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Water, Vol. 16, Pages 1200: Spatio-Temporal Characteristics of Land Subsidence and Driving Factors Analysis in Shenzhen
Water doi: 10.3390/w16091200
Authors: Shuanglong Wang Guoyang Wang Min Huang Jun Song Xiaoyu Yang Tingyu Zhang Wenyu Ji Shuai Zhang Weili Wu Chengwen Wei Jian Xiao
Analyzing land subsidence using Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology holds significant importance for the secure development of urban areas. Shenzhen, being a crucial component of the Pearl River Delta, faces the threat of land subsidence, similar to most deltaic cities. Numerous studies have already indicated the presence of severe land subsidence in certain localities of Shenzhen. However, due to limitations in data scope and research methodologies, the comprehensive spatial-temporal distribution of land subsidence across the entire city of Shenzhen remains unclear. This study initially employed MT-InSAR technology to process a total of 534 Sentinel-1A SAR images from three different frames (P11F71, P113F71, P11F65), covering the entire city of Shenzhen. This processing resulted in the generation of subsidence rate maps and subsidence time series. Subsequently, the temporal evolution patterns of the subsidence were analyzed while significant subsidence regions were identified. By integrating information from optical images reflecting human activities on the Earth’s surface, the study deduced the subsidence mechanisms in various significant subsidence areas. Research findings indicate that land subsidence in Shenzhen is primarily caused by construction activities, with a concentration in the western coastal areas of Shenzhen, reaching a maximum rate of 80 mm/yr, located at the estuary of Dongbao River (113.770385, 22.745305). The cumulative subsidence from March 2017 to June 2023 amounts to 500 mm. The expansion of the Qinglinjing Reservoir has led to an increased demand for water, resulting in a significant rise in formation pressure and subsequent land subsidence. InSAR land subsidence monitoring and analysis in urban areas can address the spatial and temporal resolution limitations of traditional subsidence monitoring methods, providing effective recommendations for widespread subsidence prevention and control.
