Assessing GNSS Reflectometry for High‐Latitude 3D Ionospheric Imaging
Brenna Royersmith, Brian Breitsch, Jade Y. MortonAbstract
This study investigates whether Global Navigation Satellite System Reflectometry (GNSS‐R) measurements from low‐Earth‐orbit (LEO) satellites can improve three‐dimensional (3D) ionospheric electron density imaging, with a focus on high‐latitude regions. Conventional ionospheric tomography is limited by sparse ground‐based receiver coverage over oceans and high latitudes, as well as the restricted viewing geometry of Global Navigation Satellite System radio occultation (GNSS‐RO) measurements. GNSS‐R measurements provide grazing‐angle ray paths over polar regions that have not previously been incorporated into ionospheric tomographic imaging. Using simulated observations, we incorporate total electron content estimates from reflected GNSS‐R ray‐paths and low‐elevation (less than ) ground‐based signals into a voxel‐based tomographic inversion. These measurements are combined with conventional data sources, including ground‐based receivers, GNSS‐RO, and precise orbit determination links. Reconstruction performance is evaluated with and without the inclusion of GNSS‐R and low‐elevation measurements. Reconstructed electron density fields are evaluated using voxel‐intersection statistics across signal geometries and by quantifying reconstruction error relative to the simulated truth. Results show that GNSS‐R and low‐elevation signals substantially increase the number and spatial diversity of ray‐paths, particularly at high latitudes. This increased ray‐path diversity improves spatial resolution and reduces reconstruction error relative to inversions using only ground‐based and GNSS‐RO observations. To explore future mission capabilities, we test conceptual LEO constellation configurations to examine how satellite number and orbital configuration influence imaging performance. These findings highlight GNSS‐R as a valuable complementary data source for next‐generation 3D ionospheric imaging and provide a framework for incorporating real GNSS‐R measurements into future space‐weather monitoring systems.