This PDF file contains the front matter associated with SPIE Proceedings Volume 11857, including the Title Page, Copyright information, and Table of Contents.

Coastal zones are at the interface of land and sea and provide a buffer to storms, wave action, and coastal inundation. Bathymetric mapping of the submerged littoral zone is essential for the understanding of sediment transport and good coastal management and planning. Surf zones are dynamic areas, ever-changing, so there is a need for low-cost, rapid response aerial remote sensing techniques that can provide high temporal and spatial coverage of nearshore bathymetry. However, this is a challenging task given water turbidity, wave action, seafoam, and other issues. With this motivation, this study used a small unoccupied aircraft system (UAS) equipped with a digital RGB camera to collect video footage of wave action on the water surface. The video data was then used to apply a spectral depth inversion algorithm called cBathy and estimate nearshore bathymetry at high resolution. Ground truth data were collected using cross-shore transect surveys to a depth of 2 m for assessment of the UAS-based bathymetry estimates. The video data was split into frames with a frequency of 2 frames per second (fps), and ground control points (GCPs) laid out in the scene were used to perform image georectification. A time stack of image pixel values was then generated from the video data for the cBathy depth inversion algorithm. Accuracy assessment resulted in an overall RMSE of 0.2056 m for an area of 390 m offshore and 400 m alongshore, and the maximum depth achieved was up to 3 m. Results show the potential of the cBathy algorithm to provide reasonable depth accuracies in dynamic and turbid water surf zones. However, results also show that this method has constraints for which users need to be aware of prior to applying it, including the study site’s physical characteristics.

During the last years, many studies related to Satellite-Derived Bathymetry (SDB) emphasize the potential use of optical satellite remote sensing sensors for bathymetric estimation. For this study, ten multispectral SPOT 6/7 satellite images with a medium resolution covering the coastal waters of the study areas were analyzed. These images were geometric, radiometric, and atmospheric corrected and acquired in three different sensing dates having coverage with at least 30% of lidar data. A number of 5284 random depth measurements with 0 to 50 meters depth were acquired for the ratio conversion algorithm with absolute depths and error assessment. A series of steps were performed to obtain reliable results using satellite optical data such as, sun glint process, land/sea extraction, kernel filters. The study area was divided into three sub-regions, based on the sensing date of the satellite imageries. The light attenuation in the water column increases at a depth of about thirty meters as seen in other related studies. This study identified the depth of light attenuation to determine the maximum depth that can be estimated through optical sensors. The results show that better correlation was identified up to 15 meters depth. Results of the regression analysis show the following correlation coefficients R² :0.90, 0.87, 0.80, and 0.89 with the Root Mean Square Error (RMSE) for the respective study areas to be 1.34, 1.53 1.70 and, 1.15.

Coastal bathymetry is a fundamental component in numerous research and engineering applications, however obtaining such data in detail for large areas is rather complicated due to several natural and technical limitations. In order to overcome these difficulties, we developed a pipeline of novel methods based on drone-imagery for producing high resolution, accurate coastal bathymetric data. As a result, this study was based on combination of state-of-the-art, high resolution datasets, including drone-based imagery and reference bathymetric points using a sonar sensor mounted on a remote-controlled unmanned surface vehicle (USV). Our approach relies on fusion of two conceptually different yet complementary methods for bathymetry estimation, a geometric one and a spectral one using deep learning. In the first approach, we developed a novel structure-from-motion technique, that natively incorporates the correct geometrical optics accounting for water refraction. In the second method we built upon the reconstructed surface of the geometric method together with the reference bathymetric data for training a deep convolutional network (CNN) using a set of spectral features from the RGB imagery. The CNN produced high resolution coastal bathymetry with vertical accuracy varying in the decimeter scale. The main advantage of this approach is that it exploits both the spectral and the multi-view aspects of drone imagery which function complementary to each other. The geometric method yields accurate 3D bathymetry over any kind of seafloor that shows sufficient texture on the images and where seafloor texture is absent, spectral information is utilized for harmonizing the bathymetry surface for the entire scene. Finally, this study demonstrates that modern, unmanned platforms can perform accurate coastal bathymetry mapping far more efficiently than traditional boat surveys, although ideal sea-state conditions are required for obtaining imagery data with optimal quality. This work is part of the ACTYS project (https://actys.ims.forth.gr/) that has received funding from FORTH-Synergy grant.

The performance of various standard algorithms for the retrieval of suspended particulate matter (SPM) from Sentinel-2 MSI and Landsat-8 OLI satellite data obtained in 2019 and 2021 is discussed. The study was conducted for the estuaries of 2 mountainous rivers originating in the Caucasus Mountains: the Sulak River flowing to the Caspian Sea and the Mzymta River flowing to the Black Sea. The rivers differ in the degree of flow control and the composition of terrigenous suspended matter carried to the sea. The main objective of the study was to compare SPM retrieval results of the C2RCC (Case 2 Regional Coast Color) processor and the ACOLITE (Atmospheric Correction for OLI ‘lite’) algorithms Nechad 2009, Nechad 2015 and Dogliotti. The satellite data were verified against in situ measurements of turbidity and SPM performed synchronously with the satellite survey. Field measurements from a small boat were performed in April and May 2019, 2021 in the northeast Black Sea, in the mouth area of the Mzymta, and in May 2021 in the Sulak mouth area. The measuring instruments and methods included a turbidity sensor mounted on a CTD (conductivity / temperature / depth) probe, a portable turbidimeter, and water sampling for further laboratory analysis. It was established that for low SPM, 20-30 g/m³, performances of C2RCC and Nechad 2015 practically coincided and correlated well with the in situ data. For large SPM, over 300 g/m³, the best performance was demonstrated by Dogliotti, an algorithm designed especially for extreme SPM values.

Sun-induced fluorescence (SIF) emission can be used as a proxy for chlorophyll-a concentration (chl a) and as an indicator of phytoplankton physiological status. However, retrieving and interpreting the SIF signal is challenging due to physiological factors, optical properties of the atmosphere and the water body, instrumental effects, and assumptions inherent to retrieval schemes. Due to the complexity of factors determining SIF occurrence, lack of measurement protocols and few studies on retrieval methods, its exploitation remains limited, especially in lakes. To address some of these challenges, we assess SIF estimate sensitivity to quantum yield of fluorescence (φF). Often assumed constant, φF exhibits a diel cycle related to light stress, which impacts the interpretation of fluorescence signals. Determining φF allows us to estimate and predict SIF diurnal variability, evaluate how this variability impacts chl a estimates, and gain insight into photosynthetic activity of the phytoplankton community. We address the dynamics of φF by using high-frequency optical measurements in Lake Geneva. From this dataset, we are able to calculate φF, infer the strata where non-photochemical quenching occurs and how this affects the SIF signal detected above water. We compare chl a estimates obtained from fluorescence and absorption measurements to assess at which conditions fluorescence-derived estimates are underestimated, and at which conditions saturation irradiance for photosynthesis is reached. The increasing availability of hyperspectral satellite data could improve SIF retrieval since algorithms utilizing contiguous bands can be implemented. Subsequently, an improved retrieval scheme will allow for better φF estimation. An upcoming satellite mission from ESA, the Fluorescence Explorer,designed to measure terrestrial SIF globally, can potentially be used in aquatic environments. Through this study, we demonstrate how hyperspectral measurements improve SIF signal interpretation by understanding φF dynamics. Despite using in-situ data, our findings can also contribute to the evaluation of SIF estimates from hyperspectral satellite data.

Ocean surface monitoring, especially oil slick detection, has become mandatory due to its importance for oil exploration and risk prevention on ecosystems. For years, the detection task has been performed manually by photo-interpreters using Synthetic Aperture Radar (SAR) images with the help of contextual data such as wind. This tedious manual work cannot handle the increasing amount of data collected by the available sensors and thus requires automation. Literature reports conventional and semi-automated detection methods that generally focus either on oil slicks originating from anthropogenic (spills) or natural (seeps) sources on limited data collections. As an extension, this paper presents the automation of offshore oil slicks on an extensive database with both kinds of slicks. It builds upon the slick annotations of specialized photo-interpreters on Sentinel-1 SAR data for 6 years over 3 exploration and monitoring areas worldwide. All the considered SAR images and related annotation relate to real oil slick monitoring scenarios. Further, wind estimation is systematically computed to enrich the data collection. Paper contributions are the following : (i) a performance comparison of two deep learning approaches: semantic segmentation using FC-DenseNet and instance segmentation using Mask-RCNN. (ii) the introduction of meteorological information (wind speed) is deemed valuable for oil slick detection in the performance evaluation. The main results of this study show the effectiveness of slick detection by deep learning approaches, in particular FC-DenseNet, which captures more than 92% of oil instances in our test set. Furthermore, a strong correlation between model performances and contextual information such as slick size and wind speed is demonstrated in the performance evaluation. This work opens perspectives to design models that can fuse SAR and wind information to reduce the false alarm rate.

A non-homogeneous water column Monte Carlo model is utilized for predicting underwater light fields in shallow estuarine waters with suspended muds/flocs and a bottom lutocline boundary layer. This previously developed model creates synthetic water wave surfaces. Outputs are presented to demonstrate the response to water wave facet slopes at the surface and a lutocline bottom fluid mud boundary layer. Synthetic reflectance spectrums and radiometric quantities are modeled with different solar and sensor zenith and azimuth angles. Measurements in Space Coast Florida waters are used as model inputs. Averaged depth dependent concentration profiles for particulate matter suspended from the bottom fluid mud and lutocline layers are estimated from sondes. In this report vertical profiles of shape factors with various water surface slopes and fluid mud assumptions are shown. Model results suggests that suspended muds in the water column will have unique higher absorption influences upon the photosynthetically active light region of the underwater light field.

The Arabian Gulf is characterized by high water scarcity and quickly growing populations over the years. This led to increase of desalination plants in the coastal areas surrounding it. The aim of this paper is to study the effect of the brine discharge from the desalination plant on the water quality of the Gulf over the years. Remote sensing data was collected for Chlorophyll-a (Chl-a) and Sea Surface Temperature (SST) for the period from 2002 to 2020, while Sea Surface Salinity (SSS) was tracked from 2010 to 2019, obtained from SMOS. Anomalies' graphs for each parameter were obtained and K-mean clustering was used. Additionally, seasonal Mann Kendall Test and time-series analysis were performed at three desalination plants for SST prediction. SST has generally increased especially on the coast inside and outside the Gulf, As for Chl-a, it had a less uniform change, but it had shown an increase outside the Gulf and a decrease inside it with the exception of some coastal areas. K-means clustering generated a best fit with 7 clusters on the map. The most predominant clusters were 1, 3, 4, 6, 7 representing the east of the gulf and the simulated area outside the gulf, coastal areas inside the gulf, the western side of the gulf, and the neck of the gulf and the coastal areas. It can be concluded that there are changes in SSS, Chl-a and SST over recent years in the Gulf. Moreover, SSS data needs further investigation with higher resolution and model enhancement.

As an important area of wetland protection in the Yangtze River Economic Belt, Honghu Lake, is a habitat or wintering place for many wild animals. In the past two decades, the water environment of Honghu Lake has been suffering from serious deterioration due to the rapid development of agriculture and industry in the urbanization of surrounding areas. Under the combined effect of multiple non-point source pollution and point source pollution, cyanobacteria blooms often occur here. As a proxy of eutrophication, chlorophyll-a (Chl-a) has been considered to be an important indicator of water quality parameters. For better understanding of the change of the water quality of Honghu Lake, an improved empirical model for estimating chlorophyll-a concentrations (Chl-a) from different multi-spectral satellites images was established and validated. (1) We combined the results of two-band algorithms(2BDA), three-band algorithms(3BDA), normalized difference chlorophyll index (NDCI) and fluorescence line height (FLH) into support vector machine model (SVM) for better multi-nonlinear relationship establishment between Chl-a concentration and surface water reflectance, which acquired higher model accuracy.(2) Based on the long-term time series data derived from Landsat-7, Landsat-8 and Sentinel-2, the variation of Chl-a concentration of Honghu Lake over long term was obtained. (3) Our results demonstrate that the average chlorophyll concentration has been at a very high level and showing an increasing tendency in recent years, which may indicate the eutrophication in the Honghu Lake is still getting worse.

Remote sensing technologies are useful tools when gathering spatial and temporal information about dynamic coastal regions. A method is presented using an in-situ space-time drone imaging technique for evaluating wave periods and wavelengths of surface water waves in shallow urban coastal water environments. High-definition drone video imagery (viewed near-nadir) of shallow water waves was acquired and time synchronized with littoral video imagery. Drone video imagery records of a wave patch with reflected sun glint and non-affected sun glint surface water waves facets, staff gauges, and a simultaneously deployed line target were used to determine wave periods and wavelengths. Time series analyses was applied to the video derived time series imagery. Wave energy spectrums can be extracted and used to simulate synthetic images using a gravity wave model based upon a Weibull probability distribution that simulates the sea state. Applications in shallow water coastal environments continue to benefit from knowledge of wind driven water waves. Data extracted from the Banana River in Florida was used to determine the procedure and techniques.

The type of bottom resting below the water surface may be difficult to be determined due to the contamination of pixels by glint in satellite reflectance imagery. A nearest neighbor non-glint pixel search algorithm was written in Python to correct glint contaminated pixels. An algorithm was then utilized to estimate water visibility and to analyze the results of the glint corrected pixels by using light attenuation depths and attenuation coefficients based upon glint corrected satellite reflectance in the WorldView-3 (WV-3) image channels. The aforementioned correction was undertaken for sand, mud, and seagrass bottom types. The results suggest that it is possible to estimate attenuation depths (a measure of water visibility).

The paper is focused on investigation of microwave backscattering from wind waves on a clean water surface. Field experiments were carried out in the coastal zone of the Black Sea using dual co-polarized Doppler X-band scatterometer and a three-band Doppler dual co-polarized radar (X-, С-, S-bands). The radar incidence angles were about 50 - 60 degrees, the wind changed in a wide range of speeds. We assumed that microwave backscattering at VV and HH polarizations is composed by a Bragg (polarized) component associated with Bragg waves and a non-polarized component (NBR). Analysis of Doppler spectra of NBR allowed us to remove the effect of strong wave breaking (overturning wave crests) from the time series and to study the backscatter associated only with dm-scale waves. Measurements of wind waves with a wire gauge were carried out simultaneously with the radar monitoring. It is shown that the velocities of non-Bragg scatterers not associated with strong wave breaking in X-, С-, S-bands correspond to the velocities of short dm waves and weakly depend on radar wavelength. The speeds of the scatterers in X-, С-, S-bands associated with overturning wave crests are also close to each other (within the measurement error). The intensity of NBR in X-, С-, S-bands grows with wind speed as well as with the intensity of dm-waves measured by the wire gauge. Strong suppression of NBR and simultaneously measured decrease of short dm-wave intensity are demonstrated, thus confirming the assumption that the intensity of the NBR in X-, С-, S-bands is determined by dm waves.

This paper discusses satellite data usage in monitoring the surface oil pollution in the off-shore oil-producing area "Oil Rocks" in the Caspian Sea. For this study, we examined satellite SAR and VIS data taken over the region of interest in 2017-2020. All in all, we analyzed 558 satellite images. First of all, we present some statistics on the detectability of natural oil showings in SAR and VIS data. We show that the frequency of reliable detection of natural oil showings in satellite images varies greatly and depends sufficiently on a sensor type and the season. We state that the seasonal variability in the detection rate of natural oil showings on the sea surface is most pronounced for the VIS data. We show statistics on individual sizes of oil patches and describe their seasonal and inter-annual variability. We also present statistics on the frequency of detecting oil patches in the Oil Rocks oil-producing area in SAR images depending on near-surface winds and ocean-atmosphere boundary layer conditions. We compare our new results with those obtained in our previous long-term satellite survey of the Caspian Sea and discuss the reliability of quantitative estimates of surfaced oil amounts based on the satellite images. Further, we depict a detailed map of the spatial distribution of oil slicks detected in satellite imagery taken over the oil-producing area and outline high-risk areas of the sea surface oil pollution and areas exposed to potential risks.

Investigation of the mixing processes in river confluence zones is an important problem, particularly in application to ecological monitoring of rivers and inland waters. Such processes can be very complex and can affect riverbed deformation, mixing and pollution transport, etc. A typical example is a confluence zone (CZ) of the Volga and Oka rivers (Russia). In this paper, the Volga/Oka CZ signatures in satellite optical (Sentinel-2 MSI) and radar (Sentinel-1 SAR) images are analyzed for the period of ice/snow melting in early spring. First, the ice melting appears immediately downstream of the meeting point of the rivers. At the early stages of the ice cover melting the CZ is seen as a narrow dark band in the optical images and as a bright band in radar images because of wet snow/ice cover within the CZ. The faster melting in the CZ than outside can be associated both with the emissions of warm water from factories/thermal power plants and with water transportation towards the CZ by weak transverse currents in the river flows. The enhanced radar backscatter in the CZ can be associated with an increase of the snow/ice moisture and thus an increase of snow/ice cover roughness. With air temperature increase, an open water patch with fragmented ice appears transformed later into a long polynia. The polynia is manifested as a dark band in radar imagery presumably due to the suppression of wind waves associated with fragmented ice. This happens even at moderate wind velocities normally exceeding the threshold of wind wave excitation.

The present paper considers the possibility of a system for environmental monitoring of film pollution in the Gorky reservoir. A combination of the well-known approachs for calculating the drift trajectories of passive particles on the sea surface with the data of remote sensing, which provides primary detection of a pollution spill in the scanned area is proposed. X-band digital coherent radar was the source of remote data. Based on this radar, an automated radar system is being developed for the automatic detection of film contaminants on the water surface. The results of tests of the algorithm for automatic detection of film pollutions based on field measurements are presented. The model component of the system is based on the results of measurements of current velocities and the physical dependence of the slick drift. As a result of comprehensive studies, the developed system has demonstrated operability for detecting and predicting the spread of film pollution in the Gorky reservoir.

On the base of laboratory experiments on co- and cross-polarized microwave signal scattering on a wavy water surface, the dependences of NRCS on wind speed and friction velocity at high wind speeds were obtained. It is shown that the cross-polarized NRCS demonstrates sensitivity to the wind speed when its value is more than 20 m/s, in contrast to the co-polarized NRCS. Based on the analysis of the Doppler spectra, it was suggested that the backscattered signal is formed mainly on wave breakers. This assumption was verified by analyzing the dependence of the scattered signal power at both polarizations on the area of the white-cap coverage, which revealed their direct dependence. Based on the phenomenological approach used in statistical physics, a parametrization of the dependence of the white-cap coverage fraction on the wind friction velocity was proposed. This parametrization was based on the use of the universal Gibbs method, the central concept of which is a canonical ensemble or an ensemble of a thermodynamic system states which are in a weak thermal contact with a "thermostat". In this case, the atmospheric boundary layer acts as a thermostat, and the entire ensemble of states of the sea surface, including breakers, is the canonical ensemble. Based on this parametrization, the GMF was proposed to retrieve the wind speed and wind friction velocity for wind speeds above 40 m/s, which also considering the angular dependence of the NRCS.

The preliminary attempts to develop of the geophysical model function (GMF) for the retrieval of wind speed and wind stress in hurricanes, based on a dependency between the cross-polarized satellite SAR data from Sentinel-1 and wind speed or turbulent stress obtained from collocated NOAA GPS-dropsondes data array. Field measurements in the Atlantic Ocean during hurricane season in the period 2001-2018 were analyzed. Using the data measured by GPSdropsondes, due to the ensemble averaging, mean wind velocity profiles were obtained, and the atmospheric boundary layer parameters drag coefficient and turbulent stress (or friction velocity) were retrieved from the “wake” part of the velocity profiles taking into account a self-similarity property of the velocity profile “defect”. The parameters were retrieved for 25 major hurricanes of categories 4 and 5. The collocation of Sentinel-1 images and GPS-dropsonde data was made for the hurricanes Irma 2017/09/07, Maria 2017/09/21 and 2017/09/23, taking into account the assumption that turbulent boundary layer parameters in the hurricanes remain quasistationary. The dependencies of the cross-polarized normalized radar cross-section (NRCS) on the wind speed and wind friction velocity were obtained, the results were compared to the data for small and moderate winds, represented in [1], a good agreement is demonstrated. In the region of high wind speeds the relation between NRCS and the wind friction velocity becomes ambiguous, it may be explained by the dependency on the hurricane sector.

The present paper is devoted to the development of a multisensory approach to hydrophysical measurements of the ocean surface layer. The combination of coherent acoustic sounding, coherent radar sounding, and optical stereo photography is considered. Each of the methods separately has several advantages, but it is not free from disadvantages. Coherent acoustic sounding, described by resonant scattering, has a large observation area but has a poor spatial resolution. Coherent radar sensing, also described by resonant scattering, has a smaller observation area but a higher spatial resolution. Both methods allow us to recover the velocity characteristics of scattering associated with the velocities of resonant scatterers. Using the relationship between sea surface elevations and orbital wave velocities, it is possible to determine the spatial spectra of wind waves in a wide range of wind wavelengths up to several meters. To determine the characteristics of shorter-scale wind waves, it is proposed to use data from optical stereo photography. As a calibration for the optical system, information on the elevation of the sea surface obtained from coherent acoustic and radar sounding data can be used. Thus, the spatial spectrum of wind waves can be reconstructed in the range of energy-carrying to gravitational-capillary waves. In addition to the spectral characteristics of waves, it is possible to determine the velocity of the near-surface flow by measuring the Doppler shift of acoustic and radar signals. High-resolution data obtained from stereo photography of the sea surface allow clarifying the relationship between the velocities of acoustic and radar scatterers and hydrometeorological parameters.

It is well known that marginal ice zones are characterized by different forms of initial stages of ice such as, e.g., grease and fragmented ice which act as surface wave absorbers and thus affect microwave radar backscattering. As a result, mapping of boundaries between solid ice and open water areas using radar may become rather complicated. Another aspect of the problem of wind wave damping due initial stages of ice is that the areas of strong wave damping due to ice can be erroneously interpreted as surface pollutions in radar imagery. Studies of wave damping due to ice floes are still insufficient, and relations between the floe geometry and wave damping are poorly established. The motivation of this study is to improve our understanding of the process of wave damping due to ice floes for elaboration of physical models of wave damping. New wave tank experiments were carried out to investigate the damping of regular mechanically generated waves and of irregular wind waves due to drifting floe imitators (washing sponges) as well as for the case of stationary, non moving floes. Dependencies of the damping coefficient on wave frequencies for regular and wind waves for different floe sizes and different areas occupied by the floes were obtained. One of the most interesting results was that the damping coefficient indicated a local maximum when the floe size was about half the wave length. A physical interpretation of the results was given, based on the analysis of floe movement under the action of the orbital wave motion taking into account the floe added mass.

It is known that organic and mineral films appear in microwave radar or optical aircraft/satellite images as the areas of reduced intensity due to suppression of short wind gravity-capillary waves (GCW) - slicks. The suppression of GCW with wavelengths ranged from some millimeters to decimeters can be characterized in terms of film elasticity. Hence, marine slicks in radar/optical images can be quantitatively described if the film elasticity is known. The elastic properties of monomolecular films have been thoroughly studied, while the problem for thick films, particularly for crude oil films remains poorly investigated. The latter are characterized by strong inhomogeneity in thickness. This paper is focused on laboratory analysis of GCW attenuation due to non-uniform films. The damping of GCW was measured in laboratory using a method of parametric excitation of standing GCW in a vertically oscillating cuvette mounted on a vibration table. Laboratory measurements were performed for highly inhomogeneous films of pure dodecyl alcohol. When the surfactant concentration exceeded the values corresponding to the saturated monomolecular layer, the surfactant excess was concentrated in non-spreading drops (lenses) of macroscopic thickness of 1-3 mm. The GCW attenuation coefficient was studied for GCW frequencies of 10 to 20 Hz and for different sizes and number of lenses. It was found that the attenuation coefficient increased with the relative area of the lenses. A physical explanation of this effect was proposed based on the “lens-wall” model, when assuming that the lenses reduced the area of the monomolecular film and, accordingly, increased the wave attenuation. Theoretical analysis of wave damping based on a “lens-wall” hypothesis has demonstrated good consistence with the experiment. The effective elasticity of a two phase film -a monomolecular layer with a lens phase- is introduced, which replaces the two-phase film with an effective monomolecular film.