Synthetic aperture radar (SAR) tomography (TomoSAR) technology effectively provides precise three-dimensional (3D) forest vertical structure information. However, conventional TomoSAR methods require abundant acquisitions for accurate 3D reconstruction, which is time-consuming and low-efficiency for forest vertical structure mapping. To address these limitations, this paper proposes a novel micro-stack TomoSAR imaging approach utilizing four images, referred to as the double iterative adaptive residual approach (DIARA). The DIARA innovatively combines iterative inner-outer adaptive spectral estimation and residual optimization to enhance both processing efficiency and accuracy. For validation purposes, both simulated and airborne TomoSAR experiments are analyzed at P- and L-band. The P-band results from the BorTomoSAR campaign indicate that the DIARA acquires higher accuracy for estimating forest height than the conventional methods, i.e., improvingR2from 0.443 to 0.628, mean absolute error (MAE) from 0.779 m to 0.625 m, mean absolute percentage error (MAPE) from 4.629% to 3.680%, and root mean square error (RMSE) from 0.941 m to 0.771 m. Additionally, the performance is further validated by the TropiSAR P-band campaign, which confirms the robustness of the proposed DIARA in high-canopy, complex forest environments. Furthermore, the L-band results from HaiTomoSAR campaign demonstrate that the DIARA method successfully detects the weak ground scatterers beneath dense forest canopies, which validates its super-resolution capability in vertical structure reconstruction.

Airborne Synthetic Aperture Radar (SAR) Tomography (TomoSAR) technology facilitates the extraction of three-dimensional (3D) information of target scatterers. However, phase screens induced by radar platform trajectory errors causes TomoSAR defocusing, which adversely affects the accuracy of the forest vertical structure retrieval. Additionally, the phase screens exhibit space-variant characteristics, which significantly degrade the calibration performance estimated by traditional phase gradient autofocus (PGA). This paper proposes an improved phase calibration method synthesizing unconstrained optimization model and weighted PGA (WPGA), which effectively address the above challenges. Firstly, the SAR data stack is segmented into multiple subareas by assuming that phase screens are space-invariant within each small area, which reduces complexity and improves computational efficiency. Secondly, a WPGA method synthesizing the scatterer heights derived from an unconstrained optimization model is proposed to estimate the phase screens. Simulation experiments are conducted to validate the effectiveness of the proposed phase calibration method. Furthermore, the full-polarization SAR data stacks acquired by the BorTomoSAR campaign are used for tomographic focusing analysis. Experimental results demonstrate that the proposed method accurately estimates the phase screens at the P, L, and S bands, providing a efficient solution for the forest vertical structure retrieval.

Synthetic aperture radar (SAR) is a crucial technique in Earth observation, providing vast amounts of data for monitoring the Earth’s surface. However, SAR’s side-looking imaging characteristics often result in significant geometric distortions in complex terrains such as mountainous gorges. Current methods struggle to accurately compute both active and passive geometric distortions when orbital state vector information is not available. This study aims to address this challenge by concentrating on the Southeastern Tibetan Plateau (SETP) and introducing a DEM-based radar incidence angle-tracking method (Angle-Tracking) based on ray tracing principles. The fundamental aspects of this method include constructing the discrete range direction vector (DRDV) to establish calculation directions, refining grid distribution via cubic-patch cells, and identifying potential topographic occluder points (PTOPs) to minimize redundancy in iterative computations. Through the utilization of this approach, we have acquired and disclosed the distribution of geometric distortion in ascending and descending Sentinel-1 data over the SETP region. Cross validation with results computed from precise orbital data showcases the efficacy of the angle-tracking method in identifying geometric distortions in the absence of satellite state vector information. Furthermore, the angle-tracking method demonstrates effectiveness when implemented on cloud computing platforms such as Google Earth Engine (GEE), thereby enhancing the feasibility of SAR-based research in mountainous areas.

Ionospheric phase delay is a critical error source in Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) for the purpose of monitoring ground surface deformation with SAR data obtained from low-frequency radar systems. Recently, the Range Split-Spectrum Interferometry (RSSI) method has been employed to estimate and rectify ionospheric errors in TS-InSAR. However, the performance of the RSSI method is largely restricted by the significant linear scale factors resulting from the current small SAR bandwidth. In this study, we propose a Reformulating RSSI (Re-RSSI)-based method for correcting the ionospheric error in TS-InSAR by optimizing the linear scale factors, with the aim of improving the accuracy of TS-InSAR measurements. We evaluate the Re-RSSI method using 121 ALOS-1 PALSAR images that cover two distinct regions: the low-latitude Lazufre volcano region and the high-latitude Anaktuvuk River tundra fire region. Our results demonstrate that the Re-RSSI method can effectively remove time series ionospheric errors at both test sites, where we detected ionospheric delays of approximately 2.5 cm/yr and 2.0 cm/yr, respectively. Using Global Navigation Satellite System (GNSS) measurements as ground truth, we achieved an 86.59% improvement rate in root mean square error (RMSE) with the Re-RSSI method, which is significantly higher than the 66.40% improvement rate achieved with the traditional RSSI method.

The interferometric synthetic aperture radar (InSAR) image shift measurement technique is of great significance in processing high-precision digital elevation model (DEM) generation and deformation measurements. It can be used in steps such as image fine coregistration, interferometric phase unwrapping and absolute phase calibration in the InSAR processing flow without an external DEM. However, the shifts estimated by current methods are of low resolution and have high measurement noise, which may have adverse impacts on subsequent applications. In this paper, a lightweight, high-resolution and low-noise interferometric stereo-radargrammetric shift estimation network (SSENet) is proposed to solve the aforementioned problems. It introduces deep learning technology to the InSAR shift estimation task for the first time. We propose forming multiscale 3D coherence coefficient cubes by projecting the shift values of the images onto the third dimension and then using a 3D convolutional network for multiscale fusion and encoding, followed by decoding with linear layers. In addition, a dataset generation and augmentation scheme based on real data is designed for model training and evaluation. Several sets of real SAR images from different regions of the world were used to evaluate SSENet. Compared with the typical coherent cross-correlation approach, SSENet reduces the mean absolute error of the estimated shifts by approximately 79% while improving the resolution by a factor of 4×4, making it possible to restore the absolute interferometric phase. Finally, we demonstrate a stitching strategy for processing large-scale SAR images and discuss the multiple potential uses of SSENet in the InSAR processing chain.