Integrating TS-InSAR and machine learning for spatiotemporal analysis and influencing factors identification of land subsidence in Nansha, Guangzhou, China - He, Qin; Zhou, Cong; Yang, Xiaoge; Wang, Hua (Frontiers in Earth Science, 2026)
Rapid urban expansion and construction on compressible coastal soils have induced significant land subsidence across Nansha District. To quantify this subsidence, we applied Time-Series Interferometric Synthetic Aperture Radar (TS-InSAR) on Sentinel-1A data collected between 2019 and 2023, utilizing the RapidSAR method to identify high-quality points for precise deformation monitoring. On this basis, a multi-source database was developed that incorporated not only common factors (e.g., soft soil thickness, elevation, and construction activity) but also aquaculture areas and most notably a detailed building-type classification that specifically accounts for widespread custom rural houses. A Random Forest regression model was applied to quantitatively identify the main drivers of subsidence. The results reveal a maximum subsidence rate of 283.7 mm/year in Nansha, with 4.5% of monitored points exceeding 40 mm/year and 10.3% exceeding 20 mm/year. Significant subsidence was predominantly concentrated near major engineering construction sites and coastal soft soil zones. The model results indicate that construction activity is the most important factor driving significant subsidence. Additionally, soft soil thickness, aquaculture, and custom rural houses in riverine zones were also identified as leading contributing factors. The findings of this study can provide a scientific basis for future urban planning and flood prevention strategies in Nansha.