InSAR for Environmental Remote Sensing: Current Progress and Future Vision
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3183
Special Issue Editors
Interests: InSAR data processing
Special Issues, Collections and Topics in MDPI journals
Interests: geological hazards and ground instability; landslide monitoring; remote sensing data interpretation and validation; engineering geological characterization and modelling
Special Issues, Collections and Topics in MDPI journals
Interests: SAR; radar Interferometry; geosynchronous SAR; MIMO radar; radar constellations
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
After a slow uptake, SAR interferometry based on satellite and airborne radar sensors is now becoming a standard technology for environmental monitoring. Subsidence phenomena, landslides, seismic events, sinkholes, and volcanic eruptions are all natural hazards where InSAR data can play a key role for mitigating risk or making informative decisions. In fact, although with different levels of maturity, InSAR can already provide invaluable information to decision makers in a variety of applications: from DEM reconstruction to displacement monitoring and from biomass and soil moisture estimation to permafrost and glacier analysis. Significant advances in InSAR data processing are expected in the next few years when new data sources, characterized by fast revisiting times and high-resolution imagery, will become available. Indeed, the synergy of agile, small sensors operated by private companies with large satellite SAR instruments operated by national and international space agencies will become an important research topic, triggering new monitoring solutions and new data fusion algorithms. Significant advances are also expected in the joint use of change detection of InSAR algorithms for the exploitation of so-called “Temporary Scatterers”. The aim of this Special Issue is to provide a snapshot of state-of-the-art monitoring solutions based on InSAR technology, while providing an overview of the current lines of research. Contributions addressing the role of new SAR constellations, cloud computing, and machine learning algorithms are especially welcome.
Dr. Alessandro Ferretti
Prof. Dr. Nicola Casagli
Prof. Dr. Andrea Monti Guarnieri
Guest Editors
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Keywords
- InSAR algorithms
- PolInSAR algorithms
- SAR constellations
- machine learning
- cloud computing
- InSAR monitoring solutions
- data fusion
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