The diffuse attenuation coefficient for the downwelling irradiance $K_d\left(z,\lambda \right)$ is a critical parameter in terms of the optical properties of the ocean. In the northwestern South China Sea, there are complex physical processes, and the accurate estimation of $K_d\left(z,\lambda \right)$ in the northwestern South China Sea is critical for the study and application of the underwater light field and water constituents. In this study, using Hydrolight 6.0 (HL60) software, $K_d\left(z,\lambda \right)$ was simulated based on the inherent optical properties (IOPs) and chlorophyll a concentration dataset in the northwestern South China Sea. The simulations were in good agreement with the results calculated by the model of Lee (2005), and the spectral characteristics of $K_d\left(z,\lambda \right)$ were consistent with several oceanic types according to Jerlov’s classification. The horizontal and vertical distribution characteristics of $K_d(z,490)$ were studied in the two typical upwelling areas of eastern Hainan Island and eastern Vietnam. $K_d(z,490)$ in eastern Hainan Island exhibited an overall decreasing trend from west to east at the same depth, while the vertical depth of the maximum value of $K_d(z,490)$ in eastern Hainan Island was found to increase from west to east, which was significantly associated with the distribution trend of the temperature and salinity. $K_d(z,490)$ in eastern Vietnam exhibited unique horizontal and vertical distribution characteristics due to upwelling, with a low temperature and high salinity. A satisfactory linear relationship between $K_d(z,490)$ and $K_d\left(z,\lambda \right)$ was found from $420 \mathrm{t}\mathrm{o} 580 \mathrm{n}\mathrm{m}$ with $R^2$ > 0.76, root mean square (RMSE) $\le$ 0.010 ${\mathrm{m}}^{-1}$, and mean absolute percentage error (MAPE) < 9%, and this result indicated that $K_d\left(z,\lambda \right)$ from $420~580 \mathrm{n}\mathrm{m}$ could be estimated with $K_d(z,490)$. The regression accuracy sharply decreased after 580 nm, indicating that $K_d\left(z,\lambda \right)$ estimation based on $K_d(z,490)$ can be more suitably achieved from 420~580 nm and becomes inaccurate after 580 nm. Based on the simulations, an empirical relationship for $Z_{SDD}$ estimation involving $K_{d,10\%}\left(490\right)$ was developed, and $Z_{SDD}$ in the northwestern South China Sea was calculated, with a range of 5–23 m and a suitable agreement with $Z_{SDD}$ obtained via the method of Lee (2018).

The vertical distribution of the diffuse attenuation coefficient K(z, λ) is critical for studies in bio-optics, ocean color remote sensing, underwater photovoltaic power, etc. It is a key apparent optical property (AOP) and is sensitive to the volume scattering function β(ψ, z, λ). Here, using three machine learning algorithms (MLAs) (categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and random forest (RF)), we developed a new approach for estimating the vertical distribution of K _d (z, 650), K _Lu (z, 650), and K _u (z, 650) and applied it to the South China Sea (SCS). In this approach, based on in situ β(ψ, z, 650), the absorption coefficient a(z, 650), the profile depths z, and K _d (z, 650), K _Lu (z, 650), and K _u (z, 650) calculated by Hydrolight 6.0 (HL6.0), three machine learning models (MLMs) without or with boundary conditions for estimating K _d (z, 650), K _Lu (z, 650), and K _u (z, 650) were established, evaluated, compared, and applied. It was found that (1) CatBoost models have superior performance with R ² ≥ 0.92, RMSE≤ 0.021 m⁻¹, and MAPE≤ 4.3% and most significantly agree with HL6.0 simulations; (2) there is a more satisfactory consistency between HL6.0 simulations and MLMs estimations while incorporating the boundary conditions; (3) the estimations of K _d (z, 650), K _Lu (z, 650), and K _u (z, 650) derived from CatBoost models with and without boundary conditions have a good agreement with R ² ≥0.992, RMSE ≤0.007 m⁻¹, and MAPE≤0.8%, respectively; (4) there is an overall decreasing trend with increasing depth and increasing offshore distance of K _d (z, 650), K _Lu (z, 650), and K _u (z, 650) in the SCS. The MLMs for estimating K(z, λ) could provide more accurate information for the study of underwater light field distribution, water quality assessment and the validation of remote sensing data products.

The underwater light field and water quality parameters serve as important indicators for evaluating ecosystem structures and the capacity of the marine carbon cycle. Investigating the underwater light field and water quality is essential for the sustainable management of the marine ecological environment. The coast around the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), an important blue economic region in China, is characterized by a complex hydrodynamic system. Based on in situ optical properties and water quality parameters, the underwater light field along the coast around the GBA was investigated using the downward diffuse attenuation coefficient K _d , euphotic depth Z _eu , and Secchi disk depth Z _SD through radiative transfer numerical simulation software. K _d with or without the bottom irradiance reflectance was studied, and the relationships between the underwater light field and water quality parameters (especially for chlorophyll-a concentration (Chla) and turbidity) were analyzed. The results indicate that 1) Bottom irradiance reflectance has a negligible impact on K _d values in this study area; 2) K _d exhibits significant positive correlations with both Chla and turbidity. A superior linear regression is found between K _d (570) and turbidity (R = 0.894), while a strong exponential regression is observed between K _d (430) and Chla (R = 0.718); 3) The spatial distributions of the underwater light field and water quality parameters are influenced by the Pearl River plume (PRP) and upwelling in eastern Guangdong. Specifically, the eastern coast exhibited lower Chla and turbidity together with higher water transparency than the western coast, while the eastern offshore region showed higher Chla and turbidity along with lower water transparency compared to the western offshore region; 4) Comparative analysis revealed a decrease in Z _SD average from 2020 (11.1 m) to 2023 (6.8 m), potentially attributed to intensified PRP effects and increased regional precipitation during 2023. This study provides essential data support for evaluating the ecological health along the coast around the GBA.

An instrument named as Volume Scattering and Attenuation Meter (VSAM) is presented. The VSAM can simultaneously measure the attenuation coefficient and the volume scattering function (VSF) from 10° to 170° with an interval of 10° at 659 nm. Using ultrapure water and NCRM-traceable polystyrene microsphere beads, the VSAM was calibrated, and the conversion factor χ _bθ for estimating the backscattering coefficient from the backward VSF was obtained based on Mie theory in the laboratory. For χ _bθ , the average relative deviation was no more than 7.77% in the range of 100°-160° between the modeled result based on VSAM and the theoretical result by Boss. Subsequently, the VSAM and ECO-VSF3 were deployed in situ in Zhanjiang Bay. The backscattering coefficient and VSF at the same angles measured by the two instruments were quite consistent. Some remarkable changes in the shape and magnitude of the VSF profile at different stations were found, with land-based pollutants composing an important suspicious source of these changes.

Accurate satellite retrieval of oceanic particulate organic nitrogen (PON) vertical profile is essential for understanding global biogeochemical processes; however, no dedicated retrieval models currently exist. This study developed a novel PON profile retrieval model using the eXtreme Gradient Boosting (XGBoost) algorithm, based on a comprehensive global dataset that includes in situ PON measurements, MODIS-Aqua bio-optical data, and 3D reanalysis physical data. The XGBoost-retrieved PON profiles were compared with those derived from Copernicus particulate backscattering coefficient ($b_{\mathit{bp}}$) profiles and were further used to estimate the euphotic-zone PON stocks through an optimally performing regression model. The results showed that the proposed model significantly outperformed models constructed without physical inputs, achieving $R^2$ of 0.83, RMSE of 1.49 $\mathrm{mg} {\mathrm{m}}^{-3}$ and MAPE of 18.07%. Compared to the $b_{\mathit{bp}}$-based profiles, the XGBoost-retrieved profiles exhibited higher accuracy. The model also provided reliable estimates of euphotic-zone PON stocks, with $R^2$ of 0.76, RMSE of 200.31 mg ${\mathrm{m}}^{-2}$ and MAPE of 15.09%. These findings demonstrate the potential of the proposed retrieval model for investigating oceanic nitrogen dynamics and biogeochemical cycles.