“Wind Pump” is an important concept that has drawn significant attention in the recent years. Wind Pump is defined as a series of wind-driven processes that influence ocean currents and water movement, which subsequently affect marine ecological conditions. Wind Pump can change the transport of nutrients and promote the cycling of major elements in the ocean. It thus drives primary production and marine ecosystem and affects carbon fixation and global fishery resources (Tang, 2004). This presentation will introduce “Wind Pump” effects on marine systems and take some examples in the South China Sea.

Algal bloom is defined as a rapid increase or accumulation in biomass in an aquatic system. It not only can increase the primary production but also could result in negative ecological consequence, e.g., Harmful Algal Blooms (HABs). According to the two classical theories of algal blooms “critical depth” and “eutrophication”, oligotrophic waters are difficult to form a large area of algal blooms. Cruise observations were only able to capture sporadically the existence of algal blooms. Due to limitations of in-situ observational methods, most of previous studies investigated occasional or regional blooms along coastal eutrophic waters, without much success of understanding of main processes responsible in the offshore deep-ocean oligotrophic waters. Based on previous studies by taking a full advantage of remote sensing technology and multiple satellite data, we proposed the mechanism model of “Wind Pump effects”, which represent the oceanic dynamic mechanism of the bloom growth. Except for the classical coastal Ekman transport, the Wind Pumping effects explain that wind forcing affects the formation of algal bloom through a variety of mechanisms, including Ekman pumping, clip volume, stirring and mixing, and transport by wind and wind-induced surface currents.

The conventional way to study upwelling regions by remote sensing is to use infrared and optical sensors by which the sea surface temperature (SST) and the chlorophyll-a (Chl-a) concentration is measured. However, also synthetic aperture radars (SARs) are useful instruments to study upwelling regions. Upwelling regions are areas of high biological activity, where the marine beings (plankton and fish) secrete surface active substances which rise to the sea surface and damp there the short surface waves, which are responsible for the radar backscattering. Thus upwelling areas manifest themselves on SAR images often as areas of reduced normalized radar cross section (NRCS). However, not only biogenic slicks associated with upwelling regions cause a reduction of the NRCS, but also the change the stability of the air-sea interface (from neutrally-stable to stable) because in upwelling regions the SST is usually lower than over the adjacent areas. Biogenic slicks visible on SAR images as areas of reduced NRCS are often confounded with mineral oil films. Criteria for discriminating between both types of surface films are presented. Furthermore, the correlation between Chl-a distribution and biogenic slick coverage in upwelling areas, like in the South China Sea east of Hainan, the East China Sea north of Taiwan, the Atlantic Ocean west of South Africa, and the Agulhas Return Current in the Indian Ocean, is investigated. These upwelling events are studied by using Sentinel-1 SAR images, Modis SST and Chl-a maps and model data of geostrophic surface currents. It is shown that this synergism yields new insights into upwelling mechanisms.

Sea Surface Temperature (SST) is the most important parameter, which is widely applied for studying water masses, air-sea interaction, marine ecosystem and environment, and other subjects. With the development of half century, satellite remote sensing has become the dominant technique to detect the global SST. However, the satellite measured SST is more closely related to the skin temperature than the subsurface bulk temperature. It is not convictive to validate the satellite measured SST with the subsurface bulk temperature, which is generally measured at a depth of one meter or even deeper. In order to validate the satellite retrieved SST, it is necessary to measure skin temperature.

A new version of the Buoyant Equipment for Skin Temperature (BEST), has been recently manufactured. The new instrument consists of 1050 thermistors, which are integrated in one pole, and 840 thermistors are on the top part (505mm in length) of the pole at 0.6mm distance each and 210 thermistors are on the other part (1015mm in length) of the pole at distance about 5mm. The pole works with a liquid level meter, the liquid level meter uses the electrical capacitance sensors which were also arrayed at 0.6mm distance corresponding to the thermistors. The new instrument BEST was then calibrated in a thermal isolation calibration system, and totally 21 temperature points from temperature -4℃～45℃ were measured for the calibration. The calibration results show the accuracy of the BEST is 0.01K.

The new instrument was vertically floated in Haizhu lake, Guangzhou from January 30 to 31, 2018, continuously for 2 days when the weather is quite cold. It synchronically measures the temperatures of the bottom layer of the air, the skin layer and the subsurface layer of the water at every second and more than hundred thousand temperature profiles were measured. All the temperature profiles have similar distribution pattern. In the bottom of air, the closer to the water surface, the higher temperature. and under the water surface, there is a thin thermocline (or metalimnion) which is just several centimeters thick. In the thermocline the temperature increases with water depth quickly. The water generally increases in temperature by 0.65 degrees Celsius every centimeter. The thermocline has very strong intensity, which is thousand times stronger than normal thermocline occurs in the ocean columns.

Mapping the Yangtze River discharge and freshwater plume spreading is highly important for in the understanding of phytoplankton blooming and nutrient distribution and transportation from the estuary to the East China Sea. Satellite sensor synergy building on passive microwaves, imaging spectrometer and radars are explored together with in-situ observations and dynamic modeling. With new EO satellite data available, such as Chinese Gaofen-4 and the ESA Sentinel-1,2 and 3 there exist possibilities that the freshwater plume mix and transportation process on weekly to seasonal basis can be observed and modelled. Moreover, in this study the Yangtze River Plume transportation dynamics may also be studied by mapping the plumes over the past decades, which may link the variations with large damming in the catchment. We adapt some of the classical methods for retrieval of sea surface salinity distribution with optical remote sensing data by establishing relationships between colored dissolved organic matters (CDOM) and salinity. We will also opt for sea surface brightness temperature methods with which sea surface salinity is obtained by using K-S model, where the brightness temperature is derived from scattering coefficient of SAR data. A Debye Equation based synergic method for sea surface salinity inversion will be thoroughly explored, in which sea surface temperature is synergically derived from brightness temperature through high resolution optical images and sea surface emittance calculated from SAR data.

Large scale green tide (macroalgae blooms of Ulva prolifera) have ocurred in every summer in the Yellow Sea since 2007, causing serious damages on coastal ecological environment, aquaculture, tourism, transportation and so on. The green macroalgae of Ulva prolifera originate from the seaweed aquaculture zone in the Jiangsu shoal, and the blooms are mainly caused by the activity of recycling the seaweed aquaculture facilities. In this work, Gaofen (GF) optical images with high spatial resolution (16m) and high revisit frequency (4 days) and Sentinel-1 IW-GRD microwave data are used to monitor the seasonal changes in the seaweed aquaculture in Jiangsu Shoal (120.8–122°E, 31.9–33.5°N) in 2016 and 2017 with the aim of exploring the reasons on the changes in the magnitude of green tide in the Yellow Sea in the summer of 2016 and 2017.

Macroalgae have the similar spectral signature as that of green vegetation. The normalized differential vegetation index (NDVI) derived from the GF-1 reflectance spectra is used to extract the seaweed aquaculture zone. Considering the difficulty of detecting seaweed aquaculture zone under the ebb tide and bad weather conditions, Sentinel-1 IW-GRD images are used to determine whether it is seaweed aquaculture zone or not.

The result shows that the seaweed aquaculture facilities was recycled mainly in April and May. However, the area of the aquaculture zone was only 1.3 km2 on May 3rd, 2016 while it remained 137.4 km2 on May 7th, 2017. In 2017, the area of the aquaculture zone reduced to 0.7 km2 till June 9th, which shows that the completion time of recycling the seaweed aquaculture facilities in 2017 was about one month later than in 2016. We deduced that the lower magnitude of green tides in 2017 in the Yellow Sea than 2016 may be due to the delay of recycling the seaweed aquaculture facilities. In 2017, the late time of recycling the seaweed aquaculture facilities slowed down the speed of the green macroalgae into the sea, therefore, the scale of the Yellow Sea green tide decreased significantly due to the reduced release of green tide species.

It is well known that rain events leave fingerprints on synthetic aperture radar (SAR) images acquired over the ocean, but it is not always easy to identify them unambiguously, especially not on C-band SAR images. Rain becomes visible on SAR images acquired over the ocean via several mechanisms: 1) by variations of the sea surface roughness caused by downdraft winds associated with rain cells and by rain drops impinging onto the sea surface (surface scattering) generating ring waves, splash products (including stalks), and turbulence, and 2) by scattering and attenuation of the radar beam by rain drops in the atmosphere (volume scattering). Surface scattering is particularly intricate at C-band because the Bragg waves responsible for the radar backscattering at this radar frequency lie in the transition region, where the impinging raindrops can increase (usually) or decrease the backscattered radar power, and also because scattering at stalks generated by impinging rain drops can significantly enhance the radar backscattering. In addition, at very high rain rates, volume scattering and attenuation can also contribute.

In this paper we report about progress that has been made in our study of C-band radar signatures of rain over the ocean. Such studies are relevant also for retrieving sea surface wind fields from C-band scatterometer data. Rain is a main source of error in wind retrieval algorithms, especially when co- and cross-polarized scatterometer data are used, which will be the case in the future. In this study we have analyzed mainly Sentinel-1 SAR images acquired over the South China Sea and have compared them with rain data from the weather radar of the Hong Kong Observatory and from the Global Precipitation Measurement (GPM) mission. In contrast to previously analyzed ERS and Envisat SAR data, the Sentinel-1 SAR data are acquired at VV and VH polarization simultaneously, which allows investigating the role of scattering at stalks, consisting of small cylinders of water emanating from the sea surface, in more detail. Theoretical investigations show that coherent scattering at stalks is responsible for the large values of the normalized radar cross section (NRNCS) at VV and VH polarizations often observed in radar signatures of strong rain cells. This interpretation is supported also by data acquired by the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) of NASA/JPL over the Gulf of Mexico.

Yangtze Estuary is located in the margin of land, facing East China Sea. It is influenced by the interaction of land and ocean, developed special environmental characteristics. Riverine freshwater plumes appear in the estuarine area specially, which play an important role in the study of material transport and Yangtze River runoff. Salinity can directly reflect the distribution of freshwater plumes. Therefore, research on the spatial and time distribution and variation of Yangtze River salinity is significant to understanding the importance of freshwater plum and estuarine environment. Compared to the significance of salinity, the measurement of salinity cannot provide sufficient and timely dataset. Remote sensing as a new monitoring technique, is able to provide the real-time synchronous monitoring of large area fast and timely. Existing salinity satellite SOMS and Aquarius cannot apply to the estuarine area because of their low spatial and time resolution. Optical satellite like MODIS, has high spectral resolution, proved suitable to retrieve salinity in estuarine area. This study uses MODIS Terra/Aqua L1b data and field data from voyage and hydrometric station of year 2013 to 2017 to establish a half-experienced retrieval model of Yangtze Estuary. This study divides the study area into inside and outside the Yangtze river estuary. Statistical models are used to the salinity retrieval outside the estuary. A dynamic model is established to t retrieve the salinity inside the estuary, taking runoff volume and tide into consideration, because of the complex hydrological and dynamic environments. The salinity retrieval model is used to reconstruct the salinity distribution of Yangtze Estuary during recent 30 years and analyze the seasonal and spatial salinity variations.

Retrieval of ocean color information is one of the most important missiona of Sentinel-3 Ocean and Land Color Instrument (OLCI). As the successor to Medium Resolution Imaging Spectrometer (MERIS) aboard ENVISAT, OLCI shows significant superiority compared with MERIS as well as Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The superiority shows in such aspects: the sensor has 21 bands, compared to 15 bands on MERIS, a design optimized to minimize sun-glint and a resolution of 300 m over all surfaces. In this study, we estimated the water quality in the Pearl River using Sentinel-3 OLCI. We appraise the precision of the water quality, including suspended sediment, Chlorophyll-a concentration, CDOM retrieved from OLCI, MERIS, MODIS and SeaWiFS. The results shows that the OLCI shows a good improvement in water quality detection in Pearl River Estuary. The additional bands enhance the ability to extract the information of coastal water quality.

Both the green tide caused by the outbreaks of Ulva prolifera and the golden tide caused by the outbreaks of Sargassum have appeared in the Yellow Sea and the East China Sea in recent years (Xing et al, 2017). The spectral characteristics of floating macroalgae are the basis for the remote detection by optical satellite remote sensing. A total of 10 samples of Ulva prolifera and Sargassum were collected from June 9, 2017 to June 19, 2017 in the Yellow Sea (33º37´～36º30´N, 120º00´～123º30´E). The spectral reflectance of them were measured by a hyperspectral spetroradiometer and a multi-spectral imager, respectively. The hyperspectral data was used to analyze spectral characteristics. The threshold method and neural network method based on the multi-spectral image were tested for the classifying of Ulva prolifera and Sargassum.
In this work, the Virtual-Baseline Floating macroAlage Height (VB-FAH) was calculated for extracting the floating macroalgae (Xing et al, 2016). The reasonable threshold was chosen for the classifying of the two macroalgae based on the trough depth (T-depth) and the Virtual-Baseline Floating macroAlage Height (VB-FAH) (Xing et al, 2013). And the pixel is regard as Ulva prolifera if the value of T-depth is larger than 0.30 or the value of VB-FAH is larger than 0.44. For the neural network method, we did 3 tests with different inputs: the 3-band reflectance image (Image_{_R}), the 3-band reflectance and the T-depth (Image_{_R+T-depth}), the 3-band reflectance and the VB-FAH (Image_{_R+VB-FAH}). The classification results of the above two methods were compared.

Xing Q G, Yu D F, Lou M J, et al, 2013. Using in-situ reflectance to monitor the Chlorophyll concentration in the surface layer of Tidal Flat. Spectroscopy and Spectral Analysis, 33(8): 2188—2191.

References

Xing Q G, Hu C, 2016. Mapping macrolagal blooms in the Yellow Sea and East China Sea using HJ – 1 and Landsat data: Application of a virtual baseline reflectance height technique. Remote Sensing of Environment, 178: 113—126.

Xing Q G, Guo R H, Wu L L, et al, 2017 . High-Resolution satellite observations of a new hazard of "Golden Tides" caused by floating Sargassum in Winter in the Yellow Sea. IEEE Geoscience and Remote Sensing Letters.