EGU24-17505, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Determining snow material properties from near-infrared photography

Lars Mewes1, Benjamin Walter1, Jon Buchli2, Valeria Büchel2, Markus Suter2, Martin Schneebeli1, and Henning Löwe1
Lars Mewes et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland (
  • 2Davos Instruments AG, Davos Platz, Switzerland

It is well understood that snow is a complex, porous material, whose microstructural changes directly affect its physical properties. Therefore, – to gauge the snow's role within the climate system – it is of interest to accurately measure and characterize the spatio-temporal variability of snow surfaces and snowpacks.

On a local scale, for example inside a snowpit during a field campaign, snow measurements are often taken in a manual, point-like fashion resulting in single, one-dimensional profiles with a sampling resolution of a few centimeters. At this resolution thin layers are difficult to observe and spatial inhomogeneities of the snowpack are missed. State-of-the-art X-ray microtomography (μ‑CT) scans of snow provide excellent spatial resolution,1 however, the added experimental constraints prevent sampling extended spatio-temporal domains.

To address some of these limitations, we propose to use near-infrared (NIR) photography2 with 940 nm illumination to determine the snow's specific surface area (SSA) and density. Our device – called SnowImager – achieves millimeter resolution and covers a spatial extent of a few square meters, such as the surface area of a snowpit wall. While the SSA is determined directly from the measured NIR image using the well-established asymptotic radiation transfer theory,3–6 the density dependence is introduced by physically truncating the illuminating and back-scattered light. It results non-trivially from the lateral component of the sub-surface scattering process and enables us to recover density profiles that compare well to reference data from density cutter and μ‑CT measurements. As a demonstration, we present the spatial variability of an Antarctic snowpack at an unprecedented level of detail, revealing an extremely high spatial variability of the snow microstructure.

Using near-infrared photography enables accurate and fast determination of snow material properties, whenever millimeter spatial resolution and a spatial extent of several square meters are required. It is thus ideally suited to simultaneously capture thin layers within the snowpack and spatial inhomogeneities over a centimeter to meter scale, which is relevant as ground truth measurement for climate research, remote sensing and avalanche forecasting among others.


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2. Matzl, M. & Schneebeli, M., J. Glaciol. 52, 558–564 (2006).

3. Bohren, C. F. & Barkstrom, B. R., J. Geophys. Res. 79, 4527–4535 (1974).

4. Warren, S. G., Rev. Geophys. 20, 67–89 (1982).

5. Kokhanovsky, A. A. & Zege, E. P., Appl. Opt. 43, 1589–1602 (2004).

6. Libois, Q. et al., The Cryosphere 7, 1803–1818 (2013).

How to cite: Mewes, L., Walter, B., Buchli, J., Büchel, V., Suter, M., Schneebeli, M., and Löwe, H.: Determining snow material properties from near-infrared photography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17505,, 2024.