Zhiqiang Xiong

Phase consistency is a critical prerequisite for achieving high-precision time-series InSAR deformation retrieval. However, conventional SBAS-InSAR methods provide only limited consideration of phase consistency during the inversion process. Within the SBAS-InSAR workflow, two principal categories of error sources are primarily responsible for phase inconsistency, manifested as non-zero closure phase (NCP): (1) fading biases introduced during multilooking and filtering prior to phase unwrapping; and (2) unwrapping errors caused by large deformation gradients, low coherence, or inappropriate selection of unwrapping algorithms. To address these issues, this study introduces an improved SBAS-InSAR processing workflow, termed NCP-SBAS, designed to improve the accuracy of deformation field estimation and thereby enhance its applicability to geological hazard monitoring. The key idea of the method is to enforce phase consistency as a constraint, jointly accounting for the spatiotemporal characteristics of fading biases and the valid deformation signals, thereby enabling effective correction of NCP. To evaluate the effectiveness of NCP-SBAS, this study conducted a detailed analysis of deformation differences in Hualong County, Qinghai Province, before and after NCP correction, highlighting the significant advantages of the proposed approach. The results indicate that the influence of fading biases on deformation estimates depends on both the magnitude and direction of deformation, while unwrapping errors primarily lead to an underestimation of deformation. In addition, the study provides an in-depth discussion of how fading biases and unwrapping errors affect landslide monitoring and identification.

Interferometric Synthetic Aperture Radar (InSAR) is a crucial technology for monitoring large-scale surface deformation. Recent advancements have increasingly emphasized the automation of InSAR data processing. However, terrain complexity, environmental variability, and diverse deformation patterns in wide-area monitoring introduce multiple error sources. Conventional correction models based on singular assumptions struggle to achieve adaptive processing, often resulting in low processing efficiency and distortion of some deformation results. To address these challenges, this study proposes an adaptive error correction method guided by deformation prior information, enhancing automated workflows for wide-area InSAR processing. By integrating prior deformation and terrain information to create mask files, this method adaptively distinguishes deformation signals from error components, enabling precise error correction. A case study conducted in the North China Plain demonstrates the method’s adaptive error-correction capabilities. Experimental results indicate that the proposed method achieves robust error separation while maintaining solution accuracy across deformation scales from regional subsidence to localized deformations. This method provides novel algorithmic support for automated InSAR data processing in wide-area applications, significantly improving processing efficiency and result reliability.

The combined effects of global warming and human activities have intensified land subsidence (LS), limiting sustainable development of economy in delta regions. Despite the potential of Interferometric Synthetic Aperture Radar (InSAR) for monitoring LS, its application across vast delta regions may be hindered by complex data processing, high computational demands, and the need for standardized results. To overcome these challenges, we adopted the multi-temporal InSAR (MT-InSAR) technique, integrating a frame data parallel processing strategy and an overall adjustment correction method, to obtain the temporal deformation sequences of the entire Yangtze River Delta (YRD) region in China from Jan. 2019 to Dec. 2021. We calculated the annual average deformation rate and identified deformation areas, with 73.5% concentrated along the Yangtze River, along the coastline, and within the northern Anhui mining area. A significant correlation was observed between LS and anthropogenic activities, such as economic development and land reclamation activities. Further analysis reveals that the increase of GDP growth rate may contribute to LS. Approximately 38% of reclaimed area in the YRD is at risk of LS. Land reclamation activities present a dichotomy, with Hangzhou Bay as the dividing line. This study provides a new perspective and scientific basis for understanding and analyzing LS in deltaic environments, contributing to sustainable development and advancing wide-area InSAR deformation monitoring.

The optical image sub-pixel correlation (SPC) technique is an important method for monitoring large-scale surface deformation. RapidEye images, distinguished by their short revisit period and high spatial resolution, are crucial data sources for monitoring surface deformation. However, few studies have comprehensively analyzed the error sources and correction methods of the deformation field obtained from RapidEye images. We used RapidEye images without surface deformation to analyze potential errors in the offset fields. We found that the errors in RapidEye offset fields primarily consist of decorrelation noise, orbit error, and attitude jitter distortions. To mitigate decorrelation noise, the careful selection of offset pairs coupled with spatial filtering is essential. Orbit error can be effectively mitigated by the polynomial fitting method. To address attitude jitter distortions, we introduced a linear fitting approach that incorporated the coherence of attitude jitter. To demonstrate the performance of the proposed methods, we utilized RapidEye images to extract the coseismic displacement field of the 2019 Ridgecrest earthquake sequence. The two-dimensional (2D) offset field contained deformation signals extracted from two earthquakes, with a maximum offset of 2.8 m in the E-W direction and 2.4 m in the N-S direction. A comparison with GNSS observations indicates that, after error correction, the mean relative precision of the offset field improved by 92% in the E-W direction and by 89% in the N-S direction. This robust enhancement underscores the effectiveness of the proposed error correction methods for RapidEye data. This study sheds light on large-scale surface deformation monitoring using RapidEye images.

The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500–3700 m with slope angles of 10–30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures.

China's first group of L-band interferometric synthetic aperture radar (InSAR) satellites LuTan-1 (LT-1) were successfully launched in 2022. This satellite mission is mainly designed for global digital elevation model (DEM) mapping and rapid surface deformation monitoring. However, the group of satellites also acquired L-band SAR observations in both bistatic and monostatic modes, which presented an opportunity for large-scale forests vertical structure detection and underlying terrain estimation. This article aims at vegetation height and underlying terrain inversion using LT-1 spaceborne bistatic InSAR data with a model-based method. Preliminary cross-validation was performed on the inversion results using terrain and forests height products obtained from spaceborne ICESat-2 products. Both results demonstrated the potential of LT-1 for large-scale vegetation height and underlying terrain inversion. This work will also provide reference for the upcoming TanDEM-L project in studying the regional and global forests parameter estimation using spaceborne bistatic InSAR data.

The processing of wide-area and long-term Interferometric Synthetic Aperture Radar (InSAR) data is characterized by its computing-intensive nature. This challenge significantly constrains the accessibility of wide-area deformation monitoring and updating for the majority of InSAR users who lack access to cloud computing facilities. We present a strategy that leverages Alaska Satellite Facility (ASF) On-Demand InSAR products for the efficient implementation of wide-area deformation monitoring. Our strategy includes a multi-patch method designed to adaptively correct wide-area elevation-dependent atmospheric delays. We tested our strategy in a specific region of Sichuan province, China, utilizing 507 ascending and 498 descending differential InSAR (DInSAR) products obtained from ASF. We identified 283 deformation areas from the ascending deformation rate map and 248 from the descending one, both obtained through a stacking method. Subsequently, time-series InSAR was employed to compute the deformation time series for the regions of interest (ROI). The proposed strategy can also be applied to self-processed interferograms and other publicly available products, such as those anticipated from future missions like NISAR.

The ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) plays an important role in the scientific task of global terrain mapping. ICESat-2 records high-precision object information in the form of photon clouds. Ground photon extraction is a key step in retrieving high-precision terrain. However, it is a difficult task to extract ground photons in mountainous forest areas due to the influence of terrain slopes. We proposed a slope correction method to mitigate the effect of terrain slope on ground photon extraction. First, the background noise photon is removed by a photon cloud filtering method. Second, we used the Douglas–Peucker algorithm to find the special terrain points (STPs), the photon cloud then was divided into a series of segments by STPs. We calculated the slope in each segment to form a rotation matrix, and corrected the slope of the photon cloud in each segment. The results show that the obtained STPs have high accuracy, and these STPs divided the photon cloud into a series of segments accurately. Compared with the reference slopes in each segment, the obtained slopes have a small RMSE of 2.3° and a high R ² of 0.96. Additionally, the results indicate that the discrimination between ground photons and canopy photons in mountainous forest areas is significantly obvious after a slope correction.