The synthetic aperture radar (SAR) is a type of active radar that can obtain polarization scattering information of ground objects, which is an important supplement to optical remote sensing. This paper designs a high-precision quantitative SAR system that combines radiation and polarization calibration processing to achieve a subtle perception of the changes in soil moisture and straw coverage. In Yushu, Jilin, we conducted the first S-band agricultural remote sensing application experiment. The backscattering coefficient was measured under different water content and straw coverage conditions, and the results showed that the backscattering coefficient increased by about 2 dB and 6 dB, respectively. We estimated that the soil water content increased by about 0.01 cm³/cm³, which was consistent with the theoretical analysis. The polarization scattering characteristics also showed significant differences under different straw coverage. The results indicated that S-band quantitative SAR had an excellent response ability to water content and straw coverage, which provided a technical basis for further radar agricultural applications in the future.

For the challenges of high-precision mapping in complex terrain, a novel airborne Interferometric Synthetic Aperture Radar (InSAR) system is designed. This system, named ASMIS (Airborne Short-Baseline Millimeter-Wave InSAR System), adopts the coplanar antenna and a pod-type structure. This design makes the system lightweight and highly integrated. It can be compatible with small general aviation flight platforms. The baseline is millimeters in size, which greatly simplifies the unwrapping process. The coplanar antennas have two advantages: they maximize the baseline utilization and minimize the Doppler decorrelation and the motion error inconsistency. Acquisition campaigns of the system have been carried out in Boao, Bayannur, and Chengde, China. In the Chengde experimental area, we designed an antiparallel flight experiment to account for the topographic relief. High-precision Digital Orthophoto Maps (DOMs) and Digital Surface Models (DSMs) at a scale of 1:5000 were obtained. The coordinate Root Mean Square Error (RMSE) of the checkpoints within the obtained DSM is less than 0.82 m in altitude and 3 m horizontally. The RMSE of the Sparse Ground Control Points (GCPs) within the obtained DSM is less than 0.3 m in altitude. Experimental results from different areas, including plains, mountains, and coastlines, demonstrate the system’s performance.

To date, various methods have been proposed for calibrating polarimetric synthetic aperture radar (SAR) using distributed targets. Some studies have utilized the covariance matching estimation technique (Comet) for SAR data calibration. However, practical applications have revealed issues stemming from ill-conditioned problems due to the analytical solution in the iterative process. To tackle this challenge, an improved method called Comet IS is introduced. Firstly, we introduce an outlier detection mechanism which is based on the Quegan algorithm’s results. Next, we incorporate an initial search approach which is based on the interior point method for recalibration. With the outlier detection mechanism in place, the algorithm can recalibrate iteratively until the results are correct. Simulation experiments reveal that the improved algorithm outperforms the original one. Furthermore, we compare the improved method with Quegan and Ainsworth algorithms, demonstrating its superior performance in calibration. Furthermore, we validate our method’s advancement using real data and corner reflectors. Compared with the other two algorithms, the improved performance in crosstalk isolation and channel imbalance is significant. This research provides a more reliable and effective approach for polarimetric SAR calibration, which is significant for enhancing SAR imaging quality.

Spaceborne bistatic Frequency Modulated Continuous Wave (FMCW) Synthetic Aperture Radar (SAR) faces several challenges, including the nonlinear motion trajectory of the satellite platform, intra-pulse motion of the transmitting and receiving satellites, spacevariant characteristics of echo signals, and the breakdown of azimuth shift invariance. The “stop-and-go” hypothesis is no longer applicable in spaceborne bistatic FMCW SAR. In this paper, an extended ground Cartesian back-projection (EGCBP) algorithm for bistatic FMCW SAR is proposed under the “non-stop-and-go” hypothesis. First, to address the peak positioning deviation of the range migration curve caused by intra-pulse modulation effects, the least squares method is employed to perform range-dimensional linear fitting of the signal propagation delay, thereby obtaining the theoretical peak position of the range migration curve under the “non-stop-and-go” hypothesis. Second, since the traditional GCBP algorithm is applicable only to mono-static systems, the spectrum compression function is enhanced to accommodate bistatic systems. Prior to this, to overcome the high computational cost of echo simulation using the iterative method under the “non-stop-and-go” hypothesis, a signal propagation delay approximation method (SPDAM) is introduced. This method not only meets accuracy requirements but also significantly im-proves simulation efficiency, providing a foundation for subsequent verification of the imaging algorithm. Simulation experiments involving both point and scene targets are provided to demonstrate the effectiveness and potential of the proposed algorithms for practical applications.

Based on the high-precision quantitative measurement ability, the S-band subsystem of China's high- resolution aviation system carried out the agricultural application test for the first time in Jilin elm, China. The experiment is mainly carried out in combination with vegetation growth, soil moisture content inversion, wheat straw returning and ground feature classification. Through the design of quantitative measurement scheme, the results of different straw coverage and observation at different times are compared, which shows the prospect of S-band full polarization synthetic aperture radar system in the field of agricultural application.

Spaceborne interrupted frequency-modulated continuous-wave synthetic aperture radar (iFMCW SAR) employs a single antenna on a single spacecraft operating in a time-division transmit/receive mode, effectively avoiding mutual interference between transmitted and received signals and thereby overturning the design paradigm of spaceborne FMCW SAR systems. However, the periodic switching of the antenna between transmit and receive states results in periodic data gaps along the azimuth direction in the echo signal, leading to spurious artifacts in the reconstructed images and severely degrading image quality. Sparse signal recovery techniques based on compressive sensing models have been shown to effectively suppress such spurious targets. Nevertheless, the generalized orthogonal matching pursuit (GOMP) algorithm requires prior knowledge of the signal sparsity, a condition that is often impractical in real-world scenarios. To address this limitation, this paper investigates the variation pattern of the residual norm with respect to sparsity in the GOMP algorithm and proposes a modified GOMP algorithm based on binary search. This approach enables rapid and accurate determination of the true sparsity level without prior knowledge, thereby achieving sparsity-adaptive reconstruction with GOMP and significantly enhancing the imaging quality of iFMCW SAR. Simulation experiments involving both point and scene targets are provided to demonstrate the effectiveness and potential of the proposed algorithms for practical applications.