According to the data of four voyages of zooplankton survey in the southwest of Bohai Sea from August 2020 to May 2021, the community structure and seasonal variation of small medusae were analyzed, and the effects of environmental factors on the abundance of small medusae were discussed. There were seasonal variations in species composition and abundance distribution of small medusae in the southwest of Bohai Sea, a total of 13 species of small medusae were found throughout the year, including 11 species of hydromedusae and 2 species of ctenophores. The number of medusae species in spring, summer, autumn and winter were 4, 9, 7 and 2 species respectively. The mean abundance values were 30.74 ind./m³, 30.78 ind./m³, 12.08 ind./m³ and 0.57 ind./m³ respectively. The dominant species were Sugiura chengshanense, Rathkea octopunctata, Eirene ceylonensis, Clytia hemisphaerica, Pleurobrachia globosa. The average seasonal replacement rate of dominant species was 91.67%, showing obvious seasonal succession. Water temperature and salinity were main environmental factor affecting the seasonal variation of total abundance of small medusae in the southwest of Bohai Sea. The increase of water temperature and nutrients in spring promote the growth and reproduction of small medusae. In summer, copepods provided rich bait for small medusae to promote its growth, the community was mainly affected by salinity in autumn. According to the risk species of small medusae in the southwest of Bohai Sea, it was speculated that the peak of risk species in summer and autumn was a risk to the planned nuclear power.

In order to improve the success rate and accuracy of species identification of fish eggs and larvae, fish eggs and larvae samples collected from the Zhujiang River Estuary in spring were identified by DNA barcoding technology based on the mitochondrial COⅠand 12S rRNA genes. A total of 391 samples were amplified and 60 species in 7 orders, 25 families, 42 genera were successfully identified (2 species were not identified). Among them, Perciformes had the most species and quantity accounting for 51.6% and 47.91% respectively, followed by Clupeiformes with 25% and 34.56% respectively. There were 10 dominant species, among which Coilia mystus had the highest dominance of 0.071, Collichthys lucidus had the lowest dominance of 0.014. The amplification results of COⅠand 12S rRNA gene fragments showed that the success rate of 12S rRNA gene amplification in eggs and larvae (95.60%) was significantly higher than that of COⅠgene (43.22%). Genetic distance and ABGD analysis showed that the intraspecies genetic distance of COⅠ gene was 0−0.005 (average was 0.003), and the interspecific genetic distance was 0.061−0.376 (average was 0.253), and there was an obvious “barcode gap” between them. The intraspecific genetic distance of 12S rRNA gene was 0−0.011 (average was 0.007), and the interspecific genetic distance was 0.007−0.487(average was 0.283). which do not form a “barcode gap” between the interspecific and intraspecies genetic distances. The Bayesian phylogenetic tree based on COⅠ and 12S rRNA genes showed that all species could be clustered into independent branches and can be effectively distinguished. The above results show that both COⅠ gene and 12S rRNA gene can be used for the identification of most fish eggs and larvae, but the success rate of COⅠ gene amplification is low, and it is difficult to distinguish some closely related species of 12S rRNA gene. The combined use of the two genes can improve the success rate and accuracy of species identification of fish eggs and larvae.

Ocean is an important carbon sink in nature. The sea-air carbon dioxide flux is usually estimated by the difference of partial pressure of carbon dioxide (pCO₂) between the atmosphere and the sea surface. Due to the imbalance of observation data on temporal and spatial distribution and datasets used for prediction, there is still large room for improvement in spatial resolution for present reconstruction of pCO₂ on sea surface. In order to fit the temporal and spatial variability under high spatial resolution better, based on the sea surface fugacity of carbon dioxide (fCO₂) observations of the Surface Ocean CO₂ Atlas (SOCAT) and other multi-source data including remote sensing data, the nonlinear relationship between sea surface pCO₂ and physical, biological, optical factors was established by a XGBoost model and a weight model was built based on spatiotemporal frequency of samples. A 0.041 7°×0.041 7° monthly sea surface pCO₂ dataset in Atlantic from 2000 to 2018 was finally constructed with correlation coefficient of 0.966, mean squared error of 8.087 μatm and mean error of 4.012 μatm on prediction dataset. The reconstruction is highly consistent to other similar reconstruction results on temporal and spatial trend and also gains advantage in spatial resolution.

The sea surface temperature (SST) products, obtained from the Chinese Ocean Color and Temperature Scanner (COCTS) on the two haiyang satellites (HY1C and HY1D), play an important role in oceanic and atmospheric researches. It is important to know whether they are consistent with products from other satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua satellites. The data quality of SST global images from COCTS on HY1C/1D is evaluated by the average bias, absolute difference, root mean square error, and correlation coefficient based on in-situ SST measurements and the MODIS products. The results show that the spatial patterns of the daily and monthly global SST of HY1C/1D are similar to those of Terra/Aqua. The average bias, absolute difference, root mean square error and correlation coefficient of the global daily SST/HY1C products at daytime, based on the SST values of Terra on June 2020, are 0.04℃, 0.60℃, 0.78℃ and 0.98, respectively, and that of the nighttime products are −0.16℃, 0.78℃, 0.95℃ and 0.86, respectively. Similarly, the values of the daytime SST products of HY1D comparing with the SST of Aqua on June 2020 are 0.02℃, 0.59℃, 0.79℃ and 0.98, and that of the nighttime products are −0.09℃, 0.61℃, 0.82℃ and 0.96, respectively. The evaluation on other seasons indicates that the SST products from COCTS are very stable. However, the data quality control scheme and inhomogeneity correction still need to be developed to improve the performance of SST products of HY1C/1D. As a whole, the SST products of HY1C/1D can be used in some applications, similar to the Terra/Aqua products.

Bacteria play an important role in the aquatic ecosystem. In our work, bacterial community structure and assembly mechanisms were studied in Sansha Bay in Fujian Province based on DNA and RNA high-throughput sequencing. Our results revealed that: (1) A total of 1476 operational taxonomic units (OTUs) were detected, and γ-proteobacteria, α-proteobacteria, cyanobacteriia and bacteroidia were the most diverse bacterial groups. (2) γ-proteobacteria, α-proteobacteria, cyanobacteriia and bacteroidia were also the most abundant groups in Sansha Bay both in the DNA and RNA high-throughput sequencing results. The metabolic activities of these four dominant groups were different, and their metabolic activities were mainly regulated by salinity, total nitrogen, nitrite nitrogen and inorganic phosphate concentrations. (3) The bacterial community structure in Sansha Bay was different in spatial scale, and the closer the geographical location was, the more similar the bacterial community structure was. Neutral process was the main assembly mechanism affecting the construction of bacterial community in Sansha Bay. Our results were useful for the understanding the bacterial community structure and assembly mechanism in Sansha Bay, Fujian.

Mangroves forests, as a coastal zone ecosystem dominated by mangrove plants in the tropics and subtropics, are one of the important coastal wetland types. In this paper, multi-source and multi-phase satellite data were used to form a data atlas of shoreline, reclamation, aquaculture area, mangrove distribution in the Guangdong-Hong Kong-Macao Greater Bay Area from 1969 to 2020, and the time series analysis of the evolution of mangroves in the Greater Bay Area was obtained by using the combine mangrove recognition index (CMRI). The results show that the existing mangrove forests data set can be obtained by interpreting the multi-source remote sensing data, and the CMRI time series data can establish the history of the existing mangrove forest change, and then effectively estimate the mangrove forest age. The temporal and spatial distribution of mangroves in the Guangdong-Hong Kong-Macao Greater Bay Area has undergone obvious changes, with the existing mangroves being about 3 316 hm², and the existing forest age in various regions in the Greater Bay Area is quite different, and the overall average forest age is 20 a. In the past 50 years, the shoreline as a whole has moved towards the sea, and the changes in shoreline, reclamation, and breeding areas have significantly affected the area, spatial distribution, and age of mangroves. Artificial cultivation has been the main reason for the restoration of mangroves in the past 20 years.

Mangroves are important for maintaining biodiversity as well as ecological balance. Therefore, it is necessary to extract mangrove vegetation information efficiently and accurately and to monitor it in real time. A deep learning method for pixel-level accurate extraction of mangroves from high-resolution remote sensing images is presented in this paper. For the problem of low accuracy of mangrove remote sensing classification, CU-Net model for mangrove identification is constructed by introducing CLoss loss function by strengthening image center information and weakening edge information, and adding Dropout and Batch Normalization layers. And a new prediction model is constructed by sliding overlap splicing method, which effectively solves the problem of insufficient edge information and splicing traces in the prediction results. The recognition results of the proposed method are compared with the prediction results of U-Net, SegNet and DenseNet models as well as the traditional SVM and RF methods. The results show that the proposed model has stronger generalization ability and better recognition effect compared with other deep learning models. In the two test areas, the average OA and MIoU reach 94.43% and 88.12%, respectively. The average F1-score in mangrove and ordinary trees reach 95.96% and 90.49%, respectively. The accuracy is significantly higher than that of traditional SVM and RF methods, as well as several other neural networks. The effectiveness of the model in the field of mangrove recognition is verified, which can provide a new idea for the field of high resolution remote sensing mangrove recognition.

The majority of global fish stocks lack adequate data for their stock statuses to be assessed using conventional stock assessment methods. Data-limited methods, such as CMSY, have been increasingly recommended as new solutions for stock assessment and fishery management. However, CMSY is highly dependent on data quality, and the reliability of the method is yet to be verified under circumstances of limited length of time series data and variable observational errors. In this study, we investigated effects of lengths of catch time series, stages of fishery development, and levels of observational errors in catches on stock assessment of three economically-important species in the Yellow Sea using CMSY method. The results show that chub mackerel (Scomber japonicus), hairtail (Trichiurus lepturus), and silver pomfret (Pampus argenteus), all have been overfished (B/B_MSY<1 and F/F_MSY>1), with their yields higher than estimated MSY since 2000, and although their fishing intensities have been reduced over the most recent decade, their biomasses remain at low levels (B/B_MSY<1). The retrospective analysis show small differences in the results of stock assessment for the three species, indicating that the assessments are robust enough with long time series data. As to effects of lengths of catch time series, the assessments are more stable using time series data covering a period of both rise and fall in catches. The effect of observational errors in catches is also tested, showing that when the error is >20%, the model tend to overestimate MSY and B_MSY, but the assessment remains robust enough. This study suggests that cautions should be undertaken in the application of CMSY by using longer time series of catch data and, in the presence of high uncertainty in the assessment, more conservative measures should be taken in fishery management.

Separation of wind-wave and swell is the basis for studying the respective characteristics of wind-wave and swell. However, due to the lack of wave spectrum data, it is difficult to popularize and apply separation methods based on wave spectrums. An effective solution is to use wave observations that are easy to obtain, namely basic wave elements to separate wind-wave and swell. Existing methods cannot use basic wave elements to comprehensively calculate the proportions and characteristic parameters of wind-wave and swell. For this reason, this paper introduces machine learning into the separation of wind-wave and swell. Based on the multi-layer perceptron model, a method using wave elements and wind elements to accurately estimate wind-wave and swell parameters is proposed. This method requires each station to provide at least 466 training samples of wave data and 766 or more training samples are recommended. The method is suitable for 3 stations in the Taiwan Strait with its accuracy significantly better than traditional methods based on wave spectrums. The proposed method can provide alternative calculation schemes of wind-wave and swell for stations lacking wave spectrums in this sea area. It helps expand the source of measured data of wind-wave and swell, therefore strengthening the research on the characteristics and early warning and forecasting of wind-wave and swell.

In order to investigate the melting process at the ice-water lateral interface and to quantify the dominant factors affecting the lateral melting rate of ice layer, an ice melting experiment was carried out in a low-temperature water tank. Simultaneous measurements of the ice bottom and surface processes and the lateral melting process of the ice layer were carried out, while the laboratory air temperature, ice temperature at different depths inside the ice samples and water temperature at different depths in open water were recorded, the relationships between different elements and their influence patterns on the lateral melting rate of ice were investigated using correlation analysis methods. The results show that the lateral melting rate at different depths inside the ice samples was slow and uniform in the early stage of melting, with an average melting rate of 0.05 mm/h. The lateral melting rate at different depths in the middle and late stages of melting increased significantly and was no longer uniform, with an average melting rate of 0.15 mm/h. The correlation coefficient of the average lateral melting rate and air temperature (r=0.82) was better than that between the average water temperature (r=0.74) and the water-ice temperature difference (r=0.48). The quantitative relationships of lateral melting rate with temperature (air temperature, water temperature) and depth were established to accurately describe the non-uniformity of the lateral melting process of ice layer. It also verifies the feasibility of conducting non-uniform lateral melting test techniques, and lays the foundation for sea ice tests that more closely resemble real Arctic conditions considering wind speed and light source conditions.