Spatiotemporal evolution characteristics of ground deformation in the Beijing Plain from 1992 to 2023 derived from a novel multi-sensor InSAR fusion method

Abstract

The Multiple Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology is capable of effectively generating ground deformation information derived from high-precision and continuous observation by satellites. However, due to the limited operational lifespan of a single SAR satellite, the derived ground deformation result of the study area cannot be ensured long-term (several decades), and merely a few years. With the increasing number of SAR satellite launches, it has become possible to conduct long-term continuous monitoring of ground deformation by combining data from multiple platforms. Nevertheless, several existing methods (e.g., model fitting method, predictive splicing method, etc.) have lower fusion accuracy and are limited to specific deformation patterns. In this study, a Piecewise Exponential Fitting with Weighted Average (PEFWA) method is proposed, which takes into account both the trend and accuracy of the preceding and following deformation time series in the fusion. The experimental results on the simulation data prove that the accuracy and robustness of this method are higher than several other methods. We applied the proposed method to characterize the evolution of ground deformation in the Beijing Plain from 1992 to 2023 using data from four different SAR satellites. The results show that: (1) With the implementation of various policies (e.g., the South-to-North Water Diversion Project, the Ecological Water Replenishment Project, etc.), ground subsidence has generally followed a trend of “worsening initially, then improving”. (2) The spatial variability of ground subsidence is primarily influenced by the locations of fault zones. (3) The periodic changes in the ground deformation time series are mainly driven by fluctuations in groundwater levels. The above findings indicate that the method proposed in this study can effectively integrate deformation series with temporal discontinuities, which helps detect the long-term trends and formation mechanisms of ground deformation.