| 28 Aug 2026
Technical note: Comparison of Modeled Snow Permittivity and Density Estimates of Shallow, Wet Snowpacks using UAS-Lidar and UAS-GPR Observations
Abstract. Seasonal snow density is a key determinant of snow water equivalent (SWE) and melt dynamics. However, characterizing its spatiotemporal variability is challenging, particularly in the shallow, ephemeral snowpacks of the eastern United States. This study evaluates the potential of integrating Ground Penetrating Radar (GPR) and LiDAR onboard an Uncrewed Aerial System (UAS) to assess the applicability of dielectric permittivity models for density retrieval in a transitional melt environment. Three field surveys were conducted at Kingman Farm, New Hampshire, where we collected UAS-GPR and in situ measurements of snow depth, density, and liquid water content (LWC) across open and sheltered fields between late February and early March, 2025. LiDAR‑derived snow depth and GPR travel time were used to retrieve snow permittivity; these values were compared against theoretical estimates derived from seven dielectric models, including the physically-based Complex Refractive Index Model (CRIM) and six empirical formulations that account for snow wetness. Field observations revealed a rapid transition from dry to wet snow, with GPR-derived permittivity exhibiting a non-linear increase as LWC exceeded 3–4 %. Comparisons showed that the physically based CRIM and the empirical Lundberg and Denoth models achieved comparably close agreement with GPR-derived permittivity (r = 0.70–0.71, RMSE ≈ 0.35). The remaining empirical models showed moderately larger error (RMSE = 0.40–0.48), while the Webb model showed weaker agreement (r = 0.54, RMSE = 0.79). These findings suggest that while empirical relationships performed reasonably well in transitional snowpacks outside the regions where they were established, applying physically-based models to UAS-GPR data offers an alternative and promising pathway for characterizing bulk snow properties in hydrologically complex environments.
Summary:
This study presents a novel and relevant dataset combining UAS-based LiDAR, GPR, and in-situ measurements to investigate snow permittivity and density in shallow, wet snowpacks. The dataset is particularly valuable because observations under transitional and wet-snow conditions are challenging and remain relatively scarce. I appreciate the authors’ effort in collecting this dataset and for submitting their work to HESS. The manuscript is well written.
However, I have several concerns that should be addressed and that will hopefully enhance the scientific relevance of this work.
General Comments:
Technical Comments:
Given that I expect larger changes to the manuscript if the authors follow (at least some) of my major comments, I focus my minor comments on the most important ones.
Title: It is unclear in the title what you are comparing the snow permittivity and density estimates with. Depending on how the authors will specify this study’s contribution you could change this to something like:
Technical Note: Comparing snow permittivity estimates from UAS-LiDAR and UAS-GPR with empirical and physically based models for shallow, wet snowpacks.
L 50 – 52: Several grammar issues
L 54 TWT, L 127 UAS and L128 GPR: Abbreviations are already introduced.
Figure 1: It’ hard to see the trees in panel b (SF), especially south of your area of interest.
L125: The methodology of the LWC measurements should be described in greater detail, including measurement depths, direction and sampling configuration.
L204: remove “retrieved”.
L268-269: This sentence is unclear and I would be careful here. As the authors also say later on, snow density measurements with snow tubes are extremely challenging and have high uncertainties for snow depths <10 cm.
Figure 4: The black symbol representing the mean value for OP 03/09 in the last panel (snow wetness <-> permittivity) does not seem to be located where I would expect the mean?
L329: The authors argue that the Denoth model was developed for snow densities up to 0.58 g/cm³. Following the data from Figure 4, only one data point of the presented dataset has a density higher than this value, which does not make this argument very convincing to me.
Section 4.4.: I generally find that references are missing from the discussion, specifically in this section and around L353ff.
L373ff: I cannot fully follow the reasoning behind this point. Canopy gaps can often host highly variable snowpacks in forested environments (see, for instance, Geissler et al. 2025), particularly during spring. However, this depends on the gap diameter relative to canopy height and on the dominant environmental drivers (e.g., climate and topography; see Mazzotti et al. 2023). Since this information is not provided for the SF site, it is difficult to understand what the authors mean by a “stable” snowpack. Based on the potential effects of climatic controls and gap size, I would not necessarily expect lower variability in snow structure and density compared to the OF site.
L387: I am not sure how the authors can justify the statement that density and liquid water content are the primary controls based on the presented data, particularly when considering Figure 4.
L398: I strongly encourage and support the authors in their efforts to make the dataset openly available following FAIR data principles, which would further enhance its value to the community and facilitate its use in future research.
References: Please include the DOI for references, following the journal's reference style. Also, please avoid capital letters (L450).
References
Dupuy, B., Grøver, A., Garambois, S., et al. (2026). UAV-borne GPR for snowpack characterization: Potential, limitations and operational guidelines. Cold Regions Science and Technology, 241, 104641., https://doi.org /10.1016/j.coldregions.2025.104641
Geissler, J., Mazzotti, G., Rathmann, L., Webster, C., & Weiler, M. (2025). Forest snow patterns derived using ClustSnow are temporally persistent under variable environmental conditions. Water Resources Research, 61, e2024WR038442. https://doi.org/10.1029/2024WR038442
Mazzotti, G., Webster, C., Quéno, L., Cluzet, B., and Jonas, T.: Canopy structure, topography, and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests, Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023, 2023.
Valence, E., Baraer, M., Rosa, E., Barbecot, F., and Monty, C.: Drone-based ground-penetrating radar (GPR) application to snow hydrology, The Cryosphere, 16, 3843–3860, https://doi.org/10.5194/tc-16-3843-2022, 2022.