Soil salinization is a global problem, which seriously damages the ecological environment and considerably reduces agricultural productivity, especially in arid regions. Synthetic aperture radar (SAR) has been widely used in remote sensing due to its weather and sunlight independence. Polarimetric SAR has great potential for large-scale mapping and monitoring salt-affected soils. In this study, we investigate the characteristics of saline soil in extremely arid regions using dual-band quadrature-polarimetric (quad-pol) SAR images acquired by GF-3 (C-band) and ALOS-2 (L-band). Firstly, the effectiveness of the modified dielectric mixing model and integral equation model (IEM) in describing saline soil is evaluated. Secondly, the potential relationships between polarimetric parameters and salinity are discussed in both the C- and L-band, respectively, such as co-polarization ratio, scattering entropy H, and scattering angle $\alpha$. Finally, a linear regression model for monitoring salt content is established. The main contributions of this article are as follows: (1) Simulation results suggest that the radar backscattering coefficient is a weak function of salinity at low water content, but our experimental data show that soil salinity significantly contributes to the radar backscattering coefficient, which indicates the modified dielectric mixing model and IEM model is not applicable in extremely arid areas. (2) A negative correlation between the co-polarization ratio and salinity is observed, and the correlation coefficients are 0.64 (C-band) and 0.71 (L-band). Besides, scattering entropy and scattering angle exhibit a positive correlation with salinity in the C-band with correlation coefficients 0.686 and 0.669, respectively, whereas a negative correlation is found in the L-band with correlation coefficients 0.682 and 0.680, respectively. This can be attributed to the different penetration depths and sensitivity to the surface roughness of the electromagnetic waves at two frequencies. (3) A regression model for salinity estimating based on radar backscattering coefficient, co-polarization ratio, and scattering entropy is established, with a determination coefficient ($R^2$) of 0.79 and a root mean square error (RMSE) of 6.56%, allowing us to determine soil salinity from quad-pol SAR images without using backscattering models. Therefore, our results can be a reference for future soil salinity monitoring and inversion.

Polarimetric synthetic aperture radar (SAR) provides an effective way to understand the physical properties of planetary surfaces. Rocks are the products of specific geological events, recording the evolution of impact craters on airless bodies. Multiple-scattered radar echoes from the surface or near-surface rocks generally exhibit high circular polarization ratio (CPR). In this paper, we systematically investigate the relationship between radar CPR and rock abundance of lunar regolith using Mini-RF SAR data. A total of 51 craters with diameters of 11.8-200 km are selected for analysis. The mean values of Mini-RF CPR and Diviner rock abundance are calculated for the interior and exterior of each crater. Statistical results show a close correlation between Mini-RF CPR and Diviner rock abundance, where high radar CPR and Diviner rock abundance are observed at the blocky ejecta. However, we find the difference of mean CPR value between crater interior and exterior is smaller than that of Diviner rock abundance. This may be attributed to the near surface rocks that contribute to radar CPR but are not considered in the inversion of Diviner rock abundance. Therefore, the concept of volumetric rock abundance is introduced, indicating the proportion of rocks with diameters larger than one-tenth of the radar wavelength within the detection area. A three component CPR model is then developed to link radar CPR to volumetric rock abundance. Furthermore, an inverse powerlaw relationship between volumetric rock abundance and crater ages is discovered, which further indicates the feasibility of using SAR data to estimate rock abundance of lunar regolith.

The channel imbalance and crosstalks are the two major factors for the polarimetric calibration of the circular quad-polarimetric (CQP) synthetic aperture radar (SAR) systems. In existing methods using distributed targets, the latter is usually ignored, which may lead to unbearable errors in target classification, surface parameter inversion, and so on. To address this issue, this article proposes a modified iterative calibration method using distributed targets that satisfy quasi-azimuth symmetry to calibrate both the channel imbalance and crosstalks of the CQP SAR systems. First, a stable distributed target selection strategy is proposed based on the correlation coefficient of LL polarization and RR polarization, the cross- and co-polarization backscatter ratio, and the equivalent number of looks (ENL). These parameters are insensitive to polarization distortion, and their typical ranges are determined via numerical simulations. Their combination helps select the targets that satisfy the quasi-azimuth symmetry, which is critical for calibrating the crosstalks. Then, the calibration can be conducted using the selected target. Finally, the phase ambiguity of the receive channel imbalance ratio, commonly found in distributed-target-based algorithms, is eliminated using a dipole target. Through the calibration of Gaofen-3 data and the statistical analysis of residual distortion, the effectiveness of the proposed method is verified.

During the process of the airborne synthetic aperture radar (SAR) system platform in space, platform attitude deflection is inevitable. However, large attitude deflection angles are unacceptable for polarimetric calibration using point targets, especially the dihedral, which is very sensitive to the pointing angle of the radar. To mitigate the impact of attitude angles on calibration accuracy, attitude compensation of the corner reflector is necessary during the calibration process. The conventional approach to attitude compensation typically maps the three-dimensional attitude angle information to the one-dimensional polarimetric orientation angle (POA) information. However, the reduction of dimension inevitably results in information loss, leading to errors that affect calibration performance when the attitude angle is large. In order to ensure the accuracy of point target calibration, this paper proposes a novel point target compensation method based on the reflection theory of electromagnetic waves. This method is based on three-dimensional attitude angle information and has higher reliability than the POA method. Finally, this paper calculates the distance between the scattering matrices obtained after compensation based on the proposed method and the POA method to obtain the difference in the performance of the two methods. Through a simulation, this paper finds that when the attitude angle is small, the results of the two schemes are approximately the same, but as the attitude angle increases, the error between the two gradually increases. This suggests that the proposed method has greater advantages in the case of attitude deflection. Furthermore, the proposed method does not require additional information supplementation compared with the equivalent POA method, making it highly practical.

Manual selection of the distribution target and determination of its spatial location is not only time consuming but also has a serious impact on the polarimetric channel imbalances assessment if the area is incorrect. However, the existing automatic extraction methods are affected by the morphology of the features, and their local feature extraction capability is limited. On the other hand, only local information is considered, while the feature channels and global information are ignored, which in turn leads to the limited application scenarios, especially in some urban areas. The polarimetric channel imbalance estimation accuracy is so low that it exceeds the system tolerance limit. Therefore, in order to more effectively mine the polarimetric features of distributed targets in PolSAR images, we propose an attention-based multipath hybrid convolutional distribution target extraction network(AMHC-DTENet) for polarimetric channel imbalance assessment. Firstly, in order to develop the local feature extraction capability of the hierarchical network, we design a hybrid convolutional module with adaptive adjustment of the receptive field size. Secondly, a polarimetric feature channel reconstruction module is constructed in order to utilize the spatial information of the polarimetric feature channels. Then, considering that the polarimetric information of the ground feature is sensitive to the relative geometry of the target attitude to the radar line of sight, the Vision Transformer architecture is used to capture polarimetric global information and extend it to multipath. Finally, Gaofen-3(GF-3) full-polarimetric data is used for experimental verification. Experimental results demonstrate the effectiveness and reliability of the proposed method.