Evaluating the influence of ETAD product for PSI estimation
Synthetic Aperture Radar Interferometry (InSAR) technology is widely used for ground deformation monitoring, including infrastructure safety, subsidence, landslides, and earthquakes, due to its all-weather,wide range, and high-precision characteristics. InSAR technology obtains high-resolution and large-scale surface deformation information by comparing the phase differences between two SAR images obtained at different times, and combining sensor parameters and geometric relationships. However, this technology encounters various errors during the processing, including phase decoherence, atmospheric delay phase, phase unwrapping errors, and accuracy issues with digital elevation models (DEM). The Extended Timing Annotation Dataset (ETAD) for Sentinel-1 allows for the correction of range and azimuth timing to account for geophysical effects and inaccuracies in data focusing. In this study, we evaluate the influence of ETAD correction on PSI estimation, specifically on interferogram quality, velocities, and geolocation accuracy. We integrated multiple correction layers in the ETAD data, including tropospheric delay, ionospheric delay, solid earth tidal displacement, bipartite azimuth shift, distance shift caused by Doppler effect, and azimuth shift caused by frequency modulation mismatch. By applying ETAD data to phase correction of Sentinel-1 data, the experimental results showed that the average phase standard deviation of the original interferogram decreased from 1.8747 rad to 1.6917 rad, a reduction of 10.12%. The RMSE of deformation time series improved from 9.8814 mm to 5.8717 mm post-correction, confirming better alignment with GNSS data.The geographic positioning of PS points showed a notable shift towards building locations, enhancing overall positioning accuracy in the PSI process.This indicates that ETAD data has a significant effect on improving the accuracy of Sentinel-1 data processing.
Authors
- Mengshi Yang (ORCID: https://orcid.org/0000-0003-1449-3494)
- Wu Hao (ORCID: https://orcid.org/0000-0001-7748-737X)
- Mingchun Wen
Institutions
- Yunnan University (CN)
Publication Details
- Published
- 2024-11-22
- DOI
- https://doi.org/10.1109/icsidp62679.2024.10869158
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Space Agency
- National Natural Science Foundation of China