Surface Reflectivity Variations of GNSS Signals from a Mixed Ice and Water Surface

Roohollah Parvizi, Shahrukh Khan, Li Jun Pan, Seebany Datta-Barua

Abstract:	This paper presents estimates of signal-to-noise ratio (SNR) and surface reflectivity (SR) over time of GPS L1 signals scattered off a partially frozen lake surface. A portable ground-based sensor system is used to collect both scattered Global Positioning System (GPS) signals and independent validation data (lidar and camera) from the surface. GPS front-end signals are collected from both a direct receiving antenna facing upward and from a reflection-receiving antenna facing downward. A data campaign is conducted on the Lake Michigan waterfront in Chicago during a period in which the lake surface consisted of a mixture of surface ice and water. The lidar surface scans are mapped with camera images and estimated specular reflection point positions to indicate the surface reflection type and to provide surface height relative to the sensors. A customized software defined receiver processes signals reflected from the lake surface. One ms of coherent integration and 1000 ms of incoherent integration of the reflected signal are used for correlation. The SNR is averaged over one-minute intervals. For two satellites whose reflection points scan across ice and water over time, the SNR and SR are computed over time. The SR is shown to be about 0.5 dB lower for liquid water than lake ice in unambiguous conditions. However, a variable surface type over an area smaller than the scattering zone can reduce the variation in SR. This system concept may be used in the future for more complete mapping of phase changes in the cryosphere.
Published in:	Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020) September 21 - 25, 2020
Pages:	3909 - 3919
Cite this article:	Parvizi, Roohollah, Khan, Shahrukh, Pan, Li Jun, Datta-Barua, Seebany, "Surface Reflectivity Variations of GNSS Signals from a Mixed Ice and Water Surface," Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), September 2020, pp. 3909-3919. https://doi.org/10.33012/2020.17756
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