Efficient reconstruction of nearshore sea state and bathymetry from marine radar images

In-situ monitoring of waves is usually limited to provide spatio-temporal information. However, remote sensing tools (RSTs) can be complementary to eliminate this limitation. In this study, a novel framework, Lightweight Image Assimilation (LMS), has been developed for RSTs which is capable to gather information on hydro-morphodynamics by utilising Radon transformation, advanced signal processing methods, and histogramming to eliminate outliers. LMS was tested at our study site where two different RSTs were deployed: an X-Band Radar and a video monitoring system. LMS has been benchmarked with field observations and alternative processing tools by utilising radar images, which are collected every hour with 0.5 Hz frequency. The statistical measurements showed that LMS was capable to reconstruct the evolution of wave characteristics (e.g. significant wave height, r 2 = 0.79 , R M S E = 0.21 m ) and the bathymetry ( r 2 = 0.70 , R M S E = 0.55 m ). LMS outperformed alternative tools despite less computational demand. However, a systematic overestimation was observed for all methods and the performance was affected by cross-sea altered signals where LMS allowed to diagnose the source of alteration. The wave characteristics derived from the framework were used to force the phase-resolving model to investigate coastal inundation and compare with empirical formulations. Consequently, the framework can be used as a comprehensive tool to investigate hydro-morphodynamic processes, their impacts on the coastal regions, and to have an insight on the coastal resilience.