Coal-mining subsidence causes ground fissures and destroys surface structures, which may lead to severe casualties and economic losses. Time series interferometric synthetic aperture radar (TS-InSAR) plays an important role in surface deformation detection and monitoring without the restriction of weather and sunlight conditions. In addition, the probability integral method (PIM) is a surface movement model that is widely used in the field of mining subsidence. In recent years, the integration of TS-InSAR and the PIM has been extensively studied. In this paper, we propose a new method to estimate mining subsidence with the PIM based on TS-InSAR results. This study focuses on the improvement of a boundary constraint and dynamic parameter estimation in the PIM through the inversion of the line-of-sight (LOS) time series deformation derived by TS-InSAR. In addition, 45 Sentinel-1A images from 17 June 2015 to 27 December 2017 of a coal mine in Jiaozuo are utilized to acquire the surface displacement. We apply a time series deformation analysis using small baseline subsets (SBAS) and place the results into an improved PIM to estimate the mining parameters. The simulated mining subsidence is highly consistent with the leveling data, exhibiting an RMSE of 0.0025 m. Compared with the conventional method, the proposed method is more accurate in discovering displacement in mining areas. In the final section of this paper, some sources of error that affect the experiment are discussed.

Time series interferometric synthetic aperture radar (TS-InSAR) has been a powerful tool for monitoring land surface deformation over the last two decades. Atmospheric effects cause large-scale delays in InSAR observations, which is one of the difficulties facing deformation calculations from differential InSAR and time-series InSAR. Currently, the atmospheric delay is derived mainly from auxiliary data from sources such as the global navigation satellite system (GNSS) and moderate-resolution imaging spectroradiometry (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data may also not temporally match SAR image acquisition, which leads to low accuracy in atmospheric phase correction. This article presents a decomposition method to remove the atmospheric delay. We consider the atmospheric phase to be caused by the combined changes in spatial position and elevation. Therefore, quadtree segmentation is applied to divide the topographic units, and we improve the drift function of universal kriging by adding an elevation component. We then interpolate the whole atmospheric phase space from reliable sampling points estimated by the coherence coefficient. Using Sentinel-1 data, we test the proposed method in discriminating and monitoring a mining subsidence area in Shanxi Province and compare the results with the results from interferometric point target analysis and the network-based variance-covariance estimation method. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation inverted from TS-InSAR.

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.

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.