Modelling and Estimation of Sea-Ice Reflectivity: MOSAiC Results on GNSS Reflectometry

Abstract

The L-band signals transmitted by Global Navigation Satellite Systems (GNSS) are a promising source for sea-ice remote sensing as they partly penetrate into ice upon reflection. Reflecting interfaces of sea-ice layer and snow cover contribute to the reflected signal. We present calculations of sea-ice reflectivity for GNSS signals based on a multilayer reflection model. Therefore, we assume coherent reflection conditions, satellite elevation angles of 3° to 30° and layers of the different media (with their relative permittivity): dry snow cover (~1), low/high-salinity ice layer (~3/~5) and underlying sea water (~70). Furthermore, we analyze reflectivity profiles estimated from GNSS reflectometry data of the first MOSAiC drift period (10/2019 to 06/2020) in the central Arctic. The comparison of model and estimation results shows that oscillation patterns occur when low permittivity coincides with low conductivity (low-salinity ice or dry snow). The patterns are particularly strong in late April 2020 after three days of warm air intrusion at the ice floe. We conclude that structural changes of the sea-ice/snow layers upon temperature change can be detected by reflectometry measurements. Upcoming satellites (e.g. ESA's nano-satellite PRETTY) will provide further opportunities to study reflectometry data over sea ice in the Arctic and Antarctic.