  The availability of Synthetic Aperture Radar (SAR) data from different
sensors and observation of the Global Navigation Satellite System (GNSS) has
been growing worldwide. The complementary nature of InSAR and GNSS observations
demands methodological advancements for integrating these datasets of variable
accuracy, spatiotemporal sampling rate, and geometries to generate seamless
maps of 3D time series that account for both observations advantages. Here, I
present an approach based on Kalman Filter, which recursively resolves the 3D
displacement field time series by combining line-of-sight time series from at
least one SAR viewing geometry and horizontal velocities from GNSS networks. I
apply this method to 3 overlapping SAR frames in ascending and descending
orbits of Envisat C-band and ascending orbit of ALOS L-band acquired over the
San Francisco Bay Area from 2007 to 2011. The experimental results and
validation tests against independent observations indicate that the presented
approach can resolve 3D displacement field time series at mm-level accuracy
comparable to GNSS accuracy but at 10s m spatial resolution.
