Land subsidence modeling and assessment in the West Pearl River Delta from combined InSAR time series, land use and geological data

West Pearl River Delta (WPRD) is sinking as a result of the jointed effect of natural and anthropogenic factors. Land subsidence has increasingly become a concern because of tremendous population growth and rapid urbanization over this region in the last few decades. In this study, sixty-seven Sentinel-1 images, acquired between 2016 and 2021, were analyzed with the persistent scatterer interferometry technique (PSI), to monitor and reveal the ground subsidence characteristics in the WPRD. It is found that the overall vertical deformation velocities observed in the WPRD ranged between −70 mm/year and 10 mm/year. Three subsidence bowls were found in the study area (Gaolan island of Zhuhai, the junction area of Zhuhai and Zhongshan, and the junction area of Zhongshan and Jiangmen). The spatial–temporal subsidence characteristics have been analyzed. It is discovered that the ground subsidence is mostly dispersed in Quaternary deposits and is highly relevant to the thickness of sediments, indicating that soft soil consolidation is one of the primary causes contributing to land subsidence. Furthermore, land use maps for 2016 and 2021 were generated using Landsat-8 images for the investigation on the relationship between land subsidence and land use. The results obtained from analysis demonstrated that the rapid subsiding areas mainly occurred in the land-use classes as follows: aquaculture, urban land, and agricultural land. The land use conversion pattern with more significant anthropogenic influence usually causes a higher subsidence rate. In addition, based on soft soil thickness, groundwater exploitation, land use, elevation, and strata lithology, a Random Forest Regression (RFR) model was used to predict subsidence rates (R2 = 0.631, RMSE = 2.7 mm/year). The importance of these influencing factors of land subsidence was calculated based on the RFR algorithm. The results indicated that soft soil thickness, elevation, groundwater exploitation, strata lithology, and landcover type are the most significant factors affecting subsidence. The applicability of geological data and land-use history for land subsidence prediction has been demonstrated with the use of the RFR algorithm.