Exploring the performance of machine learning models for the GNSS-IR retrieval of seasonal snow height

Snow is a key variable of the global climate system and the hydrological cycle, as well as one of the most critical sources of freshwater. Therefore, measurements of snow-related parameters such as seasonal snow height (SSH) or snow-water-equivalent (SWE) are of great importance for science, economy and society. Traditionally, these parameters are either measured manually or with automated ground-based sensors, which are accurate, but expensive and suffer from low temporal and spatial resolution.

A new alternative for such systems is the use of GNSS observations, by application of the GNSS interferometric reflectometry (GNSS-IR) method. The technique enables users to infer information about soil moisture, snow depth, or vegetation water content. Signal-to-Noise Ratio (SNR) observations collected by GNSS receivers are sensitive to the interference between the direct signal and the reflected signal (often referred to as “multipath”). The interference pattern changes with the elevation angle of the satellite, the signal wavelength, and the height of the GNSS antenna above the reflecting surface. By comparing this reflector heights estimated for snow surfaces with those from bare soil conditions, snow height can be determined.

The estimation of reflector heights, and respectively SSH, is typically carried out using Lomb-Scargle Periodogram (LSP) spectrum analysis. This study investigates the potential of machine learning methods for this task, using similar input parameters as the standard GNSS-IR retrieval. Results from different supervised algorithms such as Random Forest (RF) or Gradient Boosting (GB) are shown for different GNSS sites and experimental setups. First investigations indicate that snow heights can be successfully obtained with machine learning, with results less noisy than with classical approaches.