{ "id": "ep9z1pvhn0", "slug": "persistent-scatterer-insar", "title": "Persistent scatterer InSAR", "updated": "2026-10-15", "topic_path": [ { "id": "physical", "label": "Physical world and mathematics", "api_url": "https://www.edgepedia.org/api/v1/topics/physical" }, { "id": "physical.earth", "label": "Earth sciences", "api_url": "https://www.edgepedia.org/api/v1/topics/physical.earth" }, { "id": "physical.earth.geology", "label": "Geology and mineralogy", "api_url": "https://www.edgepedia.org/api/v1/topics/physical.earth.geology" } ], "geo": [ { "id": "geo.nongeo.t1946.physical.earth", "label": "Non-geographic · 1946 to 2000: Earth sciences", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical.earth", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t1946", "label": "Non-geographic · 1946 to 2000", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946" }, { "id": "geo.nongeo.t1946.physical", "label": "Physical world and mathematics", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical" }, { "id": "geo.nongeo.t1946.physical.earth", "label": "Earth sciences", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical.earth" } ] }, { "id": "geo.nongeo.t2001.physical.earth", "label": "Non-geographic · 2001 to 2020: Earth sciences", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.physical.earth", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t2001", "label": "Non-geographic · 2001 to 2020", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001" }, { "id": "geo.nongeo.t2001.physical", "label": "Physical world and mathematics", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.physical" }, { "id": "geo.nongeo.t2001.physical.earth", "label": "Earth sciences", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.physical.earth" } ] } ], "excerpt": "Persistent scatterer InSAR (PSInSAR, or PSI) is a radar technique that measures surface deformation with millimeter-scale velocity precision at phase-stable ground points in time series of interferometric SAR images.", "snippet": "Persistent scatterer InSAR (PSInSAR, or PSI) is a radar technique that measures surface deformation with millimeter-scale velocity precision at phase-stable ground points in time series of interferometric SAR images.", "node": "physical.earth.geology", "markdown": "# Persistent scatterer InSAR\n\nPersistent scatterer InSAR (PSInSAR, or PSI) is a radar technique that measures surface deformation with millimeter-scale velocity precision at phase-stable ground points in time series of interferometric SAR images.[1](https://doi.org/10.1109/36.898661) It belongs to a family of multi-temporal DInSAR methods that exploit the same area repeatedly to overcome the temporal and geometric decorrelation, phase unwrapping (recovering true phase from measurements wrapped into one cycle), and atmospheric errors that limit single-interferogram DInSAR.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) Typical applications are subsidence and uplift monitoring, landslides, and tectonic or volcanic deformation, with wide-area coverage that single-sensor ground surveys cannot match.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf)\n\n| Key fact | Value |\n|---|---|\n| What is measured | Line-of-sight deformation velocity and time series at persistent scatterer points, with millimetric accuracy[1](https://doi.org/10.1109/36.898661) |\n| Velocity precision | About 0.5 mm/yr on the original ERS data set; typically up to 1 mm/yr in later practice[1](https://doi.org/10.1109/36.898661) • [3](https://link.springer.com/chapter/10.1007/1345_2024_269) |\n| Minimum acquisitions | 15–20 images for C-band analysis; 20 cited for reliable PS analysis[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) • [4](https://www.sarmap.ch/tutorials/PS_v562.pdf) |\n| Revisit interval | Sentinel-1 revisits every 6 or 12 days; older sensors 11–46 days[3](https://link.springer.com/chapter/10.1007/1345_2024_269) • [5](https://www.mdpi.com/2220-9964/2/3/797) |\n| PS density in cities | 0.5%–2.5% of pixels, about 50–400 points per km²[5](https://www.mdpi.com/2220-9964/2/3/797) |\n| Coverage per frame | 100 × 100 km (ERS/Envisat StripMap), 250 × 250 km (Sentinel-1 IW)[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) |\n\n## How it works\n\nInterferometric phase comparison of SAR images gathered at different times and with different baselines can provide DEMs with meter accuracy and terrain deformation with millimetric accuracy, on a dense grid (4–20 m spacing for ERS images).[1](https://doi.org/10.1109/36.898661) Single interferograms are limited by temporal and geometrical decorrelation and by atmospheric inhomogeneities, the atmospheric phase screen (APS).[1](https://doi.org/10.1109/36.898661)\n\nThe DInSAR observation equation separates displacement from residual topographic error (RTE), atmospheric, orbital, and noise phase components, plus an integer phase ambiguity \\( k \\) that results from the wrapped nature of the phases, bounded in \\( (-\\pi, \\pi] \\).[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) PSI resolves these terms by using only pixels whose radar response is dominated by a strong reflecting object that is constant over time, the persistent scatterers, rather than distributed scatterers whose response comes from different small objects at each acquisition.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) At such points, sub-meter DEM accuracy and millimetric motion detection are achievable even where surrounding areas are incoherent.[6](https://doris.tudelft.nl/Literature/sousa11.pdf)\n\nPS reliability depends on the phase stability or coherence of the scatterers, and the assumed deformation model is a second key factor.[7](https://link.springer.com/content/pdf/10.1007/s40534-016-0108-4.pdf) The atmospheric component has high spatial correlation but low temporal correlation, which a second inversion exploits to estimate and remove it.[4](https://www.sarmap.ch/tutorials/PS_v562.pdf) PSInSAR algorithms are data-driven: they estimate atmospheric delays and orbit errors without auxiliary geodetic data beyond a priori orbits, and measure deformation relative to a presumably stable reference location.[3](https://link.springer.com/chapter/10.1007/1345_2024_269)\n\nWhat qualifies as a scatterer: PSs are usually abundant on buildings, monuments, antennas, poles, and exposed rocks or outcrops.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) They are typically man-made objects such as statues, lamp standards, antennas, and metallic roof structures, or specially fabricated reflectors; natural rock outcrops also qualify.[8](https://media.geolsoc.org.uk/iaeg2006/PAPERS/IAEG_284.PDF) In urban areas the introducing paper hypothesized that PSs are railings and corners of reinforced-concrete buildings smaller than the resolution cell.[1](https://doi.org/10.1109/36.898661) [Vegetation](https://www.edgepedia.org/vegetation) fails because its scattering changes between acquisitions, so pixels there are distributed rather than persistent scatterers.\n\n## How it is done\n\nA representative processing chain runs as follows.[4](https://www.sarmap.ch/tutorials/PS_v562.pdf)\n\n1. **Connection graph generation** over the stack of co-registered scenes, then interferogram generation and flattening.\n2. **First inversion**: from the amplitude dispersion index (MuSigma), coherence, and a linear deformation model, estimate deformation velocity and residual topographic error, using the [periodogram](https://www.edgepedia.org/periodogram) method on wrapped, APS-free interferograms.[4](https://www.sarmap.ch/tutorials/PS_v562.pdf) • [9](https://air.unimi.it/retrieve/handle/2434/607034/1356518/22797254.2018.pdf)\n3. **Second inversion**: estimate the atmospheric phase screen with spatio-temporal filters on unwrapped phase images, remove it, and re-estimate residual heights and displacement.[4](https://www.sarmap.ch/tutorials/PS_v562.pdf) • [9](https://air.unimi.it/retrieve/handle/2434/607034/1356518/22797254.2018.pdf)\n4. **2+1D phase unwrapping** of a redundant stack of multi-look interferograms generates, for each pixel, a set of temporally ordered deformation estimates without a deformation model assumption; the velocity map is derived from the time series.[10](https://isprs-archives.copernicus.org/articles/XLII-2-W7/597/2017/isprs-archives-XLII-2-W7-597-2017.pdf) • [9](https://air.unimi.it/retrieve/handle/2434/607034/1356518/22797254.2018.pdf)\n5. **Geocoding** of the PSI results is the final step.[9](https://air.unimi.it/retrieve/handle/2434/607034/1356518/22797254.2018.pdf)\n\nEstimates assume a linear deformation model; non-linearity can be detected if displacement between two consecutive acquisitions does not exceed \\( \\lambda/4 \\).[4](https://www.sarmap.ch/tutorials/PS_v562.pdf) An alternative chain based on the integer least-squares principle uses differential phase observations between persistent scatterer candidates to reduce atmospheric signal delay and orbit errors.[11](https://www.isprs.org/proceedings/XXXVI/1-W3/PDF/101-vanleijen.pdf)\n\n## Origin\n\nThe Permanent Scatterers technique was introduced by A. Ferretti, C. Prati, and F. Rocca in \"Permanent scatterers in SAR interferometry\", IEEE Transactions on Geoscience and Remote Sensing, 2001.[1](https://doi.org/10.1109/36.898661) The approach was developed to overcome decorrelation and atmospheric errors by using time series of images acquired at different times.[7](https://link.springer.com/content/pdf/10.1007/s40534-016-0108-4.pdf) According to a later review, these publications were preceded by a patent of the PSInSAR algorithm and by the foundation of Tele-Rilevamento Europa (TRE), a spin-off of the Politecnico di Milano.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) The technique identifies single targets that remain coherent over long time intervals and wide look-angle variations, building on the same group's earlier work.[6](https://doris.tudelft.nl/Literature/sousa11.pdf)\n\n## Variants\n\nPSI methods split into two families: those selecting pixels mainly by their phase variation in time, and those using mainly spatial correlation of phase.[6](https://doris.tudelft.nl/Literature/sousa11.pdf) The temporal-model family includes the original Permanent Scatterers approach and the Delft approach (DePSI, the Delft PSI processing package); the spatial-correlation family includes StaMPS (the Stanford Method for Persistent Scatterers), whose advantage is phase-based PS selection with no assumption of an a priori deformation model.[12](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2008GL034654) • [13](https://repository.tudelft.nl/file/File_118e1b7a-78c8-4317-87e2-73e3dcfd3dc7) Applied to the same data set, DePSI and StaMPS give broadly similar results but differ mainly in PS density and location.[6](https://doris.tudelft.nl/Literature/sousa11.pdf)\n\nSmall baseline (SB) methods form a third group, using small distances among satellite positions or acquisition times; a combined method incorporates both persistent scatterer and small baseline approaches.[7](https://link.springer.com/content/pdf/10.1007/s40534-016-0108-4.pdf) • [12](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2008GL034654) The SqueeSAR algorithm extends PSInSAR to distributed scatterers.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) All PSI algorithms use a single-master stack of differential interferograms to process PS pixels.[14](https://www.sciencedirect.com/science/article/abs/pii/S0034425721000249)\n\n## Applications\n\nPSI applications fall into four main, partially overlapping classes: urban, peri-urban, and built environments; subsidence and uplift; landslides; and geophysics.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) Over its first decade, InSAR was used to study surface displacements related to active faults, volcanoes, landslides, aquifers, oil fields, and glaciers at spatial resolution below 100 m and cm-level precision; PSI extends this to individual buildings and infrastructures while retaining wide-area coverage.[15](https://www.fig.net/resources/proceedings/2006/baden_2006_comm6/PDF/ISR1/Ferretti.pdf) • [2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) Very high resolution X-band data, available since 2007, has remarkably increased application potential, and [Sentinel-1](https://www.edgepedia.org/sentinel-1) data are freely available.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf)\n\n**By the numbers**: a minimum of 15–20 images is typically needed for C-band analysis, with shorter datasets possible at X-band.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) PSI precision is millimeter-level versus sub-millimeter for precise leveling, but PSI offers hundreds of points per km² in built-up areas versus tens per km² for leveling lines, and data every 11–46 days with processing in hours to days, versus roughly 20–50 benchmarks per day for leveling campaigns requiring 3–4 personnel.[5](https://www.mdpi.com/2220-9964/2/3/797)\n\n## Limitations and alternatives\n\nPSI is an opportunistic measurement method: it measures deformation only where persistent scatterers exist. PS density is usually low in vegetated, forested, and low-reflectivity areas and in steep terrain facing the radar; snow cover, construction works, and street re-pavement can cause partial or complete loss of PSs.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) Amplitude-stability-based selection does not suffice to interpolate atmospheric phase screens in natural and non-urban areas, which motivated phase-based selection methods such as StaMPS.[13](https://repository.tudelft.nl/file/File_118e1b7a-78c8-4317-87e2-73e3dcfd3dc7) Traditional surveying remains necessary where vegetation dominates, where time series are short, where subsidence is temporally complex, or where rates are too high or too low for PSI.[5](https://www.mdpi.com/2220-9964/2/3/797)\n\nTropospheric delay greatly limits InSAR measurement accuracy, with vertically stratified tropospheric components a key error source in time-series analysis.[16](https://www.sciopen.com/article/10.1016/j.geog.2021.12.002) Because SAR satellites fly near-polar orbits, sensitivity to South-North displacement is very low, and only East-West and vertical displacements can be reconstructed with sufficiently high accuracy.[3](https://link.springer.com/chapter/10.1007/1345_2024_269) The wrapped nature of the observations also limits measurement of fast deformation.[2](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf) For earlier sensors such as ERS-1/2 and Envisat, the relatively large orbital tube is a distinguishing processing condition compared with Sentinel-1.[14](https://www.sciencedirect.com/science/article/abs/pii/S0034425721000249)\n\n## References\n\n1. [A. Ferretti, C. Prati, F. Rocca (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing.](https://doi.org/10.1109/36.898661)\n2. [Persistent Scatterer Interferometry: A review (Crosetto et al.)](https://air.unimi.it/retrieve/handle/2434/349581/575968/CrosettoCrippa_Persoistent.pdf)\n3. [Relevance of PSInSAR Analyses at ITRF Co-location Sites](https://link.springer.com/chapter/10.1007/1345_2024_269)\n4. [PS Tutorial (SARMAP)](https://www.sarmap.ch/tutorials/PS_v562.pdf)\n5. [A Comparison of Precise Leveling and Persistent Scatterer SAR Interferometry for Building Subsidence Rate Measurement](https://www.mdpi.com/2220-9964/2/3/797)\n6. [Persistent Scatterer InSAR: A comparison of methodologies based on a model of temporal deformation vs. spatial correlation selection criteria (Sousa et al., Remote Sensing of Environment)](https://doris.tudelft.nl/Literature/sousa11.pdf)\n7. [A technical review on persistent scatterer interferometry](https://link.springer.com/content/pdf/10.1007/s40534-016-0108-4.pdf)\n8. [Satellite interferometry for monitoring ground deformations in the urban environment](https://media.geolsoc.org.uk/iaeg2006/PAPERS/IAEG_284.PDF)\n9. [Data analysis tools for persistent scatterer interferometry based on Sentinel-1 data](https://air.unimi.it/retrieve/handle/2434/607034/1356518/22797254.2018.pdf)\n10. [Deformation measurement using Sentinel-1A/B imagery (ISPRS 2017)](https://isprs-archives.copernicus.org/articles/XLII-2-W7/597/2017/isprs-archives-XLII-2-W7-597-2017.pdf)\n11. [Persistent Scatterer Interferometry: Precision, Reliability and Integration (van Leijen et al., ISPRS)](https://www.isprs.org/proceedings/XXXVI/1-W3/PDF/101-vanleijen.pdf)\n12. [A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches (Hooper, GRL 2008)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2008GL034654)\n13. [Radar Interferometry: 20 Years of Development in Time Series Techniques and Future Perspectives (TU Delft repository)](https://repository.tudelft.nl/file/File_118e1b7a-78c8-4317-87e2-73e3dcfd3dc7)\n14. [Benchmarking and inter-comparison of Sentinel-1 InSAR velocities and time series (Remote Sensing of Environment)](https://www.sciencedirect.com/science/article/abs/pii/S0034425721000249)\n15. [PSInSAR: Using satellite radar data to measure surface deformation remotely (Ferretti, FIG 2006)](https://www.fig.net/resources/proceedings/2006/baden_2006_comm6/PDF/ISR1/Ferretti.pdf)\n16. [Mitigation of time-series InSAR turbulent atmospheric phase noise: A review](https://www.sciopen.com/article/10.1016/j.geog.2021.12.002)\n\n---\n*Topic: Encyclopedia › Physical world and mathematics › Earth sciences › Geology and mineralogy*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/persistent-scatterer-insar", "markdown_url": "https://www.edgepedia.org/persistent-scatterer-insar.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Persistent scatterer InSAR\", Edgepedia (EdgeChat), https://www.edgepedia.org/persistent-scatterer-insar. Edgepedia Community License 1.0.", "credit_md": "\"[Persistent scatterer InSAR](https://www.edgepedia.org/persistent-scatterer-insar)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/persistent-scatterer-insar](https://www.edgepedia.org/persistent-scatterer-insar). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Persistent scatterer InSAR\", Edgepedia (EdgeChat), https://www.edgepedia.org/persistent-scatterer-insar. Edgepedia Community License 1.0.", "speakable": "Persistent scatterer InSAR is a radar technique that measures surface deformation with millimeter-scale velocity precision at phase-stable ground points in time series of interferometric SAR images." }