In the past two decades, ground-based synthetic aperture radars (GB-SARs) have developed rapidly, providing a large amount of SAR data in minutes or even seconds. However, the real-time processing of big data is a challenge for the existing GB-SAR interferometry (GB-InSAR) technology. In this paper, we propose a near-real-time GB-InSAR method for monitoring slope surface deformation. The proposed method uses short baseline SAR data to generate interferograms to improve temporal coherence and reduce atmospheric interference. Then, based on the wrapped phase of each interferogram, a network method is used to estimate and remove systematic errors (such as atmospheric delay, radar center shift error, etc.). After the phase unwrapping, a least squares estimator is used for the overall solution to obtain the initial deformation parameters. When new data are added, a sequential estimator is used to combine the previous processing results and dynamically update the deformation parameters. Sequential estimators could avoid repeated calculations and improve data processing efficiency. Finally, the method is validated with the measured data. The results show that the average deviation between the proposed method and the overall estimation was less than 0.01 mm, which could be considered a consistent estimation accuracy. In addition, the calculation time of the sequential estimator was less sensitive than the total amount of data, and the time-consuming growth rate of each additional period of data was about 1/10 of the overall calculation. In summary, the new method could quickly and effectively obtain high-precision surface deformation information and meet the needs of near-real-time slope deformation monitoring.

To meet the needs of the rapid development of new energy sources, China is currently accelerating the construction of pumped storage power stations (PSPS). However, the complex geological environment and large-scale construction can easily cause deformation areas such as collapse and sliding on the surface of PSPS. This paper adopts ground-based interferometric synthetic aperture radar (GB-InSAR) technology to monitor surface deformation using 206 ground-based radar (GBR) data collected from 2022/07/31 10:24 to 2022/08/06 14:57 with a PSPS in the construction stage in western China. In the selection of permanent scatterers (PSs) for GBR data processing, statistical homogeneous pixels (SHPs) information is considered in this paper. Based on the fast SHP selection (FaSHPS) algorithm, we introduce intensity deviations ( ${D_I}$ ) to exclude pixels with unstable intensity performance in the temporal dimension and propose the ${D_I} + FaSHPS$ algorithm, which is experimentally proven to have higher SHPs selection accuracy and computational efficiency. By combining with the traditional PSs selection algorithm, the distribution density of monitoring points is effectively improved. In addition, based on the deformation results and on-site survey, this paper analyzes and discusses the identified deformation areas in detail from the aspects of natural geography and man-made activities. The results show that the surface is relatively stable overall, except for the effects of man-made activities. This study has demonstrated the remarkable reliability and superiority of the GB-InSAR technology in identifying and analyzing deformation areas in the construction stage of PSPS.

Satellite synthetic aperture radar interferometry (InSAR) has become a recognized and reliable surface deformation monitoring technology in recent years. However, the complexity of surface deformation, driven by various physical mechanisms, presents itself in different forms such as secular trend, cyclical fluctuations, and irregular variations in time series. Consequently, the limitations of conventional InSAR technology, which relies on linear deformation assumptions, make it challenging to meet the requirements of monitoring complex nonlinear deformation. Additionally, the commonly used parameter inversion method based on the least squares approach is unsuitable for non-Gaussian observation error distribution with gross errors. To address these issues, we propose an InSAR interferometric phase nonlinear function model that considers zero-mean second-order stochastic differential equations and periodic changes. This model can quantitatively describe the law of surface deformation driven by multiple physical factors. Furthermore, we utilize an efficient M-estimation method, known for its high robustness, to optimize the model parameters and mitigate the impact of non-Gaussian noise and/or gross errors in InSAR observation and data processing. By conducting simulation experiments, it verifies that the proposed method is more robust than the conventional InSAR method. Finally, the processing and analysis of Sentinel-1 data in the overlying rock glacier area confirm the effectiveness of the proposed method in extracting nonlinear surface deformation.

The upper Jinsha River, located in a high-mountain gorge with complex geological features, is highly prone to large-scale landslides, which could result in the formation of dammed lakes. Analyzing the movement characteristics of the typical Xiaomojiu landslide in this area contributes to a better understanding of the dynamics of landslides in the region, which is of great significance for landslide risk prediction and analysis. True displacement data on the surface of landslides are crucial for understanding the morphological changes in landslides, providing fundamental parameters for dynamic analysis and risk assessment. This study proposes a method for calculating the actual deformation of landslide bodies based on multi-track Interferometric Synthetic Aperture Radar (InSAR) deformation data. It iteratively solves for the optimal true deformation vector of the landslide on a per-pixel basis under a least-squares constraint based on the assumption of consistent displacement direction among adjacent points on the landslide surface. Using multi-track Sentinel data from 2017 to 2023, the line of sight (LOS) accumulative de-formation of the Xiaomojiu landslide was obtained, with a maximum LOS deformation of −126 mm/year. The true surface displacement of the Xiaomojiu landslide after activation was calculated using LOS deformation. The development of two rotational sub-slipping zones on the landslide body is inferred based on the distribution of actual displacements along the central profile line. Analysis of temporal changes in water body area data revealed that the Xiaomojiu landslide was activated after a barrier lake event and continuously moved due to the influence of higher water levels’ in the river channel. In conclusion, the proposed method can be applied to calculate the true surface displacement of landslides with complex mechanisms for analyzing the movement status of landslide bodies. Furthermore, the spatiotemporal analysis of the Xiaomojiu landslide characteristics can support analyzing the mechanisms of similar landslides in the Jinsha River Basin.

InSAR technology has emerged as a powerful tool for the early identification and monitoring of geological hazards in the Loess Plateau. Since the SAR satellite adopts the range-azimuth two-dimensional side-looking imaging mode, the corresponding field area of the pixel will change with slope, aspect, and incident angle, especially in areas with severe changes in terrain and landforms, which causes serious area distortion, leading to abnormal deformation rate mapping. Considering the SAR imaging geometry, terrain slope and aspect conditions, this paper proposes a quantitative analysis method for accurate geometric distortion based on pixel area and corresponding field area and constructs a weighted fusion method based on distortion values for InSAR deformation rate maps in overlapping regions of adjacent tracks. Taking Lingtai County, Gansu Province as the research area and Sentinel-1 data as the research data, we utilize the SBAS method to process the Sentinel-1 data and estimate the deformation rate, and then use the geometric distortion value as a weight factor to estimate the fusion rate of the overlapping area of adjacent tracks. The results demonstrate that the weighted fusion method based on distortion values can achieve a high-quality mosaic effect of deformation rate maps of adjacent tracks, effectively improve the geometric distortion influence in deformation rate mapping, and the identification of geological hazards.

Lutan-1 is the first L-band SAR satellite launched by China with the core mission of geohazard monitoring, but few studies have been conducted to apply it in the field of earthquakes. In this paper, the capability of Lutan-1 data in coseismic deformation analysis and seismogenic fault parameter inversion was discussed by taking the 2023 Mw6.0 Jishishan earthquake as an example. Firstly, we utilized Lutan-1 data to acquire the coseismic deformation field of the Jishishan earthquake. Subsequently, the seismogenic fault parameter and slip distribution were inverted using both uniform slip and distributed slip models. Finally, a comprehensive comparison was conducted with Sentinel-1 data in terms of the coseismic deformation field, seismic source parameters, and coherence. The comparative results demonstrate that the coseismic deformation and seismogenic fault parameter inversion derived from Lutan-1 data are consistent with those obtained from Sentinel-1 data. Moreover, Lutan-1 data exhibit superior image quality and better coherence, confirming the effectiveness and superiority of Lutan-1 data for coseismic deformation and seismogenic fault analysis. This study provides a theoretical foundation for the application of Lutan-1 in the field of earthquake disaster monitoring.