Forest fires significantly impact permafrost degradation in the subarctic regions. However, inter-annual and seasonal variations in surface deformation due to permafrost thawing in burned areas were poorly understood. Measuring the ground surface displacement in fire scars helps us understand the freeze–thaw dynamics of near-surface ground and predict the future state, particularly in ice-rich permafrost regions. This study used the L- and C-band interferometric synthetic aperture radar (InSAR) technique to reveal inter-annual subsidence and seasonal thaw settlement/frost heave in a fire scar near Mayya, Sakha Republic in Eastern Siberia burned in 2013. We found that the cumulative subsidence was up to 7 cm between 2014 and 2020, most of which had occurred by 2016. The magnitude of seasonal thaw settlement and frost heave varied each year from 2017 to 2020 after the fire, but the inter-annual change in frost heave corresponded to the temporal variation in precipitation during the thawing season from 2017 to 2020. This suggests that the precipitation amount during the thawing season is related to the magnitude of segregation–ice formation in the sediments, which determines the frost heave amount. The observed seasonal displacements could not be quantitatively explained by models inferred from the Stefan’s equation and volume changes associated with ice–water phase change. This implies that other models associated with segregated ice (ice lens) formation/thaw are required to explain the observed seasonal displacement.

In this study, a simple stochastic representation of the microscale spatial variability in thaw depth in permafrost regions was proposed. Thaw depth distribution measured in the two larch-type forests in eastern Siberia, Spasskaya Pad and Elgeeii, showed different spatial, seasonal, and interannual variability, respectively. Minor year-to-year variation in active-layer thickness was observed in Spasskaya Pad, where a transient layer may constrain further thawing. A gamma distribution accurately represented the thaw depth spatial variability in both sites as the cumulative probability. Thus, a simple model illustrating the spatiotemporal variation in thaw depth as a function of the mean thaw depth was developed using the gamma distribution. A hierarchy of models was introduced that sequentially considered the constant state, linearity, and non-linearity in the dependence of the rate parameter of the gamma distribution for the mean thaw depth. Although the requirements of the model levels differed between Spasskaya Pad and Elgeeii, the proposed model successfully represented the spatial variability in thaw depth at both sites during different thaw seasons.