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Roughness-Dependent Empirical Models of Grazing Angle GNSS-R Amplitude and Phase
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DOI：10.1109/tgrs.2026.3717656.png)
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Abstract
En 中文
This study aims to improve understanding of the effect of surface roughness on global navigation satellite system (GNSS) reflected signals. More than 60 million samples of Spire Global Inc., grazing angle GNSS-reflectometry (GNSS-R) data collected in 2021 over the Ross Ice Shelf (RIS), Antarctica, are analyzed in combination with surface height from the reference elevation model of Antarctica Digital Elevation Model (REMA DEM). Amplitude and phase rate (PR) distributions are observed to vary with surface roughness. An extended Suzuki (ES) model is applied to fit the amplitude distributions, and a two-component von Mises (VM)-uniform mixture model is applied for PR. These empirical models are then used for preliminary efforts to retrieve roughness from real GNSS-R tracks, with an average RMSE of 6.2 cm over one month of data, computed using 4-s windows. The statistical amplitude and phase modeling process identified here could serve as a basis for future grazing angle GNSS-R modeling, simulation, and geophysical variable retrieval studies over rough land surfaces.
Keywords:
Extended Suzuki (ES)
global navigation satellite system reflectometry (GNSS-R)
ice sheets
Rayleigh parameter (RP)
Ross Ice Shelf (RIS)
rough surface scattering
surface roughness
von Mises (VM)
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8.6
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2.1W
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10.7W
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