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The freezing and thawing processes of the active permafrost layer, driven by temperature variations between summer and winter, lead to surface seasonal uplift and subsidence, which can be captured by time-series InSAR techniques and associated permafrost modeling. As climate change introduces variations in factors like soil moisture and temperature, the seasonal surface deformation experiences interannual and fluctuating variations. However, these variations have often eluded capture due to either the spatiotemporal filtering processes to mitigate atmospheric delay of temporal InSAR and the approximate assumptions in permafrost deformation models. To better capture the dynamic changes in surface deformation caused by permafrost freeze-thaw processes, we develop a seasonally varying deformation method based on Autoregressive Integrated Moving Average Model (ARIMA) time series analysis. Through both synthetic data and real data experiments, we validate that the proposed method can provide more accurate deformation results while capturing the interannual variations in permafrost deformation. The real-data experiment, utilizing Sentinel-1 data, reveals that the maximum seasonal deformations in the continuous permafrost region of northern Alaska exhibit an increasing-decreasing trend from 2017 to 2021, with 2019 showing a relative maximum, correlating with the number of thawing days and air temperature in that year. This study contributes to a deeper understanding of freeze-thaw processes in permafrost regions, providing robust support for analyzing the impact of climate change on surface deformations in permafrost areas.
Yu et al. (Sat,) studied this question.