Chiayi County is located in the largest alluvial plain of Taiwan with extensive aquaculture and rice farming sustained by water extracted from groundwater wells. Chiayi is a typical aquaculture area affected by land subsidence, yet such lands worldwide combine to provide nearly 90% of global aquaculture products, greatly reducing oceanic overfishing problems. This study uses precision leveling, multi-layer compaction monitoring well (MLCW) and spaceborne SAR interferometry (InSAR) to examine the cause and effect of land subsidence in Chiayi associated with groundwater extractions and changes. Heights at benchmarks in a leveling network are measured annually and soil compactions at 24–26 layers up to 300-m depths at 7 MLCWs are collected at one-month intervals. Over 2007–2011, 15 ALOS/PALSAR images are processed by the method of TCPInSAR to produce subsidence rates. All sensors show that land subsidence occur in most parts of Chiayi, with rates reaching 4.5 cm/year around its coast, a result of groundwater pumping from shallow to deep aquifers. MLCWs detect mm-accuracy seasonal soil compactions coinciding with groundwater level fluctuations and causing dynamic compactions. Compactions near Taiwan High Speed Rail may reduce the strength of the rail’s supporting columns to degrade its safety. The SAR images yield subsidence rates consistent with those from leveling and compaction wells after corrections for systematic errors by the leveling result. Subsidence in Chiayi’s coastal area leads to relative sea level rises at rates up to 15 times larger than the global eustatic sea level rising rate, a risk typical for world’s aquaculture-rich regions. At the fish pond-covered Budai Township, InSAR identifies subsidence spots not detected by leveling, providing crucial geo-information for a sustainable land management for aquaculture industry.

Extracting groundwater for agricultural, aquacultural, and industrial use in central Taiwan has caused large-scale land subsidence that poses a threat to the operation of the Taiwan High Speed Railway near Yunlin County. We detected Yunlin subsidence using the Sentinel-1A Synthetic Aperture Radar (SAR) by the Small BAseline Subset (SBAS) method from April 2016 to April 2017. We calibrated the initial InSAR-derived displacement rates using GPS measurements and reduced the velocity difference between the two sensors from 15.0 to 8.5 mm/a. In Yunlin’s severe subsidence regions, cumulative displacements from InSAR and GPS showed that the dry-season subsidence contributed 60%–74% of the annual subsidence. The InSAR-derived vertical velocities matched the velocities from leveling to better than 10 mm/a. In regions with few leveling measurements, InSAR increased the spatial resolution of the vertical velocity field and identified two previously unknown subsidence spots over an industrial zone and steel factory. Annual significant subsidence areas (subsidence rate > 30 mm/a) from leveling from 2011 to 2017 increased with the declining dry-season rainfalls, suggesting that the dry-season rainfall was the deciding factor for land subsidence. A severe drought in 2015 (an El Niño year) dramatically increased the significant subsidence area to 659 km². Both InSAR and leveling detected similarly significant subsidence areas in 2017, showing that InSAR was an effective technique for assessing whether a subsidence mitigation measure worked. The newly opened Hushan Reservoir can supply surface water during dry seasons and droughts to counter rain shortage and can thereby potentially reduce land subsidence caused by groundwater extraction.

Land subsidence is a significant problem around the world that can increase the risk of flooding, damage to infrastructure, and economic loss. Hence, the continual monitoring of subsidence is important for early detection, mechanism understanding, countermeasure implementation, and deformation prediction. In this study, we used multiple-sensor observations from the Continuous Global Positioning System (CGPS), the small baseline subset (SBAS) algorithm, interferometric synthetic-aperture radar (InSAR), precise leveling, multi-layer compaction monitoring wells (MLCWs), and groundwater observation wells (GWs) to show the spatial and temporal details of land subsidence in the Choushui River alluvial fan (CRAF), Taiwan, from 1993 to 2019. The results showed that significant land subsidence has occurred along the coastal areas in the CRAF, and most of the inland subsidence areas have also experienced higher subsidence rates (>30 mm/yr). The analysis of subsidence along the Taiwan High Speed Rail (THSR) revealed a newly formed subsidence center between Tuku and Yuanchang Townships in Yunlin, with high subsidence rates ranging from 30 to 70 mm/yr. We propose a map showing, for the first time, the distribution of deep compactions occurring below 300 m depth in the CRAF.

Over 1992–2018, groundwater overexploitation had caused large-scale land subsidence in the Choshui River Alluvial Fan (CRAF) in Taiwan. The Taiwan High Speed Railway (THSR) passes through an area of severe subsidence in CRAF, and the subsidence poses a serious threat to its operation. How to effectively monitor land subsidence here has become a major issue in Taiwan. In this paper, we introduce a multiple-sensor monitoring system for land subsidence, including 50 continuous operation reference stations (CORS), multi temporal InSAR (MT-InSAR), a 1000 km levelling network, 34 multi-layer compaction monitoring wells and 116 groundwater monitoring wells. This system can monitor the extent of land subsidence and provide data for studying the mechanism of land subsidence. We use the Internet of Things (IoT) technology to control and manage the sensors and develop a bigdata processing procedure to analyse the monitoring data for the system of sensors. The procedure makes the land subsidence monitoring more efficient and intelligent.

During 1992–2013, pumping of groundwater caused large-scale aquifer-system compaction and land subsidence in the Choshui River Alluvial Fan (CRAF) in Taiwan. The subsidence has already endangered the operation of Taiwan High Speed Rail (THSR). In this paper, we introduce the multiple sensors monitoring system to study the extent of subsidence in CRAF and its mechanism, including GPS (Global Positioning System), PSI (Persistent Scatterer Interferometry), leveling and multi-layer compaction monitoring well. These sensors complement each other in spatial and temporal resolutions.

Land subsidence driven by excessive groundwater extraction in the Choushui River alluvial fan of central Taiwan poses a significant threat to the structural integrity of the Taiwan High-Speed Rail (THSR). This study presents an integrated approach combining multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR), continuous and campaign Global Navigation Satellite System (GNSS) measurements, and precise leveling surveys to characterize both vertical and horizontal surface displacements along the THSR corridor. Sentinel-1 C-band SAR data from ascending (A69) and descending (D105) tracks were processed using the Small Baseline Subset (SBAS) technique over the period 2015–2021, and decomposed into east-west (EW) and vertical components via 2.5D decomposition. The InSAR-derived EW velocity field was calibrated using GNSS Ordinary Kriging interpolation, improving R2 from 0.147 (RMSE = 4.24 mm/yr) to 0.992 (RMSE = 0.24 mm/yr). The vertical velocity field was corrected using a polynomial trend surface fitted to 922 leveling benchmarks and 38 CGPS stations, reducing RMSE from 6.03 to 4.85 mm/yr (R2 from 0.889 to 0.898) and virtually eliminating the systematic bias from +3.47 to −0.47 mm/yr. Maximum subsidence exceeding 60 mm/yr was identified in the Yunlin Tuku area, while three secondary subsidence centers exist in Changhua. The horizontal velocity field reveals a convergent pattern directed toward subsidence centers, with magnitudes of 2–10 mm/yr, confirming that aquifer compaction induces significant lateral deformation. Along the THSR corridor, differential EW velocities across the Xizhou and Tuku subsidence zones highlight potential risks to rail alignment and structural safety, with horizontal strain rates reaching approximately 10⁻⁶/yr. GNSS observations additionally provide the north-south velocity component that InSAR cannot detect, enabling a more complete three-dimensional deformation characterization.