Mitigation of tropospheric delay induced errors in TS-InSAR ground deformation monitoring

ABSTRACT

Interferometric Synthetic Aperture Radar (InSAR) is capable of detecting crust deformation. However, the accuracy is limited by spatiotemporal changes in the lower troposphere. In this paper, we constructed a periodic zenith total delay negative exponential function (PZTD-NEF) model of atmospheric spatiotemporal variation characteristics based on ERA-5 data to alleviate the temporal oscillation bias introduced by tropospheric delay and improve the accuracy of time series InSAR (TS-InSAR) inversion of surface deformation. We evaluated the model’s performance using the phase standard deviation (STD), atmospheric delay correlation coefficient with topography and the spatial structure function. The results were compared with a linear topography-dependent empirical model, generic atmospheric correction online service (GACOS) and ERA-5 methods. Our method reduces the STD of the phase of 83% of the interferograms by 12.8%. For vertical stratification delay correction, the correlation between the proposed method and the Linear, GACOS, and ERA-5 reached 0.734, 0.708, and 0.729, respectively. We found that accounting for spatiotemporal variation characteristics of tropospheric delay can alleviate the seasonal oscillations of vertical stratification delay and improve the accuracy of the deformation time series solution by 40.04%. We also used the Kunming continuous operation reference station system (KMCORS) to verify the displacement results of our method.

KEYWORDS:

• TS-InSAR
• ERA-5
• Tropospheric delay
• Time oscillation deviation
• Deformation

Acknowledgements

The authors would like to acknowledge ESA for providing the Copernicus Sentinel-1A dataset and ECMWF for the ERA-5 data. Thanks to Andy Hooper for developing and releasing StaMPS as well as David Bekaert for developing open source TRAIN for atmospheric delay correction. Thanks to Yunnan Geo-Environmental Monitoring Institute for providing GAMMA software for SAR data processing. In addition, we would like to thank the Yunnan Basic Mapping Technology Center for providing the CORS data. We would also like to thank the three peer reviewers and the editor-in-chief for their guidance on the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the Natural Science Foundation of China [grant numbers 42161067 and 42004006] and Major Science and Technology Project of Yunnan Province, China [grant number 202202AD080010].