InSAR processing for volcano monitoring and other near‐real time applications

Abstract Radar interferometry (InSAR, interferometric synthetic aperture radar) is routinely used to measure surface deformation prior to, during, and after volcanic events, although not in a monitoring capacity. The improved data availability of some current satellite missions presents us with the opportunity to do just that. We present here a fast and flexible algorithm to estimate coherence and select points on an interferogram‐by‐interferogram basis, which overcomes limitations of the conventional boxcar ensemble method in areas of marginal coherence. Time series methods, which offer an alternative way to select coherent points, are typically slow, and do not allow for insertion of new data without reprocessing the entire data set. Our new algorithm calculates the coherence for each point based on an ensemble of points with similar amplitude behavior throughout the data set. The points that behave similarly are selected prior to new images being acquired, on the assumption that the behavior of these nearby points does not change rapidly through time. The resulting coherence estimate is superior in resolution and noise level to the boxcar method. In contrast to most other time series methods, we select a different set of coherent points for each interferogram, avoiding the selection compromise inherent to other time series methods. The relative simplicity of this strategy compared to other time series techniques means we can process new images in about 1 h for a typical setup.