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.

大气水汽是时序合成孔径雷达干涉测量（time-series InSAR）中延迟误差的主要来源。即便采用常规时空滤波及基于GACOS（Generic Atmospheric Correction Online Service for InSAR）的大气校正方法，仍会存在残余误差，削弱形变监测精度。本研究以天津为研究区域，先通过GACOS完成大气校正，再采用哨兵-1A（Sentinel-1A）合成孔径雷达（Synthetic Aperture Radar，SAR）影像与永久散射体合成孔径雷达干涉测量（Persistent Scatterer InSAR，PS-InSAR）技术提取时序地面沉降信息。通过整合15个全球导航卫星系统（Global Navigation Satellite System，GNSS）站点的观测数据，本研究对经过GACOS校正后的时序InSAR形变结果中残余的大气影响开展系统分析。此外，本研究借助ERA5大气可降水量（Precipitable Water Vapor，PWV）数据集，构建了针对不同天气情景下时序InSAR残余大气误差的校正模型，以期提升基于时序InSAR的形变监测精度。结果表明，在降雨、雾霾及降雪天气条件下，经过GACOS大气校正后的时序InSAR形变结果中仍存在残余影响。夏季降雨对InSAR形变精度造成的大气残余误差最为显著，影响幅度可达8毫米。相比之下，秋冬春三季的雾霾与降雪对形变精度的影响小于雨季，约为2毫米。降雨、雾霾及降雪事件期间的时序InSAR大气残余误差与同期的PWV变化量（ΔPWV）之间存在显著负相关关系，且相关系数均大于-0.85。随后，本研究利用11个GNSS站点数据与ERA5 ΔPWV数据集，通过线性回归方法构建了时序InSAR大气残余误差模型。基于4个GNSS站点的观测结果与两项工程的精密水准测量数据，对该模型的可靠性进行了验证。经模型校正后，时序InSAR的形变精度优于1毫米。经GACOS大气校正后，时序InSAR生成的形变产品中仍存在残余大气误差。ERA5水汽变化量与时序InSAR的大气残余误差之间存在显著负相关关系，相关系数超过0.85。通过整合ERA5水汽数据、GNSS形变数据及时序InSAR形变数据，本研究构建了夏季降雨时段及雾霾、降雪时段的时序InSAR大气残余误差模型。该模型通过多组独立数据集进行了严格验证，包括未参与模型构建的4个GNSS站点数据以及两项工程的精密水准测量数据。经验证后的模型可显著提升时序InSAR形变监测精度。经GACOS大气校正后，时序InSAR生成的形变产品中仍存在残余大气误差。ERA5水汽变化量与时序InSAR的大气残余误差之间存在显著负相关关系，相关系数超过0.85。通过整合ERA5水汽数据、GNSS形变数据及时序InSAR形变数据，本研究构建了夏季降雨时段及雾霾、降雪时段的时序InSAR大气残余误差模型。该模型通过多组独立数据集进行了严格验证，包括未参与模型构建的4个GNSS站点数据以及两项工程的精密水准测量数据。经验证后的模型可显著提升时序InSAR形变监测精度。