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The foot of the continental slope is an important topographical feature of the continental margin. Its the basis for coastal states to extend its continental shelf rights and to delimit the outer limit of the continental shelf beyond 200 nautical miles. Its also an important technical parameter that the Commission on the Limits of the Continental Shelf pays special attention to when considering the submissions of coastal states. The formulation of the continental shelf regime in Article 76 of the United Nations Convention on the Law of the Sea originates from the typical passive continental margin. However, due to the diversity and complexity of the global continental margin, especially the transformation and influence of late tectonic activities and sedimentation on the continental margin, the seabed topography is extremely complex and changeable, which makes it very difficult to identify the foot of the continental slope. In addition, in order to obtain the largest extent of the outer continental shelf, each coastal state has interpreted the relevant provisions of the foot of the continental slope in their own favor, making the foot of the continental slope a hot and controversial issue in the delimitation of the outer continental shelf. Based on the provisions of the United Nations Convention on the Law of the Sea and the "Scientific and Technical Guidelines of the Commission on the Limits of the Continental Shelf" on the foot of the continental slope, combined with the geological characteristics of different types of continental margins and the delimitation practice of various coastal states, the determination of the base of the continental slope, the selection of the point of greatest change and the application of the evidence to the contrary are discussed.

Sea surface temperature (SST) is the critical factor for depicting the marine thermal distribution. Daily global SST data sets support the typhoon elaborated monitoring and other marine disasters forecast. SST products retrieved by the visible infrared radiometers and mediumresolution imagers have high spatial resolution, while the SST products retrieved by infrared remote sensing are affected by clouds, fog and haze, and therefore a large areas under the clouds are lack of value. SST products retrieved by the microwave radiometer have low spatial resolution, while the microwave could penetrate the cloud layer to achieve all-weather sea surface observation. The data interpolation empirical orthogonal function method (DINEOF) was used to reconstructed the global SST products, and FY-3 (Fengyun 3) SST data sets were applied in this study, which included the SST data sets from the FY-3B/FY-3C Visible and Infra-Red Radiometer, FY-3D Medium Resolution Spectral Imager and FY-3D Micro-Wave Radiation Imager. Accuracy of the reconstructed data sets was verified using OISST measurements to demonstrate the validity and reliability of the DINEOF method. The results show that DINEOF reconstructed sea surface temperature (DSST) data are validated reliable. Root mean square error of the original data is ranging from 0.59 ℃ to 0.70 ℃, while the reconstructed data is relatively stable, ranging from 0.10 ℃ to 0.34 ℃. Correlation coefficient obvious raises from 0.33-0.48 to 0.78-0.98. Multi-sensors reconstructed SST products is continuous and credible in spatial distribution and monitor the variation of warm pool from spring to winter. Addition of FY-3D microwave SST products has significantly improved the spatial continuous distribution and temporal resolution of reconstructed SST.

Based on the sea surface wind data at 10 m during 1979 to 2018 from European Center for MediumRange Weather Forecasts (ECMWF), the Growing Hierarchical Self-Organizing Map (GHSOM) method were used to analyze the seasonal variation and interannual anomaly variation characteristics of near-surface wind field over the South China Sea (SCS). Four feature patterns are extracted in the first-layer GHSOM from original wind field data, which highly summarize the seasonal variation characteristics, and the second-layer results reveal the monthly variation characteristics. Four anomaly feature patterns also are extracted in the first-layer GHSOM network and they are anticyclonic wind anomaly, cyclonic wind anomaly, southwest wind anomaly and northeast wind anomaly patterns, respectively. Anticyclonic and cyclonic wind anomaly patterns are closely related to ENSO events with time lags by three months and five months comparing with Niño3.4 index. Anticyclonic and cyclonic wind anomalies also show asymmetry, that is, the amplitude of anticyclonic wind anomaly is obviously larger than that of cyclonic wind anomaly. The occurrence frequency of the northeast wind anomaly pattern is greater than that of the southwest wind anomaly pattern. The more SOM patterns in the second layer expose particulars of anomaly wind.

The seafloor is characterised by active sand bodies of different scales, and  a large number of small-scale sand waves (ripples) is often superimposed on the large active sand bodies. The analysis of the small-scale sand waves (ripples) can reveal the genetic mechanism of these active sand bodies. However, their scales are small, and conventional analysis methods often filter them out as high-frequency noise, making it difficult to separate and quantify sand waves on the seafloor at this scale. To address this problem, a method was designed and implemented for decomposing small-scale submarine sand wave topography based on wavelet analysis, and achieved the decomposition and quantitative analysis of small-scale submarine sand wave topography: background topography, small sand waves and sand ripples (wavelength less than 0.6 m) by using high-precision bottommoored in situ observation data from a typical area of the Taiwan Banks. A quantitative method for decomposition of small-scale submarine sandwave topography was proposed, which can be widely applied to the study of the development and evolution of highly active landforms in shallow seas and the sediment dynamics of submarine boundary layer, and also has practical value for assessing the stability of marine engineering.

In order to study the deep resistivity structure of intraplate volcanoes and their formation processes, a marine magnetotelluric survey at Suda Seamount in the western Pacific Ocean was conducted. The mainstream data processing method SSMT2000 was used for data processing. The measured data after rotation and the two groups of rotation invariants obtained from the measured data were tested for magnetotelluric response under the assumption of one-dimensional structure, and one-dimensional inversion was conducted for the YX direction data with the best response. Combined with one-dimensional forward modeling and other geological data, the inversion results were comprehensively interpreted. The inversion results show that the thickness of the crust at Suda Seamount is about 21.5 km. The thick volcanic clastic rocks indicate that the formation of Suda Seamount is mainly eruptive and weakly intrusive.

Rapid land subsidence is a kind of geological disaster, which is related to the sustainable development of society and even threatens the safety of human life and property. InSAR technology can obtain long-term and large-scale surface deformation data, analyze potential land subsidence problems, and then provide reliable means for preventing geological disasters. How to predict land subsidence based on InSAR data has always been the key direction and problem that researchers focus on. Thus, on the basis of previous research on land subsidence prediction, a land subsidence prediction method was proposed, which combines the Auto Regressive Integrated Moving Average (ARIMA) model with the Long Short-Term Memory (LSTM) model in deep learning. The difference between the deformation data and the prediction result of the ARIMA model was obtained and then LSTM was used to train and predict the difference. Taking the InSAR monitoring data of Hangzhou Bay from 2017 to 2019 as an example, the method was verified. The result shows that compared with the traditional single prediction algorithm, the root mean square error of the method is reduced by at least 2.23 mm, and the mean absolute error is reduced by at least 0.98 mm, and the average prediction accuracy is improved by at least 15.19%, which verifies the feasibility of this method and provides some ideas and methods for early warning of ground subsidence.

Wave diffraction will happen when it encounters the island in the wave propagation process. Based on the modified slope equation, the hybrid element method was introduced to solve the equation, and the analytical solutions proposed by KUO et al was used to verify the present numerical solutions. Wave diffraction from a 3D circular island for engineering scale was conducted. The ratio of the wave height to the incident wave height (defined as the relative wave height) along the section of the wave propagation direction and around the island coastline was calculated for different incident wave periods, shoal shape parameters and island sizes. The results indicate that with the decrease in the incident wave period and the size of island, as well as the increase in the shoal shape parameters, the oscillation amplitude of the relative wave height at the head wave side of the circular island, the value of the relative wave height at the back wave side of the circular island, and both the oscillation amplitude and the value of the relative wave height around the island coastline all increase. However, for the different cases, the majority of the maximal relative wave heights happen at the head wave point, and several maximal relative wave heights happen at about 20-25 degrees on the both sides of the head wave point. The minimum relative wave heights are located about 30 degrees on the both sides of the back wave point.

Based on the measured hydrological and water quality data of Xincun lagoon in December 2020, the characteristics of tide in Xincun lagoon were studied, and the effects of tidal elevation changes on nutrients in Xincun lagoon were discussed. The results show that the tide of Xincun lagoon is irregular diurnal. The tidal current at the inlet of the lagoon is a reciprocating current, lasting 15 h for flood tide and 10 h for ebb tide. The concentration of dissolved inorganic nitrogen (DIN), phosphate (PO3-4) and silicate (SiO2-3) vary from 0.91 to 20.87 μmol/L, 0.11 to 5.92 μmol/L and 2.36 to 134.75 μmol/L respectively. The concentrations of DIN, PO3-4 and SiO2-3 at the observation sites in the lagoons changed with the flood and ebb of the tidal process. The flow velocity at the inlet of the lagoon has an important influence on the change of tidal elevation at the observation sites, and the tidal flux basically determines the change of tidal elevation at the observation sites. There is a significant negative correlation between the nutrient concentration at the inlet of the lagoon and at the mariculture area and the tidal elevation, and the tidal current velocity has an important effect on the nutrient concentration at the inlet of the lagoon and the mariculture area. The results will provide scientific basis for pollution control and ecological restoration of Xincun lagoon.

Based on the measured water level data of Xiuying Station from 1980 to 2017, the distributional characteristics and influencing factors of storm surge in Haikou were studied by using the methods of statistical analysis and numerical model. It is found that Haikou is frequently affected by typhoon storm surge, with an annual average of 2.79 times, which usually occurs from July to October, and the probability of tidal disaster caused by storm surge is high. The horizontal distribution of storm surge shows that the storm surge intensity of Dongzhai Port ranks first, followed by Xiuying Port, Haidian Island and South Port. Influencing factors show that the weight of topography is usually greater than the moving path of tropical cyclone storm surge in Haikou; when the moving speed of tropical cyclone is 14-19 km/h, the storm surge in Haikou is usually the largest.

Hyperspectral remote sensing water depth inversion which has many advantages such as convenience, quickness, and economy is a supplement to traditional measurement methods of water depth, and is worth studying. The research area of this study is located in Hengsha, Shanghai, which is a typical shallow water area of tidal flats. The research data was obtained from GF5-AHSI hyperspectral remote sensing and measurement of water depth at the same time. The modeling parameters were extracted through data transformation and correlation analysis. The single-band ratio model, multivariate linear regression model, optimal scale regression model and BP neural network model were used for water depth inversion in this area. By comparing and verifying the accuracy of the four models, it is found that the optimal scale regression model is better than the other three models, with R2 reaching 0.972 and RMSE of 0.47 m, which is suitable for inversion of shallow water depth in Hengsha.

The meiofaunal abundance and biomass in the Changjiang (Yangtze River) Estuary and adjacent waters was evaluated based on samples collected from July 13 to August 30, 2006. The group composition, the horizontal and vertical distribution of abundance and biomass, environmental factor were analyzed. A total of 11 meiofauna groups and Naupius were found from the surveyed area, including Nematodes, Copepods, Polychaetes, Oligochaetes, Ostracoda, Acari, Bivalves, Gastrotricha, Kinorhyncha, Amphipods and Isopods. The results showed that average abundance was 453.22±355.34 ind/10 cm2. Nematode accounted for 81.37% of the total abundance of all meiofauna, followed by Copepod (10.13%) and Polychaetes (2.96%). The average biomass of the meiofauna was 622.65±505.07 μg/10 cm2. Polychaetes accounted for 30.21% of the total biomass of all meiofauna, followed by Nematode (23.69%) and Oligochaetes (19.44%). The horizontal distribution showed that the meiofaunal abundance increased from the estuary to the East China Sea Shelf. The average abundance in the Hangzhou Bay was 240.96±223.47 ind/10 cm2. The average abundance in the Changjiang Estuary was 442.91±304.16 ind/10 cm2. The average abundance in the East China Sea was 865.42±553.88 ind/10 cm2. The analysis of vertical distribution showed that the abundance of meiofauna decreased with the depth in the sediment. The highest abundance of meiofauna was 290.28±250.03 ind/10 cm2 in 0-2 cm layer. The second highest abundance of meiofauna was 132.81±128.74 ind/10 cm2 in 2-5 cm layer. The lowest abundance was 30.14±31.91 ind/10 cm2 in 5-10 cm layer. The Nematodes, Polychaetes, Oligochaetes, Copepods had the same vertical distribution trend. The correlation analysis of environmental factors indicated that dissolved oxygen, depth and salinity were the main factors to influence the meiofaunal abundance in the surveyed area. Result of BIOENV analysis showed that the best combination of environmental factor was dissolved oxygen and salinity.