With the onset of solar activities of the 25th solar cycle, ionospheric perturbations have become increasingly frequent, posing significant challenges to various applications of Global Navigation Satellite Systems (GNSS). The Rate of Total Electron Content (TEC) Index (Rate of TEC Index (ROTI)), commonly derived from GNSS data sampled at 30‐s intervals, fails to detect ionospheric irregularities smaller than the first Fresnel scale due to its low temporal resolution. This study investigates the efficacy of using 1‐s sampling intervals of multi‐GNSS (GPS, GLONASS, Galileo and BDS) data for monitoring ionospheric perturbations, comparing it to the conventional 30‐s sampling approach. Using real‐time multi‐GNSS observations in China, we computed ROTI values at both 1‐s (1s‐ROTI) and 30‐s (30s‐ROTI) intervals. Results indicate that 1s‐ROTI demonstrates higher magnitude and larger inconsistency across different GNSS constellations compared to 30s‐ROTI, with peak 1s‐ROTI values reaching 10 TECU/min and a maximum discrepancy of 1.0 TECU/min between different GNSS constellations. The inconsistency in 1s‐ROTI was found to correlate with different receivers types (e.g., Septentrio, Unicore, Trimble and Leica). Additionally, 1s‐ROTI exhibits better consistency and higher correlation with scintillation indices from Ionospheric Scintillation Monitoring Receivers (ISMR), with overall correlation coefficient exceeding 0.85 for all systems except for GLONASS. To mitigate inconsistencies in 1s‐ROTI, we propose different GNSS‐ROTI indices, with ROTI_mean and ROTI_ele showing the best performance in terms of correlation with scintillation indices and reflecting GNSS positioning performance degradation. The potential of 1s‐ROTI is highlighted for improving small‐scale ionospheric irregularities monitoring and indicating the GNSS positioning performance, especially in regions lacking ISMR.

BeiDou Satellite Based Augmentation System (BDSBAS) can notably improve the GNSS L1 positioning performance by transmitting the orbit, clock and ionosphere corrections. Aside from terrestrial geodetic receivers, the mass-market smartphones also benefit from the augmentation service. In this paper, the smartphone-based GPS-L1 standard point positioning (SPP) augmented by BDSBAS corrections is analyzed using Huawei-P40 smartphone. The quality of BDSBAS-B1C ionospheric information is evaluated by ionospheric observables retrieved from the selected 20 stations. Compared to the BDS-3 global ionosphere model (BDGIM) and the Global Ionosphere Maps (GIM) of the International GNSS Service (IGS), the accuracy of BDSBAS ionospheric correction is about 28% better than BDGIM and 11% better than the IGS-GIM. Compared to the GPS-L1 SPP, the accuracy of BDSBAS augmented smartphone positioning increases by 27% in the horizontal component and 41% in vertical component in the open-sky environment, and 50% improvement in both directions in the second experiment where the smartphone connects with an external geodetic GNSS antenna to reduce the negative effects of multipath errors.

Global Ionospheric Maps (GIMs) provide ionospheric corrections together with associated Root-Mean-Square (RMS) maps intended to characterize model uncertainty. However, the interpretation and reliability of these RMS values vary among the Ionospheric Associated Analysis Centers (IAACs) of the International GNSS Service (IGS) due to their generation methodologies. This study investigates the statistical characterisrics of unmodeled ionospheric errors in GIM products from IGS IAACs during 2016–2021.The results show that the residuals generally follow symmetric but leptokurtic distributions. After excluding outliers, the residuals can be well approximated by a normal distribution. Spectral and Allan variance analyses further reveal the presence of deterministic components with dominant periodicities of 1/6, 1/3, and 2/3 day. To improve the consistency between GIM-provided RMS values and actual ionospheric errors, we propose the Factor-Adjusted Ionospheric Residual Statistics (FAIRS) approach, which adaptively scales RMS values based on the residual distributions at contributing GNSS stations.The proposed FAIRS method has been implemented in generating the RMS maps of Chinese Academy of Sciences (CAS) GIM products. The validation using the Ionospheric Errors-Accuracy Diagram (IEAD) and RMS Bounding Percentage (RMSBP) demonstrates that FAIRS provides more realistic and responsive precision information than the original CAS RMS generation method, which employs a function fitting and global inflation approach. Furthermore, when applied to Precise Point Positioning (PPP) with ionospheric constraints, FAIRS reduces convergence time while maintaining reliable error bounding.

This study utilizes ionospheric total electron contents (TECs) data from China and its surrounding areas to compute the regional ionospheric semi‐variance using the Gaussian variogram. The regional ionospheric model is then constructed by introducing an additional stochastic term, named adjusted Spherical Harmonics Adding KrigING method (SHAKING), in which the stochastic Vertical Total Electron Content estimated using Kriging interpolation based on Ionospheric Variogram Model Parameters (IVMP) predict model. In the IVMP prediction model, two notable periodic patterns, that is, diurnal and annual signals with periods of 365.25/i days and 24/j hours, are recognized as Fourier series decompositions. These patterns capture the regular daily and yearly periodic variations in variogram parameters, and the IVMP prediction model is established using harmonic estimation methods. IVMP‐SHAKING is employed to generate the regional ionospheric map in China using multi‐GNSS data from 27 reference stations during days of the year 150–250 in 2022, with 14 rover stations used for evaluation. Results indicate that the proposed model exhibits a standard deviation of 4.85 TECu compared to the difference of slant TEC extracted from rover stations. Compared to existing models, including BDS‐3 broadcast ionospheric model (BDGIM), GPS broadcast model (Klobuchar), Galileo broadcast model (NeQuickG) and post‐processing Global Ionospheric Map released by IGS (IGSG), the accuracy of IVMP‐SHAKING improves by 6%–52%. Additionally, the Standard Point Positioning performance with different ionospheric corrections is analyzed on test stations. Results demonstrate that the horizontal and vertical positioning accuracy of the IVMP‐SHAKING model improves by 31%–55% and 16%–33%, respectively, compared to other three solutions.