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.

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.

Sea ice growth and consolidation play a significant role in heat and momentum exchange between the atmosphere and the ocean. However, few in situ observations of sea ice kinematics have been reported owing to difficulties of deployment of buoys in the marginal ice zone (MIZ). To investigate the characteristics of sea ice kinematics from MIZ to packed ice zone (PIZ), eight drifting buoys designed by Taiyuan University of Technology were deployed in the open water at the ice edge of the Canadian Basin. Sea ice near the buoy constantly increased as the buoy drifted, and the kinematics of the buoy changed as the buoy was frozen into the ice. This process can be determined using sea ice concentration, sea skin temperature, and drift speed of buoy together. Sea ice concentration data showed that buoys entered the PIZ in mid-October as the ice grew and consolidated around the buoys, with high amplitude, high frequency buoy motions almost ceasing. Our results confirmed that good correlation coefficient in monthly scale between buoy drift and the wind only happened in the ice zone. The correlation coefficient between buoys and wind was below 0.3 while the buoys were in open water. As buoys entered the ice zone, the buoy speed was normally distributed at wind speeds above 6 m/s. The buoy drifted mainly to the right of the wind within 45° at wind speeds above 8 m/s. During further consolidation of the ice in MIZ, the direct forcing on the ice through winds will be lessened. The correlation coefficient value increased to 0.9 in November, and gradually decreased to 0.7 in April.

Given the advances in satellite altimetry and multibeam bathymetry, benthic terrain classification based on digital bathymetric models (DBMs) has been widely used for the mapping of benthic topographies. For instance, cobalt-rich crusts (CRCs) are important mineral resources found on seamounts and guyots in the western Pacific Ocean. Thick, plate-like CRCs are known to form on the summit and slopes of seamounts at the 1 000–3 000 m depth, while the relationship between seamount topography and spatial distribution of CRCs remains unclear. The benthic terrain classification of seamounts can solve this problem, thereby, facilitating the rapid exploration of seamount CRCs. Our study used an EM122 multibeam echosounder to retrieve high-resolution bathymetry data in the CRCs contract license area of China, i.e., the Jiaxie Guyots in 2015 and 2016. Based on the DBM construted by bathymetirc data, broad- and fine-scale bathymetric position indices were utilized for quantitative classification of the terrain units of the Jiaxie Guyots on multiple scales. The classification revealed four first-order terrain units (e.g., flat, crest, slope, and depression) and eleven second-order terrain units (e.g., local crests, depressions on crests, gentle slopes, crests on slopes, and local depressions, etc.). Furthermore, the classification of the terrain and geological analysis indicated that the Weijia Guyot has a large flat summit, with local crests at the southern summit, whereas most of the guyot flanks were covered by gentle slopes. “Radial” mountain ridges have developed on the eastern side, while large-scale gravitational landslides have developed on the western and southern flanks. Additionally, landslide masses can be observed at the bottom of these slopes. The coverage of local crests on the seamount is ~1 000 km², and the local crests on the peak and flanks of the guyots may be the areas where thick and continuous plate-like CRCs are likely to occur.

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.

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.

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.

Ship detection using synthetic aperture radar (SAR) plays an important role in marine applications. The existing methods are capable of quickly obtaining many candidate targets, but numerous non-ship objects may be wrongly detected in complex backgrounds. These non-ship false alarms can be excluded by training discriminators, and the desired accuracy is obtained with enough verified samples. However, the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation, which is difficult and time consuming. To address this issue, a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets. Specifically, various features commonly used in SAR image target discrimination are extracted, and several acknowledged classification models and their classic variants are investigated. Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model. Then, the performance of all the discriminators is tested, and better discriminators are selected for implementing the semisupervised training process. These strategies enhance the diversity and reliability of the discriminators, and their heterogeneous ensemble makes more correct judgments on candidate targets, which facilitates further positive training. Experimental results demonstrate that the proposed method outperforms traditional tri-training.

Eutrophication in coastal area has become more and more serious and mariculture potential is a main cause. Although there are some quantitative research on nutrient loads in national and global perspective, the calculation method problems make the results controversial. In this paper, the farming activities are divided into fed culture types (include cage culture and pond culture) and extractive culture types (e.g. seaweed, filter-feeding shellfish culture). Based on the annual yield of China in 2019 and feed coefficient of fed culture types and carbon (C), nitrogen (N), and phosphorus (P) content of extractive culture types, the annual nutrient loads was estimated. The results showed that to coastal region of China (1) annual nutrient released by fed culture types were about 58 451 t of N, 9 081 t of P, and annual nutrient removed by harvest of extractive culture types were 109 245 t of N, 11 980 t of P and 1.86×10⁶ t of C. Overall, the net amount of nutrient removed annually by mariculture industry were 50 794 t of N and 2 901 t of P. (2) The nutrient released from mariculture industry influenced nutrient stoichiometry. Pond farming and seaweed farming had the potential of increasing the molar concentration ratio of N and P (N:P), while cage farming and bivalve farming decreased the N:P. (3) Due to different mariculture types and layouts in the coastal regions in China, N and P loading were regional different. Among the coastal regions in China, net release of nutrient from mariculture occurred only in Hainan and Guangxi regions, while in the other regions, N and P were completely removed by harvest. We suggest decrease the amount of fed culture types and increase the amount of integrated culture with extractive culture types. This study will help to adjust mariculture structure and layout at the national level to reduce the environmental impact.