Estimating tropospheric phase delay in SAR interferograms using Global Atmospheric Models
Résumé
The main limiting factor on the accuracy of Interferometric SAR (InSAR) measurements comes from phase propagation delays through the Earth's troposphere. The delay can be divided into a stratified component, which correlates with the topography and often dominates the tropospheric signal in InSAR data, and a turbulent component. The stratified delay can be expressed as a function of atmospheric pressure P, temperature T, and water vapor partial pressure e vertical profiles. We compare the stratified delay computed using results from global atmospheric models with the topography-dependent signal observed in interferograms covering three test areas in different geographic and climatic environments: Lake Mead, Nevada, USA, the Haiyuan fault area, Gansu, China, and Afar, Republic of Djibouti. For each site we compute a multi-year series of interferograms. The phase-elevation ratio is estimated for each interferogram and the series is inverted to form a timeline of delay-elevation ratios characterizing each epoch of data acquisition. InSAR derived ratios are in good agreement with the ratios computed from global atmospheric models. This agreement shows that both estimations of the delay-elevation ratio can be used to perform a first order correction of the InSAR phase. Seasonal variations of the atmosphere significantly affect the phase delay throughout the year, aliasing the results of time series inversions using temporal smoothing or data stacking when the acquisitions are not evenly distributed in time. This is particularly critical when the spatial shape of the signal of interest correlates with topography. In the Lake Mead area, the irregular temporal sampling of our SAR data results in an interannual bias of amplitude ~2~cm on range change estimates. In the Haiyuan Fault area, the coarse and uneven data sampling results in a bias of up to ~0.5~cm/yr on the line of sight velocity across the fault. In the Afar area, the seasonal signal exceeds the deformation signal in the phase time series. In all cases, correcting interferograms from the stratified delay helps removing these biases. Finally we suggest that the phase delay correction can potentially be improved by introducing a non-linear dependance to the elevation, as consistent non-linear relationships are observed in many interferograms as well as in global atmospheric models.