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<p>The proliferation of Synthetic Aperture Radar (SAR) satellite missions, complemented by the refinement of Differential SAR Interferometry (DInSAR) techniques, has resulted into a significant progress in the capability to monitor ground deformation, particularly in the context of landslide analysis. The present study includes a comprehensive global analysis for landslide investigations using satellite DInSAR data, compiling a geo-referenced inventory derived from a systematic review of scientific literature spanning 1995–2024. Employing the Web of Science database, a total of 2,739 initial contributions were screened with a detailed analysis. The geo-tagging of each landslide in the analysed study was performed with a high degree of accuracy, thereby resulting in the creation of an inventory comprising a total of 1,480 Point Identification Number (PINs) that is capable of differentiating between site-specific and area-wide analyses. The global dissemination of case studies underscores the predominance of specific regions, such as China, Italy and the US, which reflect the most common users in this context. The positioning of the PINs within the developed database was validated by a comparison using open and freely available products, including the global landslide susceptibility map and a range of independent landslide inventories. Specifically, the Italian landslide inventory demonstrated a strong correspondence with the PINs. The database was developed for the purpose of supporting researchers by offering spatial access to a broad spectrum of peer-reviewed DInSAR-based landslide studies.</p>

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A database of landslides investigated using satellite DInSAR

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Francesco Poggi,
  • Gabriele Fibbi,
  • Francesco Becattini,
  • Francesco Caleca,
  • Claudio De Luca,
  • Matteo Del Soldato,
  • Federico Raspini

摘要

The proliferation of Synthetic Aperture Radar (SAR) satellite missions, complemented by the refinement of Differential SAR Interferometry (DInSAR) techniques, has resulted into a significant progress in the capability to monitor ground deformation, particularly in the context of landslide analysis. The present study includes a comprehensive global analysis for landslide investigations using satellite DInSAR data, compiling a geo-referenced inventory derived from a systematic review of scientific literature spanning 1995–2024. Employing the Web of Science database, a total of 2,739 initial contributions were screened with a detailed analysis. The geo-tagging of each landslide in the analysed study was performed with a high degree of accuracy, thereby resulting in the creation of an inventory comprising a total of 1,480 Point Identification Number (PINs) that is capable of differentiating between site-specific and area-wide analyses. The global dissemination of case studies underscores the predominance of specific regions, such as China, Italy and the US, which reflect the most common users in this context. The positioning of the PINs within the developed database was validated by a comparison using open and freely available products, including the global landslide susceptibility map and a range of independent landslide inventories. Specifically, the Italian landslide inventory demonstrated a strong correspondence with the PINs. The database was developed for the purpose of supporting researchers by offering spatial access to a broad spectrum of peer-reviewed DInSAR-based landslide studies.