be impaired by deforestation that is overgrown with fast-growing vegetation in a year or two, which we have encountered in our research.  

It is not clear from the text of the article why the Random Forest classification algorithm was chosen. This algorithm is very popular among researchers, although there are other effective algorithms whose results would be interesting to compare, especially on a national scale.

Perhaps, to improve the quality, it is worth dividing the territory of Poland, for example, by geographical provinces, although such a division is rather arbitrary. 

The suggestions I have made do not in any way affect the quality of the work, and therefore I recommend it for publication.

Dear Authors,

this manuscript covers a topic of constant relevance, is clearly written and for the most part easy to follow. The results are also sensibly discussed and summarised.

However, there are still some questions that need to be addressed with regard to the methods. For the reader, it is not clear how the multi-year aggregated features were created; in some places STMs are mentioned (e.g. lines 136, 138, 141), in others composites (lines 142, 147, 150). If these are composites, it would be important to know which rule set was used to create them. In the case of temporal statistics, it would be important for traceability to know which metrics were included as features in the classification. A list/overview of all features included in the classification would also be desirable. This also applies to the additional explanatory variables (from line 167). Has the effect of these variables on the classification results been tested?

The period of the time windows for the creation of the composites/stms seems to overlap, at least in 2021 (Table 2.). Why was the summer period not shifted back? Is it possible that the same information was received in both STMs/composites?

To compensate for the differences in the number of training data, additional training data was taken from less common tree species from stands with a 60-80% mixing ratio (line 105). What proportion of the total number of trainingpixel in these classes did this account for? I think this is definitely relevant for the interpretation of the results. Perhaps this could be added to the appendix?

In general, however, I think that once these aspects have been clarified, nothing stands in the way of publication and I look forward to receiving comments on my remarks.