Covariance estimation for dInSAR surface deformation measurements in the presence of anisotropic atmospheric noise

Handle URI:
http://hdl.handle.net/10754/561573
Title:
Covariance estimation for dInSAR surface deformation measurements in the presence of anisotropic atmospheric noise
Authors:
Knospe, Steffen H G; Jonsson, Sigurjon ( 0000-0001-5378-7079 )
Abstract:
We study anisotropic spatial autocorrelation in differential synthetic aperture radar interferometric (dInSAR) measurements and its impact on geophysical parameter estimations. The dInSAR phase acquired by the satellite sensor is a superposition of different contributions, and when studying geophysical processes, we are usually only interested in the surface deformation part of the signal. Therefore, to obtain high-quality results, we would like to characterize and/or remove other phase components. A stochastic model has been found to be appropriate to describe atmospheric phase delay in dInSAR images. However, these phase delays are usually modeled as being isotropic, which is a simplification, because InSAR images often show directional atmospheric anomalies. Here, we analyze anisotropic structures and show validation results using both real and simulated data. We calculate experimental semivariograms of the dInSAR phase in several European Remote Sensing satellite-1/2 tandem interferograms. Based on the theory of random functions (RFs), we then fit anisotropic variogram models in the spatial domain, employing Matérn-and Bessel-family correlation functions in nested models to represent complex dInSAR covariance structures. The presented covariance function types, in the statistical framework of stationary RFs, are consistent with tropospheric delay models. We find that by using anisotropic data covariance information to weight dInSAR measurements, we can significantly improve both the precision and accuracy of geophysical parameter estimations. Furthermore, the improvement is dependent on how similar the deformation pattern is to the dominant structure of the anisotropic atmospheric signals. © 2009 IEEE.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Crustal Deformation and InSAR Group
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Geoscience and Remote Sensing
Issue Date:
Apr-2010
DOI:
10.1109/TGRS.2009.2033937
Type:
Article
ISSN:
01962892
Appears in Collections:
Articles; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKnospe, Steffen H Gen
dc.contributor.authorJonsson, Sigurjonen
dc.date.accessioned2015-08-02T09:14:31Zen
dc.date.available2015-08-02T09:14:31Zen
dc.date.issued2010-04en
dc.identifier.issn01962892en
dc.identifier.doi10.1109/TGRS.2009.2033937en
dc.identifier.urihttp://hdl.handle.net/10754/561573en
dc.description.abstractWe study anisotropic spatial autocorrelation in differential synthetic aperture radar interferometric (dInSAR) measurements and its impact on geophysical parameter estimations. The dInSAR phase acquired by the satellite sensor is a superposition of different contributions, and when studying geophysical processes, we are usually only interested in the surface deformation part of the signal. Therefore, to obtain high-quality results, we would like to characterize and/or remove other phase components. A stochastic model has been found to be appropriate to describe atmospheric phase delay in dInSAR images. However, these phase delays are usually modeled as being isotropic, which is a simplification, because InSAR images often show directional atmospheric anomalies. Here, we analyze anisotropic structures and show validation results using both real and simulated data. We calculate experimental semivariograms of the dInSAR phase in several European Remote Sensing satellite-1/2 tandem interferograms. Based on the theory of random functions (RFs), we then fit anisotropic variogram models in the spatial domain, employing Matérn-and Bessel-family correlation functions in nested models to represent complex dInSAR covariance structures. The presented covariance function types, in the statistical framework of stationary RFs, are consistent with tropospheric delay models. We find that by using anisotropic data covariance information to weight dInSAR measurements, we can significantly improve both the precision and accuracy of geophysical parameter estimations. Furthermore, the improvement is dependent on how similar the deformation pattern is to the dominant structure of the anisotropic atmospheric signals. © 2009 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectCovariance analysisen
dc.subjectCovariance functionsen
dc.subjectError analysisen
dc.subjectGeophysical inverse problemsen
dc.subjectGeostatisticsen
dc.subjectRemote Sensingen
dc.subjectSynthetic aperture radar (SAR)en
dc.titleCovariance estimation for dInSAR surface deformation measurements in the presence of anisotropic atmospheric noiseen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentCrustal Deformation and InSAR Groupen
dc.identifier.journalIEEE Transactions on Geoscience and Remote Sensingen
dc.contributor.institutionInstitute of Geophysics, Swiss Federal Institute of Technology (ETH) Zürich, 8092 Zürich, Switzerlanden
dc.contributor.institutionInstitute of Geotechnical Engineering and Mine Surveying, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germanyen
dc.contributor.institutionInstitute of Geophysics, ETH Zürich, 8092 Zürich, Switzerlanden
kaust.authorJonsson, Sigurjonen
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