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Anisotropy of atmospheric delay in InSAR and its effect on InSAR atmospheric correction
https://orcid.org/0000-0003-4575-5258
Abstract Reconstruction of interferometric synthetic aperture radar (InSAR) atmospheric delay maps is important for the correction of tropospheric artifacts in differential InSAR (D-InSAR) and for the improvement in persistent scatterer (PS) target identification in PS-InSAR. In this study, we explored the spatial structure of atmospheric delay datasets and assessed its effect on InSAR atmospheric delay correction. Two-dimensional (2D) experimental variogram maps of turbulent mixing components derived from 12 GPS zenith wet delay (ZWD) datasets, 12 MERIS ZWD datasets, and 3 ERS-1/2 tandem interferograms showed that spatial anisotropy is common in these datasets. An anisotropic variogram model was then developed and applied to fit the experimental variograms. The results showed that the proposed 2D anisotropic variogram model is superior to the isotropic model, with average improvements in 31.92 and 33.57% in terms of root-mean-square error and correlation coefficients, respectively. With the proposed anisotropic variogram model, the atmospheric delay maps were reconstructed by kriging interpolation and used to correct the atmospheric artifacts in InSAR interferograms. The results showed that the model considering the anisotropy of atmospheric delay produces better results than that with the isotropy assumption. Finally, the effects of the anisotropy ratio, sampling density, and correlation distance of external water vapor data on the atmospheric delay correction were investigated. The results showed that when the anisotropy ratio is less than 0.3, or the sampling density is less than 1% or more than 60%, the impact of anisotropy on kriging prediction is not obvious.
Anisotropy of atmospheric delay in InSAR and its effect on InSAR atmospheric correction
https://orcid.org/0000-0003-4575-5258
Abstract Reconstruction of interferometric synthetic aperture radar (InSAR) atmospheric delay maps is important for the correction of tropospheric artifacts in differential InSAR (D-InSAR) and for the improvement in persistent scatterer (PS) target identification in PS-InSAR. In this study, we explored the spatial structure of atmospheric delay datasets and assessed its effect on InSAR atmospheric delay correction. Two-dimensional (2D) experimental variogram maps of turbulent mixing components derived from 12 GPS zenith wet delay (ZWD) datasets, 12 MERIS ZWD datasets, and 3 ERS-1/2 tandem interferograms showed that spatial anisotropy is common in these datasets. An anisotropic variogram model was then developed and applied to fit the experimental variograms. The results showed that the proposed 2D anisotropic variogram model is superior to the isotropic model, with average improvements in 31.92 and 33.57% in terms of root-mean-square error and correlation coefficients, respectively. With the proposed anisotropic variogram model, the atmospheric delay maps were reconstructed by kriging interpolation and used to correct the atmospheric artifacts in InSAR interferograms. The results showed that the model considering the anisotropy of atmospheric delay produces better results than that with the isotropy assumption. Finally, the effects of the anisotropy ratio, sampling density, and correlation distance of external water vapor data on the atmospheric delay correction were investigated. The results showed that when the anisotropy ratio is less than 0.3, or the sampling density is less than 1% or more than 60%, the impact of anisotropy on kriging prediction is not obvious.
Anisotropy of atmospheric delay in InSAR and its effect on InSAR atmospheric correction
https://orcid.org/0000-0003-4575-5258
Abstract Reconstruction of interferometric synthetic aperture radar (InSAR) atmospheric delay maps is important for the correction of tropospheric artifacts in differential InSAR (D-InSAR) and for the improvement in persistent scatterer (PS) target identification in PS-InSAR. In this study, we explored the spatial structure of atmospheric delay datasets and assessed its effect on InSAR atmospheric delay correction. Two-dimensional (2D) experimental variogram maps of turbulent mixing components derived from 12 GPS zenith wet delay (ZWD) datasets, 12 MERIS ZWD datasets, and 3 ERS-1/2 tandem interferograms showed that spatial anisotropy is common in these datasets. An anisotropic variogram model was then developed and applied to fit the experimental variograms. The results showed that the proposed 2D anisotropic variogram model is superior to the isotropic model, with average improvements in 31.92 and 33.57% in terms of root-mean-square error and correlation coefficients, respectively. With the proposed anisotropic variogram model, the atmospheric delay maps were reconstructed by kriging interpolation and used to correct the atmospheric artifacts in InSAR interferograms. The results showed that the model considering the anisotropy of atmospheric delay produces better results than that with the isotropy assumption. Finally, the effects of the anisotropy ratio, sampling density, and correlation distance of external water vapor data on the atmospheric delay correction were investigated. The results showed that when the anisotropy ratio is less than 0.3, or the sampling density is less than 1% or more than 60%, the impact of anisotropy on kriging prediction is not obvious.
Anisotropy of atmospheric delay in InSAR and its effect on InSAR atmospheric correction
Wei, Jianchao (author) / Li, Zhiwei (author) / Hu, Jun (author) / Feng, Guangcai (author) / Duan, Meng (author)
Journal of Geodesy ; 93
2018
Article (Journal)
English
BKL:
38.73
Geodäsie
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