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Remote Sensing
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New Perspective of InSAR Data Time Series Analysis
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New Perspective of InSAR Data Time Series Analysis

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (29 December 2023) | Viewed by 4371

Special Issue Editor

Dr. Jung-Rack Kim

E-Mail Website
Guest Editor
RSS Hydro, 100 Rte de Volmerange, 3593 Dudelange, Luxembourg
Interests: remote sensing on Earth and planetary surfaces; topographic mapping; monitoring of topographic migrations on Earth and planetary bodies; activate remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

 Since the scientific community has adopted time series analysis as a way to stabilize the technical difficulty of InSAR, there have been tremendous achievements, both technical and applied. Various time series analysis techniques for stacked interferograms have proven the capability to handle error sources and trace tiny topographic migrations that have existed as enormous barriers to the practical applications of InSAR. We can therefore expect to achieve significant scientific goals, such as monitoring of pre-seismic activities, detecting preludes to landform disasters such as landslide and tracing continuous glacial migrations that have never been captured with the precision of any other remote sensing tool. The application of time series techniques is not only limited to phase angle analysis but extended to the phase coherence and amplitude changes. These advances in time series technology have pushed the scope of InSAR applications to the flood, aeolian/fluvial degradation, and anthropogenic footprints.  In addition, the newly installed space-based constellations and UAV equipped with multiple bandwidths SAR sensors covering X to P bands are providing a huge number of raw data sets in multiple space/spectrum/time domains.

 Therefore, this Special Issue is designed to summarize the technical achievements and applicable cases of InSAR time series analysis. In this Special Issue, we intend to evaluate existing approaches, refine the methodology, and propose innovations in InSAR time series analyses. The applications of InSAR time series analysis in new remote locations such as arctic regions, arid deserts, and even extraterrestrial objects are also of interest to us.

 Accordingly, in this Special Issue, submissions of papers focusing on but not limited to the following areas will be given priority:

- Notable technical improvements in time series methodologies, error removal strategies, and PS and DS configurations;

- Exploration of new time series techniques, such as SAR tomography;

- Cases of application to multiple different targets, such as volcanoes, glaciers, plate tectonics, active flaws, cryospherical objects, and sand deserts;

- Machine learning add-ons related to InSAR time series output;
- The application and technique of time series based on amplitude and phase angle to exploit information on natural disaster and climate change.

 

Dr. Jung-Rack Kim
Guest Editor

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

  • time series of interferograms
  • phase coherence
  • natural hazard
  • topographic migrations
  • climatic changes
  • machine learning

Published Papers (3 papers)

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Research

22 pages, 21441 KiB  
Article
Development of a Proof-of-Concept A-DInSAR-Based Monitoring Service for Land Subsidence
by Margherita Righini, Roberta Bonì, Serena Sapio, Ignacio Gatti, Marco Salvadore and Andrea Taramelli
Remote Sens. 2024, 16(11), 1981; https://doi.org/10.3390/rs16111981 - 31 May 2024
Viewed by 414
Abstract
The increasing availability of SAR images and processing results over wide areas determines the need for systematic procedures to extract the information from this dataset and exploit the enhanced quality of the displacement time series. The aim of the study is to propose [...] Read more.
The increasing availability of SAR images and processing results over wide areas determines the need for systematic procedures to extract the information from this dataset and exploit the enhanced quality of the displacement time series. The aim of the study is to propose a new pre-operational workflow of an A-DInSAR-based land subsidence monitoring and interpretation service. The workflow is tested in Turano Lodigiano (Lombardy region, Italy) using COSMO-SkyMed data, processed using the SqueeSAR™ algorithm, and covering the time span from 2016 to 2019. The test site is a representative peri-urban area of the Po plain susceptible to land subsidence. The results give insight about new value-added products and enable non-expert users to exploit the potential of the interferometric results. Full article
(This article belongs to the Special Issue New Perspective of InSAR Data Time Series Analysis)
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19 pages, 9670 KiB  
Article
Trend Classification of InSAR Displacement Time Series Using SAE–CNN
by Menghua Li, Hanfei Wu, Mengshi Yang, Cheng Huang and Bo-Hui Tang
Remote Sens. 2024, 16(1), 54; https://doi.org/10.3390/rs16010054 - 22 Dec 2023
Cited by 3 | Viewed by 1178
Abstract
Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose [...] Read more.
Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention. Full article
(This article belongs to the Special Issue New Perspective of InSAR Data Time Series Analysis)
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Graphical abstract

20 pages, 8471 KiB  
Article
Predicting Short-Term Deformation in the Central Valley Using Machine Learning
by Joe Yazbeck and John B. Rundle
Remote Sens. 2023, 15(2), 449; https://doi.org/10.3390/rs15020449 - 11 Jan 2023
Cited by 4 | Viewed by 2066
Abstract
Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. [...] Read more.
Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers’ integrity. Full article
(This article belongs to the Special Issue New Perspective of InSAR Data Time Series Analysis)
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