## 1 Introduction
The method first inverts the linear trend of the IED over a long period of time. Then, modelled residuals are used to obtain the deviation term. The accuracy of this method is 10-40% higher than the voxel-based traditional node parameterization and linear time-dependent methods. However, most of these studies focused on improving the inversion methods within localized regions. The fundamental cause of the ill-posed problem is the insufficient number of observation rays, especially on a global scale. Thus, increasing the number of observation rays is critical for improving the accuracy of global CIT. Multi-source data such as ionosonde, incoherent scattering radar, ionospheric occultation, and satellite in situ measurements can effectively improve the accuracy of the reconstructed IED [13; 14; 15; 16; 17]. [PERSON] et al. [18] improved the discomfort of the inversion problem by imposing a priori constraints through the addition of virtual observations, and [PERSON] et al. [19] applied the side-ray to the CIT, which reconstructed the IED with 56.3% better accuracy during the ionospheric quiet period. To reduce the number of unilluminated voxels and improve the reconstruction accuracy of ionospheric IED, [PERSON] et al. [20] proposed a CIT algorithm based on a virtual reference station (VRS), which showed a 70% improvement in discomfort compared to the traditional method in the main reconstruction region. In addition, more effective 3D electron density modelling methods for the ionosphere have been developed based on compressed sensing and machine learning theory [21; 22; 23; 24; 25]. In recent years, the development of low earth orbit (LEO) satellites has made it possible to improve the accuracy of wide-area GNSS tomography; for example, large LEO constellations have been deployed or are planned to be deployed by SpaceX, OneWeb, and China Satellite Networks Group Co., Ltd. to carry out global communication applications [26]. In addition, GNSS occultation LEO constellations such as Spire, Tianmu, and Yunyao provide an important opportunity for the in-depth development of CIT [27; 28; 29]. Related scholars have also carried out exploratory studies. [PERSON] et al. [30] reconstructed a global 3D IED model with the help of the MART algorithm based on empirical orthogonal functions using global ground-based GNSS and satellite-based GNSS observations with a spatial resolution of \(10^{\circ}\times 5^{\circ}\times 50\) km (longitude \(\times\) latitude \(\times\) altitude), which significantly improved the accuracy of the model in the region of increased satellite-based GNSS observations. [PERSON] et al. [31] reconstructed the electron density distributions in the top ionosphere and plasma layer, based on slant TEC (STEC) observations from 27 LEO satellites; the results showed excellent agreement with satellite in situ measurements. However, due to the current limited number of LEO satellites, as only a relatively small number of actual observations can be provided globally, it is not yet feasible to realize global CIT with higher spatial and temporal resolution using ground-based and space-based measured GNSS data. [PERSON] et al. [32] used the MART method to analyze the effect of increasing LEO observations on CIT results based on global ground-based GNSS and LEO simulation observations. Their study demonstrated that integrating LEO observations could improve STEC retrieval accuracy by 15-20% under both high and low solar activity conditions, providing a valuable benchmark for optimizing emerging LEO constellations in ionospheric monitoring. However, they only used simulated observations of 192 LEO satellites at an orbital altitude of 1000 km, and did not discuss the effect of heterogeneous LEO constellations on GNSS CIT at different altitude and number levels. Additionally, the existing LEO constellations were not designed to take into account the effect of the improvement on the accuracy of GNSS CIT.