In this study, a moored array optimization tool (MAOT) was developed and applied to the South China Sea (SCS) with a focus on three-dimensional temperature and salinity observations. Application of the MAOT involves two steps: (1) deriving a set of optimal arrays that are independent of each other for different variables at different depths based on an empirical orthogonal function method, and (2) consolidating these arrays using a K-center clustering algorithm. Compared with the assumed initial array consisting of 17 mooring sites located on a 3°×3° horizontal grid, the consolidated array improved the observing ability for three-dimensional temperature and salinity in the SCS with optimization efficiencies of 19.03% and 21.38%, respectively. Experiments with an increased number of moored sites showed that the most cost-effective option is a total of 20 moorings, improving the observing ability with optimization efficiencies up to 26.54% for temperature and 27.25% for salinity. The design of an objective array relies on the ocean phenomenon of interest and its spatial and temporal scales. In this study, we focus on basin-scale variations in temperature and salinity in the SCS, and thus our consolidated array may not well resolve mesoscale processes. The MAOT can be extended to include other variables and multi-scale variability and can be applied to other regions.

Complex perturbations in the profile and the sparsity of samples often limit the validity of rapid environmental assessment (REA) in the South China Sea (SCS). In this paper, the remote sensing data were used to estimate sound speed profile (SSP) with the self-organizing map (SOM) method in the SCS. First, the consistency of the empirical orthogonal functions was examined by using k-means clustering. The clustering results indicated that SSPs in the SCS have a similar perturbation nature, which means the inverted grid could be expanded to the entire SCS to deal with the problem of sparsity of the samples without statistical improbability. Second, a machine learning method was proposed that took advantage of the topological structure of SOM to significantly improve their accuracy. Validation revealed promising results, with a mean reconstruction error of 1.26 m/s, which is 1.16 m/s smaller than the traditional single empirical orthogonal function regression (sEOF-r) method. By violating the constraints of linear inversion, the topological structure of the SOM method showed a smaller error and better robustness in the SSP estimation. The improvements to enhance the accuracy and robustness of REA in the SCS were offered. These results suggested a potential utilization of REA in the SCS based on satellite data and provided a new approach for SSP estimation derived from sea surface data.

Oceanic noise is the background interference in sonar performance prediction and evaluation at high sea states. Statistics of underwater ambient noise during Typhoons Soulik and Nida were analyzed on the basis of experimental measurements conducted in a deep area of the Philippine Sea and the South China Sea. Generated linear regression, frequency correlation matrix (FCM), Burr distribution and Gumbel distribution were described for the analysis of correlation with environmental parameters including wind speed (WS), significant wave height (SWH), and the inter-frequency relationship and probability density function of noise levels (NLs). When the typhoons were quite close to the receivers, the increment of NLs exceeded 10 dB. Whilst ambient noise was completely dominated by wind agitation, NLs were proportional to the cubic and quintic functions of WS and SWH, respectively. The fitted results between NLs and oceanic parameters were different for “before typhoon” and “after typhoon”. The fitted slopes of linear regression showed a linear relationship with the logarithm of frequency. The average observed typhoon-generated NLs were 5 dB lower than the Wenz curve at the same wind force due to the insufficiently developed sea state or the delay between NLs and WS. The cross-correlation coefficient of FCM, which can be utilized in the identification of noise sources in different bands, exceeded 0.8 at frequencies higher than 250 Hz. Furthermore, standard deviation increased with frequency. The kurtosis was equal to 3 at >400 Hz approximately. The characteristics of NLs showed good agreement with the results of FCM.

Frontal upwelling is an important phenomenon in summer in the Yellow Sea (YS) and plays an essential role in the distribution of nutrients and biological species. In this paper, a three-dimensional hydrodynamic model is applied to investigate the characteristics and influencing factors of frontal upwelling in the YS. The results show that the strength and distribution of frontal upwelling are largely dependent on the topography and bottom temperature fronts. The frontal upwelling in the YS is stronger and narrower near the eastern coast than near the western coast due to the steeper shelf slope. Moreover, external forcings, such as the meridional wind speed and air temperature in summer and the air temperature in the preceding winter and spring, have certain influences on the strength of frontal upwelling. An increase in air temperature in the previous winter and spring weakens the frontal upwelling in summer; in contrast, an increase in air temperature in summer strengthens the frontal upwelling. When the southerly wind in summer increases, the upwelling intensifies in the western YS and weakens in the eastern YS. The air temperature influences the strength of upwelling by changing the baroclinicity in the frontal region. Furthermore, the meridional wind speed in summer affects frontal upwelling via Ekman pumping.

Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents. Multispectral images have the advantages of high spatial resolution, short revisit period, and wide imaging width, which is suitable for large-scale oil spill monitoring. However, in wide remote sensing images, the number of oil spill samples is generally far less than that of seawater samples. Moreover, the sea surface state tends to be heterogeneous over a large area, which makes the identification of oil spills more difficult because of various sea conditions and sunglint. To address this problem, we used the F-Score as a measure of the distance between forecast value and true value, proposed the Class-Balanced F loss function (CBF loss function) that comprehensively considers the precision and recall, and rebalances the loss according to the actual sample numbers of various classes. Using the CBF loss function, we constructed convolution neural networks (CBF-CNN) for oil spill detection. Based on the image acquired by the Coastal Zone Imager (CZI) of the Haiyang-1C (HY-1C) satellite in the Andaman Sea (study area 1), we carried out parameter adjustment experiments. In contrast to experiments of different loss functions, the F1-Score of the detection result of oil emulsions is 0.87, which is 0.03–0.07 higher than cross-entropy, hinge, and focal loss functions, and the F1-Score of the detection result of oil slicks is 0.94, which is 0.01–0.09 higher than those three loss functions. In comparison with the experiment of different methods, the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks, supports vector machine and random forests models, and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods. To verify the applicability of the CBF-CNN model in different observation scenes, we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments, which include two studies areas (study area 2 and study area 3). The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88, which is 0.16–0.24 higher than that of other methods, and the F1-Score of the detection result of oil slicks is 0.96–0.97, which is 0.06–0.23 higher than that of other methods. Based on all the above experiments, we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills, and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images.

Reasonably understanding of the long-term wave characteristics is very crucial for the ocean engineering. A feedforward neural network is operated for interpolating ERA5 wave reanalysis in this study, which embodies a detailed record from 1950 onwards. The spatiotemporal variability of wave parameters in the Bohai Sea, especially the significant wave height (SWH), is presented in terms of combined wave, wind wave and swell by employing the 71 years (1950–2020) of interpolated ERA5 reanalysis. Annual mean SWH decreases at −0.12 cm/a estimated by Theil-Sen estimator and 95th percentile SWH reflecting serve sea states decreases at −0.20 cm/a. Inter-seasonal analysis shows SWH of wind wave has steeper decreasing trend with higher slopes than that of swell, especially in summer and winter, showing the major decrease may attribute to the weakening of monsoon. The inner Bohai Sea reveals a general decreasing trend while the intersection connecting with the Yellow Sea has the lower significance derived by Mann-Kendall test. Meanwhile, 95th percentile SWH decreases at a higher rate while with a lower significance in comparison with the mean state. The frequencies of mean wave directions in sub-sector are statistically calculated to find the seasonal prevailing directions. Generally, the dominant directions in summer and winter are south and north. A similar variation concerning to SWH, the trend of the mean wave period is provided, which also shows a decrease for decades.

Submesoscale processes in marginal seas usually have complex generating mechanisms, highly dependent on the local background flow and forcing. This numerical study investigates the spatial and seasonal differences of submesoscale activities in the upper ocean of the South China Sea (SCS) and the different dynamical regimes for sub-regions. The spatial and seasonal variations of vertical vorticity, horizontal convergence, lateral buoyancy gradient, and strain rate are analyzed to compare the submesoscale phenomenon within four sub-regions, the northern region near the Luzon Strait (R1), the middle ocean basin (R2), the western SCS (R3), and the southern SCS (R4). The results suggest that the SCS submesoscale processes are highly heterogeneous in space, with different seasonalities in each sub-region. The submesoscale activities in the northern sub-regions (R1, R2) are active in winter but weak in summer, while there appears an almost seasonal anti-phase in the western region (R3) compared to R1 and R2. Interestingly, no clear seasonality of submesoscale features is shown in the southern region (R4). Further analysis of Ertel potential vorticity reveals different generating mechanisms of submesoscale processes in different sub-regions. Correlation analyses also show the vertical extent of vertical velocity and the role of monsoon in generating submesoscale activities in the upper ocean of sub-regions. All these results suggest that the sub-regions have different regimes for submesoscale processes, e.g., Kuroshio intrusion (R1), monsoon modulation (R2), frontal effects (R3), topography wakes (R4).

The mesopelagic communities are important for food web and carbon pump in ocean, but the large-scale studies of them are still limited until now because of the difficulties on sampling and analyzing of mesopelagic organisms. Mesopelagic organisms, especially micronekton, can form acoustic deep scattering layers (DSLs) and DSLs are widely observed. To explore the spatial patterns of DSLs and their possible influencing factors, the DSLs during daytime (10:00–14:00) were investigated in the subtropical northwestern Pacific Ocean (13°–23.5°N, 153°–163°E) using a shipboard acoustic Doppler current profiler at 38 kHz. The study area was divided into three parts using k-means cluster analysis: the northern part (NP, 22°–24°N), the central part (CP, 17°–22°N), and the southern part (SP, 12°–17°N). The characteristics of DSLs varied widely with latitudinal gradient. Deepest core DSLs (523.5 m±17.4 m), largest nautical area scattering coefficient (NASC) (130.8 m²/n mile²±41.0 m²/n mile²), and most concentrated DSLs (mesopelagic organisms gathering level, 6.7%±0.7%) were observed in NP. The proportion of migration was also stronger in NP (39.7%) than those in other parts (18.6% in CP and 21.5% in SP) for mesopelagic organisms. The latitudinal variation of DSLs was probably caused by changes in oxygen concentration and light intensity of mesopelagic zones. A positive relationship between NASC and primary productivity was identified. A four-months lag was seemed to exist. This study provides the first basin-scale baselines information of mesopelagic communities in the northwest Pacific with acoustic approach. Further researches are suggested to gain understandings of seasonal and annual variations of DSLs in the region.

The macroalgal blooms of floating brown algae Sargassum horneri are increasing in the Yellow Sea and East China Sea during the past few years. However, the annual pattern of Sargassum bloom is not well characterized. To study the developing pattern and explore the impacts from hydro-meteorologic environment, high resolution satellite imageries were used to monitor the distribution, coverage and drifting of the pelagic Sargassum rafts in the Yellow Sea and East China Sea from September 2019 to August 2020. Sargassum blooms were detected from October 2019 to June 2020 and presented two successive drifting paths that both initiated from around 37°N. The first path spanned smaller spatial scale and shorter period, starting with a bloom of 3 km² distribution area near the eastern tip of Shandong Peninsula in late October 2019 and drifted southwards, hit the Pyropia aquaculture area in early January 2020, then vanished in the northwest of East China Sea (ca. 32°N) around end of January. The second path began with a large distribution area of 23 000 km² east of 123°E in late January 2020, firstly moved southwards in the central Yellow Sea and northern East China Sea (north of 29°N) till late April, then turned northwards with monsoon wind and vanished from late June to August. The mean sea surface temperature of 8°C to 20°C in the Sargassum bloom areas corresponded to in situ observed temperature range for vegetative growth and floating of S. horneri. There was no observed floating Sargassum blooms during July through September in the Yellow Sea and East China Sea. The results indicate that floating S. horneri is unable to complete life cycle in the Yellow Sea and East China Sea, and provide insights to the future management of Sargassum blooms. Further studies are needed to validate the pattern and source of annual Sargassum bloom in the Yellow Sea and East China Sea.

The concentration and composition of nutrients, such as N, P, and Si, respond to biogeochemical processes and in turn, impact the phytoplanktons’ community structure and primary production. In this study, historical data was systematically analyzed to identify long-term variations in nutrient trends, red tide frequency, phytoplankton community abundance, and dominant species succession in the southern Yellow Sea (SYS). Results showed that N/P concentration ratios dramatically increased as a function of increasing dissolved inorganic nitrogen concentrations, and Si/N concentration ratios were generally larger than 1, indicating that N limitation morphed to P limitation and potentially to Si limitation, which impacted the phytoplankton community. Furthermore, inter-annual trends over the past 50 years show that phytoplankton community abundance has been higher in spring and summer, relative to autumn and winter. Moreover, with respect to red tide frequency, diatom abundance gradually decreased, while that of dinoflagellates gradually increased. Dominant species succession showed that the phytoplankton community exhibited an evident tendency to transform from diatoms to dinoflagellates. These research results clearly depict the presence of an important correlation between the phytoplankton community and nutrient structure in the SYS.