This PDF file contains the front matter associated with SPIE Proceedings Volume 10784, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

We examine the potential for ocean color (OC) retrievals using a neural network (NN) technique recently developed by us to make up for the lack of a 678 nm florescence band on VIIRS, previously available on MODIS and important for Karenia brevis harmful algal bloom (KB HABs) retrievals.. NN uses VIIRS Remote Sensing Reflectance (Rrs) at 486, 551 and 671 nm to retrieve phytoplankton absorption at 443nm, from which both KB HABs and chlorophyll [Chla] concentrations can be inferred. NN retrievals are compared with retrievals obtained using other algorithms, including OCI/OCx and Semi-analytical algorithm for both complex and open ocean waters. VIIRS KB HABs retrievals in the WFS, using NN and other algorithms, are first compared against all co-incident in-situ cell count measurements available between 2012-16. Next, we compared retrievals obtained for different algorithms using in-situ radiometric Rrs measurements against sample measurements, 2017-18, for both the WFS and Atlantic coasts. Retrieval statistics showed (i) the important impact of short term (15-20 minutes) temporal variations and sample depth considerations in complex bloom waters. These limit satellite retrieval accuracies and utility; and (ii) particularly for high chlorophyll bloom waters, better retrieval accuracies were obtained with NN followed by OCI/OCx algorithms. Likely rationales: the longer Rrs wavelengths used with NN are less vulnerable (i) to atmospheric correction inadequacies than the deeper blue wavelengths used with other algorithms, and (ii) to spectral interference by CDOM in more complex waters.

Frequency and intensity of the harmful algal blooms (HABs) increased globally since 1970s. The increase in HABs have negatively affected aquatic ecosystem and aquaculture industry. The economic losses were about $ 1 billion in Europe, $ 100 million in USA and $ 121 billion in Korea per year. There were various field monitoring campaigns for ecological and biological researches. However, traditional HABs monitoring has limitations on both spatial and temporal coverage. In these days, multispectral remote sensing methods using satellite sensors have been widely used to monitor HABs in ocean and coastal areas. However, the satellite systems used in ocean and coastal research, such as MODIS, SeaWiFS and etc. have limitations in study on complex coastline, because of their coarse spatial resolution (~ few km). In this research, we conducted two-year intensive monitoring on the South Sea of Korea from 2016 to 2017 at 62 sampling station and used landsat-8 operational land imager (OLI) satellite that has 30m spatial resolution. We used 4 band (band 1 to 4), 4-band ratio (band 1 over band 3 and 4, and band 2 over band 3 and 4) and mixed dataset of 4 band and 4-band ratio. The empirical OC algorithms showed poor performances, under 0.25 of r-squared. The machine learning techniques, i.e., artificial neural network (ANN) and support vector machine (SVM) were applied to enhance performance of estimating chl-a on landsat-8 application. Parameters for developing ANN and SVM model were optimized using a pattern search algorithm in MATLAB toolbox. All dataset were divided into 80 % of training and 20 % of validation data. In the training step, mixed dataset showed the best performance in both ANN and SVM models, whereas 4-band ratio and 4 band dataset in the validation step showed the best performance in ANN and SVM, respectively. The ANN model showed poor performance in low chl-a concentrations but SVM had more accurate performance in low and mid concentrations. Both models under-estimated chl-a in mid to high concentration range. For the mapping results, the ANN model using 4 band dataset showed very low concentration of chl-a in most of research area, whereas SVM showed high concentration of chl-a in coastal area and bay. The result using 4-band ratio dataset showed similar chl-a distribution in ANN and SVM. For mixed dataset results the ANN model estimated over 8 mg m-3 of chl-a at some of coastal, almost zero in near coastal area and over 2 mg m-3 chl-a concentration for off-shore area. In case of SVM, all region showed approximately 2 mg m-3 of chl-a concentration. Landsat-8 OLI was not proper system for OC algorithms. Machine learning techniques were effective tools for enhancing ocean chl-a estimation performance using landsat-8 OLI. Thus, this study showed potential of landsat-8 OLI application to coastal HAB monitoring.

Vortex structures of different types are common in the Southeastern Baltic Sea. Intensive western winds, a complex coastline and an absence of steady currents make this region very appealing for studying the nature of vortex processes. These processes are clearly identified from Space. We present results of a multi-year satellite monitoring of mesoscale and submesoscale vortex structures in the Southeastern part of the Baltic Sea and of supporting field studies. An important part of our work was accumulation of remote sensing data as the first step in evaluation of circulation patterns and vortex structures presented on the sea surface. An analyzed remote sensing data include color composite images from MSI Sentinel-2, OLI Landsat-8 and ETM+ Landsat-7 as well as radar images from Sentinel-1 and Radardsat-2 that also provide a powerful tool for an identification of circulation processes on the sea surface. Continuous monitoring of the Southeastern Baltic Sea with an analysis of satellite visual and radar images show that the Gulf of Gdańsk is the most frequent area with mesoscale and submesoscale eddies. They appear in this area mostly in summer and mainly under the atmospheric influence. Some eddies remain stable for at least of 8 days and could be easily tracked on color composite images. The other area of the vortex structure was determined to the north of the Cape Taran. In summer months from 2014 to 2018, we performed oceanographic concurrent experiments in the southeastern part of the Baltic Sea. Field studies of spatial and temporal characteristics of vortex structures proved results found by an analysis of satellite images.

Canada’s coastline presents challenges for navigational charting. Within Arctic regions, in situ surveying presents risks to surveyors, is time consuming and costly. To better meet its mandate, the Canadian Hydrographic Service (CHS) has been investigating the potential of remote sensing to compliment traditional charting techniques. This paper focuses on an evaluation of sensors and techniques for operational Satellite Derived Bathymetry (SDB) implementation. Analysis focused on Cambridge Bay, Nunavut using Pléiades, SPOT, WorldView and PlanetScope imagery. Multiple SDB techniques were applied to evaluate their agreement with in-situ bathymetric measurements: • An empirical logarithm band ratio approach. • A multiple band modeling technique. • A multi-dimensional Look-Up-Table approach. Through this analysis, CHS attempted to answer critical questions for operational SDB implementation: • Do specific optical sensors offer advantages for SDB? • Are there advantages/disadvantages with the application of SDB techniques within the examined environment? • Can multiple SDB techniques improve CHS’s understanding of the confidence it can place in remotely sensed bathymetry estimates? Early results have achieved overall root mean square errors of 0.56 to 0.99 m relative to in situ survey depths for all sensors and techniques. These similarities suggest that CHS can be confident in the accuracies observed from various SDB approaches. Results do not indicate significant advantages or disadvantages of particular optical sensors, suggesting other factors contain greater importance for SDB image selection (e.g. sea floor visibility). While this analysis provides excellent information for operational empirical SDB implementation within Arctic environments, further work is required within other Canadian coastal regions to support national SDB application.

In this study an optical empirical algorithm was used to evaluate the robustness of some experimental processes to derive Satellite-Derived Bathymetric (SDB) models for shallow waters in the Minho river mouth, Caminha, Portugal. Multiple procedures to calibrate and modelling SDB calculation were tested. Regarding to these procedures, the following approaches were studied: i) Based on TOA and WL reflectance, a linear regression, as proposed by Stumpf et al.[3], and a quadratic regression to modelling the SDB algorithm were tested; (ii) several in situ data sets acquired at different epochs were used in order to investigate the dependency of the temporal and the spatial density of the calibration sample; (iii) The tidal level was considered in order to provide a tide height at the time of image acquisition to depth models calibration. Numerous SDB models from Sentinel-2 imagery were derived and compared with a reference hydrographic survey data. The results show that SDB models present a good level of reliability regardless of the acquisition date or source of the in situ datasets. Coefficients of determination (R²) higher than 50% were obtained for the majority of the tested procedures. The quadratic modelling approach also appear to retrieve SDB models in agreement with hydrographic surveys data. In addition, the results demonstrate that the SDB information is influenced by the spatial density variation of the calibration datasets. Furthermore, the operational capabilities of the synergy of optical and synthetic aperture radar (SAR) data to derive SDB information in shallow waters will be briefly discussed.

The system waveform (SWFM) of a pulsed LiDAR is obtained from the pulse shape received when pointing the sensor towards a flat, extended target with the surface normal equal to the laser beam direction. The SWFM is determined by the shape of the outgoing laser pulse and the transfer characteristics of the receiver. Knowing the SWFM is essential for performing highly accurate range measurements, for interpreting the LiDAR waveforms correctly, and to derive additional attributes for detected target returns. Often the actual SWFM is not known explicitly, and a Gaussian pulse shape is used in lieu thereof. However, the Gaussian pulse, despite its advantageous properties, does not properly address asymmetries and ringing effects typically present in real-life SWFMs. We present a model of the SWFM composed of harmonic and exponential terms which is able to account for these effects while at the same time being mathematically easy to handle. Unfortunately, the approximation of data by a sum of harmonics and exponentials belongs to the class of ill-posed problems. Nevertheless, we present a pragmatic solution to the problem and demonstrate the versatility of the resulting model.

Two flow irradiance model solutions are tested using various bottom depths and hyperspectral in-situ bottom reflectance signatures for Elkhorn Coral, Bleached Reef, Sand, Sand and Seagrass, and Turtle Grass. Bottom reflectance signatures are used to simulate a water surface reflectance signatures from analytical solutions that can account for the effect of a collimated irradiance signal or direct sunlight within the water column. Simulated reflectance signatures are generated as a function of depth and wavelength in an optically clear water column and a turbid estuarine water column containing concentrations of chlorophyll-a, seston, and dissolved organic material. Simulated surface reflectance signatures as a function of water depth are then used to predict bottom reflectance signatures. Comparison of in-situ bottom reflectance signatures to simulated bottom reflectance signatures predicts model depth sensitivity at a 95% confidence level. Spectral window solutions for water column depths are detected for bottom types and estuarine type water column concentrations. Model can be coupled with bathymetry and high resolution water surface sensor derived reflectance signatures to monitor or map bottom variations for surveillance, environmental monitoring, fishing, or dredging applications in coastal waters or very shallow estuarine waters.

Going on toward the objectives of NAOMI (New Advanced Observation Method Integration) research project, Total and ONERA are working on hyperspectral imagery to detect, characterize and quantify spills at sea. An important part of this work consists in building a database of oil and water-in-oil emulsion reflectance. This database of spectral signatures will be used to analyze the properties of a slick thanks to hyperspectral imagery in the VNIR+SWIR domain and spectral matching techniques. The characterization of the hydrocarbons performed first in laboratory has been completed with a pool experiment. The aim of such an experiment is to measure more realistic spectral signatures in term of background and thickness than in laboratory. Starting from the sample of the emulsion released at sea during NOFO 2015 experiment, emulsion has been remixed once for laboratory measurements and second for the pool experiment. Indeed, its reflectance was measured in the laboratory but for a quite large thickness and it was difficult to predict how the thickness would be once the emulsion freely spread at the water surface. Moreover, depending on the thickness, a signature mixing emulsion and water background could be obtained. In such case, the signatures measured in the lab and in the pool may differ significantly. As a consequence, the use of spectral signature measured in laboratory may give poor spectral matching results. In order to get the answers, a pool experiment, piloted by ONERA in the frame of the NAOMI collaboration with TOTAL, was organized at CEDRE in Brest (France). CEDRE’s pool is usually used for oil recovery equipment testing or people operating such equipment training. Thus, the pool is large (1900 m²), fairly deep (3 m) and filled with ocean water. Known volumes of several products, including the NOFO 2015 emulsion, were successively poured into a delimited area within the pool. Two hyperspectral cameras put on board a cherry picker located on the pool quay were lifted at about 15 m above the delimited area in order to take images of each small spill. The obtained spectral signatures have been compared with the laboratory ones. Detection algorithms have been applied to the pool hypercubes in order to identify the pixels covered by the NOFO 2015 emulsion and a thickness assessment has been performed. A wide characterization of the NOFO2015 emulsion has been done thanks to laboratory measurements, in pool experiment and airborne images from the NOFO campaign in 2015. The paper will present the pool experiment and the corresponding image processing. A quick recall of the laboratory measurements will be done before presenting the pool and laboratory spectral signatures comparison. Then the spectral signatures will be compared with data from an airborne image of the 2015 NOFO campaign. Finally, a conclusion will be drawn concerning the information that can be extracted from hyperspectral airborne imagery for such kind of emulsion and more generally.

Unmanned Aerial Vehicles (UAVs) have shown great capability for disaster management due to their fast speed, automated deployment and low maintenance requirements. In recent years, disasters such as flooding are having increasingly damaging societal and environmental effects. To reduce their impact, real-time and reliable flood monitoring and prevention strategies are required. However, the limited battery life of small lightweight UAVs imposes efficient strategies to subsample the sensing field in this context. This paper proposes a novel solution to maximise the number of inspected flooded surface while keeping the travelled distance bounded. Our proposal solves the so-called continuous Travelling Salesman Problem (TSP), where the costs of travelling from one location to another depend not only on the distance, but also on the presence of water. To determine the optimal path between checkpoints, we employ the fast sweeping algorithm using a cost function defined from hyperspectral satellite maps identifying flooded regions. Preliminary results using MODIS flood maps show that our UAV planning strategy achieves a covered flooded surface approximately 3.33 times greater for the same travelled distance when compared to the conventional TSP solution. These results show new insights on the use of hyperspectral imagery acquired from UAVs to monitor water resources.

Hyperspectral in-situ attenuation measurements have been conducted within the Indian River and Palm Bay estuaries. Station data is used to help assess the light attenuation depths obtained from satellite imagery such as WorldView-3. Measurements made during 2016-2018 suggest that optimal photosynthetic light needed for submerged plant species may not be available or optimal plant growth. The attenuation data indicates that in the estuarine waters influenced by dissolved organic matter, gelbstoff, and suspended particulates and seston, only the red portion of the light spectrum reaches deeper waters. In these waters, submerged vegetation are not present but underwater videos shows the eater bottom is readily visible except during high wind and wave events that resuspend bottom boundary layer mud and muck sediments. The measurements of water attenuation is also affected by water wave facet reflectance as shown by Monte Caro model results.

Optimization techniques are used in inversion of ocean color remote sensing reflectance measurements, where the error between forward modelled spectra and observed spectra is minimized. In this study, NASA Bio – optical Marine Algorithm Dataset (NOMAD) is used to test the performance of global optimization technique based on Multi-Verse Optimization (MVO) for retrieval of Bulk and Individual Inherent optical properties (IOPs) from Remote sensing reflectance (Rrs). The results are compared with other global optimization algorithms such as Particle Swarm Optimization (PSO) and Genetic algorithms (GA) in terms of their statistical goodness of fit and computational time requirements. MVO (743.82 secs) offered computational fastness over both PSO (1261.8 secs) and GA (3818.8 secs). The RMSE values in log space, obtained for bulk IOPs, i.e., total absorption coefficient at 440 nm and total backscattering coefficient at 555 nm using MVO (0.264,0.265), PSO (0.264,0.265) and GA (0.264, 0.274) respectively show that MVO performed either better or similar to PSO and GA. In case of individual IOP retrieval i.e., log scale RMSE values obtained for absorption due to phytoplankton at 440 nm (MVO – 1.038, PSO – 1.200, GA – 1.215), absorption due to gelbstoff at 440 nm (MVO – 0.272, PSO – 0.272, GA – 0.273) and backscattering due to particulate matter at 555 nm (MVO – 0.228, PSO – 0.227, GA – 0.238) showed similar performance as in bulk IOP retrieval. MVO can thus be used effectively on satellite imagery data for retrieval of IOPs owing to its faster computational capability and comparable or better performance to existing global optimization algorithms.

Abstract: The measurement of ocean optical parameters is an important part of ocean research. According to the transmission of the blue and green laser pulse, the lidar return signals were analyzed, and the vertical profiles of the lidar attenuation coefficient were studied with airborne polarization lidar. Simultaneously, the absorption coefficient and extinction coefficient of South China Sea were measured using AC-S. Comparing the lidar and in situ measurements, we found that the lidar attenuation coefficient is between the absorption and extinction coefficient. The correlation analyses of lidar attenuation coefficient with absorption and extinction coefficient were carried out respectively, and it was shown that they have a good correlation. Overall, the results indicated that the airborne polarization lidar is an efficient way to detect the profiles of ocean and the combination of airborne lidar and in situ measurements provide comparable and complementary information about ocean optical parameters.

The catastrophic explosion which destroyed the DeepWater Horizon (DWH) oil rig in the Gulf of Mexico, in April 2010, caused, in a period of three months, the discharge of some 4.9 million barrels of oil. The DWH remains the largest accidental marine oil spill in the history of petroleum industry. In order to detect the oil slick, and to measure its extent and geo-location, we present a methodology based on the use of SAR images and of stochastic process theory. The task of scene interpretation makes use of pixel potential functions supported by the Ising model. The scheme is applied to an ASAR image of the Gulf of Mexico, at the time when the DWH oil spill had already completely developed. The result shows both the labelled field of the image elements and the extent of the oil spill itself. The MRF model and the parameters of the stochastic optimization procedure are fully described.

A shape of marine slicks is an important characteristic which can be used when solving a problem of detection and identification of oil spills on the sea surface. Slick shape and its spatial-temporal evolution depend on many environmental processes, such as wind speed, nonuniform marine currents, internal waves, etc. In the context of the problem of oil spill dynamics it is very important, particularly at initial stages of oil spill evolution, to describe correctly processes of oil film spreading. Until recently the most popular was the Fay’s model of film spreading which, however, could not correctly explain some obvious effects, e.g., asymmetry of film slicks in the downwind and crosswind directions. In this paper new results of field studies of spreading of surfactant films are presented. The experiments with spills of surfactants were conducted on the Gorky water reservoir using a methodology of contouring slicks with a GPS receiver mounted on a motor boat, and also aerial photography from UAV. The following results have been obtained. First, the effect of elongation of oil spills in the wind direction, revealed in our previous experiment, is confirmed. Quantitative data on growth rates of along- and cross-wind slick axes are obtained characterizing initial stages of the spreading process. Second, new effects have been revealed which are: a) saturation of the cross- and along-wind axes at some intermediate stages of slick evolution, and b) further decrease of the along wind slick axis and the slick square, and a tendency to a circular shape at late stages of the slick evolution. A physical model, explaining qualitatively the observed effects is developed.

We calculate water-mass (WM) transformation and formation rates in thermohaline (θ-S), density (σ) and geographic coordinates over three years for three ocean basins; the North Atlantic, North Pacific and Southern Ocean by partitioning surface heat and freshwater fluxes into bins of sea surface salinity and temperature (SSS, SST) and density (σ). The three years correspond to the overlap between the SMOS and Aquarius SSS products with the SST product being that from OSTIA. Surface heat and freshwater fluxes were taken from the NOCS climatology V2.0, OAFLUX and the satellite based CMORPH dataset for evaporation and precipitation respectively. Results from SMOS and Aquarius satellite derived datasets are inter-compared followed by a comparison between the literature locations of Mode Waters (MW) in σ, θ-S and geographic co-ordinates and SMOS SSS. Then a sensitivity experiment was performed – utilising a MonteCarlo (MC) simulation – where we show the relative contributions of SSS and SST on WM formation through perturbations introduced to the satellite SSS and SST datasets. We aim to demonstrate and evaluate the feasibility of satellites at characterising the distribution and dynamics of WM’s via a comparison with literature.

The influence of wind-driven surface waves and air bubbles in water on the polarization characteristics of spaceborne oceanic lidar return signals is investigated through the meridian planes Monte Carlo method. The calculation results demonstrate that the depolarization ratio of lidar return signals increase with the increasing wind speed and nadir angle. However, compared with air bubbles the influence of surface waves could be neglected. The presence of air bubbles with high concentration in the upper ocean has a strong impact on the depolarization ratio. The polarization characteristics of the lidar return depend on the air bubble concentration, the bubble microstructure and the optical properties of oceanic waters. The change of the depolarization ratio is significant when the air bubble concentration exceeds 10⁵ m^-3 in open ocean water and 5×10⁵ m-3 in coastal water.

Dual-polarized microwave radars are of particular interest nowadays as perspective tool of ocean remote sensing. According to conventional models the microwave radar backscattering at moderate and large incidence angles is determined by resonance (Bragg) surface waves of cm-scale wavelength range, and by non polarized (non Bragg) component which is associated with wave breaking and is supposed to be independent on polarization. At present our understanding of physical origin of different components of radar return is still insufficient. In particular, an important problem of variations of Bragg and non Bragg components (BC and NBC, respectively) along the profile of a long surface wave remains poorly investigated. This paper is focused on data processing and analysis of results of field studies of BC and NBC variations over the long wave profile using dual co-polarized X-band radar. It is demonstrated that the intensities of Bragg and non Bragg components are non-uniformly distributed over the long wave profile: BC is not strongly modulated due to long surface waves and dominates near the long wave troughs. NBC is characterized by the appearance of strong spikes near the crests of intensive long waves and contributes significantly to the radar return in the spikes supposedly due to intensification of breaking of short, cm-dm-scale wind waves. It is shown that relation between BC and NBC changes in the presence of surfactants on the water surface because of different suppression of the two components in slicks.

The upwelling appears generally off the southeast of Vietnam coast in summer. Previous studies have shown that under the influence of El Niño events, the upwelling would be weakened, charactering with high sea surface temperature (SST) and low Chlorophyll-a concentration (Chl-a). However, a different pattern of upwelling appeared in summer 2016, a decaying period of a strong El Niño event. There was a high SST and low Chl-a in June and July, which were similar with that of 1998 and 2010, another two El Niño years. However, we found a strong upwelling together with a moderate phytoplankton bloom off the southeast of Vietnam in August 2016. The analysis of the wind data and SST data indicated that the southwesterly summer monsoon played an important role on this particular case. The abrupt intensification of the southwest wind in late July of 2016 resulted in the SST cooling with nearly one-week delay, which meant that the great significance of the Madden-Julian Oscillation (MJO) occurred. The continuous intensification of the southwest wind enhanced the upwelling associated with Ekman pumping and offshore Ekman transport. As a result, the high-nutrient water of the subsurface was brought into the upper layer, which induced the high Chl-a, and cold-water mass spreading northeastward offshore.

Satellite and airborne remote sensing results are shown and indicate the need for continued development of simultaneous use of sensors systems on the same platform, simultaneously collecting imagery at different spectral and spatial resolutions. When multiresolution imagery is collected simultaneously, high quality spatial and spectral image fusion techniques can be applied to the different sensor field of views that helps to improve detection of earth surface features and targets. Multispectral imagery fused with hyperspectral imagery described and results shown in this paper indicates the ability to detect subsurface water features along Florida littoral zones and coastal water regions. The use of multiresolution imaging from the same remote sensing platform provides a valuable approach for detecting sun glint regions and reflectance due to water wave facets and the presence of breaking waves. In littoral regions along Space Coast Florida and the Indian River Lagoon, surface bidirectional reflectance increases 5-6 times the background water surface reflectance, making typical water column constituent estimation questionable. The detection of the influence of surface water gravity waves can be modeled or easily detected using ground sampling distances or spatial resolutions less than or near 0.3 m. Spatial resolutions of approximately one meter or greater do not allow suitable detection of pixels affected by water waves. Satellite imagery based directional reflectance using ground sampling distance greater than 1 meter is contaminated by water wave facet reflection, breaking waves and sun glint effects. Thus bio-geophysical variables such as chlorophyll pigments, suspended matter, dissolved organic matter and light attenuation depths are biased. This bias effect can only be estimated reliably by using small spatial resolution imagery with knowledge gained from analytical and Monte Carlo radiative transfer modeling and multiresolution fused imagery. Application of transferable remote sensing algorithms for water quality applications require bidirectional reflectance corrections for water waves, sun glint, and wave facet effects. Example high quality WorldView-3 multispectral and panchromatic clearly shows the water wave facet reflectance effects that is imbedded into larger GSD imagery of water regions and littoral zones and as shown in hyperspectral Monte Carlo model results.

The work studies submesoscale fronts (with characteristic transverse scales lower than the Rossby internal radius of deformation) in inland seas on the base of remote sensing data. We show that we can significantly broaden our insights into the physics and geography of small-scale fronts by employing satellite synthetic aperture radar (SAR). During our satellite survey of the Baltic Sea, we obtained many satellite images of the sea surface bearing imprints of fronts of different formation mechanisms and different spatial and temporal characteristics. First we develop a methodology for identifying various submesoscale front signatures in SAR images and list the most common features of front manifestations. We describe characteristic features associated with fronts of different nature which are not usually detectable by traditional observational data sources because of their small scale and because they are often masked by upper-level clouds. The main problem is to discriminate between signatures of fronts in the sea and those in the nearsurface layer of the atmosphere. We validated our interpretations of front features in SAR images via the combined analysis of data on the sea surface and marine atmosphere condition, sea surface temperature, and mesoscale water dynamics.

The inversion of total suspended particulate matter (TSM) from ocean color remote sensing data in coastal waters is still highly inaccurate due to contributions of various oceanic constituents and non-linear independently variation of each other. Since the absorption and scattering by molecules, aerosols, and hydrosols and reflection, transmission over the sea surface, the initially completely unpolarized sunlight becomes partially polarized after transmitting in the coupled atmosphere-ocean system (AOS). Hence, the polarization of the sunlight, which contains embedded information on atmospheric and water optical properties, has largely been neglected. In addition, the parallel polarization radiance (PPR) has two significant advantages in effectively diminishing the sun-glint contamination and enhancing the ocean color signal at the top-of-atmosphere (TOA). In this study, the directional variations in parallel polarized water-leaving radiance of suspended particulate matters in coastal waters, based on the vector radiative transfer simulations (RT), were examined. The simulations reveal that the traditional radiation intensity (I) and parallel polarization radiance (PPR) display significant multidirectional and spectral variations with respect to the observation geometries, and TSM concentrations. Moreover, the water-leaving (Lw) radiance for I and PPR have the same angular distribution pattern and magnitude under different bands. In addition, the relative fraction of Lw to Lt for PPR is large than I, indicating that the PPR can improve to retrieve the Lw radiance at the TOA. Furthermore, an exponent relationship between the Rrs_p and the TSM concentration has been established with low corresponding AD (1.258%) and RMSE (0.202). It demonstrates that the polarization of the Lw radiance is closely related to oceanic constituents, and has great potential for the retrieval of TSM concentrations.

The polarization of water-leaving radiance (Lw) is more sensitive to microphysical properties (e.g. particles shapes, size distributions, compositions, and refractive index) of hydrosols than the unpolarised radiance. Hence, the polarized Lw can be used to extract additional information on oceanic constituents, which is complementary to the spectral and angular radiance measurements. In this study, the polarization characteristics of underwater upwelling radiance in turbid waters with respect to suspended particulate matters have been investigated. The full Stokes components of the underwater upwelling radiance in the visible spectrum are calculated using a radiative transfer model. And then, the influences of suspended particulate matter concentrations on the directional variations and the polarization of underwater upwelling radiance are examined. The results reveal that the polarization of underwater upwelling radiance (I, Q, U, DOP) shows significant multidirectional variations with respect to observation geometries, wavelengths, and solar zenith angles. Moreover, the polarization of underwater upwelling radiance is highly related to the suspended particulate matter concentrations. It demonstrates the potential of using the polarized signal to retrieve particle concentrations in coastal waters. Therefore, the development of in-situ instrumentations and next generation of ocean color sensors should able to measure the polarization properties of water-leaving radiance are recommended.

The system of optical devices for remote sensing of near surface layer of the ocean is created. The system consists of a set of original linear array of CCD-photodiodes for the recording of range – time – intensity images (RTI images) of sea surface from optical sections of sea surface and incoherent two-dimensional optical spectrum analyzer (TOSA) for the recording of the sea wave spectra by spectral analysis of the sea surface images in real time. Two these RTI images with various directions of observations enable one to receive complete information about kinematics characteristic of various manifestations on the sea surface, including sea surface waves regardless of its dispersion relation, near surface wind flow manifestations on the sea surface, internal waves (IW) manifestations, oil slicks owing to its ability to screen objects according to their velocity. The recording of short wave spectra perform by the optical spectrum analyzer synchronous with the RTI images of sea surface. The examples of investigations of long surface wave field, sea wave spectra and manifestations of wind gusts and IW are presented.

Phytoplankton are photosynthetic organisms that live in the upper part of the water's surface. A rapid growth in a short time cause the appearance of blooms which can long-term impact marine ecosystem and fisheries. Chlorophyll-a (chl-a) concentrations, the proxy pigment of phytoplankton biomass, is measured with conventional in-situ analysis methods and retrieved from satellite observations. Currently, remotely sensed ocean color data constitute a rich and important spatiotemporal database that has been exploited in many scientific studies and has been shown to be relevant in phytoplankton dynamic metrics. Here in this study, we analyze 11 years of ocean color observations, MERIS (2003-2012) and MODIS (2012-2013) archives, over Algerian coasts in order to compute the chl-a concentrations corresponding to the start and end of bloom event. The determination is based on one of the most used definition of bloom in pelagic ocean, when chl-a concentrations rise above and fall below the full time series median for each pixel plus 5%. Satellite data were preprocessed considering flags to eliminate contaminated pixels, land, cloud and open water pixels. Spatial distribution mapping of results was done showing that the highest magnitudes are concentrated near the coasts. For statistical comparison purpose, the shelf waters were divided into six regions, limited from north by the 1000 m bathymetric curve and from south by Algerian coastline. The east and west limits for each region are chosen considering the marine meteorological regions. This study highlights for the first time the spatial distribution, at high resolution (250 m), of bloom initiation magnitudes along Algerian shelf waters, taking into account the influence of important wadis (small Mediterranean river) flowing there, the marine meteorology and north Algeria pluviometry. Statistics, particularly means, based on the proposed regions, seems to be relevant because they are consistent with the dominant regional pluviometry. In fact, the precipitation causes a runoff of the lands and this flow enters the sea loaded with nutrients. The obtained values can be used to determine the bloom start timing and others phenological parameters.

A green-red quasi-analytical algorithm, QAA-GRI, was calibrated to derive inherent optical properties (IOPs) using an in situ dataset from Lake Qiandaohu (QDH). First, 510 nm was chosen as the reference band based on the general structure of the quasi-analytical algorithm (QAA). Second, a green-red index (GRI), which was calculated from the remote sensing reflectance at the three wavelengths (510, 560 and 620 nm), was used to retrieve the total absorption coefficients at the reference band, a(510) . A semi-analytical model based on a(510) and the GRI was proposed to replace the empirical model in original QAA. Subsequently, QAA-GRI, is calibrated to analytically retrieve total absorption coefficient for Lake Qiandaohu (QDH). The algorithm was further validated using the in situ data set collected in East China Sea (ECS) on January 1-12, 2016 and May 25-June 2, 2017. The QAA-GRI’s performance in ECS was compared with that of QAA-v5. Our results show that the QAA-GRI performs better in ECS with mean R² of 0.87, compared with that the QAA-v5 of 0.53, and a mean absolutely percentage error of 19.2%, compared with that the QAA-v5 of 24.8%, respectively. These results indicate the potential of QAA-GRI to accurately estimate the IOPs for coastal and inland waters.

Red tide is an ecological anomaly that phytoplankton in seawater suddenly proliferate or aggregate under certain environmental conditions and within a period of time, resulting in seawater discoloration. Red tide not only harms marine fisheries and aquaculture, deteriorates the marine environment, affects coastal tourist industry, but also causes human health problems. East China Sea (ECS) is a region of high incidence of red tide disasters. Remote sensing has been proven an effective means of monitoring red tides. Phytoplankton-specific light absorption plays a fundamental role in the remote estimation of pigment biomass and red tide. This paper retrieves the phytoplankton absorption coefficient in decade based on MODIS data from July 2002 to June 2012 using quasi-analysis algorithm (QAA), analyzes and compares phytoplankton absorption coefficient spectral curves of red tide events with multiyear monthly averaged ones, as well as phytoplankton absorption coefficient spectral differences at the same location during red tide presence and absence. A new red tide monitoring algorithm based on the phytoplankton absorption coefficient is developed to extract red tide information of the ECS. With the application of the algorithm in the ECS, the results reveal that the developed model can effectively determine the location of red tides, with good correspondence to the results from an official bulletin. This demonstrates that the algorithm can effectively extract red tide information.

An at-sensor radiance simualtion environment based on the atmosphere-ocean coupled model OSOAA for case-I and case-II waters is set up for evaluation of arbitrary combinations of sensors. Simulated results show that the 385 nm and 355 nm channels are sensitive to chlorophyll when it is less than 2.5 mg/m³ , sensitive to suspended sediment when it is less than 2.5 mg/L, and sensitive to CDOM when a_CDOM is less than 0.08 m−1. The quantification of dynamic range for TOA (top of atmosphere) radiances should help to design the forthcoming ultraviolet ocean-color satellite sensors, e.g. the UVI (UltraViolet Imager) on HY-1/C (HaiYang-1/C) of China, as well as correctly exploit the satellite data aquired by the future ultraviolet satellite sensors.

The goal of current investigation was a study of seasonal variability of remote sensing reflectance in the lake part of the Gorky reservoir, characterized by intense bloom of blue-green algae. The basis of this study includes the data of ship measurements of remote sensing reflectance, euphotic zone and chlorophyll a and dissolved organic carbon concentrations, performed from May 14, 2018 to August 27, 2018 in 7 points of the reservoir, two of which are located in shallow areas with a slow current (floodplain area), two - in the channel and the remaining three - in estuaries of three inflowing rivers. The spatial and temporal variability of remote sensing reflectance is analyzed, its variations are calculated, vertical profiles of chlorophyll a and dissolved organic carbon are constructed, variations of euphotic depth are determined and correlation between variations of remote sensing reflectance and concentrations of optically active components is established

The present work is shown results of estimating the absorption coefficient of water by underwater sky image (Snell’s window image). This paper presents theoretical model of the Snell’s window image, the algorithm to restore the absorption coefficient of water through the static moments of image, the methodology of performing full-scale measurements and processing obtained data and result of testing offered method on the basis of images obtained in fresh internal eutrophic waters of the Gorky reservoir on the Volga River, characterizing by high concentrations of dissolved organic matter and blue-green algae bloom.

Mediterranean seagrasses are represented by five species, whose most representative are Posidonia oceanica (L.) Delile and Cymodocea nodosa (Ucria) Ascherson. Spatial data analysis through remote sensing techniques is certainly a useful tool in order to understand and quantify the extent or loss of seagrass areas. Seagrass mapping and monitoring by remote sensing have been established using various optical remote sensors and mapping methods1. In most studies for habitat mapping, the most common satellite images used are Landsat, Ikonos, Quickbird, Pleiades, World View 2 and the recent Sentinel-2 etc. 1-2. The aim of this work is to compare the spatial accuracy of medium – resolution satellite images (Sentinel-2 and Landsat-8 OLI) vs. high-resolution images (MIVIS and WorldView-2) for mapping P. oceanica meadows and evaluating their conservation status. The present study was conducted in 2016 within the MPA “Capo Rizzuto” (Mediterranean Sea - Southern Ionian Sea). Remote sensing images were processed following several stages such as preprocessing phase, segmentation, supervised classification and accuracy classification assessment. Preliminary results highlighted differences in spatial and thematic accuracy between medium and very high spatial resolution images for seagrass habitat mapping.

An experimental study of statistical characteristics of fluorescent lidar echo signals and spatial distribution of optically active components in waters of the Gorky reservoir at 40 x 10 km experimental area covering the floodplain, channel and estuaries of inflowing rivers was performed. The measurements were carried out during 4 days from 1 August to 4 August 2017 and from 20 September to 22 September 2018 at the stations and continuously along the motorboat track with the help of fluorescent lidar UFL-9 which allows to restore concentrations of chlorophyll a, colored organic matter and total suspended matter in the upper water layer with thickness of about 0.2 to 1.0 m for eutrophic waters. This paper presents maps of their spatial distribution, demonstrating different scales of inhomogeneities, average values and spatial variability of chlorophyll a, total organic carbon and total suspended matter as well as data on variance and spatial spectrum of fluctuations of the elastic backscattering signal. Regressions between mean values and coefficients of variation of suspension and chlorophyll a, and between coefficients of variation of suspension and energy of the elastic backscattering signal are established.