Ocean LiDAR technology is of high interest and particularly promising for ocean applications. However, its application in complex oceanic environments is often limited by traditional elastic scattering mechanisms. Most existing ocean LiDAR simulation techniques focus primarily on elastic scattering, with limited attention given to inelastic scattering. This article presents a novel semianalytical Monte Carlo (SAMC) simulation method that integrates photon tracking algorithms with polarization state simulation, incorporating nonelastic scattering processes—such as Raman scattering, Brillouin scattering, and fluorescence—and polarization effects. By combining analytical solutions with numerical LiDAR simulations, the proposed semianalytical method improves both the precision and efficiency of simulations. Additionally, the method constructs models for fluorescence, Raman scattering, Brillouin scattering, and polarization scattering. These models were used to conduct a detailed analysis of nonelastic scattering and polarization scattering echo signals in stratified water, as well as the effects of chlorophyll concentration on these signals. Compared to traditional MC methods, the semianalytical approach offers obvious advantages in computational efficiency and accuracy. The study also investigates the impact of multiple scattering, stratified water, particle size distribution, field of view (FOV), and spectral bandwidth on nonelastic and polarization scattering echo signals. It highlights the significant influence of multiple scattering on signal detection accuracy and the critical role of changes in optical properties within stratified water. Nonelastic and polarization scattering signals play a crucial role in ocean LiDAR research, and the SAMC model developed in this article offers new insights and tools for the understanding and simulation of these signals.

This paper presents a novel Semi-Analytical Monte Carlo (SAMC) method developed for simulating the radiative transfer of full-polarization marine LiDAR signals within complex marine environments. Traditional marine LiDAR techniques, which predominantly rely on the intensity and linear polarization of backscattered signals, encounter limitations in complex settings. To acquire more detailed particulate morphological information, full-polarization LiDAR was introduced by incorporating circular polarization observation capabilities in recent years. However, the comprehensive calculation of full Stokes vectors brings about several inherent drawbacks, such as computational inefficiency and slow convergence. To address these issues, the SAMC method combines photon tracing with semi-analytical full polarization state tracking. It innovatively introduces polarization direction and phase difference variables into the radiative transfer process, thereby enabling the simulation and modeling of circular polarization echo signals. Based on the SAMC model, this study carried out simulation experiments regarding the transmission of lasers with different initial polarization states in water bodies. The impacts of LiDAR parameters, chlorophyll concentration, particle size range, and solar background light on full-polarization backscattered signals were analyzed. The results indicate that changes in polarization states are closely related to chlorophyll concentration and particle size, and multiple scattering effects have a significant influence on full-polarization signals. Moreover, the study explored the influence of the receiver field of view on signal quality and the discrimination ability of full - polarization channels for particles of different sizes. The unique advantages of circular polarized light in resisting solar background interference were also analyzed. This research not only conducts an in-depth analysis of the underwater full-polarization light transmission mechanism but also provides scientific support for the calibration of full - polarization LiDAR backscattered signals. The capacity to distinguish between different particle sizes using full-polarized channels offers a promising approach for the optical remote sensing of marine particle size distributions. The circular polarization's resistance to solar interference and its ability to maintain signal integrity under bright sunlight conditions represent significant advantages for marine remote sensing applications.

Ocean lidar technology is pivotal for observing the vertical structure of marine ecosystems. However, the accuracy of conventional inversion algorithms is often constrained in optically complex waters, as they rely ona prioriassumptions for key parameters such as the lidar ratio (r), which is highly variable and difficult to determine. This issue, coupled with uncorrected multiple scattering effects, limits the retrieval accuracy. To address these limitations, this study introduces a robust framework for a novel shipborne single-photon lidar (SPL). The core of this framework is an innovative dual-iterative hybrid inversion algorithm that adapts to various water types by simultaneously optimizing both therand the logarithmic backscatter-to-attenuation ratio (η), thus eliminating the need for prior assumptions. Furthermore, a semi-analytical model was integrated to correct for multiple scattering, a major source of error. This framework was applied to a large-scale dataset of over 58,000 lidar profiles collected along a 2,500 km track around Hainan Island. The retrieved inherent optical properties (IOPs) revealed spatial patterns that were consistent with known oceanographic features. Validation against in situ measurements demonstrated excellent performance: the diffuse attenuation coefficient (Kd) achieved a correlation coefficient (R) of 0.89 and a mean absolute percentage deviation (MAPD) of 11.38%, while the particulate backscattering coefficient (bbp) yielded anRof 0.93 and aMAPDof 16.82%. The multiple scattering correction proved to be critical, reducing theMAPDof the retrievedKdagainst satellite data from 28.66% to 9.06%. Our findings establish that this advanced SPL system, coupled with our novel inversion framework, provides a validated, reliable, and effective tool for large-scale, high-resolution monitoring of subsurface ocean optical structures.

Oceanic polarization LiDAR is a powerful tool for profiling marine optical properties, yet traditional simulation frameworks often neglect polarization effects or require excessive computational resources. Our model departs from these methods by integrating a semi-analytical approach with full Stokes vector tracking, allowing for efficient and accurate simulation of polarization evolution in multiple scattered light fields, particularly the depolarization ratio, which encodes critical information about waterborne particles and scattering processes. However, conventional Monte Carlo (MC) models suffer from computational inefficiency and insufficient handling of polarization dynamics. In this study, we propose a novel semi-analytical Monte Carlo model that significantly enhances simulation efficiency while accurately modeling the multiple scattering of polarized light in seawater. By integrating stochastic and deterministic approaches, the model captures polarization evolution via Stokes vector transformations and Mie scattering theory. Our simulations explore the influence of LiDAR system parameters, and the model’s predictions are consistent with trends observed in previous empirical studies, providing a basis for future experimental validation, inherent optical properties (IOPs), and particle characteristics on the depolarization ratio. The results reveal that multiple scattering is the dominant mechanism driving depolarization and that scattering coefficients and particle size distributions have profound effects on polarization states. The proposed model provides a robust framework for interpreting polarized LiDAR signals and supports the development of inversion algorithms for marine particle characterization.

This study proposes a deep learning (DL) approach for retrieving high wind speeds during tropical cyclones using a random forest algorithm applied to RADARSAT and Sentinel-1A/B Synthetic Aperture radar (SAR) images. The effectiveness of the proposed DL-based model is then demonstrated through a comprehensive validation of the results. Statistical analysis of the results showed that the proposed method performs well, with a low root-mean-square error, mean bias, and high correlation coefficient when compared to SFMR (Stepped-Frequency Microwave Radiometer) winds. The reconstructed wind speeds and inner-core structures were found to be in good agreement with surface wind measurements from SFMR. These findings could have significant implications for improving our understanding and prediction of tropical cyclone dynamics, as well as for operational forecasting and disaster management.

In recent years, oceanic profiling 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, 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.

Satellite-derived bathymetry (SDB) methods have been traditionally hindered by the need for in situ reference bathymetric points. However, the light detection and ranging (LiDAR) instruments on the new ICESat-2 satellite have revolutionized SDB by providing high-precision reference bathymetric point cloud datasets (RBPCDs) in shallow water. While the density-based spatial clustering of applications with noise (DBSCAN) has been effective in photon cloud processing, it has been challenging to determine key parameters due to the complexity of terrain changes. Furthermore, ICESat-2 is unable to measure deep water depths greater than 50 m, which would be less efficient if it has to process the entire track data. To overcome these challenges, we have developed an adaptive ellipse denoising algorithm with adjustable key parameters and a shallow-water feature photon (SWFP) extraction method. These innovative techniques were applied to the Caribbean Sea and the South China Sea, resulting in impressive datasets consisting of 848 395 and 438 643 RBPCDs, respectively. The mean absolute error (MAE) of RBPCDs was found to be within 0.6 m, and the RBPCDs were consistent with in situ data. By combining RBPCDs with Sentinel-2 data using a neural network (NN)-based SDB method, we have created detailed bathymetry maps over 15 islands in the Caribbean Sea. Our adaptive method has great potential for large-scale nearshore RBPCD construction, and these RBPCDs will undoubtedly enhance SDB implementations in the future.

The traditional way to detect the vertical structure of seawater optical properties and chlorophyll-a is mainly through shipboard discrete observations or Biogeochemical-Argo profiling floats, which requires considerable time to cover a limited area. In this study, the vertical distribution of seawater optical properties and chlorophyll-a concentration across two different optically-contrasted sea areas from the East China Sea (ESC) to the South China Sea (SCS) were obtained for the first time using a shipboard integrated Mie-Raman-fluorescence lidar for large-scale observations, with a total observation distance of over 3700 km. More than 74,000 lidar profiles were obtained from September 5 to September 15, 2020. In general, the lidar-estimated inherent optical properties (IOPs) and chlorophyll-a values decreased from turbid water in the ECS to clear water in the SCS. Subsurface scattering layers were often observed at depths ranging from 10 to 20 m along the SCS coast. Subsequently, the lidar-derived results were compared against in situ measurements. In addition, the diurnal hourly variation in IOPs and chlorophyll-a by lidar at a fixed coastal station was monitored for the first time, which was relatively lower in the early morning and midday yet was higher in the evening, while the relative tide height showed the reverse change trend, which revealed that the tide possibly impacted the diurnal variation in IOPs and chlorophyll-a on the SCS coast. Overall, our results indicate that the lidar remote sensing technique is effective and feasible to monitor large-scale and long-term subsurface phytoplankton structure over different optically-contrasted sea regions, and integration of multiple detection mechanisms will enhance the monitoring capacity.

The data quality of the satellite-retrieved water-leaving reflectance (Rrs) depends on the accuracy of radiometric calibration and the performance of atmospheric correction. A radiometric calibration scheme (RCS) has been developed to ensure the accuracy of Rrs through the gain adjustment factors (GAFs) to adjust the satellite calibrated data. The GAF is obtained from the ratio of the simulated reflectance at the top of atmosphere to the calibrated values. The simulated reflectance is computed by a satellite image simulation model (SISM) based on a dataset of climatological global Rrs images according to the same geometric angles of the image pixels. The dataset, taken as a kind of the pseudo-invariant calibration sites for in situ measurements, is generated from the average of standard satellite-retrieved Rrs during more than two decades (1997–2019). The SISM inputs the aerosol properties retrieved from the satellite level 1B data (L1B) and uses the same algorithms of the data-processing system. The results show that the accuracy of the calibration of the website downloaded Chinese Ocean Color and Temperature Scanner on the Haiyang-1C satellite (COCTS/HY-1C) is beyond the requirement of the operational data-processing system (higher than 10%). The daily GAFs can be used to recalibrate the L1B data and monitor the daily sensor degradations. The influences of GAFs are assessed on different meteorological conditions, indicating that the values decrease with the increase of the aerosol optical depths (AODs) but the average of the GAF image is little affected by the meteorological conditions. The uncertainty of GAFs was tested by the different inputs of Rrs values and the results show that they are actually little affected by errors of the Rrs inputs. Therefore, the RCS, taking the advantage of vicarious calibration, offers a tool to recalibrate the COCTS/HY-1C L1B data for the data reprocessing system.

Oceanic lidar is an effective tool for detecting water’s vertical structure. Cost-effective design of an oceanic lidar system and processing algorithms requires an effective lidar simulator. In this study, an oceanic lidar simulation tool was developed, which is available to the public (https://github.com/soedchen/OLE), and named the Oceanic Lidar Emulator (OLE). OLE is an improved semianalytic Monte Carlo-based lidar simulator, which has the capability of dealing with the physics of light propagating through wind-driven rough air–water interface into the ocean, being scattered by subsurface phytoplankton and reflected from the sea bottom, and returning to the lidar receiver. OLE has three main features: 1) it can deal with stratified water, while most existing lidar models can only be used for homogeneous water; 2) it can be applied to arbitrary scattering phase function (SPF) (e.g., Fournier–Forand or Petzold), while most existing lidar models can only use the Henyey–Greenstein SPF due to the difficulty of solving the inverse equation for the cumulative distribution function; and 3) it takes general consideration of lidar system observation geometry (e.g., receiver field of view, receiving aperture, altitude, incident angle, and so on) and environmental parameters (e.g., wind-driven rough water surface, vertical structure of water optical properties, and sea bottom albedo). We studied lidar extinction caused by multiple scattering and the effects of the SPF, rough sea surface, and stratified water, by comparing simulation results with measurements. These results demonstrated that our model is effective for oceanic lidar simulation.

The radiance received by satellite sensors viewing the ocean is a mixed signal of the atmosphere and ocean. Accurate decomposition of the radiance components is crucial because any inclusion of atmospheric signal in the water-leaving radiance leads to an incorrect estimation of the oceanic parameters. This is especially true over the turbid coastal waters, where the estimation of the radiance components is difficult. A layer removal scheme for atmospheric correction (LRSAC) has been developed to take the atmospheric and oceanic components as the layer structure according to the sunlight passing in the Sun-Earth-satellite system. Compared with the normal coupled atmospheric column, the uncertainty of the layer structure of Rayleigh and aerosols has a relatively small error with a mean relative error (MRE) of 0.063%. As the aerosol layer was put between Rayleigh and ocean, a new Rayleigh lookup table (LUT) was regenerated using 6SV (Second Simulation of a Satellite Signal in the Solar Spectrum, Vector version 3.2) based on the zero reflectance at the ground to produce the pure Rayleigh reflectance without the Rayleigh-ocean interaction. The accuracy of the LRSAC was validated by in situ water-leaving reflectance, obtaining an MRE of 6.3%, a root-mean-square error (RMSE) of 0.0028, and the mean correlation coefficient of 0.86 based on 430 matchup pairs over the East China Sea. Results show that the LRSAC can be used to decompose the reflectance at the top of each layer for the atmospheric correction over turbid coastal waters.

Detecting the vertical profile of optical properties is an important task in the remote sensing of the upper ocean, especially for 3-D reconstruction. Ocean color remote sensing can only provide surface information, while the light detection and ranging (lidar) technique can provide depth-resolved data. Lidar can provide global-scale observations of the upper ocean for days and nights with minimal atmospheric correction errors. Unfortunately, due to the strong multiple scattering effects that occur when light propagates in seawater, the simple lidar equation may cause some deviations between the actual measurements and the simulation of the lidar signals. In this paper, we present a shipborne oceanic lidar, which was developed to detect the optical properties of seawater. For evaluating the performance of the lidar system, a Monte Carlo (MC) model was established to simulate lidar signals based on the simultaneous in situ inherent optical properties of seawater. The lidar measurements and the MC simulation can provide both the lidar signals and the retrieved lidar attenuation coefficient α. The results of the comparison indicate that the lidar-measured signals correspond well with the MC-simulated signals at different experiment stations in the Yellow Sea and at various receiving fields of view (FOVs). We also observed strong correlations between the lidar-measured α and MC-simulated α at different stations (r = 0.95) and at various FOVs (r = 0.96). The results indicate the reliability of the developed lidar system.