Algal pigment composition is an indicator of phytoplankton community structure that can be estimated from optical observations. Assessing the potential capability to retrieve different types of pigments from phytoplankton absorption is critical for further applications. This study investigated the performance of three models and the utility of hyperspectral in vivo phytoplankton absorption spectra for retrieving pigment composition using a large database (n = 1392). Models based on chlorophyll-a (Chl-a model), Gaussian decomposition (Gaussian model), and partial least squares (PLS) regression (PLS model) were compared. Both the Gaussian model and the PLS model were applied to hyperspectral phytoplankton absorption data. Statistical analysis revealed the advantages and limitations of each model. The Chl-a model performed well for chlorophyll-c (Chl-c), diadinoxanthin, fucoxanthin, photosynthetic carotenoids (PSC), and photoprotective carotenoids (PPC), with a median absolute percent difference for cross-validation (MAPD_CV) < 58%. The Gaussian model yielded good results for predicting Chl-a, Chl-c, PSC, and PPC (MAPD_CV < 43%). The performance of the PLS model was comparable to that of the Chl-a model, and it exhibited improved retrievals of chlorophyll-b, alloxanthin, peridinin, and zeaxanthin. Additional work undertaken with the PLS model revealed the prospects of hyperspectral-resolution data and spectral derivative analyses for retrieving marker pigment concentrations. This study demonstrated the applicability of in situ hyperspectral phytoplankton absorption data for retrieving pigment composition and provided useful insights regarding the development of bio-optical algorithms from hyperspectral and satellite-based ocean-colour observations.

The diurnal variation of the diffuse attenuation coefficient for downwelling irradiance at 490 nm $(K_{\mathrm{d}}\left(490\right))$ has complex characteristics in the coastal regions. However, owing to the scarcity of in situ data, our knowledge on the diurnal variation is inadequate. In this study, an optical-buoy dataset was used to investigate the diurnal variation of $K_{\mathrm{d}}\left(490\right)$ in the coastal East China Sea, and to evaluate the $K_{\mathrm{d}}\left(490\right)$ L2 products of geostationary ocean color imager (GOCI), as well as the performance of six empirical algorithms for $K_{\mathrm{d}}\left(490\right)$ estimation in the Case-2 water. The results of validation show that there was high uncertainty in GOCI L2 $K_{\mathrm{d}}\left(490\right)$, with mean absolute percentage errors (MAPEs) of 69.57% and 68.86% and root mean square errors (RMSEs) of 0.70 and 0.71 m⁻¹ compared to buoy-measured $K_{\mathrm{d}12}\left(490\right)$ and $K_{\mathrm{d}13}\left(490\right)$, respectively. Meanwhile, with the coefficient of determination (R²) of 0.71, as well as the lowest MAPE of 27.31% and RMSE of 0.29 m⁻¹, the new dual ratio algorithm (NDRA) performed the best in estimating $K_{\mathrm{d}}\left(490\right)$ in the target area, among the six algorithms. Further, four main types of $K_{\mathrm{d}}\left(490\right)$ diurnal variation were found from buoy data, showing different variabilities compared to the area closer to the shore. One typical diurnal variation pattern showed that $K_{\mathrm{d}}\left(490\right)$ decreased at flood tide and increased at ebb tide, which was confirmed by GOCI images through the use of NDRA. Hydrometeorological factors influencing the diurnal variations of $K_{\mathrm{d}}\left(490\right)$ were also studied. In addition to verifying the predominant impact of tide, we found that the dominant effect of tide and wind on the water column is intensifying sediment resuspension, and the change of sediment transport produced by them are secondary to it.

The spectral power exponent of the particulate backscattering coefficient (b_bp), η, is a crucial parameter for assessing particle size distribution in the ocean, particularly for high-precision estimation of marine biogeochemical cycles. The development of remote sensing inversion models for η has been constrained by insufficient in situ data or faulty model mechanisms. In this study, biogeochemical Argo (BGC-Argo) data were effectively utilized to address the limitations of traditional observations, and the superior XGBoost method was employed to construct a global ocean η remote sensing inversion model (XGB model). In the 5-fold cross-validation, the Pearson correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and median absolute percentage deviation (MAPD) of the XGB model remained stable at approximately 0.65, 0.3, 0.25, and 16%, respectively, demonstrating high robustness. Via independent sample verification and comparison with major inversion models from the literature, the XGB model exhibited a high R value of 0.66 and the lowest RMSE, MAPD, and MAE, with values of 0.33, 16.81%, and 0.25, respectively, indicating its superior inversion capability. High η values were primarily found in oligotrophic regions and the iron-limited Southern Ocean, whereas low values were observed in coastal waters, exhibiting seasonal variation. The distribution of η in other ocean areas remained relatively stable, with nonsignificant seasonal changes. This study demonstrates the significant contributions of BGC-Argo observations in enhancing remote sensing estimation of global ocean η values, thereby providing valuable support for biogeochemical cycling research.

The Jiangsu Coastal Thermal Front (JCF), a persistent feature in Chinese marginal seas, plays a significant role in modulating phytoplankton dynamics and carbon cycling. However, the multi-scale spatiotemporal variability of the persistent JCF and the underlying mechanisms driving its ecological effects remain limited. Using satellite observations and reanalysis data, this study systematically investigates the JCF’s distribution and its regulatory impact on phytoplankton chlorophyll-a (Chla) and particulate organic carbon (POC). Results show the persistent JCF is most active in summer and winter, primarily in Haizhou Bay and the Jiangsu Shoal. In summer, stratification-induced nutrient limitation within the Haizhou Bay thermal front decreases Chla and POC (by ~−20% and ~−40%, respectively), whereas nutrient-replete non-frontal waters support higher biomass. In the Jiangsu Shoal, the thermal front blocks the southward transport of POC, helping to maintain stable POC levels in the nearshore non-frontal region; meanwhile, the shift from southward to northward transport leaves the offshore non-frontal area without sufficient replenishment, resulting in a ~35% decrease in POC. In winter, the Haizhou Bay thermal frontal barrier effect restricts suspended particulate matter, alleviating light limitation inside the front and enhancing Chla (up to 15%) while reducing POC due to diminished resuspension. We elucidate that the JCF shapes ecological patterns through two primary pathways: by directly acting as a barrier to material transport and by interacting with ancillary processes like upwelling. These findings advance the mechanistic understanding of frontal impacts on coastal ecosystems and provide a mechanistic basis for understanding synergistic coastal carbon sinks.