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Mangroves, coastal salt marshes and seagrass beds, as the typical coastal blue carbon ecosystems, have been widely recognized for their remarkable capacity in carbon storage. Vegetation carbon pool and sediment (or soil) carbon pool were considered to be the major carbon pools within the coastal blue ecosystems and their variations determined the overall carbon sequestration of the ecosystems. From a perspective of carbon pool interactions, this study summarized the previous research work based on literature review, including the interactions within various vegetation carbon pools and within various sediment carbon pools, as well as the interactions between vegetation and sediment carbon pools. Interspecific competition, allochthonous carbon input and biogeomorphology were found to be the key to understand the carbon pool interactions. Finally, a perspective on the current state-of-the-art of blue carbon pool study is offered, with challenges and suggestions for future directions.

Artificial intelligence in oceanography has demonstrated a great potential with the explosive growth of ocean observation data and numerical model products. This article first reviews the history of ocean big data development, and then introduces in detail the current status of artificial intelligence in oceanography applications including identifying ocean phenomenon, forecasting ocean variables and phenomenon, estimating dynamic parameters, correcting forecast errors, and solving dynamic equations. Specifically, this article elaborates the research on the intelligent identification of ocean eddies, internal waves and sea ice, the intelligent prediction of sea surface temperatures, El Ni?o-Southern Oscillation, storm surges, waves and currents, the intelligent estimation of ocean turbulence parameterization for numerical models, and the intelligent correction of waves and current forecast errors. In addition, it discusses the recent progress of applying physical mechanism fusion and Fourier neural operator for solving ocean dynamic equations. This article is based on the current status of artificial intelligence in oceanography and aims to provide a comprehensive demonstration of the advantages and potential of applying artificial intelligence methods in the field of oceanography. With the two emerging research hotspots: digital twin oceans and artificial intelligence large models, the future development direction of artificial intelligence provides enlightenment and reference for interested scientists and researchers.

In order to explore the distribution, formation mechanism and motion law of eddy around Kuroshio in East China Sea, firstly, the eddy distribution around the Kuroshio in the East China Sea during the past 27 years was analyzed by using the mesoscale eddy data set of the AVISO(Archiving, Validation and Interpretation of Satellite Oceanographic Data). The results show that there are 650 eddies generated in the meander of the Kuroshio, and 271 eddies in the middle part of the Kuroshio. The diameters of the most these eddies were between 100 to 150 km, and the amplitudes were between 2 to 6 cm. Secondly, the motion path and eddy motion process of the Kuroshio in the East China Sea are also analyzed. The results show these the cyclonic eddies are easy to be generated in the inner side of the Kuroshio cyclonic bend, with a long path. For example, at the cyclone bend of the Kuroshio axis in the northeast of Taiwan, the average length of the path was 87.6 km. Otherwise, when these anticyclonic eddies are generated, these eddies usually are wandered. In the middle part of the Kuroshio, the eddy showed the polar symmetric distribution characteristics of these cyclonic eddies in the west side of the Kuroshio main axis and these anticyclonic eddies in the east side of the Kuroshio main axis. Both types of eddies moved northeastward along the Kuroshio main axis. Finally, combined with reanalysis data sets of ocean current and sea surface height, the eddy motion law and generation mechanism were discussed. It is concluded that these eddies generation at the curve of the Kuroshio are related to the separation of the boundary layer of the Kuroshio fluid. The Kuroshio countercurrent from the south of Yanmei Island to the west of Okinawa Island played a key role in the polar symmetric distribution of these eddies in the middle part of the Kuroshio. These eddies usually experience three stages of growth, maturation and decay in the process of movement.

Costal ocean receives a bunch of carbon materials and nutrients from terrestrial sources, relates a lot of carbon-involving interactions. Meanwhile, it is normal that sedimentary reservoir-cap systems with good trap conditions beneath coastal ocean, these entrapments have potentials to storage CO₂. This review focuses on the coastal ocean as the research object, and introduces the carbon cycle processes in coastal ocean, their factors which could influence CO₂ fluxes in the carbon cycle processes, and the potential carbon storage mechanisms of the coastal marine sedimentary basins. From the perspective of “carbon peaking and carbon neutrality”, the significance of coastal oceans for “Ocean Negative Carbon Emission (ONCE)”, its potential promotion paths, carbon storage potentials in sedimentary basins and the problems faced by coastal oceans are discussed. Overall, the costal ocean is one of the important blue carbon sink areas. In the coastal marine seawater system, improving the reaction efficiency of microbial carbon pump and carbonate carbon pump have positive significance for CO₂ negative emissions; The suitable reservoir-cap systems for CO₂ storage beneath coastal ocean can not only provide extra spaces, but also guarantee the safety for CO₂ storage. In the future, the main research directions should be to inhibit the conversion process of carbon materials to CO₂ in coastal oceans and ensure the safety of CO₂ storage in sedimentary reservoirs, these could provide theoretical basis and technical guarantee for CO₂ negative emissions.

Carbon stock variation observation forms the basis for coastal saltmarsh blue carbon sink accounting. In order to accurately estimate the carbon sequestration rate of coastal saltmarshes over a short-term scale (seasonal to annual), this study carried out field observations and sample collections within a coastal saltmarsh on the south bank of Hangzhou Bay, covering different seasons of 2022. This study was primarily based on high-resolution surface monitoring by Surface Elevation Table (SET) systems. The results revealed a seasonal plant growth pattern between March and September for both the native species Scirpus mariqueter and the exotic species Spartina alterniflora. In terms of belowground biotic carbon stock changes, over the growing season, the carbon stock increase for Scirpus mariqueter reached 11 g C·m^-2 whilst this value was 56 g C·m^-2 for Spartina alterniflora. The SET data indicated a sedimentation rate of 13.02 cm·a^-1 within the Spartina alterniflora saltmarsh, higher than that of the Scirpus mariqueter saltmarsh, 12.30 cm·a^-1. Calculating the sedimentation rate data with sediment bulk density and organic carbon content, the sediment carbon accumulation rate of Scirpus mariqueter saltmarsh was estimated to be 460 g C·m^-2·a^-1, lower than 588 g C·m^-2·a^-1 of the Spartina alterniflora saltmarsh. Combining the biotic carbon stock increase and sediment carbon stock increase, the carbon sequestration rate for the Spartina alterniflora saltmarsh was found to be 644 g C·m^-2·a^-1, higher than the value of Scirpus mariqueter saltmarsh, 471 g C·m^-2·a^-1. Thus, the difference in carbon sequestration abilities of native and exotic species should be considered for future coastal blue carbon management.

Based on the mesoscale atmospheric model WRF and the regional ocean model ROMS, a two-way coupled WRF-ROMS air-sea model was constructed to simulate the super typhoon Mangkhut in 2018. The results showed that the simulation results of the coupled air-sea model were better than those of the only atmospheric or ocean model, and the error of the typhoon track obtained from the coupled model was within 60 km, which was in good agreement with the best track. Compared with the observation results, the simulation results of wind speed and sea level pressure in the coupled model were better than others model. Based on the simulation results of the coupled air-sea model, the spatial and temporal distribution of the wind field, pressure field, sea surface flow field, and storm surge under the super typhoon Mangkhut were further analyzed. The results showed that: (1) In terms of spatial distribution, after the typhoon entered the South China Sea, the radius of the seven-level wind circle was larger behind the right side of the typhoon; the cyclonic flow field showed a significant Ekman effect with the typhoon wind field, and the flow direction was 45° from the wind direction. The wind field, pressure field, wind-generated flow field and water gain distribution all had obvious asymmetry, and the typhoon intensity, flow velocity and water gain were greater on the right side of the typhoon path than on the left side. (2) In terms of time distribution, the distribution of the wind field and the pressure field were similar and synchronized with the typhoon center, while the wind-driven flow field and storm surge were three hours behind the typhoon track.

The variation characteristics of water and sediment in the Pearl River Basin over the past about 70 years were analyzed by using the runoff and sediment discharge data at three major hydrological stations in the Pearl River Basin, Gaoyao Station (Xijiang River), Shijiao Station (Beijiang River) and Boluo Station (Dongjiang River) from 1954 to 2019, and applying the Mann-Kendall nonparametric test and accumulative anomaly curves. The results showed that the change of the runoff in the Pearl River from 1954 to 2019 was not obvious while the sediment discharge was decrease, among which the sediment discharge at Gaoyao and Boluo Stations changed abruptly in 2003 and 1991, decreasing by 71.3% and 48.88%, respectively, compared with before the abrupt change, while the sediment discharge at Shijiao Station did not change abruptly. On this basis, the change of vegetation cover in the basin and its relationship with the change of water and sediment were studied by using the precipitation data and Normalized Difference Vegetation Index (NDVI) in the basin from 2000 to 2019. The NDVI in the Pearl River Basin showed an overall increasing trend from 2000 to 2019, and the improvement area of vegetations cover accounted for 89.27% of the total basin area. Among which NDVI in the Pearl River Basin was poorly correlated with sediment discharge from 2000 to 2008, NDVI in the Xijiang River Basin and Dongjiang River Basin were positively correlated with precipitation in the basin but not with sediment discharge form 2009 to 2019, and only in the Beijiang River Basin, NDVI was insignificantly negatively correlated with sediment discharge. The influence of human activities on sediment discharge in the Pearl River at different periods was discussed using the double cumulative curve method. Since the 1980s, human activities such as deforestation, reservoir construction, and soil and water conservation had a significant impact on sediment discharge. After 2000, the construction of Longtan Reservoir was the main reason for the decline of sediment discharge in the Xijiang River, but the improvement of vegetation coverage had no significant impact on the sediment discharge in the Pearl River Basin.

Mangroves, salt marshes, and seagrass beds are known as the three major coastal blue carbon ecosystems (CBCEs), which play important roles in marine biodiversity maintenance, water purification, nutrient recycling, carbon sequestration and storage. Guangdong Province and Guangxi Zhuang Autonomous Region in China, where the CBCEs are widely distributed, were selected as the research areas, to investigate the spatial relationship between CBCEs and the marine ecological redlines (MERs). The CBCE conservation gap was analyzed, and the recommended priority conservation areas outside the MERs were proposed. The CBCE distribution data obtained from the satellite images in 2019, combined with field survey and UAV remote sensing data collected during 2020 and 2021 showed that the CBCEs in Guangdong totaled 14 481.39 hm² (mangroves 11 928.87 hm², salt marshes 1 258.00 hm², seagrass beds 1 294.52 hm²), whereas the CBCEs in Guangxi totaled 11 751.30 hm² (mangroves 10 171.70 hm², salt marshes 1 450.36 hm², seagrass beds 129.24 hm²). 62.13% of the CBCEs in Guangdong and 59.88% in Guangxi were covered by the MERs. The distribution areas and protection ratios of mangroves and seagrass beds in Guangdong were both larger than those in Guangxi, while the distribution area and protection ratio of salt marshes in Guangxi were larger than that in Guangdong. As to the 3 types of CBCEs in Guangdong, 62.13% of the mangroves, 38.16% of the salt marshes and 85.41% of the seagrass beds were under protection. For the CBCEs in Guangxi, 61.44% of the mangroves, 49.58% of the salt marshes and 52.99% of the seagrass beds were protected. This research suggests the coast from Rongmujiang Bay to Maowei Sea, the areas of Tieshan Bay, Leizhou Bay, Zhelin Bay and other related locations as the recommended priority conservation areas outside the MERs.

Waves are one of the most important phenomena in the ocean. The accurate and quick updated wave forecasting is of crucial significance for ensuring marine activities safety. The development of wave forecast is presented, including the traditional statistical wave forecasting methods, numerical wave prediction models, and the rapidly developing artificial intelligence (AI) wave forecasting methods. Currently, AI wave forecast models have been demonstrated unique advantages in terms of computational efficiency and adaptive forecasting accuracy, and they are gradually being applied in practical wave forecasting operations, transitioning from the research stage. However, they also have limitations, including limited forecasting elements, underestimation of extreme wave conditions, and weak forecasting generalization ability. Based on the characteristics of AI wave prediction, key scientific and technological issues that need to be addressed in current AI wave forecasting are proposed. These include efficient utilization of observational data, incorporation of prior physical knowledge, and enhancement of AI model safety and generalization ability.

Coastal bays are greatly affected by human activities and natural changes, and the influence mechanism of variation in seawater carbon source and sink patterns is extremely complex. Due to the small spatial scale of the bay, it is necessary to use wide-bands high-spatial resolution satellite remote sensing for monitoring the air-sea CO₂ flux. Compared with the traditional kilometer-level ocean color satellite data, the retrieval of the sea surface partial pressure of CO₂ (pCO₂), the key parameter to calculate air-sea CO₂ flux, is extremely challenging in small-scale bays. Taking Xiangshan Bay in Zhejiang Province in autumn as an example, a satellite retrieval algorithm for sea surface pCO₂ was proposed based on the in situ pCO₂ data and Sentinel-2 satellite images in the past five years, using the machine learning method of support vector machine (SVM). The algorithm validation results showed a good performance with R² of 0.92 and RMSE of 23.23 μatm, and the satellite-derived results were consistent with the in situ values. On this basis, the satellite products of pCO₂ in Xiangshan Bay in autumn from 2017 to 2021 (September to November) were produced. The results revealed that the pCO₂ of Xiangshan Bay showed a decreasing trend from the top of the bay to the mouth of the bay, with an average value of 514.56 μatm, of which the average pCO₂ in the inner bay was 551.94 μatm and the average pCO₂ in the outer bay was 477.19 μatm, which implied that Xiangshan Bay was a source of atmospheric CO₂ as a whole. There was no significant trend change of pCO₂ in autumn in the past five years. Combined with the analysis of in situ data of multiple parameters, it was found that the sea surface pCO₂ of autumn in Xiangshan Bay in 2021 was jointly regulated by physical mixing and biological activities. Sea surface temperature (SST) had a good positive correlation with pCO₂, which was mainly reflected by the thermodynamic equilibrium of carbonate system. In addition, the normalized pCO₂(NpCO₂) with average temperature had a good negative correlation with seawater salinity and dissolved oxygen saturation. The relationship between NpCO₂ and salinity resulted from the exchange of sea water inside the bay and offshore coastal water under tidal effect. Long-time series satellite data analysis also confirmed that sea surface pCO₂ had a relatively consistent trend with the average tide height inside and outside the bay, and this trend was stronger in the outside bay than that in the inner bay. In this study, a set of pCO₂ remote sensing retrieval methods in the small-scale bay was constructed, which laid a good foundation for the subsequent long-time series satellite monitoring of sea-air CO₂ fluxes.

Marine gas hydrate deposits are significant temporal reservoirs for hydrocarbons migrating from deep sources. This is crucial to our understanding of ocean carbon cycling. The cold seep, a geological process regarding gas leakage from deep or shallow sources, is usually linked with gas hydrate decomposition. In this thesis, we reviewed the latest applications of in situ monitoring and detecting methods regarding the leakage plumes, migration pathways, and seafloor geomorphologies associated with gas hydrate and cold seep systems, primarily including vessel-and land-based gas plume measurements, surface ocean-lower atmosphere hydrocarbon emission detections, seafloor visualization techniques, and in situ observation networks. The integrated applications of these in situ observation methods provide a nuanced view of the temporal and spatial variability of hydrate and cold seep systems, facilitate understanding of the fate of hydrocarbons, and expand our knowledge of cold-seep biota in a watery desert.

Alkaline phosphatase activity (APA) is an important indicator of phytoplankton phosphorus limitation status in the marine science study. In the “nitrogen-excess” areas such as the Changjiang Estuary (CJE), phosphorus is a major factor controlling the primary productivity in the sea. However, the extent of phosphorus limitation is often difficult to define and little is known about the effect of phosphorus limitation on phytoplankton at different sizes. In this study, the spatial distribution of phytoplankton (Net: ≥20 μm; Nano: 2~20 μm; Pico: 0.8~2 μm) APA, bacterioplankton APA (0.2~0.8 μm) and dissolved APA (<0.2 μm) in the surface layer of the CJE in the summer of 2020 were given, and the correlation between APA and environmental factors were analyzed. The results showed that size-fractionated phytoplankton APA was negatively correlated with dissolved inorganic phosphate (DIP), indicating that DIP concentration was the main factor affecting the distribution of size-fractionated phytoplankton APA. In the spatial distribution, phytoplankton APA was lower in the light-limitation zone near the mouth, and showed an increasing trend from the mouth to the east, contrary to the distribution of DIP. Net and Nano phytoplankton APA (mean values of (40.28±32.35) nmol/(L·h) and (52.38±34.78) nmol/(L·h), respectively) were significantly higher than Pico phytoplankton APA (mean values of (28.43±20.23) nmol/(L·h)) in the CJE, implying that large size phytoplankton were more susceptible to DIP decline. In this study, the DIP concentration for inducing a rapid increase in phytoplankton APA was 0.159 μmol/L, which was close to the empirical threshold for nearshore phosphorus limitation. This study revealed the characteristics of phytoplankton response to phosphorus distribution in the CJE at different grain levels in summer, and also contributed to the understanding of the environmental regulatory mechanisms of primary production processes in the CJE.

The East China Sea Shelf Basin is a Mesozoic and Cenozoic superimposed basin with high oil and gas potential. At present, the discovery of oil and gas in the basin comes from the Cenozoic reservoirs, and various aspects of the Mesozoic residual strata are less known. Furthermore, previous qualitative or semi-quantitative studies are usually spatially limited within specific structural units in the western part of the basin and mainly involves Cretaceous and Jurassic. This study collects and sorts out the latest reflection seismic data and well data penetrated into the Mesozoic in the entire study area. Based on seismic sequences of the Cretaceous, Jurassic and Pre-Jurassic, our results show that the Mesozoic residual strata in the East China Sea Shelf Basin suffered from severe denudation in the later period. The Mesozoic residual strata generally thicken to the east and south, and the range of residual strata expanded eastward over time. Comparing the distribution characteristics of the residual strata between different periods, it is further supported that the East China Sea Shelf Basin was a passive continental margin depression basin in the Triassic, and an active continental margin fore-arc basin in the Early-Middle Jurassic, and a continental margin back-arc extensional basin in the late Jurassic to late Cretaceous. Correspondingly, the subduction of the paleo-Pacific plate began in the Late Triassic-Early-Middle Jurassic, and the retreat of the plate began in the Late Jurassic. The east boundary of the Mesozoic proto of the East China Sea Shelf Basin was located on the east side of the Diaoyu Island uplift.

Mesoscale eddies widely exist in the ocean and affect the sound propagation. Using AVISO altimeter and Argo buoy data from 2000 to 2018, the multi-year average three-dimensional structure of mesoscale eddies in the Kuroshio and Oyashio extension regions in the Northwest Pacific Ocean was constructed by synthesis method, and the structural characteristics of temperature anomalies, salt anomalies and sound velocity were analyzed. The sound propagation in eddies is simulated by using Bellhop ray acoustic model. The results show that : (1) Under the background of the cold eddy, the temperature anomaly is negative, the salinity anomaly is negative in the upper layer and positive in the lower layer, and the sound velocity contour rises. Under the background of warm eddy, the temperature anomaly is positive, the salinity anomaly is positive in the upper layer and negative in the lower layer, and the sound velocity contour is sinking. (2) The cold eddies cause the convergence region to shift towards the sound source direction and the width of convergence zone to decrease; the warm eddies cause the convergence zone to move away from the sound source and increase its width. The convergence area in the Kuroshio extension region is wider than that in the Oyashio extension region, and is further away from the sound source. (3) The cold eddies make the convergence zone turning depth shallower, while the warm eddies make the convergence zone turning depth deeper. In the Kuroshio extension region, the inversion depth is shallower with the increase of longitude,but in the Oyashio extension region, the inversion depth is deeper with the increase of longitude.

Using the data of high resolution satellite sea surface temperature (SST) from January 1, 1990 to December 31, 2020, the spatial characteristics of marine heatwaves (MHWs) in the South China Sea were identified with a deep-first-search algorithm, and the characteristics of marine heatwaves at different spatial scales were further investigated. The results indicated that the small-scale marine heatwave events in the South China Sea (Type I MHWs, area<1.8×10⁴ km²) occurred the most frequently, accounting for 94.20% of the total marine heatwave occurrences. Large-scale marine heatwaves with areas exceeding 1.2×10⁵ km² (Type III MHWs) occurred only 74 times during the 31-year period, with the largest event recorded in 2015. Further analysis revealed significant differences in the spatial distribution of intensity, duration, and frequency of marine heatwaves for different spatial scales. Compared to Type I MHWs, Type II MHWs (1.8×10⁴~1.2×10⁵ km²) exhibited a noticeable increase in the average coverage area with an intensity exceeding 1.5 ℃. Statistical analysis showed that the intensity, duration, and cumulative intensity of South China Sea MHWs increased with the spatial scale of the MHWs. The intensity of Type III MHWs was 1.4 times that of Type I MHWs and 1.2 times that of Type II MHWs. In addition, the response of South China Sea MHWs areas to the El Ni?o-Southern Oscillation (ENSO) was also investigated. The results showed a significant increase in the areas of Type I to III MHWs during El Ni?o periods, with a lag of 6 to 7 months. The duration of Type III MHWs during El Ni?o was longer by 2 days compared to La Ni?a periods. This study explored the fundamental characteristics of South China Sea MHWs areas and further analyzed the commonalities and differences of MHWs at different spatial scales, providing new insights into the characteristics and mechanisms of the formation and dissipation of South China Sea MHWs.

To solve the problem of insufficient information of offshore oil and gas platform, the method of oil and gas platform identification based on multi-source satellite remote sensing was studied. Based on Landsat-8 remote sensing images of the Bohai Sea (2018-2021), 136, 166 and 113 oil and gas platforms in the Bohai Sea were identified by threshold segmentation, K-means unsupervised algorithm and maximum likelihood classification, respectively. Based on Sentinel-1 SAR images (2018-2021), 338 oil and gas platforms were identified by threshold segmentation method. Based on the decision level fusion of the above results, 428 oil and gas platforms in the Bohai Sea were identified. The ZY-3 high-resolution images were used to verify the identification results of the fusion method. The results showed that the accuracy of the identified oil and gas platforms reached 85.2%, and the error rate and miss rate were 10.9% and 3.9%, respectively. The identified oil and gas platform locations are consistent with literature and public data. The research shows that the decision level fusion method can realize the effective identification and extraction of offshore oil and gas platforms, and has the value of popularization and application.

The monitoring of carbon flux dynamics and assessment of carbon sink functions of island forest ecosystems are rarely reported due to their special geographical location and few data sources. In this study, the forest ecosystem of the Nanji Island was used as the research object, the carbon sink potential of island forests and their driving factors were assessed. Based on eddy correlation techniques, the temporal variation characteristics and driving factors of net ecosystem productivity (NEP), gross primary productivity (GPP), and ecosystem respiration (Reco) from 2020 to 2021 were explored. Results showed that the forest ecosystem of Nanji Island was carbon sink. Net CO₂ uptake in 2020 and 2021 were 516 g C·m^-2·a^-1 and 598 g C·m^-2·a^-1, Reco were 1 037 g C·m^-2·a^-1 and 1 646 g C·m^-2·a^-1, and GPP were 1 552 g C·m^-2·a^-1 and 2 244 g C·m^-2·a^-1, respectively. Total solar radiation (Rg), photosynthetically active radiation (PAR), net radiation (Rn) and sensible heat (H) were significantly and positively correlated with NEP and GPP (p≤0.001); air temperature (Tair) and soil temperature (Tsoil) were significantly and positively correlated with Reco(p≤0.001). The photosynthesis time of Nanji Island forest was longer than the carbon sink time on the daily scale. When Tair reached 10.05-27.76 ℃ and PAR reached 110.47-429.44 μmol·m^-2·s^-1, the photosynthesis intensity of island forest was higher than that of ecosystem respiration, which showed CO₂ absorption. The monitoring and assessment of carbon fluxes in the forest ecosystems of Nanji Island will provide an important theoretical support for the establishment of a dynamic monitoring and assessment management system for blue carbon in China.

In satellite remote sensing image recognition, compared with ship recognition in a single marine environment, port ship recognition is more difficult due to the presence of a large number of interference targets such as containers and docks. In order to improve the recognition ability of GF-2 satellite data on port ships, five fusion algorithms, i.e. Intensity Hue Saturation (IHS) Transform, Brovey Transform(BT), ESRI panchromatic sharpening Transform, simple mean Transform and Gram-Schmidt Transform (GS) were used to perform the fusion experiment of panchromatic and multispectral images, and the optimal method applicable to port ship images is selected through qualitative and quantitative evaluation. The results show that GS Transform can increase spatial information while maintaining spectral fidelity, and its mean value, root mean square error, peak signal to noise ratio, and structural similarity are superior to the other four fusion algorithms, with high recognition accuracy for port ships.

The unique geographical features of the island countries in South Pacific, which are surrounded by sea and small in size, make most of the island countries in this region "ecologically fragile areas". Based on this, multi-source satellite data were used to monitor the marine ecological environment of Nauru, Palau, Tuvalu, and the Marshall Islands. It was also focused on whether there have been significant changes in the ecological environment of various countries before and after the Tonga volcanic eruption, to help to understand the impact of the Tonga volcanic eruption. The results show that: (1) In terms of temporal and spatial distribution of climatic states, the sea surface temperature and transparency of the surrounding waters of the South Pacific island countries maintain a relatively high level, while chlorophyll and net primary productivity decrease rapidly with the increase of offshore distance. (2) Warming, acidification and sea level rising are common problems faced by the sea areas of the four island countries. (3) The eruption of the Tonga Volcano has no significant impact on the coastal TSM mass concentration and SST. (4) The phenomenon of abnormally rising surface temperature and changed suspended matter mass concentration of the island in the first half month of the volcanic eruption has implications for disaster warning and forecasting using remote sensing methods.

Based on the high frequency data of sea-air interface buoys, the variation pattern and driving factors of sea-air partial pressure of carbon dioxide (pCO₂) were analyzed and the sea-air CO₂ flux in the coastal waters of Qingdao in spring was estimated. During the observation period, the sea area changed from a carbon sink of atmospheric CO₂ to a carbon source, which was mainly caused by the continuous increase of sea surface pCO₂. By analyzing the controlling factors of pCO₂, it was found that temperature was the main driving factor of pCO₂ growth, and biological processes played a certain inhibiting role. The sea surface pCO₂ showed a diurnal variation. The effects of temperature and biological factors on the diurnal variation of pCO₂ were related to solar radiation, but they had opposite effects. In addition, the analysis showed that different sampling frequencies of buoys affected the estimation of sea-air CO₂ flux and shortening the sampling interval could effectively reduce the deviation of CO₂ flux estimation and improve the accuracy of estimation.