# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 1 Introduction
Polarimetric calibration methods can be categorized into three types: point target-based calibration, distributed target-based calibration, and hybrid calibration combining both point and distributed targets [28]. Point target-based radar polarimetric calibration algorithms use passive or active calibrators as reference targets to calculate distortion parameters in the polarimetric system [22]. These algorithms are straightforward and conceptually intuitive, requiring minimal additional assumptions about the scene, which reduces constraints and increases the flexibility in selecting reference targets. In 1991, [PERSON] [29] proposed a new model for radar polarization systems and provided a clearer explanation of the physical meaning of the model and its parameters. Following this model, [PERSON] [30] further proposed several point-target-based calibration schemes for calibrating NASA's airborne SAR systems, the more representative ones being the calibration schemes for the SIR-C and DC-8 SAR systems. The algorithm utilizes three polarization radar active calibrators with known scattering matrices for polarization calibration and uses dihedral and trihedral CRs deployed in the scene for accuracy assessment. [PERSON] [31] proposed a polarimetric calibration method based on two dihedral reflectors with different rotation angles and a trihedral reflector. Considering that radar antenna characteristics and other factors can cause calibration parameters to exhibit spatial variability along the range direction, achieving more precise calibration results would require deploying a large number of polarimetric calibrators along the range direction. This inevitably increases costs and calibration complexity. Distributed targets, such as dense forests like the Amazon rainforest, large deserts, and flat grasslands, are also frequently used for the calibration of spaceborne radar systems. These targets require minimal human effort and have garnered significant research attention due to their practicality. Among the commonly used hybrid target methods combining point and distributed targets, [PERSON]'s [32] algorithm assumes that the correlation between the polarization scattering of distributed targets is minimal; [PERSON] [33] uses natural targets with known scattering matrices for calibration; the [PERSON] [34] algorithm utilizes the reflection symmetry and scattering reciprocity of the targets to solve for four different crosstalk parameters, considering system noise errors during calibration, thus achieving more accurate cross-channel imbalance errors; the [PERSON] [35] method uses the average covariance matrix of target samples for calibration. By continuously updating the covariance matrix and solving for the polarization residuals in each iteration, the algorithm converges to obtain the overall polarization error parameters for image calibration. Polarimetric calibration algorithms that rely solely on distributed targets, without the use of artificial calibrators, have been a challenging research issue in recent years. [PERSON] [36] used forest canopies and bare soil as calibration reference samples to calculate co-polarization imbalance parameters. [PERSON] [23] and [PERSON] [37] used artificial structures in urban areas as corner-like reflector targets for calibration. [PERSON] [38] utilized the scattering characteristics of bare soil to solve for polarization error parameters, demonstrating the potential of performing SAR image calibration without artificial calibrators. The Freeman method is currently used in the polarimetric calibration mission of Chinese quad-polarization SAR satellites. [PERSON] et al. [39] designed and developed a new polarimetric ARC with a dual-polarized transmit/receive antenna for GF-3, verifying that the device has a polarization isolation better than 44 \(\pm\) 0.2 dB and a polarization imbalance better than \(\pm\)4\({}^{\circ}\). In 2017, [PERSON] et al.