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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.

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.

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.

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.

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.

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.

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.

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.

Using Argo measured data combined with satellite remote sensing data and moored buoy data, the upper ocean temperature and salinity response caused by super typhoon Rammasun in 2014 was analyzed and studied. The result shows that super typhoon Rammasun resulted in cooling of sea surface temperature and deepening of mixing layer. Meanwhile, mixing length and vertical velocity induced by typhoon were calculated in this research, which explained the causes of temperature changes in the subsurface layer. Strong mixing and weak upwelling led to warming of the subsurface layer, whereas weak mixing and strong upwelling led to cooling of the subsurface layer. Compared with the change of temperature, the response of salinity was more complex. Precipitation first caused the decrease of surface salinity, and then vertical mixing led to a large increase of surface salinity. However, the effect of precipitation could greatly inhibit this process. After the typhoon departed, the vertical mixing was weakened, and the salinity was greatly reduced because of the heavy precipitation, it was even lower than that before the typhoon.

Nitrate is the main nitrogen form available for phytoplankton life activities in the ocean, and its nitracline depth (Z_N) directly affects the vertical transport of nitrate and the ocean primary productivity, and then further influences the carbon cycle. With the advancement of ocean observation technologies, the profile data of nitrate have been collected in diversified ways, such as ship-based CTD observations and BGC-Argo automatic observations. The vertical sampling resolution of these techniques varies significantly (the vertical sampling resolution of CTD is lower than that of BGC-Argo). In view of different sampling data, it is urgent to conduct systematic and quantitative comparative analysis and study on the computing methods of Z_N. In this study, three different methods: difference method, gradient method and threshold method, are adopted to compute the corresponding Z_N by using the historical ship-based CTD data and BGC-Argo buoy data in the Northwest Pacific Ocean. The results show that in the case of single nitrate profile, based on BGC-Argo data, the difference between observed Z_N and the Z_N calculated by difference method is only 0.2 m, followed by threshold method is 20.0 m and gradient method is 202.8 m at most. Based on CTD data, the difference between observed Z_N and Z_N calculated by difference method is 2.0 m, the threshold method is 49.0 m, and the gradient method is 155.0 m. Compared with the gradient method and threshold method, the difference between the Z_N calculated by the difference method and the observed Z_N is the smallest. According to the results of statistical error analysis, it is found that the Z_N calculated by the three methods based on BGC-Argo data show a good correlation with the observed Z_N. Among them, the error of difference method is the smallest (R²=0.77, RMSE=28.48 m). The R² and RMSE of threshold method are 0.64 and 34.85 m, and the R² and RMSE of gradient method are 0.52 and 53.80 m. For CTD data, due to its low vertical sampling resolution, the Z_N calculated by the three methods is quite different from the observed Z_N. However, compared with the gradient method and threshold method, the error of the difference method is still the smallest (R²=0.81, RMSE =16.13 m). The R² and RMSE of threshold method are 0.47 and 27.65 m, and the R² and RMSE of gradient method are 0.42 and 36.41 m. The applicability of each method is preliminarily explored through comparing and analyzing the characteristics and differences of them so as to provide some scientific reference for the in-depth research on the vertical distribution characteristics and upward transport process of nitrate.

The waters surrounding South Georgia Island are one of the highest primary productivity regions in the Southern Ocean with enormous carbon sequestration potential. However, the strength of the biological pump efficiency in this area is still uncertain due to the lack of continuous upper ocean observation data.In this study, the hydrological and biogeochemical parameters obtained from the Biogeochemical Argo (BGC-Argo) floats deployed in the South Georgia Island vicinity during the period of 2017-2020 were utilized to investigate the impacts of physical processes on biogeochemical processes and to estimate the carbon export flux in the Antarctic summer. Results indicated that both upstream (northeast of the Antarctic Peninsula) and downstream (Georgia Basin) regions of South Georgia Island exhibited strong seasonal characteristics in Chl-a, with the latter area having a 4-month sustained period of phytoplankton bloom, suggesting a stable and continuous supply of iron. Using the temporal variability of the seasonal particulate organic carbon (POC) export, the summer POC export fluxes of the upstream and downstream regions were estimated to be 7.12±3.90 mmol·m^-2·d^-1 and 45.29±5.40 mmol·m^-2·d^-1, respectively, indicating that the difference might be due to enhanced downward export of organic carbon after the deepening of the mixed layer. The study found that the region maintained a high biological pump efficiency, contrary to the previous conclusion that the Georgia Basin had “high productivity low export efficiency”, which might have been caused by the limited “real-time” representation of the entire seasonal characteristics during ship-based surveys. BGC-Argo provides high spatiotemporal resolution of multi-parameter observation data, and this study demonstrates that it can more accurately quantify and evaluate marine biogeochemical processes and carbon sequestration potential.

The diversity and geographical distribution pattern of deep-sea polychaete animals have been a research focus of deep-sea biodiversity science. Data from the Ocean Biogeographic Information System (OBIS) were used to analyze the characters of deep-sea benthic polychaetes diversity and distribution in the western Pacific. The results show that the collection data mainly distributed in the distinctive geographic units (e.g. seamounts and trench) near coastal countries. A total of 318 species from 51 families were recorded in the study area. Polynoidae has the highest species diversity and the largest depth distribution. The number of species decreases with depth, but increases at 2 500-3 000 m and 4 000-4 500 m. The deep-sea polychaetes exhibit high levels of endemism. Many species are endemic to hydrothermal vents. For deep-sea benthic polychaetes in the western Pacific, four biogeographical areas are recognized: Sea of Japan, biogeographic area near the continent represented by Sagami Bay, region characterized by hydrothermal vents (Okinawa Trough, Manus Basin, Fiji Basin) and regions characterized by trenches or plains (Japan Trench, Kuril-Kamchatka Trench, eastern Australia and New Zealand area).

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.

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.

Submarine earthquake is one of the most major factors causing deep-water international submarine cables damage. Understanding the process of submarine cables damage and the mechanism of submarine cables damage caused by turbidity currents after earthquake are of great significance to the security maintenance of international submarine communications. Combined with the lastest research result of global seabed topography and using professional international submarine cables engineering software Makaiplan, the process of plenty of submarine cables damage after Grand Banks Earthquake and Hengchun Earthquake were studied, then the relationship between the pattern of submarine cable damage and the developing process of turbidity currents after earthquake was found, and the mechanism of submarine cables damage caused by turbidity currents after earthquake was summarized. Study result shows that submarine cables break points are located intentively in submarine canyons and trenches. The movement speed of turbidity currents in submarine canyon and submarine trench, which caused submarine cable damage, can reach several ten kilometers to several hundred kilometres per hour. Terrestrial rivers and continental shelf undersea river channels provide materials transportation for the development of turbidity currents. Submarine canyons and trenchs are the pathes of turbidity currents movement then damage plenty of submarine cables. The turbidity currents that developed from upper continental slope in passive continental margin after earthquake can damage submarine cables laid on continental slope, continental rise and abyssal plain. This kind of turbidity currents achieves maximum speed on continental slope, then self-accelerate on abyssal plain. Multiple turbidity currents can develop at different positions of continental slope at the same time in active continental margin, then strike submarine cables which laid on canyons and trenches for multiple times. This kind of turbidity currents achieves maximum speed and self-accelerates in submarine trenches. There are several earthquake-resistance measures: submarine cable routes trying to avoid crossing submarine canyons and trenches which connected to terrestrial rivers or continental shelf channels; using shallow water type submarine cable which has outer armor protection when crossing inevitably; laying submarine cables suspended slightly on the bottom of canyons or trenches with Uraduct protection on them; changing the cross-section shape of submarine cable.

Taking Dazhuzhi Island (Dongtou, Wenzhou) as the research area, UAV equipped with multispectral sensors was used to acquire high-resolution remote sensing images, the optimal spectral band combination was selected to classify the island vegetation, and the vegetation types was divided into arbors, shrubs and herbs by supervised classification. The accuracy of vegetation classification was 99.72%, and the Kappa coefficient was 0.995 4. The spatial distribution of dominant species of arbors and shrubs was obtained by using the deep convolutional neural network (the precision rate was 0.79), and combined with the biomass equations, the spatial distribution of the biomass of dominant species of arbors and shrubs was inversed (arbors’ R²=0.97, shrubs’ R²=0.99). The biomass inversion equations of 3 shrub dominant species (Ficus erecta, Mallotus japonicas, and Eurya emarginata) were constructed by field sampling, and the other dominant species biomass inversion equations were obtained from literature. Based on the biomass and spatial distribution of dominant species, the carbon storage of arbors and shrubbys was 300.36 t and 47.59 t, respectively. Using normalized difference vegetation index (NDVI) to invert the spatial distribution of herb biomass (R²=0.99), combined with the biomass equation of the dominant herb species (Zoysia sinica) constructed from the measured data, the carbon storage of herbs was 21.59 t on Dazhuzhi Island.

Deep-sea REY-rich sediments that are rich in lanthanides and yttrium (REY) extensively distributed in regions such as the Western Pacific, Eastern Pacific, Southeastern Pacific, and the Indian Ocean. This study analyzed the mineralogical and geochemical characteristics of deep-sea REY-rich sediments from two sites in the Clarion-Clipperton Fracture Zone (CCFZ) of the Eastern Pacific. Additionally, geochemical data on elements from 92 deep-sea REY-rich sediment sites across the Pacific were collected. Based on geochemical characteristics in conjunction with mineral composition, the Pacific deep-sea REY-rich sediments are categorized into three types: Al-rich, Fe-rich, and Ba-rich. The Al-rich type, prevalent in the Western Pacific region, primarily consists of zeolite clay, with an average whole-rock Al₂O₃ content reaching up to 14.9%. The Fe-rich type, found near the Eastern Pacific Rise in the Southeastern and Northeastern Pacific, exhibits a high average TFe₂O₃ content of 18.8%. Some samples within this type show a significant positive Eu anomaly, indicating that hydrothermal activity may contribute to the enrichment of REY and associated carrier minerals. The Ba-rich type, mainly located in the CCFZ of the Eastern Pacific, consists predominantly of (siliceous) clay, with an average Ba content of approximately 8 092×10^-6. The elevated Ba levels suggest that the area of sediment formation may have experienced high primary productivity. This environmental condition likely resulted in extensive biogenic apatite deposition, which coupled with strong bottom currents in the CCFZ since the Oligocene, enhanced the accumulation of apatite, thereby promoting the enrichment of rare earth elements.