spatial resolution of SAR imagery has reached sub-meter levels, imposing more stringent requirements on the geometric positioning accuracy of the imagery [7]. Geometric positioning accuracy is a critical technical parameter for SAR satellites and a key factor influencing the processing and application of spaceborne SAR image data. This accuracy can be impacted by various factors, including sensor errors, satellite ephemeris errors, and atmospheric errors. Among these, atmospheric errors are one of the primary constraints limiting the advancement of SAR image positioning accuracy. However, many SAR satellites, particularly domestically produced ones, provide imagery and auxiliary data without atmospheric correction, which adversely affects the geometric positioning accuracy of SAR imagery [8]. In the context of spaceborne SAR technology, geometric calibration plays a crucial role in high-precision positioning. These corrections are achieved through a well-defined mathematical model that maps the three-dimensional coordinates of terrestrial targets to their two-dimensional representations in SAR images [9]. Among the prominent models employed for this purpose are the rational polynomial coefficient (RPC) model [10] and the range-Doppler (R-D) model [11; 12]. Each model facilitates the accurate rendering of the earth's surface by translating spatial data into image coordinates efficiently. Among these, the R-D model simulates the physical imaging process of satellite sensors, offering high positioning accuracy. However, its mathematical structure is complex and relies on auxiliary data such as orbital parameters, attitude information, and other satellite platform parameters [13; 14]. Due to the confidentiality of satellite parameters and the model's complexity, its practical application is often constrained. To address this limitation, researchers have derived an alternative model, the RPC model, by fitting the R-D model. As a generalized model, the RPC model is compatible with diverse types of sensors and does not require in-depth knowledge of imaging parameters or sensor system characteristics [15]. Consequently, the RPC model has gradually gained widespread adoption due to its efficiency and versatility [16]. The RPC model has become the predominant geometric correction model for SAR imagery [17]. Originally, it was developed for optical satellite imagery, with its first application by the U.S. Space Imaging Corporation in 1999, which provided RPC parameters for IKONOS satellite imagery as part of its foundational imaging products [15]. Gradually, this approach has become the predominant model for processing the geometry of optical satellite imagery [18]. Subsequently, researchers have conducted more in-depth studies on the RPC model. Two approaches, \"terrain-dependent\" and \"terrain-independent,\" have been proposed based on the methodology for deriving RPC parameters. Comparative analyses between the geometric correction outcomes of the RPC and R-D models have been carried out, summarizing the sources of errors in the RPC model and proposing error compensation strategies [15; 19; 20; 21; 22; 23; 24; 25]. The RPC model was first applied to SAR imagery in 2006, when [PERSON] et al. utilized it for SAR image geo-location. They analyzed the accuracy of nine different forms of RPC coefficients and evaluated the effects of control grid size and elevation stratification on model performance [26]. [PERSON] et al. carried out a comprehensive investigation into the practicality and interchangeability of the RPC model in SAR imagery applications. They assessed several algorithms for parameter determination and found that the highest accuracy in model substitution was achieved using a third-order RPC model with distinct denominators. Their findings were further corroborated through tests across multiple SAR satellites with varying resolution levels [27; 28; 29; 30]. Despite its successes, the model continues to manifest a range of both systematic and random errors.