Integrating Spaceborne GNSS-R Measurements in 3D Ionospheric Imaging: A Simulation Study

Both ground-based receivers and low-Earth orbiting (LEO) satellites have been used to receive GNSS signals and used to map the ionosphere. The total electron content (TEC) data along these signal paths can be used to generate two- and three-dimensional images of the electron density in the ionosphere using data assimilation techniques. However, there is a lack of data coverage across oceans and polar regions where it is difficult to have ground-based receivers. To gather information about the ionosphere in these regions and provide a more robust data resource, we can use reflected signals, or GNSS reflectometry (GNSS-R). Adding a reflected signal to existing ionospheric modeling methods requires accommodation for the geometry of the signal path. Currently, simulated ground- and space-based signals can be assimilated using inversion calculation methods which depend on the locations of the transmitted and received signals. In this project, we will simulate GNSS-R signal paths (along with ground receiver and GNSS-RO paths) from a GNSS satellite to a LEO CubeSat and generate three-dimensional images of TEC in the ionosphere using a newly developed inversion equation. This new equation is based on voxel-based inversion with the addition of reflected signal geometry. The addition of reflected signal paths will open the door to using real GNSS-R data in ionospheric imaging, allowing more signal paths to be used in the data assimilation necessary for tomography. Increasing the number of signal paths used increases the resolution and accuracy of the ionospheric images by allowing more real data to be reflected in the reconstruction. This method will allow for the adaptation of new signal geometry in ionospheric data assimilation and for better quality ionosphere modeling in the future.
Ionospheric tomography is the use of TEC measurements along a series of signal propagation paths to reconstruct the spatial and temporal variations of the electron density in the ionosphere. The spatial and temporal resolution of tomographic images of the ionosphere is sometimes limited due to the limited number of satellites utilized or by data gaps across the Earth. Some methods attempt to account for this by using interpolation of the TEC data, wavelet basis functions, or parameterized voxel-based techniques. In this study, ionospheric reconstruction is achieved using a voxel-based inversion technique. We simulate the 3D ionosphere using the IRI model. A Voronoi forward model is used to construct a matrix of ray path coordinates used in a simultaneous multiplicative algebraic reconstruction technique (SMART) of an ionospheric image (the IRI model) with given data. In previous work, this method has been used to reconstruct a simulated 3D image of the ionosphere electron density using both ground- and space-based radio occultation satellite ray paths. The purpose of adding reflected signals is to add more ray paths to the calculation as this method relies on high data density. Adding more signal beams to the inversion matrix means providing a denser ray path intersection in each voxel, therefore increasing the data density, and resulting in finer spatial resolution and better image accuracy.
We simulate GNSS-R signals ray path propagating through the ionosphere defined by IRI2016 model. The satellite positions are calculated based on real orbital parameters for GPS satellites and Spire CubeSats (such as orbit inclination, altitude, and mean anomaly) to ensure the reconstruction method will be compatible with real data. The specular point is calculated based on their positions and reflection surface and determined whether it is a coherent (usable) signal using a mask. These points will first be tested within small regions of the ionosphere until the method is sophisticated enough to generate images of the ionosphere globally. The goal of the project is to create an imaging method that will work globally, in both the Northern and Southern hemisphere, with GNSS-R signals. Adding real GNSS-R data to ionospheric modeling will provide more robust data coverage over oceans and polar regions where ground-based receivers cannot be implemented and fill data gaps in these regions, especially in the Southern hemisphere which has less land coverage than the Northern hemisphere.
Simulating the data gives us the opportunity to examine how improved the reconstruction of large- and meso-scale ionospheric disturbances is with the addition of reflected signal paths and illustrate the improvement in spatial and temporal resolution of how these disturbances evolve. These images were simulated using noisy TEC values along each ray path of a LEO satellite to 8 ground receivers. The expectation of this project is that by adding more ray paths, the quality of the image will improve. By testing the new inversion equation first with simulated signals, we will be able to integrate real GNSS-R data with ground-based and GNSS-RO data in the future and adjust the calculation if necessary. Having the ability to add a new type of geometry to the inversion method will also open the door to adding new data sources to this method in the future by adapting the equation.