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. 相似文献
Storm surge induced by hurricane is a major threat to the Gulf Coasts of the United States. A numerical modeling study was conducted to simulate the storm surge during Hurricane Michael, a category 5 hurricane that landed on the Florida Panhandle in 2018. A high-resolution model mesh was used in the ADCIRC hydrodynamic model to simulate storm surge and tides during the hurricane. Two parametric wind models, Holland 1980 model and Holland 2010 model, have been evaluated for their effects on the accuracy of storm surge modeling by comparing simulated and observed maximum water levels along the coast. The wind model parameters are determined by observed hurricane wind and pressure data. Results indicate that both Holland 1980 and Holland 2010 wind models produce reasonable accuracy in predicting maximum water level in Mexico Beach, with errors between 1 and 3.7%. Comparing to the observed peak water level of 4.74 m in Mexico Beach, Holland 1980 wind model with radius of 64-knot wind speed for parameter estimation results in the lowest error of 1%. For a given wind model, the wind profiles are also affected by the wind data used for parameter estimation. Away from hurricane eye wall, using radius of 64-knot wind speed for parameter estimation generally produces weaker wind than those using radius of 34-knot wind speed for parameter estimation. Comparing model simulated storm tides with 17 water marks observed along the coast, Holland 2010 wind model using radius of 34-knot wind speed for parameter estimation leads to the minimum mean absolute error. The results will provide a good reference for researchers to improve storm surge modeling. The validated model can be used to support coastal hazard mitigation planning.