A Novel Semi-Analytical Method for Modeling Polarized Oceanic Profiling LiDAR Multiple Scattering Signals

In recent years, oceanic profiling light detection and ranging (LiDAR) has emerged as an essential tool for detecting seawater’s detailed vertical structure. The challenge, however, lies in the necessity to comprehend the impact of polarized multiple scattering on LiDAR signals. This aspect presents considerable difficulties when addressed using conventional Monte Carlo (MC) models due to their mathematical complexities associated with Muller matrix computations and time-intensive procedures. In response to this, our study introduces a unique semi-analytical method, amalgamating improved stochastic and analytical techniques to model polarized multiple scattering LiDAR signals. A preliminary result indicates a thousandfold increase in operational efficiency in comparison to traditional MC models. Additionally, our method holistically contemplates the coupled effects of environmental parameters and the observation geometry of the LiDAR system on signals. Our research scrutinizes the footprint of multiple scattering on the time-resolved polarization state of lasers under variable environmental factors, such as complex stratified structures, scattering phase functions, and particle size distribution in seawater, as well as differing LiDAR observation conditions, like transmitter height, incident angle, field of view (FOV), and receiver detection area. Our findings reveal that multiple scattering significantly depolarizes the backscatter return from seawater particulate matter. We have pioneered the proposal of a quantitative correlation between oceanic LiDAR multiple scattering and depolarization ratio for the first time. Our methodology’s specific application is to enhance LiDAR inversion algorithms through the correction of multiple scattering from LiDAR depolarization measurements in future applications.