Abstract: The received data of synthetic aperture interferometric radiometer is susceptible to radio frequency interference (RFI) contamination, which affects the quality of subsequent products. The accurate localization of RFIs is a key to eliminating or mitigating the effects of RFI. In previous studies, the localization accuracy was improved by averaging the localization results of the same RFI in multiple snapshots. However, the localization and intensity of the same RFI vary in different snapshots, resulting in different localization accuracies of the same RFI in different snapshots. Therefore, simple averaging algorithms are difficult to achieve optimal localization accuracy. This article proposes a multi snapshot RFI localization fusion algorithm based on gaussian process regression (GPR). The algorithm obtains the corresponding localization error estimation model by using the GPR model to perform regression learning on the localization error data of the RFI in different directions. Then, the model is used to estimate the localization error of RFIs in different directions for each snapshot, and weights are assigned to the localization results of RFI in different directions for each snapshot based on this standard. Finally, the precise localization of the RFI was obtained through weighted fusion, which improved the localization accuracy of RFI. Through simulation experiments, the superiority of this method over the simple averaging algorithm was verified. In addition, this paper conducted experimental verification using soil moisture and ocean salinity satellite data, demonstrating the rationality and practicality of this method.