Automated Reference Points Selection for InSAR Time Series Analysis on Segmented Wetlands

Interferometric Synthetic Aperture Radar (InSAR) time series analysis is a powerful technique to estimate long-term water level changes in wetlands ecosystems. However, few studies have applied InSAR on wetlands that are highly segmented by canals and levees due in part to the challenge of selecting qualified reference points to minimize unwrapping errors, which, by contrast, is a relatively easy task for unsegmented wetlands. Here we developed a new method to automatically select the optimal reference point for InSAR time series analysis. The method selects reference points by considering temporal behaviors of coherence and InSAR phase connectivity from each reference point to its wetland of interest. We tested the method on six managed and highly segmented wetland units within the Sacramento National Wildlife Refuge in the Central Valley, California. We validated the InSAR measurement against water depth gauge measurements during a low water depth (<10 cm) period in 2017. The overall accuracy of the estimated water depth changes achieved an RMSE of 1.49 cm. Compared with three existing methods, our method showed significantly lower RMSE values overall. This new automatic method enables us to maximize the performance of InSAR to predict water depth and could be applied to other types of InSAR applications as well.