Stochastic modeling for time series InSAR: with emphasis on atmospheric effects
Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance–covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance–covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.
KeywordsInterferometric synthetic aperture radar (InSAR) Time series InSAR (TS-InSAR) Atmospheric delays Stochastic modeling Variance–covariance matrix (VCM)
This study was supported by the National Natural Science Foundation of China (Nos. 41474007, 41404013) and the Doctoral Innovation Foundation of Central South University (2015zzts068), and the SAR images were provided by WInSAR and the European Space Agency (ESA) Cat-1 18234.
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