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Spatiotemporal atmospheric phase mitigation in multi-temporal InSAR for low-frequency localized deformation monitoring

Jiang Long,Ligong Yang,Q. Zeng,Shangzong Lu

Published 2025 in International Journal of Remote Sensing

ABSTRACT

ABSTRACT Multi-temporal InSAR (MT-InSAR) provides a powerful tool for the monitoring of ground surface. However, large-scale MT-InSAR are usually limited by atmospheric phase, which is related to height and distance in space, presenting an unavoidable challenge to monitor mountainous surfaces. Performance of atmospheric phase modelling depends on the components and variations of the extracted phase incorporated into the model, which are varied with cases and expertise. In this work, we present a novel method to separate deformation phase by utilizing temporal and spatial errors mitigation (TSEM). The initial deformation phases are estimated using a low-pass filter and subsequently removed from the original phase to prevent them from being incorrectly modelled. Then, remaining phase is adaptively segmented to local windows by considering the deformation and atmospheric phase variation in different terrains. Atmospheric phases are modelled within the local windows in space domain. Finally, deformation and topographic error phases are re-estimated after removing the modelled atmospheric phases. We first conducted an experiment with simulation SAR data to justify the assumption and evaluate the effectiveness of TSEM. Then, the proposed method was tested at Southern California, U.S.A., with Sentinel-1 SAR data. After correction by TSEM, the mean standard deviation (STD) of the interferograms decreased by 76%. MT-InSAR deformation velocity measurements align well with GNSS measurements, with differences less than 10 mm/yr. The results demonstrate the potential of the proposed method for large-scale disaster deformation monitoring.

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