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

TL;DR

This paper introduces a Kalman Filter-based method to integrate InSAR and GNSS data, enabling high-resolution, mm-level accurate 3D displacement time series mapping over large areas.

Contribution

The novel approach combines SAR and GNSS data within a Kalman Filter framework to produce seamless 3D displacement time series with improved accuracy and spatial resolution.

Findings

Achieved mm-level accuracy comparable to GNSS.

Resolved 3D displacement fields at 10s meter spatial resolution.

Validated results against independent observations.

Abstract

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.