A New Method for Temporal Phase Unwrapping of Persistent Scatterers InSAR Time Series

The analysis of radar time series with persistent scatterer techniques usually relies on temporal unwrapping, because phase behavior can be often described by simple models. However, one of the major limitations of temporal algorithms is that they do not take advantage of spatially correlated information. Here, we focus on two types of information that can be spatially estimated, namely, observation precision and the probability density function of the model parameters. We introduce them in phase unwrapping using Bayesian theory. We test the proposed method using simulated data. We also apply them to a small area in the southern Netherlands and compare with conventional temporal unwrapping methods.