This PDF file contains the front matter associated with SPIE Proceedings Volume 10422, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

The operational monitoring of the risk areas of marine environment requires cost-effective solutions. One of the options is the use of sensor networks based on fixed installations and moving platforms (coastal boats, supply-, cargo-, and passenger vessels). Such network allows to gather environmental data in time and space with direct links to operational activities in the controlled area for further environmental risk assessment. Among many remote sensing techniques the LiDAR (Light Detection And Ranging) based on Light Induced Fluorescence (LIF) is the tool of direct assessment of water quality variations caused by chemical pollution, colored dissolved organic matter, and phytoplankton composition. The Hyperspectral LIF (HLIF) LiDAR acquires comprehensive LIF spectra and analyses them by spectral pattern recognition technique to detect and classify the substances in water remotely. Combined use of HLIF LiDARs with Real-Time Data Management System (RTDMS) provides the economically effective solution for the regular monitoring in the controlled area. OCEAN VISUALS in cooperation with LDI INNOVATION has developed Oil in Water Locator (OWL™) with RTDMS (OWL MAP™) based on HLIF LiDAR technique. This is a novel technical solution for monitoring of marine environment providing continuous unattended operations. OWL™ has been extensively tested on board of various vessels in the North Sea, Norwegian Sea, Barents Sea, Baltic Sea and Caribbean Sea. This paper describes the technology features, the results of its operational use in 2014-2017, and outlook for the technology development.

Oil spillage is one of the most common sources of environmental damage in places where coastal wild life is found in natural reservoirs. This is especially the case in the Patagonian coast, with a littoral more than 5000 km long and a surface above a million and half square km. In addition, furtive fishery activities in Argentine waters are depleting the food supplies of several species, altering the ecological equilibrium. For this reason, early oil spills and vessel detection is an imperative surveillance task for environmental and governmental authorities. However, given the huge geographical extension, human assisted monitoring is unfeasible, and therefore real time remote sensing technologies are the only operative and economically feasible solution. In this work we describe the theoretical foundations and implementation details of a system specifically designed to take advantage of the SAR imagery delivered by two satellite constellations (the SAOCOM mission, developed by the Argentine Space Agency, and the COSMO mission, developed by the Italian Space Agency), to provide real-time detection of vessels and oil spills. The core of the system is based on pattern recognition over a statistical characterization of the texture patterns arising in the positive and negative conditions (i.e., vessel, oil, or plain sea surfaces). Training patterns were collected from a large number of previously reported contacts tagged by experts in the National Commission on Space Activities (CONAE). The resulting system performs well above the sensitivity and specificity of other avalilable systems.

Radar and optical sensors are operationally used by authorities or petroleum companies for detecting and characterizing maritime pollution. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as the oil real fraction, which is critical for both exploration purposes and efficient cleanup operations. Today state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI, the airborne system developed by ONERA, during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this data set lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the electromagnetic spectrum. Specific processing techniques have been developed in order to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows to estimate slick surface properties such as the spatial abundance of oil and the relative concentration of hydrocarbons on the sea surface.

Marine pollution from oil spills destroys ecosystems. In order to minimize the damage, it is important to fast cleanup it after predicting how the oil will spread. In order to predict the spread of oil spill, remote sensing technique, especially radar satellite image is widely used. In previous studies, only the back-scattering value is generally used for the detection of oil spill. However, in this study, oil spill was detected by applying ANN (Artificial Neural Network) as input data from the back-scattering value of the radar image as well as the phase information extracted from the dual polarization. In order to maximize the efficiency of oil spill detection using a back-scattering value, the speckle noise acting as an error factor should be removed first. NL-means filter was applied to multi-look image to remove it without smoothing of spatial resolution. In the coherence image, the sea has a high value and the oil spill area has a low value due to the scattering characteristics of the pulse. In order to using the characteristics of radar image, training sample was set up from NL-means filtered images(HH, VV) and coherence image, and ANN was applied to produce probability map of oil spill. In general, the value was 0.4 or less in the case of the sea, and the value was mainly in the range of 0.7 to 0.9 in the oil spill area. Using coherence images generated from different polarizations showed better detection results for relatively thin oil spill areas such as oil slick or oil sheen than using back-scattering information alone. It is expected that if the information about the look-alike of oil spill such as algae, internal wave and rainfall area is provided, the probability map can be produced with higher accuracy.

In the shallow coastal zone (up to 4 m depth), the quality and accuracy of bathymetry data is insufficient because of the spatial variability of the seabed. For the Baltic polish coast bathymetry data are usually created in profiles based on echo sounding measurements. There are some trial applications of green laser – LIDAR known. It can be a method of shallow areas studies quality improvement. However, this method is still expensive and that is why we have decided to use the RGB digital aerial photographs to create a model for mapping the seabed of the shallow coastal zone. So far, in the 60's, researchers in the USA (Musgrove, 1969) and Russia (Zdanowicz, 1963) developed the first method of bathymetry determining from aerial panchromatic (black-white) photographs. This method was adapted for the polish conditions by Furmanczyk in 1975 and in 2014 we returned to his concept using more advanced techniques of recording and image processing. In our study, we propose to determine the bathymetry in shallow coastal zone of the Baltic Sea by using the digital color vertical aerial photographs (both single and multi-channel spectral). These photos are the high-resolution matrix (10 cm per pixel) containing values of the pixels in the individual spectral bands (RGB). This gives great possibilities to determine the bathymetry in order to analyze the changes in the marine coastal zone. Comparing the digital bathymetry maps - obtained by proposed method - in the following periods, you can develop differential maps, which reflect the movements of sea-bottom sediments. This can be used to indicate the most dynamic parts in the coastal zone. The model is based on the pixel values and relative depths, measured in-situ in geo-located points (in selected checkpoints for three areas: beach, shoal and depth parts). As a result, the relation of the pixel brightness and sea depth (the algorithm) was defined in two ways: 1) from relation of two or three bands and 2) from ratio of two channels. Using the algorithm, depth calculations for the whole scene were done and high resolution bathymetric map created to obtain three-dimensional bathymetry visualization. However, the algorithm requires numbers of adjustments resulting from, e.g., the phenomenon of vignetting, light propagation in the atmosphere or light reflection from the sea surface. We have developed the algorithm with correction formulas and created a final model in MATLAB. This model enables to determine the bathymetry of the most dynamic areas in the marine coastal zone up to 3-4 meters depth with a relatively good accuracy. In addition, the possibility to take pictures from the drone instead of a plane significantly reduces the cost of the process. We have also tried to adapt our methodology to satellite data processing. We will present the model and its results for the area of the Polish western Baltic coast. 1. Musgrove R,G., 1969. Photometry for interpretation. Photogrametric Engineering No. 10. 2. Furmańczyk K., 1975. Możliwości praktycznego zastosowania metody fotogrametrycznej do określania głębokości w strefie brzegowej morza. Gdańsk. 3. Zdanowicz W.G., 1963. Primienienije aerometodow dlia issledowanija moria. Leningrad.

Bathymetry at shallow water especially shallower than 15m is an important area for environmental monitoring and national defense. Because the depth of shallow water is changeable by the sediment deposition and the ocean waves, the periodic monitoring at shoe area is needed. Utilization of satellite images are well matched for widely and repeatedly monitoring at sea area. Sea bottom terrain model using by remote sensing data have been developed and these methods based on the radiative transfer model of the sun irradiance which is affected by the atmosphere, water, and sea bottom. We adopted that general method of the sea depth extraction to the satellite imagery, WorldView-2; which has very fine spatial resolution (50cm/pix) and eight bands at visible to near-infrared wavelengths. From high-spatial resolution satellite images, there is possibility to know the coral reefs and the rock area’s detail terrain model which offers important information for the amphibious landing. In addition, the WorldView-2 satellite sensor has the band at near the ultraviolet wavelength that is transmitted through the water. On the other hand, the previous study showed that the estimation error by the satellite imagery was related to the sea bottom materials such as sand, coral reef, sea alga, and rocks. Therefore, in this study, we focused on sea bottom materials, and tried to improve the depth estimation accuracy. First, we classified the sea bottom materials by the SVM method, which used the depth data acquired by multi-beam sonar as supervised data. Then correction values in the depth estimation equation were calculated applying the classification results. As a result, the classification accuracy of sea bottom materials was 93%, and the depth estimation error using the correction by the classification result was within 1.2m.

Hyperspectral images of coastal waters in urbanized regions were collected from fixed platform locations. Surf zone imagery, images of shallow bays, lagoons and coastal waters are processed to produce bidirectional reflectance factor (BRF) signatures corrected for changing viewing angles. Angular changes as a function of pixel location within a scene are used to estimate changes in pixel size and ground sampling areas. Diffuse calibration targets collected simultaneously from within the image scene provides the necessary information for calculating BRF signatures of the water surface and shorelines. Automated scanning using a pushbroom hyperspectral sensor allows imagery to be collected on the order of one minute or less for different regions of interest. Imagery is then rectified and georeferenced using ground control points within nadir viewing multispectral imagery via image to image registration techniques. This paper demonstrates the above as well as presenting how spectra can be extracted along different directions in the imagery. The extraction of BRF spectra along track lines allows the application of derivative reflectance spectroscopy for estimating chlorophyll-a, dissolved organic matter and suspended matter concentrations at or near the water surface. Imagery is presented demonstrating the techniques to identify subsurface features and targets within the littoral and surf zones.

Using remote sensing methods to identify valuable underwater habitats Seagrasses are submerged plants found all over the world from brackish bays to salty continental shelves. Seagrasses create high-productive habitats and are a vital part of the marine ecosystems, providing shelter, nurseries, food, and habitats for other species. Seagrasses can be seen as indicators of environmental changes and ecosystem heath as they are sensitive to water quality. Accurate and detailed spatial information of seagrasses would be important in assessments of threatened habitats, establishing MPAs and evaluating ecosystem state. However, spatially comprehensive species information is usually lacking and the knowledge of seagrass habitats around the world relies on scarce field observations and models based on inventory records. Because field work is expensive and time consuming, alternative ways of acquiring information of seagrass habitats are needed. Therefore, aerial and satellite images are useful as they are provided in different resolution, times, and prices depending on the purpose of use. We tested how aerial and high-resolution satellite images could be utilized in finding seagrass habitats in two different environments; in the brackish Finnish coast and in the clear tropical waters of Zanzibar, Tanzania. We did supervised classification to identify seagrasses of pre-processed aerial and satellite images. For the satellite image of the coast of Zanzibar, we also calculated water column correction. Both ways of the method worked, as the seagrass areas were found, although in the Finnish coast poor water visibility hinders light penetration below 4 meter. In developing countries, where marine inventories are usually non-existent, our methodology of utilizing low-cost images for identifying valuable habitats is the only reasonable way of acquiring marine biodiversity data.

Every individual attribute of a riverine environment defines the overall spectral signature to be observed by an optical sensor. The spectral characteristic of riverbed is influenced not only by the type but also the roughness of substrates. Motivated by this assumption, potential of optical imagery for mapping grain size of shallow rivers (< 1 m deep) is examined in this research. The previous studies concerned with grain size mapping are all built upon the texture analysis of exposed bed material using very high resolution (i.e. cm resolution) imagery. However, the application of texturebased techniques is limited to very low altitude sensors (e.g. UAVs) to ensure the sufficient spatial resolution. Moreover, these techniques are applicable only in the presence of exposed substrates along the river channel. To address these drawbacks, this study examines the effectiveness of spectral information to make distinction among grain sizes for submerged substrates. Spectroscopic experiments are performed in controlled condition of a hydraulic lab. The spectra are collected over a water flume in a range of water depths and bottoms with several grain sizes. A spectral convolution is performed to match the spectra to WorldView-2 spectral bands. The material type of substrates is considered the same for all the experiments with only variable roughness/size of grains. The spectra observed over dry beds revealed that the brightness/reflectance increases with the grain size across all the spectral bands. Based on this finding, the above-water spectra over a river channel are simulated considering different grain sizes in the bottom. A water column correction method is then used to retrieve the bottom reflectances. Then the inferred bottom reflectances are clustered to segregate among grain sizes. The results indicate high potential of the spectral approach for clustering grain sizes (overall accuracy of 92%) which opens up some horizons for mapping this valuable attribute of rivers using remotely sensed data.

We examine the impact of temporal changes on satellite retrievals of Karenia brevis Harmful Algal blooms (KB HABS) in the West Florida Shelf (WFS). These impacts are compared for retrievals from both VIIRS and MODIS-A using a number of retrieval techniques. The comparisons include our recently developed neural network (NN) technique. The neural network, previously developed by us, was trained on 10,000 data point part of a synthetic data of 20,000 inherent optical properties (IOPs) based on a wide range of IOP parameters for a large variety of natural conditions based on the NOMAD data. The NN then uses as inputs the remote sensing reflectance (Rrs) measurements at 486, 551 and 671 and 488, 555 and 667 nm which are available from VIIRS and MODIS-A respectively, to retrieve phytoplankton absorption at 443 nm in satellite images. This information, when combined with backscatter information has been shown by us to be effective for obtaining retrievals of KB HB HABs in the WFS. Other retrieval algorithms included in the present comparison are the blue/green OCI/OC3, the Generalized Inherent Optical Property (GIOP) model and the Quasi-Analytical Algorithm, (QAA version 5). The accuracies of VIIRS retrievals using all five techniques were then compared against all the in-situ measurements available over the 2012-2016 period for which concurrent or near concurrent match ups could be obtained with VIIRS retrievals. Analysis of retrieval statistics showed that the NN technique achieved the best accuracies. The analysis highlighted the impact of temporal variabilities on retrieval accuracies. The results showed the importance of having a shorter overlap time window between in-situ measurement and satellite retrieval. Retrievals where the maximum permissible overlap time window was shortened to 15 minutes, exhibited very significantly improved retrieval accuracies over those that were obtained with a 100 minute overlap time window, Retrievals that relaxed the overlap time window between in-situ measurement and satellite retrievals to simply the same day were hopelessly inaccurate by comparison. These results are believed to reflect the impact of temporal variabilities on retrieval accuracies. They underline the time limitations associated with satellite retrievals of inherently variable conditions and a changing scene. The temporal effects associated with KB HABs retrievals in the WFS were also examined by using images from consecutive VIIRS - MODIS-A – VIIRS overpasses overlapping the same parts of the WFS containing KB blooms. The consecutive images, all within an approximately 100 minute period, appear to confirm the changing bloom features. The temporal behavior of the KB blooms in the WFS over short time periods (less than100 minuntes) was further confirmed by a recent set of in-situ field measurements off the coast of Sarasota.

Hyperspectral signatures and imagery collected during the spring and summer of 2017 and 2016 are presented. Ground sampling distances (GSD) and pixel sizes were sampled from just over a meter to less than 4.0 mm. A pushbroom hyperspectral imager was used to calculate bidirectional reflectance factor (BRF) signatures. Hyperspectral signatures of different water types and bottom habitats such as submerged seagrasses, drift algae and algal bloom waters were scanned using a high spectral and digital resolution solid state spectrograph. WorldView-3 satellite imagery with minimal water wave sun glint effects was used to demonstrate the ability to detect bottom features using a derivative reflectance spectroscopy approach with the 1.3 m GSD multispectral satellite channels centered at the solar induced fluorescence band. The hyperspectral remote sensing data collected from the Banana River and Indian River Lagoon watersheds represents previously unknown signatures to be used in satellite and airborne remote sensing of water in turbid waters along the US Atlantic Ocean coastal region and the Florida littoral zone.

Imaging of underwater targets is challenging because of the significant attenuation of the propagating light field due to the absorption and scattering by water and suspended/dissolved matter. Some living and manmade objects in water have surfaces which partially polarize the light, whose properties can be used to camouflage or, conversely, to detect such objects. The attenuation of light by the intervening water (so-called veiling light) changes both the intensity and polarization characteristics at each pixel of the image, but does not contain any information about the target and contributes to image degradation and blurring. Its properties need to be understood in order to isolate the true optical signature of the target. The main goal of this study is to retrieve the polarization characteristics of the target from the image in different water environmental and illumination conditions by taking into account coincidentally measured inherent water optical properties (IOPs) during recent field campaigns outside the Chesapeake Bay and in New York Bight. Data, in the form of images and videos, were acquired using a green-band full-Stokes polarimetric video camera. Analysis of the acquired images show reasonable agreement in Stokes vector components with the measurements by the underwater polarimeter and modeled polarized signals. In addition, Stokes vector components of the veiling light were also estimated and compared with the models. Finally, retrieval of the attenuation coefficient for the light from the target is attempted from the measurements and compared with the results of the independent measurements of IOPs.

Dual-polarized microwave radars are of particular interest nowadays as perspective tool of ocean remote sensing. Microwave radar backscattering at moderate and large incidence angles according to conventional models is determined by resonance (Bragg) surface waves typically of cm-scale wavelength range. Some recent experiments have indicated, however, that an additional, non Bragg component (NBC) contributes to the radar return. The latter is considered to occur due to wave breaking. At present our understanding of the nature of different components of radar return is still poor. This paper presents results of field experiment using an X-/C-/S-band Doppler radar operating at HH- and VVpolarizations. The intensity and radar Doppler shifts for Bragg and non Bragg components are retrieved from measurements of VV and HH radar returns. Analysis of a ratio of VV and HH radar backscatter – polarization ratio (PR) has demonstrated a significant role of a non Bragg component. NBC contributes significantly to the total radar backscatter, in particular, at moderate incidence angles (about 50-70 deg.) it is 2-3 times smaller than VV Bragg component and several times larger that HH Bragg component. Both NBC and BC depend on azimuth angle, being minimal for cross wind direction, but NBC is more isotropic than BC. It is obtained that velocities of scatterers retrieved from radar Doppler shifts are different for Bragg waves and for non Bragg component; NBC structures are “faster” than Bragg waves particularly for upwind radar observations. Bragg components propagate approximately with phase velocities of linear gravity-capillary waves (when accounting for wind drift). Velocities of NBC scatterers depend on radar band, being the largest for S-band and the smallest at X-band, this means that different structures on the water surface are responsible for non Bragg scattering in a given radar band.

Data quality of the satellite sensors for ocean monitoring (Ocean Color –OC) like MODIS, VIIRS, MERIS, and now OLCI sensor on Sentinel-3a are often validated through matchups between normalized water leaving radiances nLw (or remote sensing reflectance R_rs) from satellite data and data from radiometric systems (SeaPRISMs) installed on ocean platforms and which are part of the NASA Aerosol Robotic Network (AERONET) and AERONET-OC networks. While matchups are usually good in open ocean waters, significant discrepancies are observed in coastal areas which are primarily due to the more complex atmospheres near the coast and therefore less accurate atmospheric correction. Satellite-derived water leaving radiances are determined by applying atmospheric correction procedures which include assumptions about the characteristics of atmospheric aerosols. At sea level, SeaPRISM makes direct measurements of nLw from the ocean, as well as observations of sky from which aerosol parameters such as aerosol optical thickness, single scattering albedo, fraction of fine and coarse aerosols, and others are determined. Using NASA SeaDAS software for OC satellite data processing, characteristics of aerosols in atmospheric correction models for VIIRS sensor are explicitly retrieved and compared with the ones from AERONET-OC data, primarily in terms of aerosol optical depth (AOD), thus characterizing the validity of the aerosol models and evaluating possible errors and reasons for discrepancies. Comparisons are presented for the coastal site at CCNY’s Long Island Sound Coastal Observatory (LISCO) and a less coastal WaveCIS Gulf of Mexico’ AERONET-OC site with variable water and atmospheric conditions.

One of the objectives of the NAOMI (New Advanced Observation Method Integration) research project, fruit of a partnership between Total and ONERA, is to work on the detection, the quantification and the characterization of offshore hydrocarbon at the sea surface using airborne remote sensing. In this framework, work has been done to characterize the spectral signature of hydrocarbons in lab in order to build a database of oil spectral signatures. The main objective of this database is to provide spectral libraries for data processing algorithms to be applied to airborne VNIRSWIR hyperspectral images. A campaign run by the NOFO institute (Norwegian Clean Seas Association for Operating Companies) took place in 2015 to test anti-pollution equipment. During this campaign, several hydrocarbon products, including an oil emulsion, were released into the sea, off the Norwegian coast. The NOFO team allowed the NAOMI project to acquire data over the resulting oil slicks using the SETHI system, which is an airborne remote sensing imaging system developed by ONERA. SETHI integrates a new generation of optoelectronic and radar payloads and can operate over a wide range of frequency bands. SETHI is a pod-based system operating onboard a Falcon 20 Dassault aircraft, which is owned by AvDEF. For these experiments, imaging sensors were constituted by 2 synthetic aperture radar (SAR), working at X and L bands in a full polarimetric mode (HH, HV, VH, VV) and 2 HySpex hyperspectral cameras working in the VNIR (0,4 to 1 μm) and SWIR (1 to 2,5 μm) spectral ranges. A sample of the oil emulsion that was used during the campaign was sent to our laboratory for analysis. Measurements of its transmission and of its reflectance in the VNIR and SWIR spectral domains have been performed at ONERA with a Perkin Elmer spectroradiometer and a spectrogoniometer. Several samples of the oil emulsion were prepared in order to measure spectral variations according to oil thickness, illumination angle and aging. These measurements have been used to build spectral libraries. Spectral matching techniques, relying on these libraries have been applied to the airborne hyperspectral acquisitions. These data processing approaches enable to characterize the oil emulsion by estimating the properties taken into account to build the spectral library, thus going further than unsupervised spectral indices that are able to detect the presence of oil. The paper will describe the airborne hyperspectral data, the measurements performed in the laboratory, and the processing of the optical images with spectral indices for oil detection and with spectral matching techniques for oil characterization. Furthermore, the issue of mixed oil-water pixels in the hyperspectral images due to limited spatial resolution will be addressed by estimating the areal fraction of each.

Stratification is of critical importance to the circulation, mixing and productivity of the ocean, and is expected to be modified by climate change. Stratification is also understood to affect the surface aggregation of pelagic fish and hence the foraging behaviour and distribution of their predators such as seabirds and cetaceans. Hence it would be prudent to monitor the stratification of the global ocean, though this is currently only possible using in situ sampling, profiling buoys or underwater autonomous vehicles. Earth observation (EO) sensors cannot directly detect stratification, but can observe surface features related to the presence of stratification, for example shelf-sea fronts that separate tidally-mixed water from seasonally stratified water. This paper describes a novel algorithm that accumulates evidence for stratification from a sequence of oceanic front maps, and discusses preliminary results in comparison with in situ data and simulations from 3D hydrodynamic models. In certain regions, this method can reveal the timing of the seasonal onset and breakdown of stratification.

This paper presents a processing scheme for the fast computation of pancake ice size distribution from aerial photographs. The test image used in this study was collected by the Twin Otter of the Naval Research Laboratory which assisted the cruise of the research ship “Sikuliaq” while carrying out an extensive study of the autumn sea ice in the southern Beaufort Sea in 2015. The processing scheme is composed of the following steps: i) image enhancement, ii) nonlinear support vector machine (SVM) analysis, and iii) ice size distribution computation. The result confirms the advantage of having immediate information on pancake ice size distribution during a field campaign in the Arctic.

The results of multi-year satellite monitoring of ice conditions in the Kerch Strait connecting the Black and Azov Seas are discussed. The issue gained importance in view of the ongoing construction of the Crimean Bridge across the strait. Our monitoring has been based on the whole variety of available satellite data including visible and radar data over the past 17 years. Every year the Azov Sea becomes fully or partially covered by ice during the cold season. In severe winters, ice often is carried to the Kerch Strait and even the Black Sea. An analysis of ice drift hydrometeorological conditions is presented. The ice conditions of 2017 are under special consideration. Everyday satellite monitoring of the Kerch Strait, including the construction area of the Crimean Bridge, revealed ice formation and drift features on the way from the Azov Sea through the Kerch Strait as well as ice interaction with the piers of the main and technological bridges under construction. It was found that, even under strong northeast winds, ice can pass neither through the piers, nor via the widest shipway. At present, it is hard to discern the impacts of the two bridges on floating ice, nevertheless when the construction is over and the technological bridge is gone, by all appearances the main bridge will strongly affect ice conditions in the Kerch Strait. This perspective calls for continuous satellite monitoring of the area that is enabled by cutting-edge systems and technologies.

The present study is aimed at bringing together the benefits of different approaches for extracting information on surface currents from satellite-derived datasets and ocean data reanalysis. As a region of interest, we use the Western Mediterranean, which is having a peculiar pattern of surface circulation, due to the inflow of Atlantic water through the Strait of Gibraltar. Among the data analysed in this study, there are fields of sea surface temperature (SST) as well as different datasets on surface currents retrieved from satellite altimetry data, satellite imagery and their synergy. In particular, we analyse (i) fields of geostrophic currents retrieved from a gridded sea level anomaly (SLA) data product, (ii) the data on total surface currents derived in the framework of the GlobCurrent project, and (iii) the data provided by reanalysis. Special attention is being paid to the performance of different datasets in manifesting largest mesoscale eddies of the region of interest. As a result of the comparison performed, the general scheme of surface circulation in the region of interest was updated. Main novelties proposed by the renewed scheme concerns the number of cyclonic gyres presenting in the field of surface currents.

Study of characteristics of sea clutter is very important for signal processing of radar, detection of targets on sea surface and remote sensing. The sea state is complex at Low grazing angle (LGA), and it is difficult with its large irradiation area and a great deal simulation facets. A practical and efficient model to obtain radar clutter of dynamic sea in different sea condition is proposed, basing on the physical mechanism of interaction between electromagnetic wave and sea wave. The classical analysis method for sea clutter is basing on amplitude and spectrum distribution, taking the clutter as random processing model, which is equivocal in its physical mechanism. To achieve electromagnetic field from sea surface, a modified phase from facets is considered, and the backscattering coefficient is calculated by Wu’s improved two-scale model, which can solve the statistical sea backscattering problem less than 5 degree, considering the effects of the surface slopes joint probability density, the shadowing function, the skewness of sea waves and the curvature of the surface on the backscattering from the ocean surface. We make the assumption that the scattering contribution of each facet is independent, the total field is the superposition of each facet in the receiving direction. Such data characters are very suitable to compute on GPU threads. So we can make the best of GPU resource. We have achieved a speedup of 155-fold for S band and 162-fold for Ku/Χ band on the Tesla K80 GPU as compared with Intel® Core™ CPU. In this paper, we mainly study the high resolution data, and the time resolution is millisecond, so we may have 10,00 time points, and we analyze amplitude probability density distribution of radar clutter.

This study is intended to demonstrate the significant shifts in DOM concentration with space and time within and across inland waters (lakes), which can be acquired by the analysis of DOM concentration using Sentinel-2A imagery. Using a remote sensing method (based on Sentinel-2A data) to evaluate DOM quantity or concentration in freshwater (lakes), optical properties shifts of CDOM of spatial and temporal observations at sites in Västerbotten and Jämtland in Northern Sweden can be understood in terms of the landscape features and hydrological events at those locations. Using Sentinel-2A as a data source for these remote sensing methods can provide an effective approach for estimating CDOM concentrations in water to obtain water quality information regularly, and also could help to accurately estimate the CDOM absorption coefficient (aCDOM ) via Sentinel-2A data of the study area. The estimation of (aCDOM ) by using Sentinel-2A could help to study DOM in inland waters at global scale by using following equation aCDOM(420)=5.13(B2/B3)-2.67 Key words: Dissolved Organic Matter component (DOM), water quality, sentinel -2A

High quality in situ radiometric observations is needed for calibration, validation, and bio-optical algorithm development of ocean color remote sensing, moreover in studying and understanding of ocean optical, biological, and biogeochemical properties. Notably, calibration and validation of ocean color satellite data depend on high quality in situ data. Therefore, to improve our understanding of optical properties of the Red Sea, radiometric field measurements performed during in 2016 oceanographic cruises. The data set measured using the SatlanticTM HyperPro instrument equipped with radiometers, includes downwelling irradiance (Ed), upwelling radiance (Lu), and surface reference irradiance (Es). Profiles of downwelling irradiances were used to calculate diffuse attenuation coefficient, the first optical depth, PAR, and depth of the euphotic zone. Remote sensing reflectance computed from the ratio of upwelling radiance to downwelling irradiance. Derivative analysis performed on the spectral remote sensing reflectance to identify the different phytoplankton pigments based on the various peaks. The results obtain from the observational data analysis will be presented in this paper and discussed for ocean color implications.

Sentinel-3A, launched in February 2016, is part of ESA's long-term commitment to climate monitoring from space. Its suite of instruments for measuring surface topography includes a Microwave Radiometer (MWR) and SRAL, the first delay-Doppler instrument to provide global coverage. SRAL promises fine spatial resolution and reduced noise levels that should together lead to improved performance over all Earth surfaces. The Sentinel-3 Mission Performance Centre (S3MPC) has been developing the methodology to evaluate the accuracy of retrievals, monitor any changes and develop solutions to known problems. The S3MPC monitors internal temperatures, path delays and the shape of the generated pulses to assess the instruments health. The MWR records over known reference surfaces are compared with those from other spaceborne instruments. Over the ocean the SRAL's return pulses are analysed to give range to the sea surface, wave height and signal strength (which can be interpreted as wind speed). The metocean data are regularly contrasted with records from in situ measurements and the output from meteorological models, which rapidly highlights the effects of any changes in processing. Range information is used to give surface elevation, which is assessed in three ways. First, flights over a dedicated radar transponder provide an estimate of path delay to within ~10 mm (r.m.s.). Second, measurements are compared to GPS-levelled surfaces near Corsica and over Lake Issyk-kul. Third, there are consistency checks between ascending and descending passes and with other missions. Further waveform analysis techniques are being developed to improve the retrieval of information over sea-ice, land-ice and inland waters.

Maritime pollution by chemical products occurs at much lower frequency than spills of oil, however the consequences of a chemical spill can be more wide-reaching than those of oil. While detection and characterization of hydrocarbons have been the subject of numerous studies, detection of other chemical products at sea using remote sensing has been little studied and is still an open subject of research. To address this knowledge gap, an experiment was conducted in May 2015 over the Mediterranean Sea during which controlled releases of hazardous and noxious substances were imaged by an airborne SAR sensor at X- and L-band simultaneously. In this paper we discuss the experimental procedure and report the main results from the airborne radar imaging campaign.

Radar altimetry provides measurements of sea surface elevation, wind speed and wave height, which are used operationally by many agencies and businesses, as well as for scientific research to understand the changes in the oceanatmosphere interface. For the data to be trustworthy they need to be assessed for consistency, and for bias relative to various validation datasets. Sentinel-3A, launched in Feb. 2016, promises, through new technology, to be better able to retrieve useful measurements in the coastal zone; the purpose of this paper is develop ideas on how the performance of this instrument can be assessed in that specific environment. We investigate the magnitude of short-term variability in wave height and range, and explain how two validation facilities in the southwest UK may be used.

For maritime remote sensing, coastline detection is a vital task. With continuous coastline detection results from satellite image time series, the actual shoreline, the sea level, and environmental parameters can be observed to support coastal management and disaster warning. Established coastline detection methods are often based on SAR images and wellknown image processing approaches. These methods involve a lot of complicated data processing, which is a big challenge for remote sensing time series. Additionally, a number of SAR satellites operating with polarimetric capabilities have been launched in recent years, and many investigations of target characteristics in radar polarization have been performed. In this paper, a fast and efficient coastline detection method is proposed which comprises three steps. First, we calculate a modified correlation coefficient of two SAR images of different polarization. This coefficient differs from the traditional computation where normalization is needed. Through this modified approach, the separation between sea and land becomes more prominent. Second, we set a histogram-based threshold to distinguish between sea and land within the given image. The histogram is derived from the statistical distribution of the polarized SAR image pixel amplitudes. Third, we extract continuous coastlines using a Canny image edge detector that is rather immune to speckle noise. Finally, the individual coastlines derived from time series of .SAR images can be checked for changes.

In August 2011, China successfully launched the Ocean II (HY-2A) satellite. HY-2A carries a dual-band radar altimeter with a calibrated microwave radiometer on the orbit. The main objective of HY-2A is to observe the elements of marine dynamic environment, including sea surface height. The evaluation of HY-2A satellite sea surface data quality is a necessary part of HY-2A satellite sea surface data application. We Used the HY-2A satellite 18th to 23th cycle data and the simultaneous Jason-2 data in orbit to analyze the deviation and evaluate of HY-2A satellite radar height data quality. The results show that the number of abnormal points in HY-2A satellite 18 to 23 cycles accounted for 12% of the total. HY-2A and Jason-2 sea level anomaly standard deviation of 7.0 cm that the accuracy of HY-2A reached the satisfaction index.

A method for ocean subsurface particulate backscatter estimation from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was demonstrated. The effects of the CALIOP receiver’s transient response on the attenuated backscatter profile were first removed. The two-way transmittance of the overlying atmosphere was then estimated as the ratio of the measured ocean surface attenuated backscatter to the theoretical value computed from wind driven wave slope variance. Finally, particulate backscatter was estimated from the depolarization ratio as the ratio of the column-integrated cross-polarized and co-polarized channels. Statistical results show that the derived particulate backscatter by the method based on CALIOP data agree reasonably well with chlorophyll-a concentration using MODIS data. It indicates a potential use of space-borne lidar to estimate global primary productivity and particulate carbon stock.

A laser induced fluorescence (LIF) technique for fast diagnosing chromophoric dissolved organic matter (CDOM) in water is discussed. We have developed a new field-portable laser fluorometer for rapid fluorescence measurements. In addtion, the fluorescence spectral characteristics of fluorescent constituents (e.g., CDOM, chlorophyll-a) were analyzed with a spectral deconvolution method of bi-Gaussian peak function. In situ measurements by the LIF technique compared well with values measured by conventional spectrophotometer method in laboratory. A significant correlation (R² = 0.93) was observed between fluorescence by the technique and absorption by laboratory spectrophotometer. Influence of temperature variation on LIF measurement was investigated in lab and a temperature coefficient was deduced for fluorescence correction. Distributions of CDOM fluorescence measured using this technique in the East China Sea coast were presented. The in situ result demonstrated the utility of the LIF technique for rapid detecting dissolved organic matter.

Changes of oceanic salinity are highly related to the variations of evaporation and precipitation. To understand the influence of rainfall on the sea surface salinity (SSS) in the waters adjacent to Taiwan, satellite remote sensing data from the year of 2012 to 2014 are employed in this study. The daily rain rate data obtained from Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission’s Microwave Imager (TRMM/TMI), Advanced Microwave Scanning Radiometer (AMSR), and WindSat Polarimetric Radiometer. The SSS data was derived from the measurements of radiometer instruments onboard the Aquarius satellite. The results show the average values of SSS in east of Taiwan, east of Luzon and South China Sea are 33.83 psu, 34.05 psu, and 32.84 psu, respectively, in the condition of daily rain rate higher than 1 mm/hr. In contrast to the rainfall condition, the average values of SSS are 34.07 psu, 34.26 psu, and 33.09 psu in the three areas, respectively at no rain condition (rain rate less than 1 mm/hr). During the cases of heavy rainfall caused by spiral rain bands of typhoon, the SSS is diluted with an average value of -0.78 psu when the average rain rate is higher than 4 mm/hr. However, the SSS was increased after temporarily decreased during the typhoon cases. A possible reason to explain this phenomenon is that the heavy rainfall caused by the spiral rain bands of typhoon may dilute the sea surface water, but the strong winds can uplift the higher salinity of subsurface water to the sea surface.

Due to the fact that water surface covers wide areas, remote sensing is the most appropriate way of getting information about ocean environment for vessel tracking, security purposes, ecological studies and others. Processing of synthetic aperture radar (SAR) images is extensively used for control and monitoring of the ocean surface. Image data can be acquired from Earth observation satellites, such as TerraSAR-X, ERS, and COSMO-SkyMed. Thus, SAR image processing can be used to solve many problems arising in this field of research. This paper discusses some of them including ship detection, oil pollution control and ocean currents mapping. Due to complexity of the problem several specialized algorithm are necessary to develop. The oil spill detection algorithm consists of the following main steps: image preprocessing, detection of dark areas, parameter extraction and classification. The ship detection algorithm consists of the following main steps: prescreening, land masking, image segmentation combined with parameter measurement, ship orientation estimation and object discrimination. The proposed approach to ocean currents mapping is based on Doppler's law. The results of computer modeling on real SAR images are presented. Based on these results it is concluded that the proposed approaches can be used in maritime applications.

Satellite observation has been an important way to understanding the variation of marine environments. It is noted that the satellite-retrieved ocean color products, such as the chlorophyll, have some missing data on large area where the clouds and heavy aerosols covered or other reasons. In order to monitor their changes and assess their influence on the marine ecosystems or climate, the long-term synchronous and full covered data are needed. The Geostationary Ocean Color Image (GOCI), which is one of sensors onboard COMS Geostationary satellite, observes the East China Sea hourly during the daytime (8 times observation in daytime) and provides nice opportunity to show the diurnal variation of the marine environment which different from the multi-satellite observations during a day. In this study, the hourly remote sensing data of Chlorophyll-a from July 19 to September 30, 2016 in the East China Sea is reconstructed and reanalyzed using the Data Interpolation Empirical Orthogonal Functions (DINEOF). The missing Chlorophyll-a data has been filled and the good and optimal reanalyzed image can be obtained, when considering the hourly continuous GOCI observations. Some detailed features can be found and the diurnal change of Chlorophyll-a can be shown from the reanalyzed images. In the future, the re-analyzed chlorophyll-a products could reveal its tempo-spatial variation features basically and can describe or reappear the Chlorophyll-a distribution characteristics in multi-scale processes.

Two unusual phytoplankton bloom events were identified in the southwestern Bay of Bengal from MODIS-derived chlorophyll-a concentration data collected between 2003 and 2015. The occurrence of the unusual phytoplankton bloom in December 2005 (called Bloom 1 in this study) has been reported in the literature to be triggered by multiple forcings, including strong cyclonic eddy, frequent typhoons, and strong wind-induced mixing. Interestingly, the other unusual phytoplankton bloom (called Bloom 2 in this study) was identified in almost the same location in December 2013. Further, it is the strongest bloom during our study period with large area of high Chl-a > 1.0 mg/m³ and shared some similar features with Bloom 1, such as wide coverage and long duration. At the same time, there were also frequent typhoons and a cyclonic eddy. The possible causes of Bloom 2 were examined using time series of multi-satellite datasets, including sea surface height anomalies (SSHA), sea surface temperature (SST), together with Argo profile data. We found that the cyclonic eddy might be not yet the dominant factor for Bloom 2 as the eddy was much weaker than that of Bloom 1. Specially, SST in December 2013 was lowest among all the December from 2003 to 2015. That is, the stratification is weakest. Therefore, the weak stratification can be broken easily by mixing induced by typhoons and cyclonic eddies and finally result in the strong bloom. This comparative studies could provide us some insight in understanding the role of eddies and tropical cyclones in phytoplankton dynamics in the Bay of Bengal.

Results of satellite observations of the Southeastern Black Sea are presented. Our work is aimed to the development and enhancement of satellite remote sensing technics for monitoring of sea surface oil pollution caused by the natural seepages of hydrocarbons from the seabed. We showed that multi-sensor approach to the satellite remote sensing survey contributes to a more comprehensive interpretation of the data and helps in developing a better understanding of the sea surface film pollution pattern. By using high-resolution satellite data, we were able to get a clear picture of a spatial and temporal variability of surface oil films and to show that their geographical distribution correlates with geographical locations of natural hydrocarbon seeps in this region. We used SAR for the precise estimation of the actual seafloor source location. We investigated the effect of surface winds and currents on transport, spreading, evolution, and persistence of oil slicks on the sea surface. We further demonstrated the importance of the effects of dynamic and circulation processes and natural factors (current meandering, vortical activity, and wind patterns) on the trajectory and fate of the released oil. We put together detailed maps of the sea surface oil pollution caused by natural hydrocarbons showings from the sea bottom in the south-eastern of the Black Sea and outlined the regions of the heaviest pollution.

The ocean is a huge carbon pool in the earth, and about half of the anthropogenic emissions of carbon dioxide are absorbed by the ocean each year. By converting inorganic carbon into organic carbon, the photosynthesis process of phytoplankton affords an important way for carbon sequestration in the ocean. According to previous researches, primary production (NPP) and the structure of phytoplankton community are important in regulate the efficiency of biological carbon pump. This study examined the spatiotemporal variability of satellite remote sensing derived chlorophyll a concentration (Chla), phytoplankton carbon biomass (Carbon), composition ratio of micro-, nano- and pico- phytoplankton, NPP and integrated particulate organic carbon (IPOC) during 1998-2007 in the South China Sea (SCS). Micro-, nano-phytoplankton and NPP showed similar seasonal variation with highest values in winter (January) (especially in the western ocean of Luzon Strait) and lowest values in summer (July) in SCS. Chla, phytoplankton carbon biomass, and IPOC showed different seasonal trends with one peak values occurred in winter and lowest in spring. Two sampling areas (A, N:17-21°, E:117.5-120° and B, N:12.5-15°, E:112-119°) in SCS were selected based on spatial distribution of the standard deviation of research parameters mentioned above. Compared to Chla, phytoplankton carbon biomass, NPP and IPOC, the interannual changes of phytoplankton community structure were remarkable in the two areas. The fraction of micro- and nano- phytoplankton in SCS tend to rise when La Nina events occur. Our results contribute to an understanding of the response of phytoplankton to climate change in the marginal sea. To quantify the efficiency of biological carbon pump in this area, more attention should be paid to the development of remote sensing algorithms of export NPP (or POC export flux) as well as the regulate mechanism of export NPP.

Hangzhou Bay waters are often characterized by extremely high total suspended particulate matter (TSM) concentration due to terrestrial inputs, bottom sediment resuspension and human activities. The spatial-temporal variability of TSM directly contributes to the transport of carbon, nutrients, pollutants, and other materials. Therefore, it is essential to maintain and monitor sedimentary environment in coastal waters. Traditional field sampling methods limit observation capability for insufficient spatial-temporal resolution. Thus, it is difficult to synoptically monitor high diurnal dynamics of TSM. However, the in-orbit operation of the world’s first geostationary satellite ocean color sensor, GOCI, thoroughly changes this situation with hourly observations of covered area. Taking advantage of GOCI high spatial-temporal resolution, we generated TSM maps from GOCI Level-1B data after atmospheric correction based on six TSM empirical algorithms. Validation of GOCI-retrieved normalized water-leaving radiances and TSM concentration was presented in comparison with matched-up in-situ measurements. The mean absolute percentage differences of those six TSM regional algorithms were 24.52%, 163.93%, 195.50%, 70.50%, 121.02%, 82.72%, respectively. In addition, the discrepancy reasons were presented, taking more factors such as diversified satellite data, various study area, and different research season into consideration. It is effective and indispensable to monitor and catch the diurnal dynamics of TSM in Hangzhou Bay coastal waters, with hourly GOCI observations data and appropriate inversion algorithm.

The South China Sea (SCS) is one of the largest marginal seas in the world, and the air-sea CO₂ flux in the SCS may contribute significantly to the global air-sea CO₂ flux. In the past decade, many researches on the aquatic pCO₂ and air-sea CO₂ flux mainly in the north SCS were carried out based on the underway measurement of the pCO₂, and the results revealed that the SCS is a source of the CO₂ as a whole in the annual scale. However, the air-sea CO₂ flux is high spatial variability in the SCS, for example, the north shelf of the SCS is a CO₂ sink while the basin is a source. To monitor the spatial and temporal variations of the air-sea CO₂ flux in the SCS, few satellite remote sensing algorithms have been developed to estimate the aquatic pCO₂ in the north SCS. However, these algorithms are all the empirical models which depend on the training dataset from the in situ measurement. In this study, we apply the semi-analytical algorithm MeSAA to retrieve the aquatic pCO₂ in the SCS basin. The MeSAA algorithm was proposed by the Bai et al. (2016) and was evidenced to be widely applicable to the different marginal seas including the East China Sea and Bering Sea. Based on the underway measured aquatic pCO₂ and water temperature, we found that the variation of the pCO₂ in the SCS basin is mainly controlled by the temperature. In addition, the increase of the atmosphere pCO₂ can also contribute the systematical increase of the aquatic pCO₂. Therefore, we established a semi-analytical algorithm for the aquatic pCO₂ retrieval in the SCS basin, which considers the thermodynamic effect and air-sea CO₂ fluxes. The results showed that the thermodynamic effect in the SCS basin was consistent with the theoretical result with the aquatic pCO₂ increasing 4.23% for the 1°C rising of the water temperature. Moreover, the satellite-retrieved aquatic pCO₂ match well with the in situ pCO₂. Based on the established algorithm, the monthly time-series of the aquatic pCO₂ in the SCS basins from 2003 to 2016 were generated from the MODIS datasets from both the Aqua and Terra satellite, and the long-term trends of the aquatic pCO₂ in the different parts of the SCS basin were analyzed.

Tide gauge data provided by the University of Hawaii Sea Level Center and daily sea surface temperature (SST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) product are used in this study to analyze the influence of tide on the SST in the seas of Northwestern Pacific. In the marginal region, the climatology SST is lower in the northwestern area than that in the southeastern area. In the coastal region, the SST at spring tide is higher than that at neap tide in winter, but it is lower in other seasons. In the adjacent waters of East China Sea and Yellow Sea, the SST at spring tide is higher than that at neap tide in winter and summer but it is lower in spring and autumn. In the open ocean region, the SST at spring tide is higher than that at neap tide in winter, but it is lower in other seasons. In conclusion, not only the river discharge and topography, but also tides could influence the SST variations, especially in the open ocean region.

The Bay of Bengal (BOB) is a semi-enclosed marginal sea in the northeastern part of the Indian Ocean. The South China Sea (SCS) is almost an enclosed marginal sea and is part of the northwestern Pacific Ocean. Both of them are tropical marginal seas, and have similar hydrological properties, such as high surface temperature, stable thermocline, deep euphotic zone, etc. Moreover, they are all greatly affected by East Asian monsoon and typhoons. However, there are also several significant differences between them. The current circulation structures in the South China Sea are more complex with significant season variations. A large amount of fresh water through river inputs is one of the remarkable hydrological characteristics in the Bay of Bengal. In addition, the Bay of Bengal has a large volume of precipitation. Therefore, it is naturally interesting to investigate the different response of the marine ecological properties represented by chlorophyll concentration to climate change. The Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) mission have provided a successive, long-term observations of global ocean color from space. In this study, we investigate the Chl-a trends in the BOB and SCS during the SeaWiFS and MODIS observation periods (1998-2010 and 2003-2016), respectively. In 1998-2010, Chl-a increased in the western central basin of BOB, while it decreased in almost all other parts. During 2003-2016, Chl-a significantly increased in the western part of BOB, and reduced in the southern part. In the SCS, Chl-a in almost the whole region increased during the SeaWiFS observation period. In 2003-2016, the major rising trends appeared in China and Vietnam coast, the Beibu Gulf, and the Gulf of Thailand. The Chl-a of the Straits of Malacca and the Karimata Straits showed downward trends. The rise in temperature might be the main cause of the Chl-a decrease in the BOB. The deepened MLD and/or decreasing SST may contribute to the increasing Chl-a of the BOB and SCS. Remarkably, coastal Chl-a has continued to rise over the past 19 years. The freshwater input might have significant effect on it.

Applications of different radar and optical methods for detection of oil pollutions based on the effect of damping of short wind waves by surface films have been extensively studied last decades. The main problem here is poor knowledge of physical characteristics of oil films, in particular, emulsified oil layers (EOL). The latter are ranged up to 70% of all pollutants. Physical characteristics of EOL which are responsible for wave damping and respectively for possibilities of their remote sensing depend on conditions of emulsification processes, e.g., mixing due to wave breaking, on percentage of water in the oil, etc. and are not well studied by now. In this paper results of laboratory studies of damping of gravity-capillary waves due to EOL on water are presented and compared to oil layers (OL). A laboratory method used previously for monomolecular films and OL, and based on measuring the damping coefficient and wavelength of parametrically generated standing waves has been applied for determination of EOL characteristics. Investigations of characteristics of crude oil, oil emulsions and crude OL and EOL have been carried out in a wide range of surface wave frequencies (from 10 to 25 Hz) and OL and EOL film thickness (from hundredths of millimeter to a few millimeters. The selected frequency range corresponds to Bragg waves for microwave, X- to Ka-band radars typically used for ocean remote sensing. An effect of enhanced wave damping due to EOL compared to non emulsified crude OL is revealed.

In order to detect both precise peaks of the surface and the bottom, in this study, we separated the sea waveforms into three sub-types, such as extreme shallow-water, shallow-water, deep-water after the land and sea waveform classification. Then an algorithm based on FFT was devised and the results were tested on actual airborne LiDAR measurements from a case study.

With its broad spatial coverage and fine temporal resolution, ocean color remote sensing data represents an effective tool for monitoring large areas of ocean, and has the potential to provide crucial information in coastal waters where routine monitoring is either lacking or unsatisfactory. The semi-analytical or empirical algorithms that work well in Case 1 waters encounter many problems in offshore areas where the water is often optically complex and presents difficulties for atmospheric correction. Zhejiang is one of the most developed provinces in eastern China, and its adjacent seas have been greatly affected by recent rapid economic development. Various islands and semi-closed bays along the Zhejiang coast promote the formation of muddy tidal flats. Moreover, large quantities of terrestrial substances coming down with the Yangtze River and other local rivers also have a great impact on the coastal waters of the province. MODIS, VIIRS and GOCI are three commonly used ocean color sensors covering the East China Sea. Several ocean color products such as remote-sensing reflectance (R_rs) and the concentrations of chlorophyll a (Chl-a) and total suspended matter (TSM) of the above three sensors on the Zhejiang coast have been evaluated. Cloud-free satellite images with synchronous field measurements taken between 2012 and 2015 were used for comparison. It is shown that there is a good correlation between the MODIS and GOCI spectral data, while some outliers were found in the VIIRS images. The low signal-to-noise ratio at short wavelengths in highly turbid waters also reduced the correlation between different sensors. In addition, it was possible to obtain more valid data with GOCI in shallow waters because of the use of an appropriate atmospheric correction algorithm. The standard Chl-a and TSM products of the three satellites were also evaluated, and it was found that the Chl-a and TSM concentrations calculated by the OC3G and Case 2 algorithms, respectively, were more suitable for use in the study area. Moreover, GOCI has been proved to be effective for monitoring the diurnal dynamics in coastal waters, and the concentration of TSM had a good negative correlation with water level. Overall, compared with MODIS and VIIRS, GOCI is more effective for monitoring the fine changes and diurnal dynamics in the seas adjacent to Zhejiang Province.