Sequential SBAS-InSAR Backward Estimation of Deformation Time Series

In recent years, many synthetic aperture radar (SAR) satellites have been launched to provide abundant SAR data, which creates a demand for dynamic monitoring of surface deformation. In general scenarios, we process SAR images within a fixed period to obtain the deformation time series. However, conventional interferometric SAR (InSAR) processing involves manually selecting a specific time period, making it impossible to capture the complete deformation process. Therefore, we propose a novel framework, that is, the sequential small baseline subset InSAR (SBAS-InSAR) backward estimation algorithm, which utilizes sequential least-squares adjustment to dynamically recover previous surface deformation time series. To validate the effectiveness of the proposed method, we conduct both simulated and real data experiments. Also, the simulated results obtained by new method are totally consistent with the traditional SBAS-InSAR method, while the standard deviation of the difference between the deformation time series obtained by InSAR and GNSS results is better than 6 mm. Moreover, the new method has higher computation efficiency.