Land subsidence monitoring and analysis in Qingdao, China using time series InSAR combining PS and DS - Qiuxiang Tao; Xuepeng Li; Tengfei Gao; Yang Chen; Ruixiang Liu; Yixin Xiao (Geomatics, Natural Hazards & Risk)
With the accelerated urbanization process in Qingdao, urban land subsidence has damaged infrastructure and hindered the sustainable development. To prevent disasters caused by urban land subsidence, large-scale, long-term monitoring, mechanism analysis, and risk assessment are essential. This study proposes an improved time series Interferometric Synthetic Aperture Radar (InSAR) method, extracting Permanent Scatterers (PS) and Distributed Scatterers (DS) based on a two-layer network. Using Sentinel-1 dataset, Qingdao's land subsidence from 2019 to 2024 was monitored. The results were validated using multi-source data, and a comprehensive analysis of the spatiotemporal distribution and causes of subsidence was conducted ,alongside a risk matrix based on the Hurst index. Results show that combining PS and DS increases measurement density. From 2019 to 2024, Qingdao's land was generally stable, with subsidence mainly along Jiaozhou Bay , reaching a maximum rate of −84 mm/year due to urbanization and land reclamation. Two mechanisms were identified at Jiaozhou Bay Bridge: linear and seasonal cyclic subsidence. The urban land risk matrix revealed that most sampling points in seven subsidence areas remain in a dangerous state. This study systematically investigated  Qingdao's land subsidence, evaluated urban stability, and provided insights and solutions for subsidence prevention and control.