We present a ship-borne, LiDAR-enabled marine ecosystem assessment and prediction system that overcomes sparse coverage, shallow sensing, and daylight dependence of conventional platforms. A dual-band configuration (905 nm surface LiDAR; 532 nm fluorescence/Raman channels) is coupled with deep learning: a CNN-LSTM for wave-height forecasting, a Raman-normalized 1D-CNN for chlorophyll-a inversion, and an adaptive subsurface phytoplankton layer detection method (IASPLDM). The pipeline spans data acquisition, GPU-accelerated preprocessing, and Bayesian-optimized training. Under moderate sea states, the system profiles phytoplankton beyond 50 m and suppresses biological noise by >60% relative to acoustics; wave forecasts reach correlation >0.998; chlorophyll-a inversion error is ≤0.5 μg/L; and layer detection attains ≤5% miss and ≤3% false-alarm rates. These capabilities enable early warning of harmful algal blooms, fishery habitat management, and blue-carbon assessment with high spatiotemporal resolution, robustness in low-light or turbid waters, and readiness for scalable operations.
Zhao et al. (Sun,) studied this question.