GAM-STTD: a spatiotemporal tropospheric delay correction model for time-series InSAR in complex mountainous regions

Tropospheric delay remains a critical error source in time-series interferometric synthetic aperture radar (TS-InSAR), particularly in mountainous and plateau regions where seasonal stratification and stochastic turbulence coexist. Current correction methods based on global atmospheric models (GAM) often underestimate the turbulent delay and fail to effectively address its spatiotemporal variability. We propose a novel GAM-based tropospheric correction method termed GAM-STTD that simultaneously models stratified and turbulent delays. The method integrates (1) the Prophet forecasting model to capture atmospheric variability in the temporal domain and (2) an adaptive kernel density estimation (AKDE) strategy to optimize three-dimensional zenith total delay (3D-ZTD) sampling according to the terrain gradient. We incorporated GAM-STTD into the TS-InSAR framework and validated it using both simulated and RADARSAT-2 datasets over Lijiang Basin, China. The results showed that the GAM-STTD model overcomes the underestimation of stochastic turbulent delay observed in existing atmospheric models, with a mean bias of 0.29 cm relative to the ERA5 reference ZTD. The model reduced the average phase standard deviation (STD) across 125 interferograms from 2.88 rad to 2.51 rad. In addition, the GAM-STTD model reduces the turbulence-induced interferometric phase semi-variance from 2.38 rad² to 1.49 rad², which further improves the accuracy of the TS-InSAR deformation solution.