Effects of Arctic Wetland Dynamics on Tower-Based GNSS Reflectometry Observations

A tower-based global navigation satellite system reflectometry (GNSS-R) experiment is set up in an Arctic wetland environment for investigating the possibility of monitoring wetland inundation and freeze/thaw (FT) dynamics which are additionally impacted by snow on the ground. Effects of inundation, snow cover, and soil FT state on observed GNSS-R signal-to-noise ratios (SNRs) are analyzed for horizontal (H) and vertical (V) polarizations. A simple classification approach is suggested to detect the inundated, frozen, or thawed soil state. A simple forward reflectivity model is formulated to evaluate the influence of snow cover, overlying frozen, or thawed soil, on the reflected GNSS signals. Reflectivity time series are simulated in H- and V-polarizations using in situ observations of the Arctic wetland site. The simulations are used to verify the tower-based observations, which show a significant impact of wet snow on reflectivity during melting conditions in spring. The observed SNR is strongly correlated with the Sentinel-1 backscatter coefficient. Generally, soil states detected by GNSS-R are in high agreement with ground truth soil states, especially for inundated and frozen soils. Wet snow conditions, however, complicate the correct timing estimation of soil thawing by inducing reflectivities of a similar order as thawing soil. It is recommended that GNSS-R land application models and retrieval algorithms consider snow cover effects to reduce false classification, especially in FT detection. Overall, the outcome of this study is relevant to the upcoming ESA HydroGNSS mission.

Automated summary

In this study, a tower-based GNSS-R experiment was conducted in an Arctic wetland to investigate the monitoring of wetland inundation and freeze/thaw dynamics. The effects of inundation, snow cover, and soil freeze/thaw state on the GNSS-R signal-to-noise ratios were analyzed, and a simple classification approach was suggested to detect the soil state. The findings have implications for the upcoming ESA HydroGNSS mission.