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

Abstract 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.