Iterative Weighted Least Squares Solution to InSAR Stratified Tropospheric Phase Delay
Phase-elevation regression has demonstrated the ability to mitigate the impacts of vertically stratified tropospheric phase delay on interometric synthetic aperture radar (InSAR) phase observations. To estimate stratified tropospheric phase delay, a reasonable stochastic model is required, in additi...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11103725/ |
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| author | Hailu Chen Yunzhong Shen Lei Zhang |
| author_facet | Hailu Chen Yunzhong Shen Lei Zhang |
| author_sort | Hailu Chen |
| collection | DOAJ |
| description | Phase-elevation regression has demonstrated the ability to mitigate the impacts of vertically stratified tropospheric phase delay on interometric synthetic aperture radar (InSAR) phase observations. To estimate stratified tropospheric phase delay, a reasonable stochastic model is required, in addition to a high-precision digital elevation model, because the observation noise in the interferogram (IFG) typically is spatially correlated when corrections, such as turbulence, deformation, and ionospheric delay, are not fully considered, particularly in mountainous areas with abundant water vapor. This work proposes an iteratively weighted least squares approach for stratified tropospheric delay estimation, in which the weight matrix is the inverse of the variance-covariance matrix constructed from the residuals of the last iteration. The Sentinel-1 data over Taihang Mountain, China, encompassing severe tropospheric delay signals, are processed using the proposed approach. The results show that this approach can effectively reduce the bias of the stratified component estimator associated with other spatially correlated phases, and it can achieve robust correction for the stratified tropospheric phase delay in the IFG. The average standard deviation of 46 consecutive IFGs estimated by the proposed approach was reduced by 22.9%, from 1.66 to 1.28 cm in the global correction, and by 20.9%, from 1.34 to 1.06 cm in the local correction, smaller than the values obtained via the GACOS (1.4 cm) and the raw phase (2.06 cm). This demonstrates the potential of our weighted approach to improve InSAR monitoring in areas with significant terrain variations, such as landslides, mining subsidence, and infrastructure stability assessment. |
| format | Article |
| id | doaj-art-b10b7e4454d142929df197865ade9dfd |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-b10b7e4454d142929df197865ade9dfd2025-08-20T03:07:05ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118195941960510.1109/JSTARS.2025.359405211103725Iterative Weighted Least Squares Solution to InSAR Stratified Tropospheric Phase DelayHailu Chen0https://orcid.org/0009-0006-1288-4001Yunzhong Shen1https://orcid.org/0000-0002-3447-172XLei Zhang2https://orcid.org/0000-0002-8152-2470College of Surveying and Geo-Informatics, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai, ChinaPhase-elevation regression has demonstrated the ability to mitigate the impacts of vertically stratified tropospheric phase delay on interometric synthetic aperture radar (InSAR) phase observations. To estimate stratified tropospheric phase delay, a reasonable stochastic model is required, in addition to a high-precision digital elevation model, because the observation noise in the interferogram (IFG) typically is spatially correlated when corrections, such as turbulence, deformation, and ionospheric delay, are not fully considered, particularly in mountainous areas with abundant water vapor. This work proposes an iteratively weighted least squares approach for stratified tropospheric delay estimation, in which the weight matrix is the inverse of the variance-covariance matrix constructed from the residuals of the last iteration. The Sentinel-1 data over Taihang Mountain, China, encompassing severe tropospheric delay signals, are processed using the proposed approach. The results show that this approach can effectively reduce the bias of the stratified component estimator associated with other spatially correlated phases, and it can achieve robust correction for the stratified tropospheric phase delay in the IFG. The average standard deviation of 46 consecutive IFGs estimated by the proposed approach was reduced by 22.9%, from 1.66 to 1.28 cm in the global correction, and by 20.9%, from 1.34 to 1.06 cm in the local correction, smaller than the values obtained via the GACOS (1.4 cm) and the raw phase (2.06 cm). This demonstrates the potential of our weighted approach to improve InSAR monitoring in areas with significant terrain variations, such as landslides, mining subsidence, and infrastructure stability assessment.https://ieeexplore.ieee.org/document/11103725/Interometric synthetic aperture radar (InSAR)phase/elevation ratiospatially correlated phase (SCP)stratified tropospheric phase delayvariance-covariance matrix |
| spellingShingle | Hailu Chen Yunzhong Shen Lei Zhang Iterative Weighted Least Squares Solution to InSAR Stratified Tropospheric Phase Delay IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Interometric synthetic aperture radar (InSAR) phase/elevation ratio spatially correlated phase (SCP) stratified tropospheric phase delay variance-covariance matrix |
| title | Iterative Weighted Least Squares Solution to InSAR Stratified Tropospheric Phase Delay |
| title_full | Iterative Weighted Least Squares Solution to InSAR Stratified Tropospheric Phase Delay |
| title_fullStr | Iterative Weighted Least Squares Solution to InSAR Stratified Tropospheric Phase Delay |
| title_full_unstemmed | Iterative Weighted Least Squares Solution to InSAR Stratified Tropospheric Phase Delay |
| title_short | Iterative Weighted Least Squares Solution to InSAR Stratified Tropospheric Phase Delay |
| title_sort | iterative weighted least squares solution to insar stratified tropospheric phase delay |
| topic | Interometric synthetic aperture radar (InSAR) phase/elevation ratio spatially correlated phase (SCP) stratified tropospheric phase delay variance-covariance matrix |
| url | https://ieeexplore.ieee.org/document/11103725/ |
| work_keys_str_mv | AT hailuchen iterativeweightedleastsquaressolutiontoinsarstratifiedtroposphericphasedelay AT yunzhongshen iterativeweightedleastsquaressolutiontoinsarstratifiedtroposphericphasedelay AT leizhang iterativeweightedleastsquaressolutiontoinsarstratifiedtroposphericphasedelay |