A novel approach for soil moisture retrieval via fusing multi-spaceborne GNSS-R observations in entire Tibetan Plateau

Spaceborne GNSS-Reflectometry (GNSS-R) is a powerful technology 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 fuse multi-source observations from CYGNSS, Tianmu-1, and the FY-3E/F/G constellation. The novel DG-ViT framework incorporates land cover informed prior guidance into multi-spaceborne surface reflectivity (SR) fusion, and its performance is evaluated against single-source retrievals and traditional machine learning algorithms. To assess generalization, the fused SR was coupled with six mainstream machine learning algorithms (RF, XGBoost, ANN, BP, SVM, and GBDT) for 9 km daily 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 cm 3 /cm 3 , bias = 0.000 cm 3 /cm 3 ), 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 cm 3 /cm 3 , 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 entire Tibetan Plateau, thereby compensating for the signal deficiencies of individual missions. This fusion approach provides a novel perspective for SM retrieval in high-altitude areas, specifically improving daily SR coverage and SM retrieval accuracy through fusion of multi-spaceborne GNSS-R observations with land cover informed prior guidance.