New Approaches in High-Resolution SAR Imaging

Dear Colleagues,

Scientific advancements in the last few decades in satellite technology, including the geodetic technique, have led to fast data acquisition and information on earth resources and monitoring of natural and anthropogenic phenomena in semi-real time for geoscientists and geodetic communities. Importantly, radar backscattering signals, unlike optical systems, provide multi-resolution microwave imagery of the Earth’s surface independently of day and night and adverse weather conditions. The advancement in sophisticated coherent-quality pixel selection strategies, complex phase unwrapping algorithms, different interferometric phase correction steps, and a phase inversion approach make InSAR techniques unique among those monitoring such resources. In particular, the InSAR techniques (MT-InSAR), such as PSI and SBAS, are capable of measuring the time evolution of land surface motion with centimeters to millimeters of precision at various spatio-temporal scales. Thanks to the extensive large data archive of old-to-new-generation SAR sensors (such as ESA’s Sentinel-1 A/B; JAXA’s ALOS-1 and 2; Canada’s Radarsat-1 and 2; DLR’s TerraSAR-X; ASI’s COSMO-SkyMed; and the upcoming NASA-ISRO NISAR mission) and their frequent revisits capable of providing low-to-high-resolution data products for understanding various surface and subsurface geological processes. Some InSAR-based geophysical applications include the mapping of geostructures, landslides, and land subsidence; studying the dynamics of minerals, lithological formations, and underground resources; mapping glaciers; monitoring agriculture and land cover changes; monitoring tectonic activities and infrastructures; studying coastal instability and marine oil spills; mapping flood inundation risk; etc. However, integrating InSAR-derived data products with other multi-sensor data and also with GPS, leveling benchmarks, hydro-geophysical data, and other auxiliary remote sensing data in an advanced machine learning (ML) platform can further open a broad opportunity for addressing various challenges associated with earth resources and natural and anthropogenic hazard events. Significantly, implementing ML techniques such as deep learning, artificial intelligence (AI), and other neural network techniques in the extensive SAR data archives can play a pivotal role in important decision making for risk reduction, mitigation strategies, and geophysical modeling of different hazard phenomena. This Special Issue documents the advancement of SAR technology and its applications in the monitoring and modeling of earth resources to their current status.

This Special Issue will focus on welcoming articles related to the scientific-technical advancement of satellite radar interferometric methodology and multisource geodetic data integration approaches for measuring, monitoring, and modeling various earth resources and geophysical phenomena. This will open a new paradigm for using multi-scale SAR data products for characterizing many geological and geophysical properties related to earth resources and natural and anthropogenic activities and supporting effective management strategies for hazard mitigation.

The article types may address, but are not limited to, the following points:

• Advancement of SAR, InSAR, DInSAR, and MT-InSAR techniques;
• Radar application for surface deformation monitoring and long time-series analysis;
• Natural and anthropogenic hazard monitoring;
• InSAR-derived products for hydro-geological and geophysical modeling;
• SAR monitoring of coastal oil spill and hazard evaluation;
• Mapping and monitoring of vegetation, agriculture, lithology, and earth structures;
• InSAR and multi-geodetic data integration for ground deformation measuring and monitoring;
• Retrieval of geophysical parameters using multi-scale data integration in radar remote sensing;
• Derived 3D land motion combining multi-track data fusion technique for different radar sensors;
• Integrated InSAR and machine learning approach for modeling of surface deformation.

Dr. Himanshu Govil
Dr. Chandrakanta Ojha
Dr. Ramji Divedi
Dr. Antonio Pepe
Dr. Sankaran Rajendran
Guest Editors

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