Separation of Water Level Change From Atmospheric Artifacts Through Application of Independent Component Analysis to InSAR Time Series

Abstract

In recent years, synthetic aperture radar (SAR) interferometry (InSAR) has emerged as a valuable tool for measuring water level change (WLC) to study hydrodynamic processes in coastal wetlands. However, the highly dynamic wet atmosphere conditions common in these areas have a significant impact on InSAR observations, producing errors in the derived values. Standard methods for estimating atmospheric noise in InSAR time series lack the spatial or temporal resolution needed to adequately correct for wet tropospheric delays. In this study, we utilize the Independent Component Analysis (ICA) signal decomposition technique to identify the likely WLC signal and eliminate atmospheric noise in a time series derived from rapid repeat measurements made with the L-band uninhabited aerial vehicle synthetic aperture radar airborne instrument. The method compares in-situ water level measurements with the independent components (IC) to identify the ICA components corresponding to WLC. The signal-to-noise ratio between the WLC after the ICA-based filtering and in situ water level gauges used for validation reaches 16 dB compared to an average of 2.6 dB before filtering. The excluded IC are used to generate maps showing a time series of likely atmospheric features. The identified features in the maps generally correspond to atmospheric features identifiable in Next Generation Weather Radar (NEXRAD) S-band ground weather radar reflectivity maps collected during the SAR acquisitions. The method is sufficiently general to be applied to any InSAR-derived surface displacement time series.