Global navigation satellite system interferometry ( GNSS-IR) can extract effective information such as soil moisture and sea surface height by analyzing interference information between direct and reflected satellite signals. In order to reduce the weight of outliers by using robust regression method, this paper proposes to reduce or offset the influence of anomalous observation data on soil moisture inversion. In order to verify the application range of the model, multi-star fusion experiments are carried out on the basis of robust regression, which effectively improve the accuracy of soil moisture inversion. The results show that compare with the traditional linear regression method, the RMSE and MAE of the proposed method are reduced by 8. 38％ and 8. 91％ on average for a single satellite, and by 15. 18％ and 16. 42％ on average for two satellites, by 21. 00％ and 22. 97％ on average for three satellites, by 26. 25％ and 28. 71％ on average for four satellites.
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