Synthetic aperture radar (SAR) interferometric baseline parameters form important input for SAR interferometry. In this paper, a nonlinear error model is established for the SAR interferometric baseline and parameterized as a polynomial based on the natural nonlinearity of the orbit of a satellite. Unlike conventional models, the proposed model takes into account the nonlinear part of the baseline error. A theoretical derivation is performed based on the imaging geometry of interferometric SAR, and the results of the analysis show that the parameters of the nonlinear baseline error model can be obtained from the relationship between the orbit, the nominal baseline, the baseline error, and the residual interferogram phase. A sample data set from the Japanese Earth Resources Satellite-1 (JERS-1) L-band SAR is used to validate the proposed model, and the results indicated that the compensation of the residual interferogram phase of the test data is superior to that provided by conventional models.

In consideration of the importance of reference phase compensation to repeat orbit SAR interferometry and the significance of the reference phase to parameter selection and baseline reestimation in InSAR processing, the effect of reference surface difference that caused reference phase change is studied as well as the method of baseline reestimation. This paper gives out the analytical form of the reference phase and reference phase frequency in different reference surface, studies the difference of the reference phase and reference phase frequency characteristic and at last analyses the possible methods in baseline reestimation using reference phase characteristic. The paper also setup the theoretic basis for baseline reestimation based on reference phase characteristic. The study shows that the baseline reestimation could be achieved by utilizing the characteristics of reference phase, and the proper surface of reference phase must be selected since different surface of reference phase will cause different reference phase.

In this paper, the time series multi-image stack processing technique is implemented based on the ERS-1, ERS-2 SAR data set of cities of Las Vegas in America. A single master approach is used in the stack data processing based on the permanent scatterers processing technique invented by Ferretti et al. [1,2]. After the differential phase model establishment and stable points selection, linear subsidence velocity and digital elevation model errors are estimated, non-linear subsidence velocity and atmospheric artifacts related to each SAR acquisition are separated, so a land subsidence history covering all the SAR data acquisitions in each city can be achieved. In our research, more test data in cities of China will be implemented in the next step.