Medium and high spatial resolution wide-band optical satellites have become the main data source for red tide monitoring, but unlike the ocean color satellite sensors, the medium and high spatial resolution satellite sensors are mainly oriented to terrestrial applications with a small number of bands and a large band width, and the resulting impact on red tide detection has yet to be studied. Therefore, this paper explores the effects of band settings, spectral response functions, signal-to-noise ratio and spatial resolution on red tide detection based on the actual hyperspectral data of different dominant species of red tide, spatio-temporally synchronized GF-1 WFV2 and GF-1 WFV3 sensor images, Sentinel-2A MSI sensor images and GF-6 WFV sensor images, and analyzes the advantages of red-edge band on red tide detection. Our results show that: the band settings have a great influence on the red tide detection, especially the central wavelength and band width of the red band and the red edge band; the red tide detection accuracy is greatly influenced by the spectral response function and less influenced by the signal-to-noise ratio under the same band settings; the spatial resolution has a greater influence on the red tide detection, and the improvement of spatial resolution helps to improve the accuracy of red tide detection. The experiments of red-edge band red tide detection show that red-edge band red tide detection has obvious advantages over red-light band red tide detection, and the F1-Score is improved by 11% on average. The results of this paper provide a theoretical basis for the data selection of red tide detection from medium and high spatial resolution satellites on the one hand, and a reference for the design of medium and high spatial resolution satellite sensors on the other hand.

Based on the field data of floc size, suspended sediment concentration and hydrodynamic data, the flocculation characteristics of tidal flat in the south of the Huanghe River Estuary are studied. The results show that the floc size of Huanghe River Estuary tidal flat range is 25.42–264.44 μm, and the average diameter is 95.20 μm. The effect of water turbulence on the flocculation of tidal flat at the Huanghe River Estuary is different, and the upper limit of turbulence on flocculation promotion is about G_l=3.76 s^–1. While the turbulence intensity of water is lower than G_l, turbulence promotes sediment flocculation, and the particle size of floc increases with the strengthen of turbulence intensity, whereas the turbulence mainly inhibits flocculation, the particle size of floc decreases with the attenuate of turbulence intensity. The suspended sediment concentration inhibits the flocculation, particle size of floc corresponding to high sediment content under the same turbulent conditions is smaller. There is a negative correlation between the effective density and the particle size of the floc, and the settling speed of the floc is mainly affected by the particle size. This study complements the understanding of sediment flocculation characteristics in tidal flat of weak tidal estuary.

The Yap Trench is an important part of the trench-arc-basin system in the western Pacific Ocean. In the northern part of the Yap Trench, the Yap Trench and the Mariana Trench are typically vertically intersected. The geomorphology of the sea area was studied in detail. The results show that the water depth, morphology and profile of the trenches change obviously near the junction of the two trenches, and have segmented. The slopes on both sides have uplift, depression and fault geomorphology, which are closely related to the special subduction position at the junction of the trenches. In addition, according to the geomorphic characteristics and plate spreading rate, the spreading center of the Parece Vera Basin should be located near 137°35'34''E before 20 Ma, and the Yap Trench is likely to be transformed from the spreading center exposed by the Parece Vera Basin.

Increasing heat stress due to global warming is the main threat to coral reef regions over the South China Sea islands. Coral reefs bleaching events are most often predicted by heat stress, which will benefit the protection and management coral reefs. Degree heating week (DHW) is used to measure the intensity and duration of heat stress experienced on coral reefs, represents the accumulation of positive sea surface temperature (SST) anomaly at that location over the past 12 week periods. This study utilizes the National Oceanic and Atmospheric Administration-Coral Reef Watch (NOAA-CRW) SST dataset to investigate spatio-temporal in the heat stress of the coral reef regions of the South China Sea islands between 1985 to 2019 and its relevance to El Niño. K-means cluster analysis was performed on the 35-year maximum degree heating week values per pixel, and the coral reefs of the South China Sea islands were divided into 6 regions: Nansha−1, Nansha−2, Nansha−3, Dongsha, Xisha and Zhongsha coral reef region. The main results are as following: (1) The maximum DHW of the coral reef regions of the South China Sea islands is 0−12.9°C-week, and it decreases from high to low in latitude. (2) The linear fitting method was used to analyze the annual maximum DHW from 1985 to 2019. The results showed that the thermal pressure intensity in the coral reef area of the South China Sea islands showed an upward trend, ranging from 0.013°C to 0.174°C per week. The maximum DHW in the coral reef area of the South China Sea islands appeared in 1998, 2010, 2014. (3) The maximum annual DHW might have caused 93.9% of coral reefs to have more than one bleaching risk event, and 19.6% of coral reefs to have at least one risk of death. (4) The cross-wavelet analysis of monthly mean DHW in the coral reef regions of the South China Sea islands and Oceanic Niño index shows that there are time-frequency characteristics and time-lag correlation of multi-period 8−32 months resonance period, which confirms that the thermal pressure of coral reefs in the South China Sea islands increases significantly with the occurrence of El Niño events. The time lag correlation analysis shows that Oceanic Niño index is positively correlated with the thermal pressure in the coral reef regions of the South China Sea islands, and the latter lags behind the former by 7−9 months.

This study compiled the data of ^239+240Pu specific activity, ²⁴⁰Pu/²³⁹Pu atom ratio and ^239+240Pu flux or inventory in the East China Sea and adjacent waters. Based on the ^239+240Pu concentration in atmospheric fallout, ^239+240Pu in seawater, ^239+240Pu in organisms, ^239+240Pu in sediment trap and ^239+240Pu in sediment, the geochemical behavior of ^239+240Pu were explained in the East China Sea and adjacent waters. The results showed that global fallout and Pacific proving grounds close-in fallout were the two major sources of ^239+240Pu. Under the influence of water masses such as the Changjing River diluted water, Zhejiang-Fujian Coastal Current, Taiwan Warm Current, Kuroshio Current and upwelling current, mixing effect and removal effect, the concentration of ^239+240Pu in coastal waters of the East China Sea showed a trend of removal over time, the burial depth of ^239+240Pu in the near shore sediments was deeper than that in the far sea area. In the northeast of Taiwan Island of China, the ^239+240Pu specific activity and ^239+240Pu inventory in Okinawa Trough increased significantly under the influence of Kuroshio current intrusion and upwelling current. At the same time, this study found that the relationship between ^239+240Pu specific activity and ²⁴⁰Pu/²³⁹Pu atom ratio in surface sediments of the East China Sea, and confirmed the existence of a tributary of the Kuroshio bottom in northeastern Taiwan, and indicated the location where the Taiwan Warm Current and a tributary of the Kuroshio bottom may intersect.

The purpose of this paper is to clarify the genesis of natural gas of Y gas field, and establish accumulation model to guide the next exploration deployment in X Sag, East China Sea Basin. Based on the analyses of natural gas composition, carbon isotope of alkane gas, light hydrocarbon and biomarker compound of condensate oil, this paper systematically studies the genetic types and sources of oil and gas, establishes the reservoir accumulation model of Y large and medium-sized gas field, and puts forward the exploration direction of large and medium-sized gas field. The analyses of carbon isotope, light hydrocarbon and burial history show that the natural gas in Y gas field is highly mature coal type gas generated by the source rocks of middle Eocene Pinghu formation in the sag during the Longjing movement period (13 Ma BP). The characteristics of pristane/phytane and regular sterane of condensate oil reflect that the source rocks of middle Eocene Pinghu formation in the central sag are developed in tidal flat and lagoon sedimentary environment with weak oxidation weak reduction, and there are a certain number of lower aquatic organisms in the hydrocarbon generating parent material. Y gas field has a spatiotemporal coupling reservoir accumulation model of “Pinghu formation source rock, Huagang formation large channel sand reservoir and Mid-Miocene compressional tectonism” in the central sag. It is clear that the compressional anticline belt in the central sag is the main exploration direction of large and medium-sized gas fields in X Sag.

There are few researches on offshore gusts at home and abroad, and most of them focus on gust prediction and application research. There is no systematic discussion on the acquisition technology of wind gust data. Based on the backscattering coefficient observed by HY-2B satellite radar altimeter and the brightness temperature information observed by correction microwave radiometer, a method for retrieving gust wind speed is proposed in this paper. The gust wind speed obtained from the joint inversion of the two remote sensing sensors is verified with the National Data Buoy Center (NDBC) buoy data from 2019 to 2021. The results show that the gust wind speed root mean square error (RMSE) is 0.98 m/s and the correlation coefficient is 0.82. The RMSE of the gust wind speed obtained based on the method using a similar satellite Jason-3 is 0.96 m/s and the correlation coefficient is 0.88. Based on the observation of sea surface wind speed with HY-2B satellite radar altimeter and the synchronous observation information of correction microwave radiometer by satellite platform, the observation of sea surface wind gust is realized jointly. The comparison results of data show that the method in this paper has high observation accuracy. At the same time, this method is also applicable to domestic and foreign satellites with the same observation system. This provides a simple and reliable means of ocean remote sensing technology for the current situation of insufficient observation capacity of offshore wind gust.

The assimilation fusion or interpolation fusion of the sea surface wind field based on multi-source data is currently restricted by computing power. This paper proposes to train the XGBoost-based machine learning ERA-5 data correction fusion model in the overlapping area of the multi-source satellite data and the ERA-5 reanalysis data, and then use the model to quickly correct (machine learning inference) ERA-5 data, of which the ERA-5 whole area correction fusion it only takes about 2 seconds. Due to the rapidity of machine learning inference, the entire sea surface fusion wind field can be constructed at a lower computational cost. This paper expands on typical wind field variables such as 10 m wind speed, 10 m wind direction, U10 component and V10 component, taking into account the difference in sea and land distribution, using land masks to eliminate land areas, and constructing D_S_A_XGBoost, D_S_O_XGBoost, U_V_A_XGBoost, U_V_O_XGBoost corrections model, and finally generate sea surface fusion wind field. By comparing the ERA-5 reanalysis data before and after the correction with the satellite data, the above four models all reduce the gap between the ERA-5 reanalysis data and the satellite data. Especially in terms of wind speed, both root mean square error (RMSE) and mean absolute error (MAE) are effectively reduced. In terms of wind direction, RMSE_d and MAE_d also show a decreasing trend. Using Tropical Atmosphere Ocean Array (TAO) buoy data to evaluate the four XGBoost models, it is found that the U_V_O_XGBoost model has the best correction results for ERA-5 data, and its correlation reaches 0.893, an increase of about 0.011, and the results show that the fusion speed is greatly improved under the condition of ensuring the accuracy of wind field.

Global navigation satellite system-reflectometry (GNSS-R) technology is an emerging technology for monitoring sea level changes. Based on the principle of the signal to noise ratio (SNR) analysis method in GNSS-R technology, this paper established a new sea level height estimation model to improve the accuracy by analyzing the process of separating the trend term and extracting the oscillation frequency. Aiming at the problem of poor signal separation in the traditional model, this paper proposed to use the variational mode decomposition (VMD) algorithm to replace the traditional least squares fitting (LSF) to separate the trend term components. On this basis, this paper combined Lomb-Scargle Periodogram (LSP) spectral analysis method and Kaiser window function (referred to as WinLSP) to reduce the inversion error caused by spectral leakage. The results of sea level inversion experiments carried out at GTGU Station in Onsala, Sweden and SC02 Station in Alaska, USA show that the estimation model established in this paper has higher inversion accuracy than traditional model. The root mean square error (RMSE), correlation coefficient and number of inversion points of the inversion results of GTGU Station based on the VMD+WinLSP estimation model are 4.70 cm, 0.98 and 5 647, respectively. The inversion accuracy and GNSS data utilization are increased by about 29.7% and 15.0%, respectively; The RMSE, correlation coefficient, and inversion points of SC02 Station are14.34 cm, 0.99 and 1 785, respectively, and the inversion accuracy and GNSS data utilization are increased by about 12.3 % and 9.4%.

Retrieving shallow water depth based on multispectral satellite imagery is highly cost-effective. However, the extensive application of satellite-derived bathymetry has been restricted by its low prediction accuracy. To improve about the accuracy of the retrieved bathymetry, spatial autocorrelation features within the in situ depth measurements and the multi-spectral image are focused in this research. To this end, we develop a machine learning method combining with spatial autocorrelation features and statistical intercorrelation features of learned samples. The experimental results of Xisha Beidao show that compared with the traditional machine learning, the accuracy of the new method is improved by 18% when the number of in situ depths is small. On the contrary, when the number of in situ depths is large, an improvement of 27% in root mean square error is achieved. This demonstrates that incorporating the spatial autocorrelation features of data sources into the machine learning can significantly improve the prediction accuracy, and then provide effective data support for shallow ocean research.