Review on the manuscript titled, “Leveraging Citizen Science, LiDAR, and Machine Learning for Snow Depth Estimation in Complex Terrain Environments” by Liljestrand et al.

The study tried to characterize and model the snow distribution using the Gaussian Process Regression (GPR) method and the Gaussian-based machine learning model (GMM). GMM and GPR method application for the Lidar observed snow distribution seems novel although they were tested using only one snapshot snow distribution. The land surface characterization in Figure 2 is nice, and the finding of elevation variability requirement for this method is interesting. However, since the transferability of this method may still be arguable, I recommend “major revision” for this review cycle for clarifications and further possible improvements. I have a few major points listed below:

1. The presentation of the data used in this study should be improved. The observed snow distribution by the airborne Lidar may be visualized and presented somewhere in the manuscript, perhaps instead of Figure 3.  It will be informative for readers to see the variability and the extent of the dataset.
2. Also, it is unclear when the LiDAR data collected. The snow distributions are highly dependent on season and year.  From the snow distributions (Figure 9), I speculate that it must be late spring.  Moreover, observation dates of the in-situ snow depth survey must be presented as well.  Were they exactly same day?  How good were they?  Were they (field data vs. Lidar) consistent each other?  It is unclear how the authors use actual field measured data.  I would suggest adding a data list table.
3. The assumption for the methodology must be further clarified. Based on my understanding, Gaussianity in local snow distribution is required while it may not be true.  I recall a recent publication in the same journal (TC) discussing non-Gaussianity of snow distribution (https://tc.copernicus.org/articles/18/5139/2024/).  Assumption of local Gaussianity may be addressed in the limitation statement in the discussion as a reminder.
4. I understand that there was no improvement by increasing sample number from 10 to100. It would be more useful if the author could quantify the ideal snow data point density (for instance, # of data point per unit area, perhaps). I understand it may be beyond scope of this study while lacking statement on potential transferability made this work just a case study based on single instantaneous snow distribution, which is rather weak.

Specific/minor point:

It is good to define the variables in the equations (2 through 4) as physical quantities (e.g. x = snow depth). Capital sigma (=covariance?) may be avoided because you use “summation” as same symbol.

See attached PDF