Refining Stacking-InSAR by Considering the Statistical Characteristics of Atmospheric Turbulence

We propose an atmospheric turbulence statistical characteristic-enhanced stacking-interferometry synthetic aperture radar (InSAR) method to more accurately derive the average deformation velocity for slow or linear surface displacements. It can be used to estimate the variance and covariance matrix (VCM) for time-series InSAR observations pixel by pixel. Moreover, a minimum variance linear estimator is used to calculate optimal weights for interferogram stacking. Compared with traditional stacking, our method is an improvement, and its advantages include the following: 1) the relevance between time-series atmospheric delays of interferograms is considered; 2) the atmospheric turbulence is better suppressed; and 3) the performance is not limited by the quantity of interferograms. The effectiveness of the new method is verified through a series of simulated experiments and real Sentinel-1 experiments over Southern California. The results demonstrate that our proposed method is more effective and robust in removing turbulent atmospheres.