Conference Agenda

Synthetic aperture radar (SAR) is broadly known for its high-resolution imaging of the ocean surface under all weather conditions during day and night. The Doppler centroid anomaly (DCA) derived from SAR imagettes has been evidenced to well capture the line-of-sight component of ocean current velocity. Several studies have reported the analytical basis of DCA method and the monitoring of major current systems. Its applicability under tropical cyclone (TC) events is not yet examined. In this study, we focus on demonstrating the spatial features of DCA obtained over TC Maria (2017) and Cimaron (2018) as well as it relation to the surface winds. We found that the Doppler velocity provides a promising spatial representation of the surface flow under tropical cyclone wind forcing. The Doppler velocity exhibits an asymmetric feature similar to the radial wind speed with larger velocity-to-winds ratio in the front than in the rear quadrant. The combined Doppler velocity resulted from a tropical cyclone and the Kuroshio Current is distinct, particularly over the regions of encountering flow. The results shall shed light on the SAR observational capability of TC-induced surface velocity and extends our understanding of how the winds and current are coupled under TC.

Due to signal degradation or saturation within tropical cyclones, accurate estimation of inner-core high winds using satellite remote sensing is still challenging. To address this, we propose a deep learning-based approach that leverages generative adversarial networks (GANs) to reconstruct the inner-core high winds from satellite remote sensing data. Our deep learning-based model integrates dilated convolution and attention mechanisms to improve this underestimation issue of synthetic-aperture radar (SAR) data in tropical cyclones. We also tackle the scarcity of SAR data by developing a GAN model that uses Hurricane Weather Research and Forecasting (HWRF) data as a proxy to simulate missing SAR data via transfer learning. To improve the transfer learning process, we explore different pre-trained models and expand the HWRF dataset used for training the deep learning-based models. Additionally, we aim to investigate other machine learning algorithms to enhance the accuracy of scatterometer wind products (e.g. Chinese Haiyang-2 and CFOSAT). We also employ machine learning approach to fuse multi-source data for synergistic monitoring of ocean dynamic environment, specifically in the context of tropical cyclones. In summary, our study demonstrates the potential of deep learning technology for tropical cyclone reconstruction and monitoring using satellite remote sensing data，which can contribute to improving the accuracy of wind products in the context of tropical cyclones.

The CFOSAT (Chinese-French Oceanic SATellite), carrying the first Ku-band scatteromenter (SCAT) with rotating fan beam, was successfully launched in October 2018. The preliminary quality assessment of CFOSAT SCAT wind data is carried out through the comparison for the period from Jan 2019 to Jun 2021 operationally released products with in situ measurements. The reference winds include in situ measurements from offshore (> 50 km) meteorological buoys of the National Data Buoy Center (NDBC) and serval research vessels. All in situ winds were converted to the 10 m equivalent neutral winds using the coupled ocean atmosphere response experiment (COARE) bulk algorithm. The temporal and spatial differences between the CFOSAT SCAT and the in situ observations were limited to less than 30 min and 12.5 km. For CFOSAT SCAT wind speed products, the comparison and analysis using the NDBC buoys yield a bias of 0.34 m/s, a root mean square error (RMSE) of 1.24 m/s. Although less accurate of CFOSAT SCAT wind direction at low winds, the RMSE of 19.76 deg with a bias of 1.13 deg is found for wind speeds higher than 4 m/s. Moreover, CFOSAT SCAT winds were evaluated against anemometers in situ onboard R/Vs , whose cruise were distributed globally. The comparison results against R/V winds are found consistent with those by the widely used NDBC buoys. The encouraging assessment results show that wind products from CFOSAT SCAT satisfy the mission specification and will be useful for scientific community.

The Surface Waves Investigation and Monitoring (SWIM) instrument onboard the China–France Oceanography Satellite (CFOSAT) can provide wave spectra using its off-nadir beams. Although SWIM shows a reasonable performance for capturing spectral peak, the accuracy of mean wave periods (MWPs) computed directly from the SWIM spectra is not satisfying due to the high noise level of the spectra. SWIM can also provide good-quality simultaneous wind speed (U10) and significant wave height (SWH) like an altimeter. The MWP can also be estimated using a U10-SWH look-up table presented in previous studies. However, the accuracy of this method is also limited as the U10-SWH look-up table is only applicable for wind-sea-dominated conditions. The two MWP retrieval methods are independent of each other, and their error properties are complementary to each other. Therefore, this study further presents a merged MWP retrieval model combining the nadir U10-SWH and the MWP from the off-nadir spectrum of SWIM using a simple artificial neural network. After training against some buoy data, the model reaches unprecedented accuracy for MWP retrievals (RMSEs of ~0.36 s for zero up-crossing periods, ~0.41 s for mean periods, and ~0.60 s for energy periods), demonstrating the usefulness of SWIM in the studies of ocean waves.

Satellite Radar Altimetry (RA) missions are one of the important means of global ocean observation and global and regional sea level changes monitoring. Satellite radar altimetry technology can provide continuous, all-weather, nearly whole coverage observations of global ocean. Atmospheric Wet Tropospheric Delay (WTD) is one of the error sources in satellite altimetry. The WTD with the range of 0~50cm is related to the variabilities of tropospheric water vapor and cloud liquid water in the radar signal propagation path, and varies spatially and temporally. The HY-2C satellite is the third China's marine dynamic environment monitoring satellite, which carries Radar Altimeter (RA), Scanning Microwave Radiometer, Microwave Scatterometer and Calibrated Microwave Radiometer (CMR). The CMR can provide WTD data for the correction of RA sea surface height. Due to the pollutions of coastal land, sea ice, rainfall and anomalies of instrument, CMR wet tropospheric delay sometimes has large errors or even is missing. In order to solve the problem of missing or low accuracy of CMR WTD data, the Wet Tropospheric Correction (WTC) of HY-2C RA is carried out by GNSS data, reanalysis data and other microwave radiometer data in this study. Taking the WTD correction data obtained by ERA5 reanalysis data as the background field, and combining the effective CMR WTD data along the ground track of the HY-2C altimeter, the nearshore WTD data obtained by the GNSS data and the WTD data obtained by other satellite microwave radiometers, multi-source data fusion was carried out on the basis of retaining the effective CMR WTD data by using spatiotemporal matching and objective analysis methods. Eventually, the missing CMR WTD data are filled and the accuracy of sea surface height measurement of HY-2C RA is improved to meet the growing data demand.

Internal tides are internal waves with tidal frequency those occur in the stratified ocean and are generated by barotropic tides flowing through steep topography. The internal tide is an important body of energy transfer, propagation and dissipation of barotropic tide in the ocean, and is also one important factor of driving the vertical transport of ocean nutrients and influencing ocean structure and ocean circulation. The study of the propagation and dissipation of internal tides at different spatial and temporal scales has been one of the important directions in marine science. Although both the theory and numerical models of internal tides indicate that they have obvious seasonal variations, the multiscale temporal modulation of internal tides is still poorly understood. This is very important to study the dissipation mechanism of internal tides and their interaction with other oceanic dynamic processes. With the development of satellite altimetry technology and the update of internal tide information extraction technology, satellite altimetry can obtain the characteristics of internal tide with large spatial and temporal coverage compared to in situ measurement. The Luzon Strait is one of the important generation sources of internal tides in the global ocean due to its steep ridges and strong barotropic tides. In this study, the seasonal variations of two major internal tides in the Luzon Strait region, M2 and K1, are analyzed based on multi-source altimeter data. Using the multi-satellite altimetry data from 1992 to 2020, a two-dimensional plane wave fitting method is used to extract the internal tide information and establish the internal tide model for different seasons. Furthermore, the origin, propagation direction, integrated energy flux and seasonal variation of the internal tide are analyzed, which is important for the parameterization of internal tide mixing in the numerical simulation.

Ocean waves are seawater fluctuation phenomena that occur on the ocean surface and are closely related to human beings. The study of ocean waves has great significance in shipping, offshore platform construction, national defense and military affairs. Though the traditional means of ocean wave observation have accurate results, the costs are high and the spatial-temporal coverage is sparse. Satellite remote sensing such as altimeter and SAR provide a new way for ocean wave observation, but their observation coverage is also sparse and large-area synchronous observation data of ocean wave cannot be obtained. The scatterometer can obtain large-area synchronous sea surface wind field data, and there is a nonlinear relationship between the sea surface wind field and ocean wave. HY-2C is the third marine dynamic environment monitoring satellite of China, and radar altimeter (RA) and microwave scatterometer are equipped which can simultaneously obtain ocean wave height and sea surface wind field. In order to obtain more ocean wave observation data with large-area synchronous spatial-temporal coverage, the wide swath significant wave height from HY-2C scatterometer is retrieved by the deep learning method in this study. The significant wave height data of HY-2C, sea surface wind field data of HY-2C and sea surface temperature data are used as the training set. For high sea state situations with relatively small data volume, the training data set is expanded by data augmentation methods. The HY-2C wide swath significant wave height is intelligently extracted by using the recurrent neural network, and the retrieval accuracy of the HY-2C wide swath significant wave height is evaluated by the data of buoys and other satellite altimeters.

Oceanic mesoscale eddy is a kind of vortex-current motion that is approximately in geostrophic balance, which are characterized by dynamic height anomalies in the surface. Vigorous mesoscale eddies are broadly distributed in the global ocean and can be readily observable from sea surface height anomaly (SSHA) field. The vertical temperature and salinity information of the subsurface ocean can be obtained from Argo floats. Combining sea surface observation (SSHA field) provided by satellites and the vertical temperature/salinity profiles provided by Argo floats, the vertical temperature and salinity structure of mesoscale eddies in the global ocean are analyzed. The result shows that cyclone eddies generally dominate the negative temperature anomalies, and anticyclonic eddies generally dominate the positive temperature anomalies in the global ocean. In the regions with strong current variation, eddy activities are vigorous, the temperature anomalies within eddies are more significant. The global distribution of vertical salinity anomalies of eddies is more complicated than that of temperature anomalies. Both the cyclonic eddies and anticyclonic eddies show positive salinity anomalies and negative salinity anomalies. Research suggests that the differences in the temperature and salt structure of mesoscale eddies in different regions of the global ocean are mainly caused by the differences in the temperature and salt characteristics of local water masses.

Laser altimetry has been shown to be able to provide high-resolution observations of ocean properties, which are crucial for global oceanographic monitoring. The Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) has demonstrated the ability to distinguish between individual ocean surface waves. This provides physical observations far from coastal regions, where most in-situ gauges are located. The CRYO2ICE campaign, which started in the summer of 2020, has provided periodic coincident orbits between ICESat-2 and CryoSat-2, allowing for the validation of ICESat-2 observations with radar altimetry. However, the data available was restricted to the northern hemisphere. Since the summer of 2022, the CRYO2ICE campaign has performed a realignment, to get coincident orbits in the southern hemisphere, enabling ocean observations in a far larger area, as well as observing the overall higher significant wave height (SWH) in this region of the oceans. This is an opportunity to extend our dataset of observed sea states and get a better understanding of the performance from ICESat-2 at extreme wave heights. In this study, the validation of ICESat-2 thereby includes data from the southern hemisphere, where we are validating two methods: individual wave observations and a statistical model. The former uses the high-resolution data from ICESat-2 to directly observe the individual surface waves and estimate the SWH. The statistical model leans closer to the conventional estimate for the SWH, which observes the general surface variance, and uses this empirical relationship to estimate the SWH. By analyzing this new data, we seek to gain insight into ICESat-2's performance at these extreme wave heights, as well as improve the accuracy of our models.

In the waters west of the Luzon Strait, a phenomenon of alternating Type A and Type B waves sometimes occurs. Previous explanations for Type A and Type B waves mostly relied on in-situ measurements at a daily or weekly scale, with fixed-point observations, short observation times, and considerable randomness due to factors such as sea conditions. Satellite SAR remote sensing, with its all-day, all-weather, large-scale observations, and relatively low-cost data acquisition, has become an important data source for ocean internal wave research.

This study uses Envisat, ERS, and Sentinel 1A/B satellite SAR remote sensing images as in-situ data and employs the MITgcm model to investigate the generation mechanisms of internal waves in the South China Sea. The model utilizes GEBCO real depth measurements as topographic input, employs the TPXO forecast model for tidal currents at the eastern ridge of the Luzon Strait and applies least squares method to extrapolate these currents to the model boundary as boundary conditions, and uses WOA13 (World Ocean Atlas 13) data as stratification input. The research results show that the moment when the model-generated internal waves reach the specified location is consistent with the moment when the internal waves captured in SAR images arrive at the same location. The numerical model results are satisfactory when verified with in-situ measurements, providing favorable conditions for investigating the generation of internal waves in the South China Sea. Furthermore, the MITgcm results are processed, and combined with internal tidal beam theory to explore the generation and evolution of Type A and Type B waves in the time series. By utilizing methods such as Hovmuller diagrams, the study traces back the timing of Type A and Type B wave generation and offers reasonable explanations.

In the previous research, we proposed a satellite SAR internal wave amplitude inversion algorithm based on Euler numerical simulation. This method simulates a steady internal solitary wave by giving an initial flow field, and then obtains the corresponding amplitude and surface flow field. The algorithm constantly modifies the initial flow field to calculate the correlation coefficient between SAR internal wave profile curve and surface flow field gradient. When the correlation coefficient reaches the maximum, the internal wave amplitude is the amplitude of inversion.

However, the initial flow field simulated in previous studies is set by the initial solution of the KdV equation. Since the internal wave theory of KdV is only valid under the condition of weak nonlinear and weak dispersion, there is a large gap between the internal solitary wave flow field given by the KdV equation and the actual internal solitary wave flow field under the condition of strong nonlinear and large amplitude, so when it is used as the initial flow field, it takes a long time for the numerical model to get a stable internal solitary wave solution.

However, the MCC internal wave theory contains high-order nonlinear terms, and the internal solitary wave solution of MCC is closer to the actual internal solitary wave in the case of large amplitude, so it can be used as the numerical model of the initial flow field to obtain the steady internal solitary wave faster. Therefore, the MCC internal wave theory is adopted to set the initial flow field, and the steady-state internal solitary waves under different stratification conditions are simulated by numerical models. The simulation results of MCC and KdV are compared.

The results show that the numerical simulation results using MCC are basically the same as those using KdV, but in the case of strong nonlinear large amplitude, the time of the numerical simulation using MCC is shorter.

The maximum wave height (Hmax) is an extremely important factor that has a significant impact on the safety of maritime activities. However, the Hmax is much less investigated than significant wave height (SWH) in the wave remote sensing. Nowadays, radar altimeters and CFOSAT provide the SWH operational but without Hmax products. A method of obtaining the Hmax from CFOSAT SWIM Level 2 parameters is presented. The buoys are the most reliable way to observe the Hmax, but the collocations between buoys and CFOSAT tracks are too few to perform the supervised learning training. The ERA5 wave reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) is one of the most accurate datasets. However, the obvious bias and scatter index of Hmax are found from the comparison between ERA5 and buoys located west of France. A machine learning model is firstly built to reduce the error of ERA5 Hmax. Then the corrected ERA5 Hmax is collocated with CFOSAT observations and used for the training target of SWIM Hmax retrieval. The SWIM parameters both from SWIM nadir and boxes, including the SWH, wavelength and wave partition information, are used to obtain the Hmax based on machine learning. The CFOSAT data in 2021 are used to train the Hmax machine learning model while the data in 2020 are used to perform the independent validation. The bias, RMSE and scatter index of CFOSAT Hmax are 0.01m, 0.51m, 16%, while 0.77m, 1.09m, 19% are for the ERA5. Therefore, this study provides a perspective to obtain the Hmax from satellite remote sensing for further applications such as marine forecasts.

Rivers provide key ecosystem services that are inherently engineered and optimised to meet the strategic and economic needs of countries around the world. However, limited water quality records of a full river continuum hindered the understanding of how river systems response to the multiple stressors acting on them. This study highlights the use of Sentinel-2 MSI data to monitor changes in water colour in two optically complex river systems: the Yangtze and Danube using the Forel-Ule Index (FUI). FUI divides water colour into 21 classes from dark blue to yellowish brown stemming from the historical Forel-Ule water colour scale, and has been promoted as a useful indicator showing water turbidity variations of water bodies. The results revealed contrasting water colour patterns in the two rivers on both the spatial and seasonal scales. Spatially, the FUI of the Yangtze River gradually increased from the upper reaches to the lower reaches, while the FUI of Danube River declined in the lower reaches, which is possibly due to the sediment sink effect of the Iron Gate Dams. The regional FUI peaks and valleys observed in the two river systems have also shown to be related to the dams and hydropower stations along them. Seasonally, the variations of FUI in both systems can be attributed to the climate seasonality, especially precipitation in the basin and the water level. Moreover, land cover within the river basin was possibly a significant determinant of water colour, as higher levels of vegetation in the Danube basin were associated with lower FUI values (7.9±0.6), whereas higher FUI values (9.3±1.5) and lower levels of vegetation were observed in the Yangtze system. This study furthers our knowledge of using Sentinel-2 MSI to monitor and understand the spatial-temporal variations of river systems and highlights the capabilities of the FUI in the optically complex environment.

Harmful algal blooms (HABs) occur due to a proliferation of phytoplankton within a body of water, resulting in deterioration of the aquatic environment which affects human and animal health. HABs are now a global issue with their frequency and severity increasing significantly due to anthropogenic activity and climate change. As with any natural or anthropogenic induced hazard, it is vital that efficient and effective monitoring strategies are put in place. Ocean colour satellites are effective in monitoring HABs around the world over long-term scales, however, species identification in dynamic coastal waters is challenging. Phytoplankton size class (PSC) is suggested to be a good indicator of cell size and considered to reflect the ecological and biogeochemical functional role of the phytoplankton present in the water column. Thus, it is important to be able to monitor PSCs, particularly in dynamic coastal waters where there are frequent changes in nutrients and phytoplankton community structure.

Our research draws on Sentinel-2 MSI and Sentinel-3 OLCI which differ in spatial, spectral, and temporal resolution. The objectives of this study are to develop and validate HAB detection and PSC algorithms for near-shore and coastal waters, with better generalisation capability and lower computational overload that could improve the identification of the optical characteristics directly associated with phytoplankton properties.

The study will be focused on four optically diverse regions of interest; The Danube Delta and Black Sea Coastline (Romania), Galician Coast (NW Spain), Shandong Peninsula Coast (China) and the Northern-South China Sea (China). Here, we will present results from the Galician coast. We used in-situ data such as hyperspectral Remote Sensing Reflectance, Chlorophyll-a concentration, phytoplankton abundance and taxonomy, along with fractionated chlorophyll-a and particle absorption properties to develop and test the algorithms. We focus on the detection of Alexandrium minutum from Sentinel-2 MSI and Sentinel-3 OLCI data, through the characterisation of the spectral properties directly associated with the bloom. Alexandrium minutum exhibit a range of spectral signatures depending on which optically active constituents are present in the water. We use an unsupervised Random Forest classification algorithm to develop clusters of similar reflectance spectra and propose a new indicator which can detect Alexandrium minutum from diatom dominated waters. Existing PSC retrieval algorithms based on pigment cover, chlorophyll-a abundance, and phytoplankton absorption for coastal and transitional waters will be tested in dynamic coastal waters. In addition, atmospheric correction models such as Polymer, Acolite and C2RCC were tested against in-situ hyperspectral data and their performance was evaluated.

We will present results on the optical characteristics of Alexandrium minutum and the potential of MSI and OLCI for their remote detection. We will discuss our plans for the development of Super Learners for HAB indicators and PSC and the evaluation of the PSC algorithms.