High-Resolution Snowpack Monitoring from a Multi-Sensing Gondola

Many snow processes operate at the slope scale and sub-daily timescales, yet observations at these scales remain scarce. We demonstrate a mobile, automated monitoring approach that captures snowpack evolution in mass, thermal state, and liquid water content at the slope scale and sub-daily resolution.

The system combines lidar and photogrammetry with a new RFID-based microwave method, which uses a cable car to repeatedly observe an entire slope. The RFID approach relies on passive tags installed on the ground and covered by snow. When the cable car passes above them, a mobile reader interrogates them at 865–868 MHz, and they respond using backscattering communication. The variation in the signal's phase and signal strength over time is used to monitor snow water equivalent and liquid water content. In parallel, lidar and photogrammetry resolve snow depth and microtopography, while temperature sensors embedded in the tags measure basal snow temperature.

The approach was first implemented on the Pischa slope near Davos, Switzerland. During the 2024–2025 winter, more than 1,000 runs were completed along a 2 km transect spanning 700 m of elevation, and a second campaign is ongoing in the 2025–2026 winter.

The resulting dataset, unique in its spatiotemporal resolution, opens new possibilities to observe rapid snow processes at the slope scale.

Related references:

• Le Breton, Mathieu et van Herwijnen, Alec. 2025. « High-Resolution Aerial Snowpack Monitoring via Passive RFID (GONDO-RFID) ». P. 1‑6 in 2025 IEEE International Conference on RFID Technology and Applications (RFID-TA).
• Charléty, Arthur, Mathieu Le Breton, Morgane Magnier, Éric Larose, Laurent Baillet, Ludovic Moreau, et Alec van Herwijnen. 2025. « Locating RFID Tags Under Snow and Vegetation ». P. 1‑5 in 2025 IEEE International Conference on RFID Technology and Applications (RFID-TA).