Assessment of noise in InSAR timeseries using least squares variance component estimation

In recent decades, Interferometric Synthetic Aperture Radar (InSAR) has progressed as an effective and reliable tool for monitoring the surface deformations of the earth. Despite the potential of this method for deformation monitoring, the quality description of InSAR timeseries in terms of precision and noise structure and, consequently, the precision description of the InSAR-derived parameters (e.g., displacement and its velocity) are still somewhat ambiguous. In this paper, we propose a data-derived methodology that directly estimates the precision and noise structure of the final InSAR products, using Least Squares Variance Component Estimation (LS-VCE). Note that due to the spatial correlation among adjacent coherent pixels and adjacent acquisitions, a multivariate LS-VCE model should be applied. We used the proposed method on deformation timeseries derived from the Sentine-l data over city of Tehran, Iran. The results show that applying the multivariate LS-VCE method in our case study improves the results by about 50% compared with the case where the noise parameters are not considered. In addition, the results confirm that InSAR timeseries are highly correlated in time and space. Particularly, the spatial correlation between a series of neighbouring targets for the noise components is significant and gradually decreases with increasing arc length. It should be noted that the observed spatial correlation should be differentiated from the well-known spatial correlation imposed by atmospheric components. In fact, due to the atmosphere filtering step, the noise structure of the final results will be different from the statistical characteristics of a raw atmospheric signal. The proposed methodology is not case study dependent and can be used as an appropriate approach to provide the precision (as a quality descriptor) of the timeseries InSAR products.