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Time Series Multi-Sensors of Interferometry Synthetic Aperture Radar for Monitoring Geographical Conditions

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing for Geospatial Science".

Deadline for manuscript submissions: 25 November 2025 | Viewed by 4058

Special Issue Editors

Dr. Chris Danezis

E-Mail Website
Guest Editor
Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus
Interests: geodesy; GNSS; InSAR; deformation monitoring; monitoring infrastructures; precise positioning; infrastructure resilience; calibration/validation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huayang Dai

E-Mail Website
Guest Editor
Department of Surveying and Land Use, China University of Mining & Technology, Beijing, China
Interests: coal mining; insar; land deformation; mining areas
Dr. Yueguan Yan

E-Mail Website
Guest Editor
College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing, China
Interests: mining damage and rock formation control; deformation monitoring and data processing; DInSAR technology; three-dimensional laser scanning technology
Prof. Dr. Phaedon C. Kyriakidis

E-Mail Website
Guest Editor
Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus
Interests: spatial analysis; geostatistics; geocomputation; geographic information systems and science; remote sensing; geoinformatics in archaeology and cultural heritage
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vassilia Karathanassi

E-Mail Website
Guest Editor
Remote Sensing Laboratory, School of Rural & Surveying Engineering, NTUA, Heroon Polytechniou 9, 15780 Zografou, Greece
Interests: remote sensing; environment; classification algorithms; neural networks; radar
Prof. Dr. Alex Hay-Man Ng

E-Mail Website
Guest Editor
1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
2. School of Civil and Environmental Engineering, UNSW Australia, Sydney 2052, Australia
Interests: InSAR; land subsidence; natural and human-induced hazards; subsidence modelling; monitoring/change detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Interferometric synthetic aperture radar (InSAR) time-series analysis has emerged as a revolutionary tool in the realms of remote sensing and geomatics, offering unparalleled insights into the Earth's dynamic processes. The integration of data from multiple SAR platforms, known as multiplatform or multi-sensor InSAR, further amplifies its capabilities, providing a more comprehensive and detailed understanding of geographical and environmental changes. Traditional InSAR approaches relied on data acquired from a single satellite. However, the advent of multiple SAR satellite missions has paved the way for multiplatform InSAR. By integrating data from different satellites, researchers can achieve higher temporal resolution, ensuring more frequent and consistent monitoring. Furthermore, the integrated use of SAR acquisitions originating from different sensors has the potential to reveal different features within the same area of study.

This Special Issue welcomes submissions on applications of multi-sensor InSAR time-series analysis monitoring a wide range of geographic conditions and changes introduced by solid earth deformation and geohazards, coastal erosion, glacier dynamics, deforestation, hydrology changes, and urban development. We also invite submissions that integrate global navigation satellite systems (GNSS) and related geodetic techniques to the InSAR time-series processing workflow.

Dr. Chris Danezis
Prof. Dr. Huayang Dai
Dr. Yueguan Yan
Prof. Dr. Phaedon C. Kyriakidis
Prof. Dr. Vassilia Karathanassi
Prof. Dr. Alex Hay-Man Ng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • InSAR
  • multisensor SAR
  • timeseries analysis
  • geospatial analysis
  • GIS
  • environment monitoring
  • geohazards
  • glacier motion
  • desertification monitoring
  • vegetation change

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

23 pages, 38747 KiB  
Article
A New Method for Extracting Three-Dimensional Surface Deformation in Underground Mining Areas Based on the Differentiability of D-InSAR Line-of-Sight Displacements
by Junjie Chen, Chunsu Zhao, Weitao Yan and Zhiyu Chen
Remote Sens. 2024, 16(21), 4085; https://doi.org/10.3390/rs16214085 - 1 Nov 2024
Cited by 1 | Viewed by 1649
Abstract
Monitoring three-dimensional (3D) deformation in underground mining areas is crucial for the prevention and control of mining-induced disasters. Differential interferometric synthetic aperture radar (D-InSAR) is limited to detecting one-dimensional (1D) deformation along the line of sight (LOS). This paper proposes a new method [...] Read more.
Monitoring three-dimensional (3D) deformation in underground mining areas is crucial for the prevention and control of mining-induced disasters. Differential interferometric synthetic aperture radar (D-InSAR) is limited to detecting one-dimensional (1D) deformation along the line of sight (LOS). This paper proposes a new method for extracting 3D mining-induced deformation based on the differentiability of D-InSAR LOS deformation fields. The method approximates the D-InSAR LOS deformation field in underground mining areas as a differentiable function and constructs a 3D deformation extraction model utilizing directional derivatives of this function. The least squares method is used for estimating and evaluating the 3D deformation. Simulation and real data experiments have been used to verify the feasibility of the method in extracting mining-induced 3D deformation. The simulation results show relative root mean square errors (RRMSES) of 1.24%, 6.05%, 0.97%, and 11.47% for vertical and horizontal displacements along the east–west and south–north directions, respectively. The real data experiments using Sentinel-1 images show that the root mean square errors (RMSES) of the up–down, south–north, and east–west directions are 14.06 mm, 7.37 mm, and 11.56 mm, respectively. Experimental results show that the method can provide a certain basis for 3D surface deformation monitoring of mining subsidence. Full article
(This article belongs to the Special Issue Time Series Multi-Sensors of Interferometry Synthetic Aperture Radar for Monitoring Geographical Conditions)
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18 pages, 5876 KiB  
Article
Prediction Method for Dynamic Subsidence Basin in Mining Area Based on SBAS-InSAR and Time Function
by Jibiao Hu, Yueguan Yan, Huayang Dai, Xun He, Biao Lv, Meng Han, Yuanhao Zhu and Yanjun Zhang
Remote Sens. 2024, 16(11), 1938; https://doi.org/10.3390/rs16111938 - 28 May 2024
Cited by 6 | Viewed by 1559
Abstract
Dynamic predictions of surface subsidence are crucial for assessing ground damage and protecting surface buildings. Based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, a method for making dynamic predictions of large-scale surface subsidence in mining areas can be established; however, [...] Read more.
Dynamic predictions of surface subsidence are crucial for assessing ground damage and protecting surface buildings. Based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, a method for making dynamic predictions of large-scale surface subsidence in mining areas can be established; however, the problem of phase coherence loss in InSAR data makes it impossible to predict the complete dynamic subsidence basin. In this study, a method combining the WeiBull time function and the improved probabilistic integral method (IPIM) model was established based on the PIM model, and a method for predicting the dynamic subsidence basin in the mining area was proposed by integrating the IPIM and the combined WeiBull time function. Time-series subsidence data, obtained using SBAS-InSAR, were used as fitting data, and the parameters of the combined WeiBull function were inverted, pixel by pixel, to predict the dynamic subsidence of the working face in the study area. Based on the predicted surface subsidence results of a certain moment in the working face, the parameters of the IPIM model were inverted to predict the subsidence value in the incoherent region. The subsidence predictions of the combined WeiBull time function and the IPIM model were fused using inverse distance weighting (IDW) interpolation to restore the complete subsidence basin in the mining area. This method was tested at the Wannian Mine in Hebei, and the obtained complete subsidence basin was compared with the measured data, with an absolute error range of 0 to 10 mm. The results show that the dynamic subsidence basin prediction method for the SBAS-InSAR mining area, involving the combination of the IPIM model and the combined WeiBull model, can not only accurately fit the time series of surface observation points affected by mining but also accurately restore the subsidence data in the incoherent region to obtain complete subsidence basin information in the mining area. Full article
(This article belongs to the Special Issue Time Series Multi-Sensors of Interferometry Synthetic Aperture Radar for Monitoring Geographical Conditions)
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