Coastal wetlands play a crucial role in supporting diverse ecosystems and providing numerous ecosystem services. The monitoring of wetland hydrodynamics is essential for understanding and assessing their vulnerability to environmental stressors. In recent years, InSAR (Interferometric Synthetic Aperture Radar) time series analysis has emerged as a valuable tool for studying wetland hydrodynamics. However, accurate wetland water level change monitoring is occasionally hindered by the presence of high amounts of atmospheric water vapor over coastal areas, which mislead the interpretation of InSAR retrievals.In this paper, we present a methodological approach based on Independent Component Analysis (ICA) combined with image segmentation as a blind source separation technique to discriminate between Water Level Change (WLC) related features and wet tropospheric delay features here referred to as 'cloud-induced features' in a UAVSAR WLC time series. Our findings provide a specific methodological case study towards addressing the challenges associated with wet tropospheric delay in Airborne InSAR, and a potential alternative solution for improved and more accurate water level change monitoring in coastal wetlands.