Multi-spectral underwater lidar evaluation based on semi-analytical Monte Carlo methods.

Area of Science:

• Marine optics and remote sensing
• Computational physics and oceanography

Background:

• Underwater multi-spectral lidar using supercontinuum lasers offers potential for marine sensing but is underexplored.
• Accurate optical profiling and target detection in complex aquatic environments require advanced simulation tools.

Purpose of the Study:

• To develop and utilize a semi-analytical Monte Carlo simulation framework for evaluating multi-spectral lidar performance in marine environments.
• To investigate the relationship between lidar parameters, seawater optical properties, and phytoplankton concentration across different wavelengths.
• To assess the capability of supercontinuum lidar for simultaneous water column characterization and benthic target identification.

Main Methods:

• Developed a semi-analytical Monte Carlo (MC) simulation framework incorporating bio-optical models.
• Reconstructed inherent optical properties (IOPs) using configurable parameters to analyze spectral signal characteristics.
• Conducted numerical experiments using 400-800 nm bands in diverse marine ecosystems.

Main Results:

• Demonstrated a strong positive correlation (R > 0.97) between lidar attenuation coefficient (k_lidar) and chlorophyll concentration (Chl) at 400 nm and 600 nm.
• Observed a sharp decline in correlation (-0.01) at 800 nm, indicating wavelength-dependent decoupling of optical attenuation from phytoplankton biomass.
• Confirmed the effectiveness of supercontinuum laser lidar in distinguishing spectral characteristics for underwater target identification.

Conclusions:

• The study provides a computational foundation for developing next-generation marine multi-spectral lidar systems.
• Findings offer critical insights into subsurface radiation physics relevant to marine sensing applications.
• Supercontinuum lidar shows dual capability for characterizing water columns and identifying benthic targets.