Multi-temporal SAR Interferometry Research Articles

Multi-temporal SAR Interferometry (MTInSAR)-based study of surface subsidence and its impact on Krishna Godavari (KG) basin in India: a support vector approach.

The surface subsidence in the Krishna Godavari (KG) basin in India has increased with the discovery of crude oil and natural gas reserves since 1983. With private players coming up to bag the exploration and refining contracts, there must be timely monitoring of the surface subsidence of the region so that remedial measures for the resettlement of the populations can be taken promptly. Regular monitoring is necessary since the region is fertile and any seawater ingress results in the loss of valuable cultivable land. Multi-temporal SAR Interferometry (MTInSAR) technique has been applied successfully all over the world for the study and regular monitoring of land surface subsidence scenarios. This study utilizes data from Sentinel-1 C-band SAR sensor for MTInSAR-based surface subsidence and RADAR Vegetation Index (RVI)-based vegetation loss for the same season estimation between 2017 and 2022 for the KG basin region. It is inferred from the study that the region has shown surface subsidence of 80mm/year between April 2020 and June 2022. This study uses support vector regressor (SVR) to predict the loss in forest cover in terms of RVI using MTInSAR-based surface subsidence, VH, and VV backscatter as parameters. It is observed that the SVR gave R2-statistics of 0.89 and 0.873 in the training and testing phases with a mean absolute error (MAE) and root mean squared error (RMSE) of 0.08 and 0.02, respectively. It is also observed that the region showed a loss of 3.21 km2 of cultivable land between 2020 and 2022.

Spatiotemporal Evolution of Ground Subsidence and Extensional Basin Bedrock Organization: An Application of Multitemporal Multi-Satellite SAR Interferometry

Since the early 1990s, the European (ESA) and Italian (ASI) space agencies have managed and distributed a huge amount of satellite-recorded SAR data to the research community and private industries. Moreover, the availability of advanced cloud computing services implementing different multi-temporal SAR interferometry techniques allows the generation of deformation time series from massive SAR images. We exploit the information provided by a large PS dataset to determine the temporal trend of ground deformation and the relative deformation rate with millimetric accuracy to analyze the spatial and temporal distribution of land subsidence induced by water pumping from a deep confined aquifer in the Northern Valle Umbra Basin (Central Italy), exploiting 24 years of Permanent Scatterers—interferometric SAR data archives. The SAR images were acquired between 1992 and 2016 by satellites ERS1/2 and ENVISAT, the Sentinel 1 ESA missions and the COSMO-SkyMed ASI mission. We observed ground velocities and deformation geometries between 1992 and 2016, with displacements of more than 70 cm and velocities of up to 55 mm/yr. The results suggest that the shape and position of the surface ground displacement are controlled by the fault activity hidden under the valley deposits.

Detection of Distributed Scatterers in Multitemporal SAR Interferometry: A Comparison Between CAESAR and SqueeSAR Detectors

Detection of Distributed Scatterers in Multitemporal SAR Interferometry: A Comparison Between CAESAR and SqueeSAR Detectors

Remote sensing-based deformation monitoring of pagodas at the Bagan cultural heritage site, Myanmar

ABSTRACT As a World Cultural Heritage site with sacred landscape featuring an exceptional range of Buddhist art and architecture, much attention has been focused on the sustainable development of Bagan (Myanmar). Particularly, the monitoring of landscape surface subsidence and monument instability is of great importance to the protection and development planning of the Bagan heritage site. In this study, we applied high resolution TerraSAR-X imagery acquired from 2019 to 2020 for deformation monitoring based on the small baseline subset (SBAS) and persistent scatterer synthetic aperture radar interferometry (PSInSAR) approaches. We identified several hotspots and pagodas with displacement anomalies linked to land cover change and previous earthquakes. The cross comparison between SBAS and PSInSAR and the precision of height estimates derived by PSInSAR indicated a millimetric precision of the derived deformation products. The combination of the two multi-temporal SAR interferometry approaches satisfies the two-scale monitoring requirements from landscapes to monuments, particularly for large-scale World Heritage sites. The non-contact monitoring method has potential when traditional methods using field accessibility and surveillance are constrained.

Assessing the Potential of Long, Multi-Temporal SAR Interferometry Time Series for Slope Instability Monitoring: Two Case Studies in Southern Italy

Multi-temporal SAR interferometry (MTInSAR), by providing both mean displacement maps and displacement time series over coherent objects on the Earth’s surface, allows analyzing wide areas, identifying ground displacements, and studying the phenomenon evolution at a long time scale. This technique has also been proven to be very useful for detecting and monitoring slope instabilities. For this type of hazard, detection of velocity variations over short time intervals should be useful for early warning of damaging events. In this work, we present the results obtained by using both COSMO-SkyMed (CSK) and Sentinel-1 (S1) data for investigating the ground stability of two hilly villages located in the Southern Italian Apennines (Basilicata region), namely the towns of Montescaglioso and Pomarico. In these two municipalities, landslides occurred in the recent past (in Montescaglioso in 2013) and more recently (in Pomarico in 2019), causing damage to houses, commercial buildings, and infrastructures. SAR datasets acquired by CSK and S1 from both ascending and descending orbits were processed using the SPINUA MTInSAR algorithm. Mean velocity maps and displacement time series were analyzed, also by means of innovative ad hoc procedures, looking, in particular, for non-linear trends. Results evidenced the presence of nonlinear displacements in correspondence of some key infrastructures. In particular, the analysis of accelerations and decelerations of PS objects corresponding to structures affected by recent stabilization measures helps to shed new light in relation to known events that occurred in the area of interest.

Multi-Temporal SAR Interferometry for Vertical Displacement Monitoring from Space of Tengiz Oil Reservoir Using SENTINEL-1 and COSMO-SKYMED Satellite Missions

This study focused on the quantitative assessment of the vertical displacement velocities retrieved using Sentinel-1 and Cosmo-SkyMed synthetic aperture radar images for the Tengiz oilfield. Tengiz oilfield was selected as a study area because of its historically reported continuous subsidence and limited up-to-date studies during recent years. The small baseline subset time-series technique was used for the interferometric processing of radar images acquired for the period of 2018–2020. The geospatial and statistical analyses allowed to determine the existing hotspots of the subsidence processes induced by oil extraction in the study area. Ground deformation measurements derived from the Sentinel-1 and COSMO-SkyMed satellite missions showed that the Tengiz oilfield continuously subsided during 2018–2020 with the maximum annual vertical displacement velocity around −77.4 mm/y and −71.5 mm/y, respectively. The vertical displacement velocities derived from the Sentinel-1 and the COSMO-SkyMed images showed a good statistical relationship with R2≥0.73 and RMSE ≤3.68 mm. The cumulative vertical displacement derived from both satellites for the most subsiding location also showed a good statistical relationship with R2 equal to 0.97 and RMSE = ± 4.69. The observed relative differences of measurements by both satellites were acceptable to determine the ongoing vertical surface displacement processes in the study area. These studies demonstrated a practical novelty for the petroleum industry in terms of the comparative assessment of surface displacement measurements using time-series of medium-resolution Sentinel-1 and high-resolution COSMO-SkyMed radar images.

Deep Learning-Based Homogeneous Pixel Selection for Multitemporal SAR Interferometry

Homogeneous pixel selection (HPS) plays an important role in the application of multitemporal SAR interferometry. The statistical goodness-of-fit testing of the temporal samples has been widely used for HPS. However, the detection rates of the existing methods are unsatisfactory under small datasets. In this paper, a stacked auto-encoder (SAE) network based method is proposed for the selection of homogeneous pixels under the idea of deep learning image classification, as termed by SAEHPS. The SAE network is used to learn the spatial distribution behavior of the average intensity image. The deep network is trained and tested on different high-resolution SAR datasets of the Hong Kong Airport and the Fuzhou City, and three pixel-wise labels (i.e., high, medium, and low reflections) are regarded as outputs of model learning. The unsupervised training and supervised fine-tuning realize the class prediction. The results show that the SAE can achieve robust accuracies above 90% based on empirically labeled samples, especially in non-architectural areas where the distributed scatterers exist. The SAE results are devoted to the multitemporal PS/DS InSAR approach to identify homogeneous pixels. Both qualitative and quantitative experiments in HPS, phase optimization, and deformation monitoring have demonstrated the superiority of the novel method.

Multitemporal SAR interferometry for monitoring of ground deformations caused by hydrocarbon production in an arid environment: Case studies from the Sultanate of Oman

Time-lapse interferometric synthetic aperture radar (InSAR) remote sensing methods of surface deformation have proven their use in desert environments. The data are acquired frequently without the need to send personnel or equipment into the field. The quality and accuracy of the data is very high. The spatial resolution of the data is excellent and matches that of surface seismic data. These characteristics make the data well suited for a variety of time-lapse monitoring tasks. In this study, we describe the accuracy of the InSAR technique relative to other measurements such as the global positioning system and precise leveling (acquired at known stable locations). We illustrate two case studies of differing natures. In one case, gas production leads to reservoir compaction, which is tracked as surface subsidence with time using frequent InSAR data. The results are used to map zones of increased deformation and identify areas with localized changes. These insights are being used to influence decisions on new wells and well interventions, to provide support for management of facility integrity, and to advise building code and material selection that can withstand the expected rate of deformation. The second case of a shallow steam flood illustrates the use of InSAR data to identify areas of surface uplift following thermal expansion of the reservoir. These data are also used to support the monitoring of the steam chamber growth and confinement in the reservoir. The information from InSAR will become more valuable for reservoir management when the steam chamber matures and conventional downhole data acquisition consequently becomes challenging. In summary, oil and gas fields located in arid environments lend themselves well to remote sensing using the InSAR technique because (1) they are sizeable (from tens to hundreds of square kilometers); (2) they are free from vegetation, snow cover, and most atmospheric distortions, although cloud and pollution can affect the data quality; and (3) they benefit from highly repeatable long-term regular monitoring.

Comment on “Pre-Collapse Space Geodetic Observations of Critical Infrastructure: The Morandi Bridge, Genoa, Italy” by Milillo et al. (2019)

We present in this comment a Multi-Temporal SAR Interferometry (MT-InSAR) analysis showing that the results published by Milillo et al. (2019) in the Remote Sensing Journal, presenting the evidence of space geodetic observations relevant to displacements occurring before the collapse of the Morandi Bridge, happened in Genova (Italy) on the 14 August 2018, are questionable. In particular, we focus on the InSAR results obtained by Milillo et al. (2019) by processing the 3 m × 3 m resolution COSMO-SkyMed (CSK) data collected from ascending and descending orbits on the area of interest. These results, thanks to the high spatial resolution and the short revisit time characterizing this multi-orbit SAR dataset, represent the cornerstone of their analysis. The main findings of their study allow Milillo et al. to conclude that the InSAR processing of this COSMO-SkyMed dataset reveals the increased deformation magnitude over time of points located near the strands of the deck next to the collapsed pier, between 12 March 2017 and August 2018. In this comment, we show the results obtained by the IREA-CNR SAR team after processing the same ascending and descending CSK dataset, but by using two alternative and independent processing techniques: the Small BAseline Subset (SBAS) and the Advanced Tomographic SAR (TomoSAR) approaches, respectively. Our analysis shows that, although both the SBAS and the TomoSAR analyses allow achieving denser coherent pixel maps relevant to the Morandi bridge, nothing of the pre-collapse large displacements reported in Milillo et al. (2019) appears in our results, leading us to deeply disagree with the findings of their InSAR analysis.

Complex Surface Displacements above the Storage Cavern Field at Epe, NW-Germany, Observed by Multi-Temporal SAR-Interferometry

The storage cavern field at Epe has been brined out of a salt deposit belonging to the lower Rhine salt flat, which extends under the surface of the North German lowlands and part of the Netherlands. Cavern convergence and operational pressure changes cause surface displacements that have been studied for this work with the help of SAR interferometry (InSAR) using distributed and persistent scatterers. Vertical and East-West movements have been determined based on Sentinel-1 data from ascending and descending orbit. Simple geophysical modeling is used to support InSAR processing and helps to interpret the observations. In particular, an approach is presented that allows to relate the deposit pressures with the observed surface displacements. Seasonal movements occurring over a fen situated over the western part of the storage site further complicate the analysis. Findings are validated with ground truth from levelling and groundwater level measurements.

Deep learning networks for selection of measurement pixels in multi-temporal SAR interferometric processing

In multi-temporal SAR interferometry (MT-InSAR), persistent scatterer (PS) pixels are used to estimate geophysical parameters, essentially deformation. Conventionally, PS pixels are selected based on the estimated noise present in the spatially uncorrelated phase component along with look-angle error in a temporal interferometric stack. In this study, two deep learning architectures, namely convolutional neural network for interferometric semantic segmentation (CNN-ISS) and convolutional long short term memory network for interferometric semantic segmentation (CLSTM-ISS), based on learning spatial and spatio-temporal behaviour, respectively, were proposed for selection of PS pixels. These networks were trained to relate the interferometric phase history to its classification into phase stable (PS pixels) and phase unstable (non-PS pixels) measurement pixels using ~10,000 real world interferometric patch images of different study sites containing man-made objects, forests, vegetation, uncropped land, water bodies, and areas affected by lengthening, foreshortening, layover and shadowing. The networks were trained using training labels obtained from the Stanford method for Persistent Scatterer Interferometry (StaMPS) algorithm. However, pixel selection results, evaluated using a combination of R-index, Similar Time Series Interferometric Pixel (STIP) maps and a classified image of the test dataset, reveal that CLSTM-ISS estimates improved the classification of PS and non-PS pixels as compared to those of StaMPS and CNN-ISS. The predicted results show that CLSTM-ISS reached an accuracy of 93.50%, higher than that of CNN-ISS (89.21%). CLSTM-ISS also improved the density of reliable PS pixels compared to StaMPS and CNN-ISS. Further, the architecture outperformed StaMPS, and is expected to compete with other MT-InSAR algorithms in terms of computational efficiency.

PS-InSAR Analysis of Sentinel-1 Data for Detecting Ground Motion in Temperate Oceanic Climate Zones: A Case Study in the Republic of Ireland

Regions of temperate oceanic climate have historically represented a challenge for the application of satellite-based multi-temporal SAR interferometry. The landscapes of such regions are commonly characterized by extensive, seasonally-variable vegetation coverage that can cause low temporal coherence and limit the detection capabilities of SAR imagery as acquired, for instance, by previous ERS-1/2 and ENVISAT missions. In this work, we exploited the enhanced resolution in space and time of the recently deployed Sentinel-1A/B SAR satellites to detect and monitor ground motions occurring in two study areas in the Republic of Ireland. The first, is a ~1800 km2 area spanning the upland karst of the Clare Burren and the adjacent mantled lowland karst of east Galway. The second, is an area of 100 km2 in Co. Meath spanning an active mine site. The available datasets, consisting of more than 100 images acquired in both ascending and descending orbits from April 2015 to March 2018, were processed by using the Permanent Scatterer approach. The obtained results highlight the presence of small-scale ground motions in both urban and natural environments with displacement rates along the satellite line of sight up to −17 mm/year. Localized subsidence was detected in recently built areas, along the infrastructure (both roads and railways), and over the mine site, while zones of subsidence, uplift, or both, have been recorded in a number of peatland areas. Furthermore, several measured target points indicate the presence of unstable areas along the coastline. Many of the detected movements were previously unknown. These results demonstrate the feasibility of adopting multi-temporal interferometry based on Sentinel-1 data for the detection and monitoring of mm-scale ground movements even over small areas (<100 m2) in environments influenced by temperate oceanic climate.

Mapping the Yellow River Delta land subsidence with multitemporal SAR interferometry by exploiting both persistent and distributed scatterers

Due to highly compressible soil and a large amount of human activity, the costal deltas are more prone to ground subsidence. Many major costal deltas in the world are facing subsidence and are consequently more susceptible to flooding, salinization and seawater infusion or even permanent submergence. Therefore, ground subsidence has been a significant problem in coastal delta areas worldwide. The Yellow River Delta (YRD) is the second largest river delta in China. On the one hand, the YRD contains a large area of wetlands rich in biodiversity, and on the other hand, industrial activities and urbanization are extensive due to abundant underground resources such as oil, gas and brine. Excessive land use has caused different degrees of ground subsidence in this area. However, a detailed and comprehensive description of the ground subsidence pattern over the YRD has not been provided. Also, widespread non-urban area in Yellow River Delta region, such as wetlands, farmland and coastal tidal areas, hinders the application of persistent scatterer interferometry method (PSI) for comprehensive subsidence measurement over the whole area. In this paper, we developed a multitemporal InSAR method to map ground subsidence over the YRD area by exploiting both persistent scatterers (PS) and distributed scatterers (DS). This method is characterized by employing the coherence-weighted phase-linking algorithm for fast and reliable optimal phase reconstruction of each DS point and a two-tier network of PS and DS for the robust analysis of land subsidence. To extract the detailed and comprehensive ground subsidence over the whole YRD, we apply our method to 30 ENVISAT ASAR images (2007–2010) and 49 Sentinel-1A (S-1A) images (2015–2018) and obtain measurements of the ground subsidence during these two periods. Forty-one Sentinel-1B (S-1B) images (2016–2018) are also exploited for cross-sensor consistency validation with the result derived from the S-1A dataset. Our method shows a great advantage over the PSI method, providing much higher measuring point (MP) density in mapping land subsidence over the YRD, including 15-fold higher density for the ASAR dataset, 5.1-fold for the S-1A dataset and 5.3-fold for the S-1B dataset, which enables a very detailed description of local ground deformation patterns. Cross-track consistency in the derived measurements from the S-1A and S-1B datasets shows a standard deviation of 9.6 mm/yr for the vertical subsidence rate. A quantitative validation of the derived subsidence results compared with leveling measurements suggests an accuracy of 4.58 mm/yr for the standard deviation term. By comparing the ground deformation over the YRD during the periods of 2015–2018 and 2007–2010, we find that the subsidence in this region shows an overall intensification trend and many new and severe subsidence depressions appear along the coastline, with a maximum vertical subsidence rate of 432 mm/yr. Subsequently, the overextraction of underground brine for salt production is identified as the primary factor causing the ground subsidence near the YRD coastal area.

Monitoring of 3D land subsidence by multi-temporal SAR interferometry with survey data

Monitoring of 3D land subsidence by multi-temporal SAR interferometry with survey data

Detection and displacement characterization of landslides using multi-temporal satellite SAR interferometry: A case study of Danba County in the Dadu River Basin

Detection of potential slope instabilities across wide areas using SAR interferometry (InSAR) techniques can aid prevention and mitigation of landslide disasters. Nevertheless, the low spatial density of coherent radar targets detected by conventional time series InSAR methods in mountainous areas with complex topography and vegetation cover introduces considerable uncertainties into the deformation measurements, and thus results in a high probability of omissions for landslide detection. In this study, a new time series InSAR method named Coherent Scatterers InSAR (CSI) is proposed to solve this problem through the joint exploitation of Persistent Scatterers (PS) and Distributed Scatterers (DS) to increase the quantity of Measurement Points (MPs) in rural environments. We applied this approach to detect potentially unstable slopes at a catchment scale, delineate sliding boundaries, and measure the deformation of major landslides. The archived ALOS PALSAR and ENVISAT ASAR data stacks covering the Danba County in the upper reach of the Dadu River Basin in southwest China, were processed. The PALSAR-measured deformation rate map revealed 17 suspected landslides and their sliding boundaries. By contrast, only six out of these 17 landslides were detected from the ASAR results, as the other 11 landslides were undetected due to the much lower density of coherent points. These detection results were indirectly verified by two different approaches. Two differential interferograms for recently acquired L-band ALOS-2 PALSAR-2 data pairs were visually inspected to check whether these landslides are still in active deformation. The reliability of the InSAR results was further evaluated by comparisons against field survey and in-situ GPS measurements. The spatial and temporal patterns of surface displacements at several slow-moving landslides were analyzed in detail, and general guidelines on the application of the CSI method for landslide investigations were discussed and summarized.

Multi-sensor monitoring of Ciudad Guzman (Mexico) ground subsidence

The study of urban subsidence with multi-temporal SAR interferometry is nowadays a well-consolidated approach. Thanks to this powerful technique, it is possible to detect and to measure ground deformation velocity and time series of displacement with high accuracy. This work focuses the analysis on the subsidence phenomenon that is threating the city of Guzman (Jalisco state, Mexico) by means of multi-temporal SAR interferometry applied to a stack of COSMO-SkyMed data, from 2011 to 2015, and a stack of Sentinel-1 TOPSAR mode images, from 2016 to 2018. The work is intended to carry on the study performed with ENVISAT images covering the time span between 2003 and 2010, allowing the continuous monitoring of the deformation process.

On the Accuracy of Topographic Residuals Retrieved by MTInSAR

Topographic residuals in differential interferometric synthetic aperture radar (InSAR) measurements are mainly caused by inaccurate external digital elevation model (DEM). Accurate separation of the phase component contributed by topographic residuals plays an important role in the retrieval of deformation time series from InSAR observations. Even though the residuals can be modeled and estimated in the framework of multitemporal SAR interferometry (MTInSAR), it is not clear what an optimal processing strategy is and how accurate the estimation can reach. We analyze here the factors that affect the accuracy of the retrieved DEM residuals by applying four commonly used MTInSAR methods in a series of simulated scenarios. The results indicate that besides the quality of interferometric observations, the thresholds of spatial and temporal baselines, the diversity of spatial baseline lengths, the connectivity of interferogram network, and improper deformation model also fluctuate the accuracy of the retrieved topographic residuals. According to these affecting factors, this paper sheds light on an optimal approach to reliably retrieve accurate topographic residuals under MTInSAR framework.

Measuring ground subsidence in Hanoi city by radar interferometry

The rapidly developing urbanization since the last decade of the 20th century leads to the strong groundwater extraction, resulting in the subsidence phenomena in the Hanoi, Vietnam. Recent advances in the multi-temporal spaceborne SAR interferometry, especially with Persistent Scatters Interferometry (PSI) approach, is the robust remote sensing technique for measuring ground subsidence in large scale with millimetric accuracy. This work has presented an advanced PSI analysis, to provide unprecedented spatial extent and continuous temporal coverage of the subsidence in Hanoi City. The correlation between the reference leveling velocity and the estimated PSI result is R2 = 0.86, and the root mean square error is 4.0 (mm/year), confirming their good agreement. The study shows that subsidence is most severe in the Haihung silt loam areas in the south of the city. The groundwater extraction resulting from urbanization and urban growth is mainly responsible for the subsidence.

Spatiotemporal Characterization of Land Subsidence and Uplift (2009–2010) over Wuhan in Central China Revealed by TerraSAR-X InSAR Analysis

The effects of ground deformation pose a significant geo-hazard to the environment and infrastructure in Wuhan, the most populous city in Central China, in the eastern Jianghan Plain at the intersection of the Yangtze and Han rivers. Prior to this study, however, rates and patterns of region-wide ground deformation in Wuhan were little known. Here we employ multi-temporal SAR interferometry to detect and characterize spatiotemporal variations of ground deformation in major metropolitan areas in Wuhan. A total of twelve TerraSAR-X images acquired during 2009–2010 are used in the InSAR time series analysis. InSAR-derived results are validated by levelling survey measurements and reveal a distinct subsidence pattern within six zones in major commercial and industrial areas, with a maximum subsidence rate up to −67.3 mm/year. A comparison analysis between subsiding patterns and urban developments as well as geological conditions suggests that land subsidence in Wuhan is mainly attributed to anthropogenic activities, natural compaction of soft soil, and karst dissolution of subsurface carbonate rocks. However, anthropogenic activities related to intensive municipal construction and industrial production have more significant impacts on the measured subsidence than natural factors. Moreover, remarkable signals of secular land uplift are found along both banks of the Yangtze River, especially along the southern bank, with deformation rates ranging mostly from +5 mm/year to +17.5 mm/year. A strong temporal correlation is highlighted between the detected displacement evolutions and the water level records of the Yangtze River, inferring that this previously unknown deformation phenomenon is likely related to seasonal fluctuations in water levels of the Yangtze River.

Mapping Ground Subsidence Phenomena in Ho Chi Minh City through the Radar Interferometry Technique Using ALOS PALSAR Data

The rapidly developing urbanization since the last decade of the 20th century has led to extensive groundwater extraction, resulting in subsidence in Ho Chi Minh City, Vietnam. Recent advances in multi-temporal spaceborne SAR interferometry, especially with a persistent scatters interferometry (PSI) approach, has made this a robust remote sensing technique for measuring large-scale ground subsidence with millimetric accuracy. This work has presented an advanced PSI analysis, to provide an unprecedented spatial extent and continuous temporal coverage of the subsidence in Ho Chi Minh City from 2006 to 2010. The study shows that subsidence is most severe in the Holocene silt loam areas along the Sai Gon River and in the southwest of the city. The groundwater extraction resulting from urbanization and urban growth is mainly responsible for the subsidence. Subsidence in turn leads to more flooding and water nuisance. The correlation between the reference leveling velocity and the estimated PSI result is R2 = 0.88, and the root mean square error is 4.3 (mm/year), confirming their good agreement. From 2006 to 2010, the estimation of the average subsidence rate is -8.0 mm/year, with the maximum value up to -70 mm/year. After four years, in regions along Sai Gon River and in the southwest of the city, the land has sunk up to -12 cm. If not addressed, subsidence leads to the increase of inundation, both in frequency and spatial extent. Finally, regarding climate change, the effects of subsidence should be considered as appreciably greater than those resulting from rising sea level. It is essential to consider these two factors, because the city is inhabited by more than 7.5 million people, where subsidence directly impacts urban structures and infrastructure.