High Spatio-Temporal Resolution Deformation Time Series With the Fusion of InSAR and GNSS Data Using Spatio-Temporal Random Effect Model

High spatio-temporal resolution deformation series can be used to improve the understanding of deformation mechanism, thereby contributing to prevention and control of geological disasters such as mine subsidence, landslide, and earthquake. Among ground deformation monitoring technologies, global navigation satellite system has high temporal resolution but low spatial resolution, and interferometric synthetic aperture radar (InSAR) has high spatial resolution but low temporal resolution. Fusing these two data may generate high spatio-temporal resolution deformation series. Existing fusion methods usually use the bi-direction interpolation, which does not consider the spatio-temporal cross correlation and is computationally extensive. We propose a dynamic filtering fusion model based on the spatio-temporal random effect (a spatio-temporal Kalman filter) model. Experiments with simulated data and real data from the Los Angeles area are conducted to validate this method. Simulated experimental results are compared with truth data and the Los Angeles experiment data results are verified using the leave-one InSAR image-out validation method. The RMS results for them are around 13.8 and 5 mm, respectively, indicating that the proposed method can achieve high accuracy and high spatial-temporal resolution deformation time series.