{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T13:50:25Z","timestamp":1772286625275,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,10]],"date-time":"2024-11-10T00:00:00Z","timestamp":1731196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals\u2019 lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas.<\/jats:p>","DOI":"10.3390\/rs16224188","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T11:34:11Z","timestamp":1731324851000},"page":"4188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Long-Term Ground Deformation Monitoring and Quantitative Interpretation in Shanghai Using Multi-Platform TS-InSAR, PCA, and K-Means Clustering"],"prefix":"10.3390","volume":"16","author":[{"given":"Yahui","family":"Chong","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Spatial Information Integration and 3S Engineering Application Beijing Key Laboratory, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1020-064X","authenticated-orcid":false,"given":"Qiming","family":"Zeng","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Spatial Information Integration and 3S Engineering Application Beijing Key Laboratory, Beijing 100871, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rigamonti, S.G., Frattini, D.P., and Crosta, G.B. (2023). A Multivariate Time Series Analysis of Ground Deformation Using Persistent Scatterer Interferometry. Remote Sens., 15.","DOI":"10.5194\/egusphere-egu23-15347"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1080\/2150704X.2019.1648903","article-title":"A safety analysis of elevated highways in Shanghai linked to dynamic load using long-term time-series of InSAR stacks","volume":"10","author":"Wang","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9803","DOI":"10.1109\/JSTARS.2021.3113672","article-title":"Detecting the Deformation Anomalies Induced by Underground Construction Using Multiplatform MT-InSAR: A Case Study in To Kwa Wan Station, Hong Kong","volume":"14","author":"Wu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/JSTARS.2022.3223027","article-title":"Spatial and Temporal Evolution of Ground Subsidence in the Beijing Plain Area Using Long Time Series Interferometry","volume":"16","author":"Zheng","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.jappgeo.2009.02.003","article-title":"Validation and intercomparison of persistent scatterers interferometry: PSIC4 project results","volume":"68","author":"Raucoules","year":"2009","journal-title":"J. Appl. Geophys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TGRS.2011.2124465","article-title":"A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR","volume":"49","author":"Ferretti","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/36.898661","article-title":"Permanent scatterers in SAR interferometry","volume":"39","author":"Ferretti","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"L23611","DOI":"10.1029\/2004GL021737","article-title":"A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers","volume":"31","author":"Hooper","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yang, M., Li, M., Huang, C., Zhang, R., and Liu, R. (2024). Exploring the InSAR Deformation Series Using Unsupervised Learning in a Built Environment. Remote Sens., 16.","DOI":"10.3390\/rs16081375"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ke, C., Shen, X., Lin, J., and Wang, R. (2023). Monitoring Land Subsidence along the Subways in Shanghai on the Basis of Time-Series InSAR. Remote Sens., 15.","DOI":"10.3390\/rs15040908"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9542","DOI":"10.3390\/rs70809542","article-title":"Extracting Vertical Displacement Rates in Shanghai (China) with Multi-Platform SAR Images","volume":"7","author":"Dai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s12665-013-2990-y","article-title":"Time-series analysis of subsidence associated with rapid urbanization in Shanghai, China measured with SBAS InSAR method","volume":"72","author":"Dong","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.isprsjprs.2012.07.002","article-title":"Shanghai subway tunnels and highways monitoring through Cosmo-SkyMed persistent scatterers","volume":"73","author":"Perissin","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qin, X., Yang, T., Yang, M., Zhang, L., and Liao, M. (2017). Health Diagnosis of Major Transportation Infrastructures in Shanghai Metropolis Using High-Resolution Persistent Scatterer Interferometry. Sensors, 17.","DOI":"10.3390\/s17122770"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1080\/17538947.2023.2171144","article-title":"Ground Infrastructure Monitoring in Coastal Areas Using Time-Series inSAR Technology: The Case Study of Pudong International Airport, Shanghai","volume":"16","author":"An","year":"2023","journal-title":"Int. J. Digit. Earth."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1109\/JSTARS.2015.2402168","article-title":"A DInSAR Investigation of the Ground Settlement Time Evolution of Ocean-Reclaimed Lands in Shanghai","volume":"8","author":"Zhao","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.rse.2018.11.003","article-title":"Ground surface response to continuous compaction of aquifer system in Tehran, Iran: Results from a long-term multi-sensor InSAR analysis","volume":"221","author":"Haghighi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1016\/j.asr.2018.11.008","article-title":"Monitoring of long-term land subsidence from 2003 to 2017 in coastal area of Semarang, Indonesia by SBAS DInSAR analyses using Envisat-ASAR, ALOS-PALSAR, and Sentinel-1A SAR data","volume":"63","author":"Yastika","year":"2019","journal-title":"Adv Space Res."},{"key":"ref_20","first-page":"102847","article-title":"A model-backfeed deformation estimation method for revealing 20-year surface dynamics of the Groningen gas field using multi-platform SAR imagery","volume":"111","author":"Zhang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2019.05.005","article-title":"Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area","volume":"154","author":"Zhao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7297","DOI":"10.1109\/JSTARS.2021.3096996","article-title":"Long-term continuously updated deformation time series from multisensor InSAR in Xi\u2019an, China from 2007 to 2021","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pepe, A., Bonano, M., Zhao, Q., Yang, T., and Wang, H. (2016). The use of C-\/X-band time-gapped SAR data and geotechnical models for the study of Shanghai\u2019s ocean-reclaimed lands through the SBAS-DInSAR technique. Remote Sens., 8.","DOI":"10.20944\/preprints201608.0083.v1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.scib.2019.04.024","article-title":"40-Year (1978\u20132017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/36.175330","article-title":"Decorrelation in interferometric radar echoes","volume":"30","author":"Zebker","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","unstructured":"Zhang, H., Zeng, Q., Liu, Y., Li, X., and Gao, L. (2005, January 25\u201329). The Optimum Selection of Common Master Image for Series of Differential SAR Processing to Estimate Long and Slow Ground Deformation. Proceedings of the IGARSS 2005\u20142005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Republic of Korea."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"B07407","DOI":"10.1029\/2006JB004763","article-title":"Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volca\u2019n Alcedo, Gala\u00b4pagos","volume":"112","author":"Hooper","year":"2007","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yu, L., Yang, T.L., Zhao, Q., Liu, M., and Pepe, A. (2017). The 2015\u20132016 Ground Displacements of the Shanghai Coastal Area Inferred from a Combined COSMO-SkyMed\/Sentinel-1 DInSAR Analysis. Remote Sens., 9.","DOI":"10.3390\/rs9111194"},{"key":"ref_29","first-page":"862","article-title":"Consolidation settlement of Shanghai dredger fill under self-weight using centrifuge modeling test","volume":"39","author":"Yang","year":"2008","journal-title":"J. Cent. S. Univ. Technol."},{"key":"ref_30","first-page":"20150202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. Math. Phys. Eng. Sci."},{"key":"ref_31","first-page":"103276","article-title":"Unsupervised detection of InSAR time series patterns based on PCA and K-means clustering","volume":"118","author":"Festa","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6572","DOI":"10.1002\/2014JB011266","article-title":"Predictability of hydraulic head changes and characterization of aquifer-system and fault properties from InSAR-derived ground deformation","volume":"119","author":"Chaussard","year":"2014","journal-title":"J. Geophys. Res. Solid Earth."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"10226","DOI":"10.1029\/2018JB016210","article-title":"Blind Signal Separation Methods for InSAR: The Potential to Automatically Detect and Monitor Signals of Volcanic Deformation","volume":"123","author":"Gaddes","year":"2018","journal-title":"J. Geophys. Res. Solid Earth."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8970","DOI":"10.1002\/2016JB013765","article-title":"Application of independent component analysis to multitemporal InSAR data with volcanic case studies","volume":"121","author":"Ebmeier","year":"2016","journal-title":"J. Geophys. Res. Solid Earth."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, Y., Tan, K., Yan, S., Zhang, K., Zhang, H., Liu, X., Li, H., and Sun, Y. (2019). Monitoring land surface displacement over Xuzhou (China) in 2015-2018 through PCA-based correction Applied to SAR interferometry. Remote Sens., 11.","DOI":"10.3390\/rs11121494"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1002\/joc.3370060305","article-title":"Rotation of principal components","volume":"6","author":"Richman","year":"1986","journal-title":"J. Climatol."},{"key":"ref_38","first-page":"103077","article-title":"Coastal subsidence detection and characterization caused by brine mining over the Yellow River Delta using time series InSAR and PCA","volume":"114","author":"Wang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","unstructured":"MacQueen, J. (1965, January 21\u201318). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1007\/s11069-018-3431-8","article-title":"A hybrid clustering-fusion methodology for land subsidence estimation","volume":"94","author":"Taravatrooy","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5206309","DOI":"10.1109\/TGRS.2021.3072037","article-title":"Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data","volume":"60","author":"Izumi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","first-page":"36","article-title":"An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data\u2014A case study in complex temperate forest stands","volume":"57","author":"Abdullahi","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113923","DOI":"10.1016\/j.rse.2023.113923","article-title":"Characterization and Prediction of InSAR-Derived Ground Motion with ICA-Assisted LSTM Model","volume":"301","author":"Peng","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2942","DOI":"10.1109\/TGRS.2010.2043442","article-title":"Decorrelation of L-Band and C-Band Interferometry Over Vegetated Areas in California","volume":"48","author":"Wei","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/TGRS.2014.2333814","article-title":"Temporal Decorrelation in L-, C-, and X-band Satellite Radar Interferometry for Pasture on Drained Peat Soils","volume":"53","author":"Morishita","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","first-page":"271","article-title":"The Influence of SAR Image Resolution, Wavelength and Land Cover Type on Characteristics of Persistent Scatterer","volume":"92","author":"Chong","year":"2024","journal-title":"PFG\u2013J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.1109\/JSTARS.2019.2896038","article-title":"Persistent Scatterer Density by Image Resolution and Terrain Type","volume":"12","author":"Huang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs.Remote Sens."},{"key":"ref_48","first-page":"96","article-title":"Construction of engineering geological structure and geological condition evaluation of Shanghai sea-land body","volume":"44","author":"Shi","year":"2017","journal-title":"Hydrogeol. Eng. Geol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4188\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:29:41Z","timestamp":1760113781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,10]]},"references-count":48,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224188"],"URL":"https:\/\/doi.org\/10.3390\/rs16224188","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,10]]}}}