Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.

The continuity and accuracy of real-time (RT) global satellite navigation system (GNSS) sensing of atmospheric water vapor can be seriously affected by a lack of connectivity. To mitigate this issue, a method has been developed that utilizes the International GNSS Service (IGS) ultra-rapid (IGU) satellite orbit and an established RT service (RTS) for satellite clock correction. This method ensures the accuracy of RT atmospheric water vapor estimation even when communication is interrupted. The established RTS utilizes variations in historical clock correction data from the user side to extrapolate its RT high-precision value. Through experimentation, it has been determined that using a quadratic polynomial and an eighth-order harmonic-based function along with 1-hour historical clock correction data is optimal for establishing this RTS method. The 2D and 3D accuracies of static Precise Point Positioning (PPP) can achieve centimeter to millimeter levels, and the estimated average Root Mean Square (RMS) of zenith tropospheric delay (ZTD) is equivalent to the RT archived products. When compared to the precipitable water vapor (PWV) results obtained from IGS ZTD products, the averaged RMS values of GPS, Galileo, and BDS-3 PWV reach 3.12 mm, 4.41 mm, and 6.43 mm, respectively, which can meet the accuracy requirements of general numerical weather forecasting and other applications during communication interruptions.

The regional global navigation satellite system (GNSS) real-time high-precision satellite clock error estimation is crucial for delivering accurate regional precise point positioning (PPP) services. To address challenges such as estimation accuracy, time consumption, uneven distribution of stations, and network latency in regional networks, which compromise the timeliness and accuracy of real-time satellite clock error services, an improved method for real-time clock error estimation and service is proposed. This method employs a satellite clock error model that utilizes polynomial and harmonic-based functions. It directly estimates the model coefficients using phase and pseudorange observation data from a regional GNSS network. PPP users can use these coefficients to derive satellite clock errors at any given time. The proposed method streamlines the process of real-time clock error estimation and service, potentially increasing service availability for GPS and BDS-3 by up to 16.1% and 21.3%, respectively. The clock error accuracy estimated by the proposed method is comparable to that of the single-epoch estimation method, while enabling PPP users to access 1–3 additional satellites clock error service. Experimental results demonstrate that the proposed clock error estimation method enhances the accuracy of both static and kinematic PPP, with a maximum improvement of approximately 33.3%. Additionally, it reduces positioning convergence time to varying degrees, with the most significant reduction being about 4.9 min. Overall, the proposed method not only improves the timeliness of real-time clock error services but also extends their availability. It proves to be both feasible and effective in enhancing positioning accuracy and accelerating convergence.

Based on the time-varying characteristics of GPS satellite differential code biases (DCB) between C1 and P1 observations, a method for directly estimating its model coefficients is proposed to streamline processing and enhance product accuracy. Evaluation over three days compares estimated DCB (C1–P1) values and assesses real-time service capabilities in single point positioning (SPP). Three-day DCB (C1–P1) estimates match single-epoch time-varying DCB (C1–P1) series fitting accuracy. SPP results show significant improvement over Orbit Determination in Europe (CODE), reducing average root mean square error in three dimensions by 12 cm and by 4 cm compared to Chinese Academy of Sciences. Real-time DCB (C1–P1) service maintains stable and accurate performance, comparable to fitted values of single-epoch time-varying series. Real-time SPP application shows the method enhances positioning accuracy compared to CODE by up to 35%, confirming the feasibility and effectiveness of the proposed model estimation method.