GNSS Reflectometry Correlation with Camera Images for Surface Type Determination

Seebany Datta-Barua, Roohollah Parvizi, Alison Banwell, Shahrukh Khan

Abstract:	We present estimates and uncertainties of surface reflectivity (SR) at L-band for a lake surface of mixed ice and water based on GNSS signals reflected from the surface with a dedicated downward-pointing GNSS antenna. The surface reflectivity, assumed to be from the first Fresnel zone of the L1 signal, is compared to two cameras’ images of the surface mapped to their location on the surface using lidar range measurements. The mean red value (MRV) of camera pixels within the Fresnel zone is used as a truth reference to distinguish surface ice from surface water. Three GPS satellites’ reflectivity of the L1 signal is examined from data collected on 14 Feb 2020 for two 20-minute segments of data collection. A correlation between SR and MRV is hypothesized, and the SR from PRN 26 is moderately positively correlated with the MRV. However, the data from PRN 16 are moderately negatively correlated, and from PRN 27 are uncorrelated although the sample size for this PRN is too small to be conclusive. Inspection of the lidar point cloud projected onto the camera image plane indicates a possible misalignment of the camera images being used with the estimated Fresnel zone position.
Published in:	Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023) September 11 - 15, 2023 Hyatt Regency Denver Denver, Colorado
Pages:	3257 - 3266
Cite this article:	Datta-Barua, Seebany, Parvizi, Roohollah, Banwell, Alison, Khan, Shahrukh, "GNSS Reflectometry Correlation with Camera Images for Surface Type Determination," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 3257-3266. https://doi.org/10.33012/2023.19456
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