InSAR
Also known as: InSAR
Interferometric Synthetic Aperture Radar (InSAR) is a radar remote sensing technique that measures millimeter-scale ground surface deformation by analyzing the phase difference between radar images acquired from slightly different orbital positions. Pioneered by Gabriel, Goldstein, and Zebker in 1989, InSAR has become essential for earthquake rupture characterization, volcanic monitoring, landslide detection, and subsidence quantification.
Key highlights
- Millimeter accuracy: resolves deformation of centimeters to millimeters over wide areas
- Cloud-independent: radar penetrates clouds, enabling observation in rainy or cloudy regions
- Wide area coverage: satellite images cover hundreds of kilometers in a single pass
- Rapid revisit: modern satellites (Sentinel-1) have 6–12 day repeat cycles, enabling weekly deformation monitoring
Intuition
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How it works
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When to use it
Use InSAR to measure co-seismic (earthquake) rupture patterns, post-seismic relaxation, volcanic deformation, slow-slip events, and ground subsidence from mining or aquifer depletion. InSAR works best on coherent terrain (rock, vegetation); urbanized areas also show good coherence. Avoid InSAR over water (low coherence) or high-relief mountains (decorrelation due to geometric differences). Combine with GPS and seismic data for multi-parameter constraints.
Strengths & limitations
- Millimeter accuracy: resolves deformation of centimeters to millimeters over wide areas
- Cloud-independent: radar penetrates clouds, enabling observation in rainy or cloudy regions
- Wide area coverage: satellite images cover hundreds of kilometers in a single pass
- Rapid revisit: modern satellites (Sentinel-1) have 6–12 day repeat cycles, enabling weekly deformation monitoring
- Coherence loss: rapid surface changes (vegetation growth, water level changes, heavy rain) destroy phase coherence, rendering images unusable
- Atmospheric sensitivity: water vapor variations in the troposphere introduce phase noise; difficult to remove without external atmospheric data
- Phase wrapping ambiguity: large deformations (> one wavelength, ~2.8 cm) cause phase wrapping; distinguishing 2πN + phase requires other constraints
- Layover and shadow: steep terrain causes radar backscatter ambiguities (layover) or signal dropout (shadow), limiting usable area
Common pitfalls
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Applications
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Frequently asked
Why do earthquake rupture patterns visible in InSAR sometimes disagree with seismic moment tensor solutions?
Seismic moment tensor solutions are averaged representations assuming simple fault geometry; InSAR reveals the actual detailed spatially-variable slip distribution. Asperities (strong patches) and barriers on the fault produce slip heterogeneity that moment tensors cannot resolve. Joint analysis combining both datasets is more complete.
What is phase coherence and why does it matter?
Coherence is the correlation between master and slave radar images, ranging from 0 (no correlation) to 1 (perfect correlation). High coherence (>0.5) is needed to extract reliable phase information. Coherence degrades due to decorrelation: temporal (surface changes between acquisitions), geometric (perpendicular baseline too large), or thermal (instrument noise). Low-coherence regions appear as noise in deformation maps.
Can InSAR detect slow-slip events (silent earthquakes)?
Yes, in favorable cases. Slow-slip events release moment gradually over days to weeks, producing continuous ground deformation. Well-positioned InSAR can detect cumulative slip of centimeters. Recent studies of Cascadia, Hikurangi, and other subduction zones have detected slow slip using InSAR stacking of multiple acquisitions.
What is the difference between ascending and descending satellite passes?
Ascending passes image from east to west (satellite moving north); descending passes image from west to east (satellite moving south). This difference in look direction means east-west and vertical deformation are separable by combining ascending and descending InSAR data: E-W motion is sensitive to geometric differences; vertical motion is more uniform between passes.
Sources
- 1.Gabriel, A. K., Goldstein, R. M., & Zebker, H. A. (1989). Mapping small elevation changes over large areas: Differential radar interferometry. Journal of Geophysical Research, 94(B7), 9183-9191.
- 2.Massonnet, D., & Feigl, K. L. (1998). Radar interferometry and its application to changes in the Earth's surface. Reviews of Geophysics, 36(4), 441-500.
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Cite this page
ScholarGate. (2026, June 3). InSAR. ScholarGate. https://scholargate.app/geophysics/insar