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Time-Series Monitoring and Analysis of Surface Deformation in Shiguilong Tailings Storage Using E-SBAS-InSAR.

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Title: Time-Series Monitoring and Analysis of Surface Deformation in Shiguilong Tailings Storage Using E-SBAS-InSAR.
Authors: Cui, Haoxin1 (AUTHOR), Han, Dongliang1,2 (AUTHOR) hdl@jlu.edu.cn, Meng, Yibo3 (AUTHOR), Shu, Chuanzeng1 (AUTHOR), Meng, Zhiguo1,2 (AUTHOR), Ding, Qing1,3 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1905. 29p.
Subjects: Tailings dams, Radar interferometry, Environmental monitoring, Rainfall, Deformation of surfaces, Synthetic aperture radar, Risk assessment
Abstract: Highlights: What are the main findings? E-SBAS-InSAR provides high-density, reliable, and long-term surface deformation monitoring results, demonstrating strong applicability for deformation monitoring of tailings storage facilities. Seasonal deformation of tailings storage facilities exhibits lagged responses to temperature variations and intense rainfall events, with intense rainfall exerting a more pronounced influence. What are the implications of the main findings? E-SBAS-InSAR offers a reliable technical framework for surface deformation monitoring and risk identification in complex tailings storage facility environments. This study reveals the lagged response of seasonal deformation in tailings storage facilities to temperature variations and intense rainfall events, highlighting the importance of short-term deformation monitoring after heavy rainfall. These findings provide a scientific basis for rainy-season risk identification and safety early warning in tailings storage facilities. Tailings storage facility (TSF) failures have caused severe casualties and economic losses. This study used Enhanced Small Baseline Subset InSAR (E-SBAS-InSAR) and 88 Sentinel-1A images to retrieve the 2022–2024 surface deformation time series of the Shiguilong TSF, located in the Fe–Cu polymetallic metallogenic belt of the middle–lower Yangtze River. The reliability of the results was assessed through consistency comparisons with Small Baseline Subset InSAR (SBAS-InSAR) and Persistent Scatterer InSAR (PS-InSAR). A time-series decomposition model was applied to extract seasonal deformation components and analyze their lagged responses to temperature and intense rainfall events. The results show that: (1) E-SBAS-InSAR achieved a monitoring-point density nearly 7 times higher than SBAS-InSAR, enabling dense and long-term deformation characterization; (2) subsidence at Shiguilong continued to increase, with cumulative subsidence reaching −76.8 mm and a maximum annual mean subsidence rate of −22.78 mm/yr; (3) deformation was mainly controlled by long-term consolidation of loose tailings and creep of dam–tailings materials, while seasonal factors induced stage-dependent fluctuations; (4) seasonal deformation showed lagged responses of 6 days to temperature variations and 2 days to intense rainfall events, with rainfall exerting a more pronounced influence. This work is significant for TSFs monitoring under complex surface conditions. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 194915038
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  Label: Title
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  Data: Time-Series Monitoring and Analysis of Surface Deformation in Shiguilong Tailings Storage Using E-SBAS-InSAR.
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  Data: <searchLink fieldCode="AR" term="%22Cui%2C+Haoxin%22">Cui, Haoxin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Dongliang%22">Han, Dongliang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> hdl@jlu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Meng%2C+Yibo%22">Meng, Yibo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shu%2C+Chuanzeng%22">Shu, Chuanzeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Zhiguo%22">Meng, Zhiguo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ding%2C+Qing%22">Ding, Qing</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1905. 29p.
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  Data: <searchLink fieldCode="DE" term="%22Tailings+dams%22">Tailings dams</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Rainfall%22">Rainfall</searchLink><br /><searchLink fieldCode="DE" term="%22Deformation+of+surfaces%22">Deformation of surfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? E-SBAS-InSAR provides high-density, reliable, and long-term surface deformation monitoring results, demonstrating strong applicability for deformation monitoring of tailings storage facilities. Seasonal deformation of tailings storage facilities exhibits lagged responses to temperature variations and intense rainfall events, with intense rainfall exerting a more pronounced influence. What are the implications of the main findings? E-SBAS-InSAR offers a reliable technical framework for surface deformation monitoring and risk identification in complex tailings storage facility environments. This study reveals the lagged response of seasonal deformation in tailings storage facilities to temperature variations and intense rainfall events, highlighting the importance of short-term deformation monitoring after heavy rainfall. These findings provide a scientific basis for rainy-season risk identification and safety early warning in tailings storage facilities. Tailings storage facility (TSF) failures have caused severe casualties and economic losses. This study used Enhanced Small Baseline Subset InSAR (E-SBAS-InSAR) and 88 Sentinel-1A images to retrieve the 2022–2024 surface deformation time series of the Shiguilong TSF, located in the Fe–Cu polymetallic metallogenic belt of the middle–lower Yangtze River. The reliability of the results was assessed through consistency comparisons with Small Baseline Subset InSAR (SBAS-InSAR) and Persistent Scatterer InSAR (PS-InSAR). A time-series decomposition model was applied to extract seasonal deformation components and analyze their lagged responses to temperature and intense rainfall events. The results show that: (1) E-SBAS-InSAR achieved a monitoring-point density nearly 7 times higher than SBAS-InSAR, enabling dense and long-term deformation characterization; (2) subsidence at Shiguilong continued to increase, with cumulative subsidence reaching −76.8 mm and a maximum annual mean subsidence rate of −22.78 mm/yr; (3) deformation was mainly controlled by long-term consolidation of loose tailings and creep of dam–tailings materials, while seasonal factors induced stage-dependent fluctuations; (4) seasonal deformation showed lagged responses of 6 days to temperature variations and 2 days to intense rainfall events, with rainfall exerting a more pronounced influence. This work is significant for TSFs monitoring under complex surface conditions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/rs18121905
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 29
        StartPage: 1905
    Subjects:
      – SubjectFull: Tailings dams
        Type: general
      – SubjectFull: Radar interferometry
        Type: general
      – SubjectFull: Environmental monitoring
        Type: general
      – SubjectFull: Rainfall
        Type: general
      – SubjectFull: Deformation of surfaces
        Type: general
      – SubjectFull: Synthetic aperture radar
        Type: general
      – SubjectFull: Risk assessment
        Type: general
    Titles:
      – TitleFull: Time-Series Monitoring and Analysis of Surface Deformation in Shiguilong Tailings Storage Using E-SBAS-InSAR.
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          Name:
            NameFull: Cui, Haoxin
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            NameFull: Han, Dongliang
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            NameFull: Shu, Chuanzeng
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            NameFull: Meng, Zhiguo
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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