Analysis of atmospheric residual error and construction of correction model for GACOS-Corrected PS+DS time-series InSAR based on ERA5 PWV

Atmospheric water vapour constitutes the primary source of delay errors in time-series InSAR. Even after implementing conventional spatiotemporal filtering and GACOS-based atmospheric correction, residual errors persist, compromising deformation monitoring accuracy. Consequently, using Tianjin as the study area. Sentinel-1A SAR imagery and PS-InSAR technique are utilized to derive time-series land subsidence information, following prior atmospheric correction via GACOS. By integrating observational data from 15 GNSS station, a systematic analysis is conducted on the residual atmospheric effects remaining in post-GACOS time-series InSAR deformation results. Furthermore, leveraging ERA5 PWV datasets, correction models tailored to residual atmospheric error in time-series InSAR across different weather scenarios are developed, with the aim of enhancing the accuracy of time-series InSAR-based deformation monitoring. Results show that residual effects persist in the time-series InSAR deformation results after GACOS-based atmospheric correction under rainfall, haze, and snowfall conditions. Rainfall in summer induces the most significant atmospheric residual error on InSAR deformation accuracy, with impacts of up to 8 mm. In contrast, haze and snowfall in autumn, winter, and spring have smaller effects on deformation accuracy than those during the rainy season, approximately 2 mm. A significant negative correlation exists between the atmospheric residual error of time-series InSAR during rainfall, haze, and snowfall events and the ΔPWV over the same periods. Moreover, the correlation coefficients are all greater than −0.85. Subsequently, using the 11 GNSS and ERA5 Δ PWV data, a time-series InSAR atmospheric residual error model was constructed via the linear regression method. Then, based on the results of the 4 GNSS and the precise levelling measurements of the two engineering projects, the reliability of the model was verified. After model correction, the deformation accuracy of time-series InSAR is better than 1 mm.

Following atmospheric correction via the GACOS, residual atmospheric errors persist in deformation products derived from time-series InSAR.
A significant negative correlation exists between the variation of ERA5 water vapour and the atmospheric residual error of time-series InSAR, with the correlation coefficient exceeding 0.85.
Based on the integration of the ERA5 water vapour data, the GNSS deformation data, and the time-series InSAR deformation data, a time-series InSAR atmospheric residual error model during the summer rainfall periods and the haze and snowfall periods was constructed.
The model was rigorously validated with multiple independent datasets, including data from four GNSS stations not used in model development and precise levelling measurements from two engineering projects. The validated model significantly enhances the accuracy of time-series InSAR deformation monitoring.

Following atmospheric correction via the GACOS, residual atmospheric errors persist in deformation products derived from time-series InSAR.
A significant negative correlation exists between the variation of ERA5 water vapour and the atmospheric residual error of time-series InSAR, with the correlation coefficient exceeding 0.85.
Based on the integration of the ERA5 water vapour data, the GNSS deformation data, and the time-series InSAR deformation data, a time-series InSAR atmospheric residual error model during the summer rainfall periods and the haze and snowfall periods was constructed.
The model was rigorously validated with multiple independent datasets, including data from four GNSS stations not used in model development and precise levelling measurements from two engineering projects. The validated model significantly enhances the accuracy of time-series InSAR deformation monitoring.