Mitigating Atmospheric Delays in InSAR Time Series: The DetrendInSAR Method and Its Validation

How to effectively mitigate atmospheric delay signals in InSAR observations has long been a pressing problem in the InSAR community. Here we propose a new method, DetrendInSAR, that addresses this issue by incorporating the spatiotemporal characteristics of displacements and atmospheric delays as a-priori information. This enables simultaneously modeling and estimating surface displacements, atmospheric delays, and other unmodeled long-wavelength errors (e.g., orbit errors) in InSAR time series. We use both simulated- and real-data experiments to validate the performance of the proposed method, and results show a 20%–70% reduction of RMSE values for the DetrendInSAR-obtained displacements compared with four commonly-used atmospheric delay reduction methods. We also show that the DetrendInSAR method is capable of capturing the spatial and temporal details of sudden deformation jumps as small as 1 cm, due to a magnitude 5.4 earthquake, in InSAR time series. Based on the DetrendInSAR-corrected ascending and descending Sentinel-1 InSAR time series, we calculate the 2-year postseismic east and vertical displacements of the 2021 M_w 7.4 Maduo earthquake, which better illuminate the postseismic deformation.