Global navigation satellite system interferometric reflectometry (GNSS-IR) is a new type of microwave remote sensing technology that can measure soil moisture content (SMC). GNSS-IR soil moisture retrieval methods based on the satellite signal-to-noise ratio (SNR) and triple-frequency signal combination have the following shortcomings: SNR does not always exist in the original GNSS file, and the number of triple-frequency signal observation satellites is small, resulting in GNSS-IR soil moisture observation time resolution being low. Based on the above problems, in this study, we constructed a soil moisture inversion method based on multisatellite dual-frequency combined multipath error is proposed: the multipath error calculation model of dual-frequency carrier phase (L4 Ionosphere Free, L4_IF) and dual-frequency pseudorange (DFP) without ionospheric effect is constructed. We selected the data of the five epochs before and after the time point of the effective satellite period to construct the multipath error model and error equation, and we solved the delay phase for soil moisture retrieval. We verified the method using Plate Boundary Observatory (PBO) P041 site data. The results showed that the Pearson correlation coefficients (R) of L4_IF and DFP methods at P041 station are 0.97 and 0.91, respectively. To better verify the results’ reliability and the proposed method’s effectiveness, the soil moisture data of the MFLE station about 210 m away from P041 station are used as the verification data in this paper. The results showed that the delay phase solved by multipath error and soil moisture strongly correlate. Pearson correlation coefficients (R) of L4_IF and DFP methods at MFLE station are 0.93 and 0.86, respectively. In order to improve the inversion accuracy of GNSS-IR soil moisture, this paper constructs the prediction model of soil moisture by using the linear regression (ULR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), and evaluates the accuracy of each model. The results showed that the soil moisture retrieval method based on multisatellite dual-frequency combined multipath error can replace the traditional retrieval method and effectively improve the time resolution of GNSS-IR soil moisture estimation. To perform highly dynamic monitoring of soil moisture, higher retrieval accuracy can only be obtained with a small epoch multipath error.

The BeiDou Navigation Satellite System (BDS-3) has officially provided services worldwide since July 2020. BDS-3 has added new signals for B1C, B2a and B2b based on old BDS-2 B1I and B3I signals, which brings opportunities for achieving high-precision time transfer. In this research, the BDS-3/BDS-2 combined penta-frequency common-view (CV) single-differenced (SD) precise time transfer model is established with B1I, B3I, B2I, B1C, B2a and B2b signals, including dual-, triple-, quad- and penta-frequency (abbreviated as DF, TF, QF and PF) ionosphere-free (IF) combination CV SD models. Taking four long baseline time links (from 637.6 km to 1331.6 km) as examples, the accuracy and frequency stability of the BDS-3/BDS-2 combined DF, TF, QF and PF SD time transfer models were evaluated. The experimental results show that the frequency stability of the TF, QF and PF SD models were improved by 2.5%, 5.3% and 8.5%, on average, over the DF SD model. Compared with the traditional DF (B1I/B3I IF combination) SD model, the standard deviation (STD) of the multi-frequency SD model was reduced by 5.9%, on average, and the frequency stability was improved by 4.0% on average, which had the most apparent effect on the improvement of short-term frequency stability. Specifically, the DF1 (B1C and B2a DF IF combination), TF1 (B1C, B2a and B2b TF IF combination), QF1 (B1C, B1I, B2a and B2b QF IF combination) and PF4 (B1C, B1I, B2a, B2b and B3I PF IF combination) SD models had better performance in timing, and the PF4 SD model had the best performance. Considering that the PF4 (one PF signal IF combination) SD model does not require an estimated inter-frequency bias and that its noise factor is minor compared with the PF1 (four DF signal IF combination), PF2 (three TF signal IF combination) and PF3 (two QF signal IF combination) SD models, we recommend the PF4 SD model for multi-frequency time transfer and the use of the PF2, PF2 or PF3 SD model to supplement the PF4 SD model in cases of penta-frequency observation loss.