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The availability of Synthetic Aperture Radar (SAR) data from differentsensors and observation of the Global Navigation Satellite System (GNSS) hasbeen growing worldwide. The complementary nature of InSAR and GNSS observationsdemands methodological advancements for integrating these datasets of variableaccuracy, spatiotemporal sampling rate, and geometries to generate seamlessmaps of 3D time series that account for both observations advantages. Here, Ipresent an approach based on Kalman Filter, which recursively resolves the 3Ddisplacement field time series by combining line-of-sight time series from atleast one SAR viewing geometry and horizontal velocities from GNSS networks. Iapply this method to 3 overlapping SAR frames in ascending and descendingorbits of Envisat C-band and ascending orbit of ALOS L-band acquired over theSan Francisco Bay Area from 2007 to 2011. The experimental results andvalidation tests against independent observations indicate that the presentedapproach can resolve 3D displacement field time series at mm-level accuracycomparable to GNSS accuracy but at 10s m spatial resolution.

A Kalman Filter Framework for Resolving 3D Displacement Field Time Series By Combining Multitrack Multitemporal InSAR and GNSS Horizontal Velocities