A B S T R A C TSpaceborne GNSS-Reflectometry (GNSS-R) is a powerful tool for soil moisture (SM) retrieval. However, complex topography and steep slopes over the Tibetan Plateau lead to significant signal loss and sampling sparsity. This study proposes a novel Dirichlet-prior-guided Vision Transformer (DG-ViT) framework to integrate multi-source observations from CYGNSS, Tianmu-1, and the FY-3E/F/G constellation. By incorporating land-cover-based prior weights into a gated fusion mechanism, the DG-ViT enhances physical consistency, significantly outperforming both traditional machine learning fusion methods and the standard ViT architecture without prior weights. To assess generalization, the fused surface reflectivity (SR) was coupled with six mainstream machine learning algorithms (RF, XGBoost, ANN, BP, SVM, and GBDT) for SM retrieval, with multi-scale validation conducted against ERA5 SM and nine in-situ stations. Results demonstrate that the DG-ViT framework achieves a daily SR coverage of 89.49%, representing an improvement of 20.13% and 14.32% over standalone CYGNSS and Tianmu-1 data, respectively. In addition, this fusion strategy overcomes the coverage gap of CYGNSS north of 38°N and compensates for the inability of individual FY-3 missions to support daily SM retrieval. In comparative assessments, the DG-ViT combined with RF yields superior performance (R=0.955, RMSE=0.039 cm3/cm3, bias=0.000 cm3/cm3), markedly enhancing retrieval accuracy relative to results derived from single-source CYGNSS or Tianmu-1 SR. In-situ validation shows ubRMSE values between 0.0232 and 0.0619 cm3/cm3, a 15.1% precision gain over ERA5 SM. The proposed DG-ViT combined with RF approach effectively exploits the complementarity among multi-spaceborne GNSS-R observations over the Tibetan Plateau, thereby compensating for the signal deficiencies of individual missions. This integrated approach provides a novel perspective for soil moisture retrieval in high-altitude areas, specifically addressing the intricate non-linear relationships between surface reflectivity and soil moisture.