Updated Method for Ionospheric Tomography using New GNSS Signals

This work investigates the incorporation of GNSS reflectometry (GNSS-R) and low-elevation GNSS signals into three-dimensional ionospheric imaging using simulated data. Accurate characterization of ionospheric electron density is especially important at high latitudes, where strong coupling between the solar wind, magnetosphere, and ionosphere drives rapid variability, plasma structuring, and large-scale convection patterns. The spatial and temporal resolution of tomographic reconstructions remains limited by the availability and geometry of observations. Ground-based GNSS receiver networks provide dense temporal sampling but are geographically restricted, leaving large observational gaps over oceans and polar regions. Space-based techniques such as GNSS radio occultation (GNSS-RO) extend coverage to remote regions but remain relatively sparse and provide limited horizontal sampling due to their limb-viewing geometry.
Here, we examine whether GNSS-R, signals reflected from Earth’s surface and received by low Earth orbiting (LEO) CubeSats, can help address these limitations in three-dimensional ionospheric imaging. GNSS-R observations provide grazing-angle ray paths that traverse long slant distances through the ionosphere, producing near-horizontal signal geometries that are largely absent from conventional ground- or space-based datasets. These signals offer the potential to improve geometric diversity in tomographic inversions and increase sensitivity to horizontal ionospheric structures, particularly in polar regions. In addition, recently demonstrated low-elevation GNSS signals (<20° elevation), which are often discarded due to multipath and noise levels, can provide useful total electron content (TEC) measurements and expand the viewing geometry of ground-based signals. TEC estimates derived from precise orbit determination (POD) links between GNSS satellites and LEO spacecraft are also incorporated to introduce additional topside sampling paths.
To assess the impact of these complementary observations, we simulate ground-based GNSS measurements, GNSS-RO, POD, GNSS-R, and low-elevation signals within a voxel-based computerized ionospheric tomography framework. Satellite orbits and receiver geometries are simulated using realistic GNSS constellations and polar-orbiting CubeSat configurations. A synthetic “truth” electron density field is generated by perturbing the International Reference Ionosphere (IRI) model, and total electron content values are produced by integrating electron density along each simulated ray path. This controlled simulation environment allows reconstructed electron density fields to be directly compared with the known truth, isolating the effects of signal geometry and enabling quantitative assessment of reconstruction accuracy without measurement noise or instrument bias.
Reconstruction performance is evaluated using both voxel-intersection statistics and error analysis relative to the simulated ionosphere to determine how additional signal geometries influence sampling coverage and image quality. Reconstruction error is defined as the difference between the truth and reconstructed images. Results show that GNSS-R and low-elevation observations substantially increase the number and spatial diversity of ray paths, particularly at high latitudes where traditional observations are sparse. The addition of these grazing-angle signals improves tomographic coverage and reduces reconstruction error compared with inversions using only conventional ground-based and GNSS-RO measurements. Additional experiments explore conceptual LEO constellation configurations to evaluate how satellite number and orbital distribution influence achievable imaging performance.
These results demonstrate the potential value of GNSS-R measurements as a complementary data source for ionospheric tomography and highlight their relevance for future space-weather monitoring systems. By increasing high-latitude sampling and introducing new observation geometries, reflected GNSS signals may enable more accurate three-dimensional imaging of the ionosphere in regions where space-weather effects are often strongest but observational coverage remains limited.