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Fusion of Multi-Satellite and Multi-Sensor SARs Data for Investigating Long-Term Surface Ground Movements and Processes

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (22 October 2021) | Viewed by 3255

Special Issue Editor


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Guest Editor
Department of Earth Sciences, University of Firenze, via La Pira, 4, 50121 Firenze, Italy
Interests: geodesy; GNSS; DInSAR; data integration; mathematical modeling and analysis

Special Issue Information

Dear Colleagues,

The determination of ground surface dynamics represents one of the most significant challenges of Earth Observation to study the impact of natural and human phenomena on the environment.
Climate, earthquakes, geodynamics, erosion, weathering, as well as human activities, primarily pumping groundwater and geothermal fluids, underground mining, and construction, can be investigated using surface ground movements.
The understanding of ground surface processes represents one of the most significant challenges to prevent loss of life, the damage of buildings and infrastructures, and to mitigate their heavy consequences on societies and economies.
The increase of the availability of multisatellite and multisensor SAR time-series specially promoted by European Space Agency through the platforms G-POD, GEP, and EO browser, the DIAS, is providing a high quality of ground displacement dataset over decades.
In this context, fusion of multisatellite and multisensor SARs series or the integration of SAR with GNSS and leveling holds great relevance.
In this Special Issue, we aim to bring together research and application contributions.
Suggestions for relevant research subjects are methods, case studies, and applications involving:

  • Data calibration and validation;
  • Data fusion from different radar platforms, operating at distinctive wavelengths and imaging modes;
  • Combination of SAR LOS-projected ground displacement with 3D GNSS components and/or the vertical displacements derived by leveling;
  • Combination of non-overlapped time frames;
  • Theoretical studies concerning future SARs and their promises, and their integration with actual SARs.

Dr. Farolfi Gregorio
Guest Editor

Manuscript Submission Information

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Keywords

  • Fusion
  • Integration
  • Long-term ground displacements
  • SAR
  • GNSS
  • Leveling

Published Papers (1 paper)

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Research

19 pages, 7727 KiB  
Article
An Improved Multi-Sensor MTI Time-Series Fusion Method to Monitor the Subsidence of Beijing Subway Network during the Past 15 Years
by Li Duan, Huili Gong, Beibei Chen, Chaofan Zhou, Kunchao Lei, Mingliang Gao, Hairuo Yu, Qun Cao and Jin Cao
Remote Sens. 2020, 12(13), 2125; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12132125 - 02 Jul 2020
Cited by 12 | Viewed by 2632
Abstract
Land subsidence threatens the stable operation of urban rail transit, including subways. Obtaining deformation information during the entire life-cycle of a subway becomes a necessary means to guarantee urban safety. Restricted by sensor life and cost, the single-sensor Multi-temporal Interferometric Synthetic Aperture Radar [...] Read more.
Land subsidence threatens the stable operation of urban rail transit, including subways. Obtaining deformation information during the entire life-cycle of a subway becomes a necessary means to guarantee urban safety. Restricted by sensor life and cost, the single-sensor Multi-temporal Interferometric Synthetic Aperture Radar (MTI) technology has been unable to meet the needs of long-term sequence, high-resolution deformation monitoring, especially of linear objects. The multi-sensor MTI time-series fusion (MMTI-TSF) techniques has been proposed to solve this problem, but rarely mentioned. In this paper, an improved MMTI-TSF is systematically explained and its limitations are discussed. Taking the Beijing Subway Network (BSN) as a case study, through cross-validation and timing verification, we find that the improved MMTI-TSF results have higher accuracy (R2 of 98% and, Root Mean Squared Error (RMSE) of 4mm), and compared with 38 leveling points, the fitting effect of the time series is good. We analyzed the characteristics of deformation along the BSN over a 15-year periods. The results suggest that there is a higher risk of instability in the eastern section of Beijing Subway Line 6 (L6). The land subsidence characteristics along the subway lines are related to its position from the subsidence center, and the edge of the subsidence center presents a segmented feature. Full article
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