Global Three‐Dimensional Ionospheric Tomography by Combination of Ground‐Based and Space‐Borne GNSS Data

The inhomogeneous distribution of Global Navigation Satellite System (GNSS) data is a significant problem for global GNSS computerized ionospheric tomography (CIT). By combining ground‐based GNSS data from 460 global stations with space‐borne GNSS data onboard eleven Low‐Earth‐Orbit (LEO) satellites, we employed the EOF (empirical orthogonal function)‐based multiplicative algebraic reconstruction technique (EMART) to reconstruct the global ionospheric electron densities. In comparison with GNSS + LEO (EMART_GL) solutions, the proportion of crossed voxels for GNSS‐only (EMART_G) solutions can rise by up to 12.54%, from 56.83% to 69.37%. Validation experiments were conducted under quiet and disturbed conditions using GNSS slant total electron content (STEC) references that were not previously used in ionospheric tomography. The EMART_GL solutions outperformed the EMART_G solutions when compared to the STEC references under quiet and disturbed conditions. The root mean square values of the CIT‐derived STEC for the EMART_G solutions can be decreased by 31.3% and 30.3% on average under quiet and disturbed conditions, respectively. The vertical TEC references from the Jason‐2 satellite were also used for validation over the ocean areas. Due to insufficient data over the ocean areas, the EMART_GL solutions showed no significant improvements. The reconstructed 3D IEDs were further validated with the COSMIC electron density profiles. Generally, the tomographic results of the EMART_GL solutions were more consistent with the COSMIC profiles when compared to those of the EMART_G solutions and the NeQuick‐2 model. It demonstrates that it is beneficial for global ionospheric tomography by combining ground‐based and space‐borne data. Plain Language Summary The computerized ionospheric tomography technique is extensively being applied to imaging ionospheric electron density (IED) distributions based on Global Navigation Satellite System (GNSS) observations. The voxel‐based model is usually utilized in ionospheric tomography, with the ionosphere being divided into many cubic voxels. However, predominant issues like unevenly distributed stations, a lack of data, and high‐elevation GNSS signals could affect the precise information about the ionosphere. The electron densities of voxels that are not crossed by GNSS rays cannot be accurately estimated. To address this problem, incorporating multisource data, such as data from Low‐Earth‐Orbit (LEO) satellites, make great sense.