Mapping ground displacement by a multiple phase difference-based InSAR approach: with stochastic model estimation and turbulent troposphere mitigation

Abstract

Tropospheric delay is one of the dominant error sources of interferometric synthetic aperture radar when measuring ground displacement. Although many methods have been presented for the correction of tropospheric effects, a large portion of them (such as the numerical weather models and the topography-correlated analysis methods) can only be used to correct the stratified delays (i.e., topography-related delays), and it is still intractable for mitigating the turbulent effects. Considering that the approximate displacement extent over an interested region often can be known based on some prior geophysical or geological information, we present here a new method to mitigate the effects of atmospheric turbulence by fusing multiple phase differences (MPDs) between the pixel of interest and those pixels whose displacement can be ignored or can be known based on external displacement datasets (e.g., from other geodetic observations). Our method involves estimating the stochastic model, i.e., variance–covariance matrix, of the MPDs for each pixel and then reconstructing the ground displacement pixel by pixel using a proposed minimum variance-based linear estimator. Two advantages of the proposed method are that: (1) no external atmospheric data are required; (2) uncertainties of the reconstructed displacements can be provided as well. In addition, our method is implemented interferogram by interferogram, so we do not need time series of InSAR datasets. The performance of the proposed approach is tested by using both simulated datasets and the real data over Mexico City regions, and the experimental results show that our method can mitigate the turbulent atmosphere efficiently and robustly, which is of great interest to a wide community of geodesists and geophysicists.