Synthetic Aperture Radar (SAR) for Monitoring the Crustal Deformation; Signal, Noise and Uncertainty Quantification
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 293
Special Issue Editor
Special Issue Information
Dear Colleagues,
Interferometric synthetic aperture radar (InSAR) is becoming an operational geodetic remote sensing technique to better understand the earth system processes, monitor hazards, and potentially mitigate natural hazards through measuring ground surface deformations caused by different geophysical processes, such as tectonics, earthquakes, volcanic unrest, hydrological processes, landslides, anthropogenic activities, and ice sheet movements. However, InSAR measurements of ground deformations can be affected by different sources of noise such as instrumental, processing or environmental effects. Therefore, understanding the different sources of noise in InSAR data, developing novel methods to separate signal from noise, and quantifying the uncertainties of the InSAR measurements are crucial towards operational InSAR monitoring of a dynamic earth.
We welcome contributions which aim to use SAR and InSAR techniques to understand geophysical and anthropogenic processes which deform the earth surface. We also welcome contributions which aim to separate ground displacement signals from different noise components, including those caused by the propagation delay of the microwave through the ionosphere and troposphere, geometrical residuals, the scattering phase due to changes in the dielectric properties of soil or those possibly caused by vegetation biomass change. Contributions which try to quantify the uncertainty of InSAR measurements of ground deformation are also well aligned with the scopes of this Special Issue.
Dr. Heresh Fattahi
Guest Editor
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Keywords
- Synthetic aperture radar (SAR)
- InSAR and InSAR time-series analysis
- Ground displacement
- Uncertainty quantification
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