Analysis and Correction of Atmospheric Residual Errors in Time-Series InSAR for the Guangzhou-Zhanjiang High-Speed Railway

This is the authors' abstract. We don't add key points for this paper.

Atmospheric delay is the primary error source limiting the monitoring accuracy of time-series Interferometric Synthetic Aperture Radar (InSAR). Although the Generic Atmospheric Correction Online Service (GACOS) can effectively mitigate atmospheric delays, significant residual errors still remain in the corrected InSAR deformation time series when facing drastic fluctuations in precipitable water vapor (PWV). Taking the Guangzhou–Zhanjiang High-Speed Railway with complex climatic environments as an example, this article proposes a time-series InSAR atmospheric residual error correction model fusing GNSS and ERA5 PWV. First, the small baseline subset interferometric synthetic aperture radar technique combined with GACOS data is adopted for preliminary correction to obtain time-series deformation, and the atmospheric residual errors in the InSAR results are systematically analyzed using GNSS monitoring data as the ground truth. Subsequently, by fusing GNSS PWV and ERA5 PWV to acquire a high-precision and high-resolution water vapor field, an atmospheric residual error correction model is established by analyzing the correlation between the atmospheric residual errors and concurrent ΔPWV (PWV variations). The results show that the atmospheric residual errors exhibit a significant negative correlation with ΔPWV, with a correlation coefficient exceeding −0.75. After correction by the atmospheric residual error model, improves the InSAR monitoring accuracy by an average of approximately 45%, effectively removing spurious deformation caused by extreme water vapor fluctuations. Finally, analysis based on the corrected InSAR results reveals that the eastern plain urban area along the railway is generally stable, whereas significant subsidence funnels exist in the western mountainous and hilly area.