Interferometric Synthetic Aperture Radar

Interferometric Synthetic Aperture Radar
NASA/JPL-Caltech · Public domain · source
Name	Interferometric Synthetic Aperture Radar
Abbreviation	InSAR
First use	1980s
Developers	Jet Propulsion Laboratory, European Space Agency, National Aeronautics and Space Administration
Applications	topography, Earthquake deformation, Volcanology, Glaciology, infrastructure monitoring
Platforms	spaceborne, airborne, UAV, Shuttle Radar Topography Mission

Contents

Interferometric Synthetic Aperture Radar is a remote sensing technique that measures surface topography and displacement by comparing the phase of coherent radar echoes from repeat observations. Developed through collaborations among institutions such as the Jet Propulsion Laboratory, European Space Agency, and National Aeronautics and Space Administration, the method underpins high-resolution mapping efforts exemplified by missions like the Shuttle Radar Topography Mission and operational constellations by European Space Agency and Japan Aerospace Exploration Agency. InSAR integrates radar engineering, signal processing, and geophysics to deliver quantitative observations used by scientists at organizations including United States Geological Survey, California Institute of Technology, and Woods Hole Oceanographic Institution.

Introduction

Interferometric Synthetic Aperture Radar combines coherent microwave sensing from platforms such as ERS-1, Envisat, Sentinel-1, ALOS and airborne systems to form interferograms that encode range differences between observations. Early demonstrations involved researchers at Jet Propulsion Laboratory and teams associated with NASA experiments, then matured with operational missions from European Space Agency and Japan Aerospace Exploration Agency. Users in communities affiliated with United States Geological Survey, California Institute of Technology, Scripps Institution of Oceanography, and University of Cambridge apply InSAR to study events like the 1992 Landers earthquake, 1999 İzmit earthquake, and volcanic crises at Mount Etna and Kilauea.

InSAR leverages the coherent phase of radar returns from instruments such as those developed by Raytheon Technologies, Thales Group, and research labs at Massachusetts Institute of Technology to infer path length differences between two acquisitions. The core theoretical constructs draw on electromagnetic scattering theory refined by contributors at Institute of Electrical and Electronics Engineers conferences and signal processing advances from researchers at Bell Labs and MIT Lincoln Laboratory. Interference between complex-valued SAR images forms an interferogram whose fringe pattern relates to topography, atmospheric path delay studied by teams at National Center for Atmospheric Research, and surface displacement caused by tectonic or cryospheric processes investigated by scientists at Lamont–Doherty Earth Observatory.

Data acquisition strategies include repeat-pass, single-pass with a dedicated interferometric pair such as in Shuttle Radar Topography Mission, and persistent scatterer methods pioneered by groups at Politecnico di Milano and Delft University of Technology. Processing pipelines use core algorithms from institutions like European Space Agency and Jet Propulsion Laboratory for coregistration, phase unwrapping, and atmospheric correction, with software implementations produced by teams at ESA, NASA, Gamma Remote Sensing, and open-source projects supported by Open SAR communities. Ancillary data from sources such as GPS networks operated by International GNSS Service and meteorological models provided by European Centre for Medium-Range Weather Forecasts are integrated to remove orbital and atmospheric errors.

InSAR supports topographic mapping exemplified by the Shuttle Radar Topography Mission used by researchers at USGS and NASA, monitoring earthquake deformation as in studies of the 2010 Maule earthquake by investigators at California Institute of Technology and University of Tokyo, volcanic unrest surveillance at Mount Etna and Kilauea tracked by teams at Italian National Institute of Geophysics and Volcanology and USGS Hawaiian Volcano Observatory, and glacier flow measurements studied by groups at Scott Polar Research Institute and Alfred Wegener Institute. Infrastructure and urban subsidence monitoring are practiced by municipal engineers collaborating with researchers at Imperial College London and Delft University of Technology. Environmental applications include soil moisture and biomass change detection used by groups at Wageningen University & Research.

Key error sources include temporal decorrelation highlighted in studies at California Institute of Technology and ionospheric disturbances analyzed by researchers at Naval Research Laboratory, plus atmospheric delays modeled by European Centre for Medium-Range Weather Forecasts and residual orbital errors corrected using precise orbits from International GNSS Service and GRACE-era products. Vegetation scattering causing coherence loss has been examined by teams at Purdue University and University of Alaska Fairbanks, while layover and shadowing complicate interpretation in steep terrain such as the Himalayas and Andes, investigated by specialists at National Institute of Standards and Technology and regional agencies.

Major spaceborne platforms include ERS-1, ERS-2, Envisat, Sentinel-1A, Sentinel-1B, ALOS-2, and commercial constellations from companies like Maxar Technologies and Airbus Defence and Space. Airborne systems developed by entities such as NASA's Jet Propulsion Laboratory and university groups provide flexible baselines for rapid-response campaigns, while UAV-based SAR prototypes are under development at laboratories including MIT Lincoln Laboratory and ETH Zurich. Ground-based and tower-mounted radar instruments deployed by researchers at USGS and Italian National Institute of Geophysics and Volcanology enable near-continuous monitoring of critical sites.

Emerging directions include multi-frequency, multi-baseline networks pursued by collaborations among European Space Agency, Japan Aerospace Exploration Agency, and NASA to improve robustness to decorrelation, assimilation of InSAR into geodetic inversion frameworks used by California Institute of Technology and GFZ German Research Centre for Geosciences, and integration with GNSS, lidar, and gravity data from missions like GRACE-FO and ICESat-2. Advances in machine learning from groups at Google DeepMind, OpenAI, and university labs aim to automate phase unwrapping, atmospheric correction, and change detection, while commercial expansion by firms such as Planet Labs and Maxar Technologies is increasing revisit frequency and operational applications led by agencies including European Space Agency and National Oceanic and Atmospheric Administration.