In efforts to mitigate these issues, a variety of error correction techniques including translational adjustments, affine transformations, and polynomial adjustments have been implemented to refine the RPC model's geometric positioning accuracy. Moreover, incorporating supplementary data, such as control points, elevation details, and orbital data, has significantly enhanced the model's geometric consistency and reliability [31; 32; 33; 34; 35]. While the RPC model exhibits commendable accuracy in substitution and positioning, the aforementioned strategies predominantly tackle systematic discrepancies, leaving a gap in addressing non-systematic errors, especially those caused by atmospheric disturbances. Notably, certain spaceborne SAR satellites, particularly Chinese SAR satellites such as GF-3 and YG-13, did not account for atmospheric errors during slant-range calibration [36]. Consequently, the RPC parameters provided with these images also lack atmospheric correction. When atmospheric correction is not considered, the positioning accuracy of the model deteriorates significantly in areas with complex terrain and in long-strip imagery [37]. For high-resolution SAR satellites, atmospheric errors reaching several meters must be effectively corrected [36]. Atmospheric errors generally refer to non-systematic delays caused by atmospheric effects during signal propagation. These delays occur when signals travel through the atmosphere, experiencing prolonged travel times due to factors such as reflection, refraction, scattering, and absorption within the atmosphere. As a result, significant ranging errors are introduced, leading to target displacement and severely compromising the geometric positioning accuracy of imagery. Currently, spaceborne SAR systems mainly depend on atmospheric delay models that utilize external datasets for corrections, as cited in [36]. These delays are categorized into ionospheric and neutral atmospheric delays. The ionospheric delay is heavily affected by fluctuations in the ionosphere's total electron content (TEC) [38]. TEC computation models like Klobuchar [39], NeQuick [40], global ionospheric map (GIM) [41], and international reference ionosphere (IRI) [42] are commonly utilized. Particularly in mid- and low-latitude areas of China, the GIM dataset from the Center for Orbit Determination in Europe (CODE) is recognized for its highest precision [43]. Neutral atmospheric delay, on the other hand, is typically addressed using models such as Hopfield [44], Saastamoinen [45], UNB series [46], GPT series [47], IGGrop [48], and ITG [49]. Widely used meteorological datasets include ERA-Interim and ERA5 from the European Centre for Medium-range Weather Forecasts (ECMWF), along with NCEP/NCAR data from the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR). For China, ECMWF datasets demonstrate superior precision over those from NCEP [50], with ERA5 exhibiting substantial improvements in accuracy compared to ERA-Interim [51]. Therefore, a feasible approach for atmospheric delay correction is to combine CODE GIM and ERA5 datasets. Currently, the research on the atmospheric correction of spaceborne SAR images is mostly based on satellite and ephemeris parameters. [PERSON] et al. [52] utilized high-precision ground control data to eliminate atmospheric delay errors in the R-D model, improving the geometric positioning accuracy of YG-13 and GF-3 imagery from 43.86 m to 2.57 m and from 30.34 m to 4.29 m. Subsequently, [PERSON] et al. [53] removed the atmospheric delay errors in the R-D model, and further fitted the RPC parameters using the R-D model, improving the geometric positioning accuracy of the GF-3 image to 3 m. This validated the feasibility of replacing the R-D model of spaceborne SAR considering atmospheric delay with the RPC model. However, due to the confidentiality of the data, ordinary users often find it difficult to access satellite and ephemeris parameters.