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The geomorphology and geological complexity around the global continental margin, and the ambiguity of the rules for the delineation of the outer limits of the continental shelf beyond 200 nautical miles according to the Convention Article 76, led to the delineation of the outer limits of the continental shelf extremely complex. The Commission on the Limits of the Continental Shelf that set up by the Convention is responsible for the consideration of submissions submitted by coastal states and providing the recommendations. Therefore the Commission plays an important role in the delimitation of the outer continental shelf. As of the end of December 2012, the Commission has received 65 submissions, and has completed the consideration of 18 submissions. Some critical issues such as the basic principles, the scientific technique adopted by the Commission during these submissions' consideration were systematically analyzed and summarized. These summary and knowledge can be use to provide the useful reference for our future delimitation of the outer continental shelf and actively respond to other country's claims which infringed on our maritime rights and interests.

Data INterpolating Empirical Orthogonal Functions (DINEOF) were used to reconstruct the chlorophyll a mass concentration from the three-level product of the MODIS-Aqua satellite. The monthly chlorophyll a mass concentration from 2003 to 2009 was obtained. The analysis on the filled data showed that the 7-years averaged chlorophyll a mass concentration in the nearshore is significantly larger than the one in the offshore. The seasonal chlorophyll-a mass concentration supports the conclusion that chlorophyll a in the East China Sea is mainly controlled by runoff of Changjiang River, sea surface temperature and monsoon. Furthermore, Empirical Orthogonal Functions were imposed on the chlorophyll a anomaly, and results showed that the first mode contribution is 37.8%, also the spatial pattern shows large value in the north-east of Changjiang River, and the corresponding temporal period is dominated by half a year or less. Meanwhile, the second mode contribution is 21.4%, the spatial pattern presents high value in the east of Changjiang River, and its temporal coefficient shows the inter-annual variance.

The Kuroshio Current undergoes large variations on various time scales in the vicinity of the Luzon Strait. Based on the output of a high-resolution regional ocean model (ROMS), this study analyzed the variability of the Kuroshio axis near the Luzon Strait and its possible causes. It is shown that the Kuroshio axis exhibits apparent interannual, seasonal and intra-seasonal fluctuations, with the intra-seasonal signal being the strongest. On the interannual and seasonal scales, the Kuroshio axis is primarily influenced by the wind-driven Ekman drift in the surface layer and by the inertia of the Kuroshio itself in the subsurface water column, while on the intra-seasonal scale, it is largely controlled by the interaction of eddies with the Kuroshio.

Red tide is one of the most serious marine hazards. It not only destroys marine fishery production, deteriorates the marine environment, affects coastal tourist industry, but also causes human health problems. The East China Sea is the region of the “high incidence” of the red tide disaster in China: both in the frequency and the area are significantly higher than others. Remote sensing technology has been proved to be one of the most effective means of red tide monitoring. From the spectral curves, we can see that the spectral of the red tide water and non-red tide water has significant difference: there are two peaks and two valleys in red tide water, while there are no these two peaks and two valleys in non-red tide water. Based on the spectral differences, a new algorithm was developed with conditions of R_rs 555/ R_rs 488> 1.5, and R_rs 678- R_rs 667 > 0 to extract the red tide information. The algorithm can determine effectively the locations from satellite data of red tide, and it is good corresponding to the results in the official bulletin. Therefore, this algorithm is suitable to monitor the red tide from satellite data in the East China Sea.

Based on gray level co-occurrence matrix(GLCM), different multi-scale texture images were extracted, and then the texture bands were combined with original bands to get new composited multispectral bands. The maximum likelihood supervised classification method was applied to new different composited images to test the differences of classification accuracy. Taking Ximen Island as a case study, through analyzing the differences of classification accuracy of different composited images, the following preliminary conclusions were got: (1)the total classification accuracy can be improved from 75.41% to 89.41% by using multi-scale texture images; (2)among different texture indices, correlation and homogeneity texture image calculated from the first band and second band of SPOT-5 image, mean texture image calculated from the third band, homogeneity texture image calculated from the fourth band can both improve classification accuracy than other texture indices images calculated from other bands.

The wind data from ASCAT were compared with those of HY-2 satellite, and their accuracy were verified with the data from oil platform in the northern South China Sea, which were collected from Jul. 19, 2011 to Sept. 17, 2012. The RMS (root-mean-square error) between ASCAT wind speed data and the platform-measured wind speed data is about 2.53 m/s. The RMS between ASCAT wind direction data and the platform-measured wind direction data is about 47.87°. The RMS between HY-2 wind speed data and the platform-measured wind speed data is about 3.41 m/s. The RMS between HY-2 wind direction data and the platform-measured wind direction data is about 58.66°(The temporal and spatial differences between the ASCAT and HY-2 date were limited to less that 30 min and 25 km). With the temporal and spatial differences between ASCAT and HY-2 date were limited to less than 150 min and 15 km, the RMS between ASCAT wind speed data and HY-2 wind speed data were also calculated in this paper, which were about 0.72 m/s. The RMS between ASCAT wind direction data and HY-2 wind direction data were about 8.50°. In conclusion, the measured data of ASCAT were a little better than those of HY-2, and it provide a basis for using seawind data of satellite in the northern South China Sea.

Luodousha Island is one of the first batches of the nonresidential islands that can be used to exploit and utilize. Luodousha Island is composed of sands. Therefore, it is unstable and its dynamic changes are highlighted. We used 12 remote sensing images spanning from 1973 to 2011, to analyze the dynamic change of Luodousha Island. The research results show that its position, shape and area changed obviously during the past forty years. The dynamic change of Luodousha Island can be roughly divided into three stages. The first was before 1987 and the island was a whole. The second was from 1987 to 2001 and the island was composed of northeastern and southwestern components. The third was after 2001 and the southwestern component of the island disappeared. These monitoring results may provide scientific basis and supporting data for compiling the protection and utilization plan, formulating the specific scheme of exploitation and utilization, putting forward the environmental protection measures. And then they may serve the ecological protection and the related management of Luodousha Island.

Seafloor sulfide is an important undersea resource after the polymetallic nodules and the cobalt rich manganese crusts. China has made great achievements since starting sulfide investigation in 2005, but the most important problem how to deal with 3D structure of sulfide has been questioning us. On land, the electromagnetic methods have always been important for that. With the first independently developed TEM device, China has gained the first data for seafloor sulfide at South Atlantic Ridge(13.2°S,14.4°W) in June 2011 during cruise DY22 segment 5. The preliminary results correspond well with the actual hydrothermal vent area, and the detection depth reached 50~100 m below seabed, which verifies the reliability of the equipment.

The total organic carbon, chloroform bitumen “A”, and its compositions ( total alkanes, saturated hydrocarbons, aromatic hydrocarbons, resin and asphalt ) in sedimental core WP02-1 from Western Pacific area were systematically analyzed. Then the nC^-₂₃/nC⁺₂₄ and CPI of n-alkanes,Pr/Ph, Pr/nC₁₇, Ph/nC₁₈ of isoprenoids were discussed. The geochemical characteristics of its original, biomarker and the relationship between organic matter and Fe, Mn were studied. The result showed that the alkanes had close relationship with diagenesis in sediments and the organic matter played an important role in mineralization process of Fe and Mn.

In order to study on real-time tracking the seabed reflection in sub-bottom profile data acquisition, the two improved algorithms were discussed: energy ratio based on amplitude information and cross-correlation based on phase information, and the best window of energy ratio and the model trace of cross-correlation were especially studied. Because of the different advantages of the two improved algorithms, we should be agile when making the choice between them. The algorithms mentioned in the article are superior to most of these applied in foreign commercial software, and could be widely used in actual survey.

To investigate the effects of different mass concentration of exogenous salicylic acid on the marine red algae Gracilaria lemaneiformis, the physiological parameters and the chlorophyll fluorescence characteristics of G. lemaneiformis were studied. Results showed that salicylic acid could increase the growth rate and the 10 μg·mL^-1 treatment group was better than other groups. The daily relative growth rate reached to 6.92%/d with a increase of 26.3% compared with the control group. When on the 3rd day, the 10 μg·mL^-1 treatment group of salicylic acid reached the maximum value with proline increased 32.5%, SOD increased 24.3%, and phycoerythrin increased 24.6% respectively. It also promoted the accumulation of Ca²⁺ and K⁺ in G. lemaneiformis. The chlorophyll fluorescence ΦPSⅡbegan to increase on the fifth day after 10 μg·mL^-1 treatment of salicylic acid and increased 9.6% compared with the control group. F_v/F_o reached a maximum on the fifth day, and increased 5.6% more than the control group. In conclusion, exogenous salicylic acid can enhance the growth of G. lemaneiformis and the 10 μg·mL^-1 treatment group of salicylic acid is the best concentration.

This study was carried out in the Daya Bay in October 2011 and April 2012. Based on the data collected from 12 stations, phytoplankton taxonomic composition, abundance, dominance species and their impacting factors were examined. Surfer 8.0 software was used for drawing and illustrating, while SPSS 17.0 software was used for analyzing principal component factors (PCA). At the same time, it used multiple stepwise regression analysis method, and principal component scores as explanatory variables, aiming at studying the effect of environmental factor for phytoplankton abundance and picking out the main factors which influenced the distribution of phytoplankton in the Daya Bay. Furthermore, its distribution characteristics and the correlation with environmental factors were discussed. A total of 51 species (4 phyla, 31 genera) were identified. Among those species, Bacillariophyta is the dominant species, Pyrrophyta is subdominant species, and Xanthophyceae and Chromophyia are even rarely. The dominant species are Heterosigma akashiwo、Skeletonema costatum、Rhizosolenia setigera、Nitzschia pungens、Thalassionema nitzschioides in autumn, and Ceratium furca、Prorocentrum minimum、Ceratium fusus、Ceratium tripos in spring. The abundance of phytoplankton range from 1.56×10⁴ to 8.03×10⁴ cell/dm³ and the average value is 3.95×10⁴ cell/dm³ in autumn and range from 1.21×10⁴ to 4.70×10⁵ cell/dm³ and the average value is 7.84×10⁴ cell/dm³ in spring. Temperature、pH、PO^3-₄-P and TP are the main factors influencing the distribution of phytoplankton in the Daya Bay.