Airborne Photon‐Counting LiDAR for Shallow‐Water Bathymetry With Robust Noise Removal and Depth Retrieval

Abstract Airborne photon‐counting LiDAR has emerged as a promising tool for shallow‐water bathymetry, offering high spatial resolution and penetration depth for coastal and nearshore applications. This study presents an integrated workflow for processing airborne photon‐counting LiDAR data to estimate shallow‐water bathymetry. The workflow combines voxel‐level false discovery rate gating, layer‐wise adaptive clustering, and wave‐driven refraction correction to improve the reliability of depth estimates. The approach was validated using two coastal data sets, representing real‐world conditions with varying water clarity and background noise. Results show that the workflow effectively separates surface, water‐column, and bottom layers, achieving high accuracy in clear‐water conditions at North Reef (RMSE 0.214 and 0.309 m) and operationally useful retrieval in noisier class‐II waters at Weizhou Island (RMSE 1.342 and 1.558 m), where spatial gradients are still preserved within the effective penetration range. This workflow is suitable for operational coastal monitoring and environmental assessment.