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

In recent years, synthetic aperture radar interferometry (InSAR) has emerged as a valuable tool for studying hydrodynamic processes in coastal wetlands by measure water level change (WLC). However, the highly dynamic wet atmosphere conditions common in these areas has 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. 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 (UAVSAR) airborne instrument. The method compares in-situ water level measurements with the independent components to identify the ICA component 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 -1 dB before filtering. The excluded independent components are used to generate a time series of likely atmospheric features masks. The identified features in the masks generally correspond to atmospheric features identifiable in Next Generation Weather Radar (NEXRAD) S-band ground weather radar reflectivity maps collected during the UAVSAR acquisitions. The method, although demonstrated on water level change, is sufficiently general to be applied to any InSAR-derived time series.