Abstract. Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely utilized for topographic, geological, and natural resource investigations. However, many existing InSAR studies on ground deformation are limited to relatively short observation periods and single sensors. This paper introduces a novel method for fusing multi-sensor InSAR time series data, specifically designed to address scenarios involving partial overlaps and temporal gaps. The method employs a new Power Exponential Knothe Model (PEKM) to fit and fuse overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses data during temporal gaps in the series. In this study, the city of Wuhan in China was selected as the experimental area. SAR datasets from COSMO-SkyMed (2011–2015), TerraSAR-X (2015–2019), and Sentinel-1 (2019–2021) were fused to map long-term surface deformation over the past decade. An independent InSAR time series analysis from 2011 to 2020, based on 230 COSMO-SkyMed scenes, was used as a reference for comparison. The correlation coefficient between the fusion algorithm’s results and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The overall correlation coefficient of 0.78 demonstrates that the proposed algorithm achieves a similar trend as the reference deformation curve. Based on the long time series settlement results obtained through fusion, a detailed analysis of the causes of settlement was conducted for several subsidence zones. The subsidence in the Houhu area is primarily attributed to the consolidation and compression of soft soil. Soil mechanics were employed to estimate the expected completion time of subsidence and calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression, gradually stabilizing over time. Furthermore, the subsidence curve observed in the area around Xinrong reveals that the construction of an underground section of subway Line 21 has caused significant settlement in that particular region. The high temporal granularity of the PSInSAR time series also enables precise detection of a rebound phase following a major flooding event in 2016. The experimental results demonstrate the accuracy of the proposed fusion method in providing robust time series for analyzing long-term land subsidence mechanisms. Additionally, these findings unveil previously unknown characteristics of land subsidence in Wuhan, thus clarifying the relationship with urban causative factors.
Read full abstract