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.

Acanthaster planci, one of the predators of reef-building corals, has attracted much attention for its catastrophic damage to coral reef ecosystems. However, the spatial and temporal distribution characteristics of A. planci are still unclear in the coral reef ecosystem of the South China Sea. In this study, using environmental DNA and real-time quantitative PCR techniques, we analyzed the concentration variation of the mitochondrial cytochrome-c-oxidase subunits I (COTS-mtCOI) fragment of A. planci in the surface seawater of the Xisha Islands in September 2020, April 2021 and January 2022, and the correlations between the concentration variation with environmental factors such as seawater temperature, salinity, pH, chlorophyll content, nutrients content and other environmental factors. The results showed that COTS-mtCOI fragment concentration in the Xisha Islands varied from 0 copies/m³ to 4.13×10⁷ copies/m³ during 2020−2022, and there were always higher concentrations in the Yongle Atoll. For Huaguang Reef, Jinqing Islands, Lingyang Reef, Quanfu Island and Zhaoshu Island, the average concentration of COTS-mtCOI fragment in September 2020 was significantly (p<0.05) higher than those in April 2021 and January 2022. In addition, COTS-mtCOI fragment concentration was significantly (p<0.05) positively correlated with surface seawater temperature. These results suggest that the population of A. planci is widely distributed in the seawater of Xisha Islands, and higher density of A. planci could appear in Yongle Atoll. Moreover, ocean warming may accelerate the outbreak of A. planci. This study is helpful to understand the population distribution characteristics of A. planci in the coral reef ecosystems of the South China Sea, and can provide a theoretical basis for the early warning and forecast of the A. planci outbreak.

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.

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.

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.

Yellowfin tuna (Thunnus albacares) is one of the most important fishes with great global economic and ecological value, and its conservation and management have received much concerns. The stock status of yellowfin tuna in the Indian Ocean based on the age-structured assessment program model is evaluated in this study, focusing on the uncertainties of its life history characteristics on the stock assessment results. The results show that the resources of yellowfin tuna in the Indian Ocean remained relatively stable from 1960 to 1985 and then declined gradually, while the fishing mortality coefficient F increased rapidly after 2010. This stock in 2020 may be overfished, since the estimated F₂₀₂₀ was greater than F_MSY (F that could attain maximum sustainable yield MSY), while spawning stock biomass, SSB₂₀₂₀ was less than SSB_MSY. Sensitivity analysis was also conducted to evaluate the uncertainties of stock assessment. Two important life history characteristics, natural mortality M and steepness of spawning-stock relationship h, were analyzed for their influence on the estimates of F, SSB and biological reference points. When h was set to 0.7, 0.8, and 0.9, SSB_MSY and SSB₀ (the unfished SSB) reduced by about 255 300 t and 340 400 t; and F₂₀₂₀/F_MSY gradually decreased (from 2.88 to 2.21 and 1.73). When the M was set to M₁ (0.963, 0.663, 0.548, 0.493, 0.463, 0.446) and M₂ (1.068, 0.735, 0.608, 0.547, 0.514, 0.495) respectively, the larger M₂ leads to lower SSB and F₂₀₂₀/F_MSY. In summary, the conservation and management of Indian Ocean yellowfin tuna should be tightened in the future to achieve long-term sustainable development of this fishery. The life history characteristics of yellowfin tuna should be fully understood, especially M and h estimation should be improved, to provide more accurate information for stock assessment and fisheries management for Indian Ocean yellowfin tuna.

Satellite derived bathymetric using multispectral imagery is an effective means to obtain shallow water depth information. However, its validity is limited to optical shallow water areas, but presents a “pseudo-shallow sea” distortion phenomenon in deep water areas. Therefore, accurately identifying the valid region of satellite derived bathymetry (SDB) data is crucial for its wide application. Based on high-spatial resolution remote sensing image, a data-driven method for evaluating the validity of SDB based on analysis of the differences in the statistical distribution of radiance in deep/shallow water regions is proposed in this paper. This method uses the local standard deviation of the radiance information of satellite images as a feature, optimizes the statistical characteristics of the optical deep water area based on the K-S test method, and uses the hypothesis test method to identify the SDB corresponding to the deep water invalid area. The experimental results in Ganquan Island region show that the method can effectively identify the invalid SDB associated with the optical deep water area by dividing the boundary between optical shallow and deep water area. After removing the invalid data, the mean absolute error (MAE) of SDB in the optical shallow region is 1.01, and the root mean square error (RMSE) is 1.52. The experimental results show that the proposed method can accurately identify the optical shallow region of SDB result, which benefits the interpretation and application of SDB results.

Using the tropical cyclone (TC) best track data from the Shanghai Typhoon Research Institute of the China Meteorological Administration (CMA-STI) and the monthly mean reanalysis data of NCEP/NCAR, the interannual variability of the basin-scale large-scale environmental steering flow and the tropical cyclone activity in the western North Pacific (WNP) during peak season from July to September from 1979−2016 are investigated. The results show that: (1) There are two typical modes of summer large-scale environmental steering flow in the WNP at the inter-annual scale. The first typical mode is a dipole circulation with a meridional distribution, which is closely related to the eastern ENSO and the sea-air coupling mode in the WNP region. (2) The TC activity (generation location, tracks, intensity and duration) differs significantly between the two typical interannual mode anomaly years of the large-scale environment steering flow, but the differences have distinctly different characteristics for the two typical inter-annual modes. (3) The spatial distribution of TC generation location shows significant differences from north to south between the years of the first typical interannual mode anomalies of large-scale environment steering flow; the TC tracks, especially the northwestward and westward prevailing tracks, also have significant differences, and their average duration and intensity also show their corresponding significant differences. In the second major interannual mode anomaly years, the TC generation locations show significant east-west distribution especially in the southeast quadrant, and the differences in TC tracks are mainly in the northwestward and offshore steering prevailing tracks, and their mean durations and intensities also show significant differences.

Paleoclimate and paleoenvironment reconstruction based on shelf mud deposits remain controversial due to the complexity of coastal dynamic conditions. In this paper, three sediment cores distributed along the sediment transport path in the North Yellow Sea were collected, and the sediment grain size composition, end member, winter storms and Yellow Sea Warm Current strength variation were also analyzed. Based on above analysis, the response mechanism and intensity of different sediment grain size end member to winter storms and Yellow Sea Warm Current were discussed, and sedimentary effect in different regions caused by the above changes was also revealed. The results show that sediment composition in the north branch of Huanghe River distal mud is dominated by the natural condition and human activity over the past 100 years, respectively. Due to the impact of human activities on the sediment flux and composition of the Huanghe River into the sea began to enhance since 1980, and the natural evolution information was destroyed. In addition, through comparing sediment end member of different cores along the sediment transport pathway, the discrepancy in response mechanism and intensity of different end member to winter storms and the Yellow Sea Warm Current were observed before 1980, which reflected significant regional sedimentation effect. Further analysis demonstrated that, the coarse-grained end member is dominated by the winter storm, reflecting intensity of cross-front transport from the north Shandong Peninsula; and the fine-grained end member is controlled by the Yellow Sea Warm Current, representing the long distance transport from the offshore mud patch of the western North Yellow Sea to the eastern coast of the Liaodong Peninsula. Above conclusions indicated that, the sediment grain size is a robust tool for paleoclimate and paleoenvironment reconstruction; however, the utility of sensitive grain size should be based on sediment source and the characteristics of the sediment hydrodynamic environment analysis, and selection of the sensitive grain size and its implication should be careful.

The short wave radiation budget on the sea surface is an important physical process of energy exchange at the sea-air interface. In this study, the sea surface short wave radiation flux budget of ERA5 reanalysis data is evaluated by using the observed data of summer scientific research voyages in the northern South China Sea in 2019. The results show that the downward short wave radiation of ERA5 is smaller than the observed data, and the deviation is the largest at 11:00 and 15:00 (Beijing time), up to −100 W/m². At the same time, the sea surface albedo of ERA5 is generally lower than observed. The bias in ERA5 is small under a high solar altitude angle, being about −0.03, but can reach −0.15 under a low solar altitude. The bias in the downward shortwave radiation and sea surface albedo jointly caused an underestimation of 25.4 W/m² in the daytime average sea surface net shortwave radiation flux in ERA5. In particular, the albedo underestimation offsets about 50% of the contribution of downward shortwave radiation bias. The results show that ERA5 has different manifestations of sea surface radiation budget deviation under different atmospheric transmittance conditions. We also found that the underestimation of sea surface albedo in ERA5 is caused by its parameterization schemes and put an optimization based on our observation.