Amongst the manifold human pressures impacting marine ecosystems, eutrophication and extreme weather events frequently result in microalgae coastal proliferation. Accurate observation of massive phytoplankton blooms in the coastal ocean is however challenging because phytoplankton composition and concentration can change over short time scales and because their spatial distribution typically displays small scale variability. While remote sensing can provide spatially and temporally resolved observations, the existing satellite missions present limitations in temporal, spatial, and/or spectral resolution when it comes to monitoring phytoplankton blooms in estuaries, bays, fjords, or coastal lagoons. Compared to previous missions, Sentinel-2 (S2) has been demonstrating improved capabilities for the detection of nearshore harmful algal blooms (HABs) due to its ability to synoptically observe inland and coastal waters every 5 days at a spatial resolution higher than 20 m in 10 spectral bands from the visible to short-wave infrared spectral domains. In addition, S2 provides accurate measurements of the water reflectance due to the development and implementation of atmospheric correction and deglinting algorithms specifically designed for optically complex coastal zones. Due to its enhanced observation capability, S2 therefore made it possible to detect and document several highly-concentrated phytoplankton blooms over a variety of coastal and inland waters during the last six years.
Despite such recent improvements, the majority of HAB remote sensing investigations generally focused on specific regional case studies. Therefore, only a modicum of bloom forming microalgae species has been separately documented so far. The objective of the present study is to propose a broader perspective for the remote sensing assessment of phytoplankton blooms, in order to better resolve the optical diversity of green, golden, red and brown seawater discolorations associated with the taxonomic and pigmentary diversity of phytoplankton coastal proliferation and/or accumulation. For that purpose, a database of high-biomass blooms and phytoplankton seawater discoloration events, both documented in situ and synchronous to S2 image acquisition, has been compiled from the IOC-ICES-PICES Harmful Algae Event Database (HAEDAT), from the French Phytoplankton and Phycotoxin Monitoring Network (REPHY), and from several phytoplankton events reported in the literature. Altogether, about 100 bloom records were compiled in more than 20 countries worldwide. For the selected blooms, available in situ information reported that the phytoplankton community was generally dominated by one or two microalgae species. The compiled S2 HABs database covered 25 different bloom forming phytoplankton species belonging mainly to the dinoflagellates, ciliates and cyanobacteria groups. The spectral characteristics of the remote-sensing reflectance (Rrs) were then extracted for each dominant species, and analyzed in order to evaluate S2 ability to distinguish phytoplankton optical types in the case of highly concentrated and quasi-monospecific coastal blooms.
Whatever the species, all Rrs spectra displayed a typical red-edge pattern with a valley at 665 nm and a peak at 705 nm, resulting from the interplay between the optical properties of pure seawater and chlorophyll-a at high concentration. Optical discrimination of the dominant bloom forming species could therefore not be based on the sole red-edge pattern. The variety of bloom optical types was better resolved when investigating Rrs spectral shape variability over all S2 spectral bands from 443 to 900 nm. Preliminary results from commonly used normalization and classification methods suggest that at least six bloom types could be distinctly identified using S2, including blooms dominated by Trichodesmium (a phycoerythryn-rich cyanobacteria), Noctiluca scintillans (a dinoflagellate causing orange seawater discoloration), and Mesodinium rubrum (a ciliate responsible of burgundy red tides). These species present typical pigment profiles and/or physiological characteristics that give them distinctive Rrs features. For the cyanobacteria other than Trichodesmium, blooms dominated by Nodularia spumigena and/or Aphanizomenon flosaquae displayed a Rrs spectrum whose shape significantly differs than those of blooms dominated by the Microcystis and/or Anabaenopsis genera. For the dinoflagellates other than Noctiluca scintillans, the Rrs variability was high at both intra- and interspecific levels, therefore generating a higher risk of confusion. However, blooms dominated by species such as Lingulodinium polyedrum or Prorocentrum sp. displayed a different Rrs shape than blooms dominated by Karenia sp. Blooms dominated by Lepidodinium chlorophorum or Margalefidinium polykrikoides could be confused with non-dinoflagellates blooms dominated by the haptophyte Phaeocystis globosa.
While the present compilation is inherently limited to the available concomitant in situ and satellite HAB datasets, it aims at shedding light on the visible tip of coastal eutrophication amenable to multispectral satellite detection. Improving the capability of remote sensing to detect massive red tides and phytoplankton seawater discoloration is indeed crucial because coastal algal proliferation is likely to increase in both frequency and amplitude in the next decades due to escalating nutrient runoffs from agriculture and nearshore urban development.
To our knowledge, this is the first time that a Rrs library of massive coastal blooms has been compiled from the S2 archive. The main reflectance types could guide a first guess identification of the dominant bloom-forming species in the absence of in situ information. Such spectral library also interestingly complements studies based on laboratory phytoplankton cultures, where accurate measurements of the inherent optical properties are performed at species level. It is however challenging to measure Rrs in the lab, as well as to extrapolate laboratory results to the scale of real phytoplankton events. By providing the bulk signature of typical bloom types that also contains the second order variability associated with non-algal colored constituents, the compiled S2 Rrs library could benefit to the development of optical models for coastal waters. From a satellite mission perspective, the current limitations of our S2-based reflectance classification are also useful to highlight which bloom forming species present a risk of confusion, and for which further work is required using either an enhanced spectral resolution and/or advanced clustering algorithms.

Since 2011, Caribbean small island developing states (SIDS) have been impacted with inundations of a large seaweed, Sargassum, creating a multitude of economic and environmental issues. Sargassum inundations impacts tourism, fisheries, endangered nesting turtles and seabirds, and coastal ecosystems including coral reefs. The source of the Sargassum is in the North Equatorial Recirculation Region (NERR), in the North Atlantic, southeast of the Caribbean Sea. A large bloom of Sargassum forms annually in the summer months and is referred to as the great Atlantic Sargassum belt (GASB). In its peak, the GASB can extend to the Gulf of Guinea, West Africa to the Caribbean Sea with a wet weight of 29 million metric tons but varies annually. Factors that might influence the growth of the GASB include increased nutrients, from either river discharge or wind-driven upwelling, and a change in sea surface temperature. Movement of Sargassum between the GASB and the Caribbean Sea, however, is more influenced by wind and waves as well as the large-scale ocean currents.

Sargassum can be observed in optical imagery using both the Alternative Floating Algae Index (AFAI) and the Maximum Chlorophyl Index (MCI). MCI data is available from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) products at 300m resolution. AFAI data is available using Moderate Imaging Spectroradiometer (MODIS) products at a 1km resolution. Due to the temporal resolution needed, AFAI images of Sargassum are used to create a time-series of the bloom distribution but smaller key areas may be examined using MCI data at a higher resolution. A combination of Earth observation data with particle tracking tools (Ocean Parcels) using a Copernicus Marine Environment Monitoring Service global ocean reanalysis model will be used evaluate the role of possibly physical and biogeochemical drivers of the GASB’s growth and variability.

This research project aims to answer three key questions to further understanding of the GASB phenomenon:
1. Why did the GASB form in 2011?
2. What is the likely cause of known intra and inter annual variability in the GASB?
3. Does the GASB form in the west of the Equatorial Atlantic, and is it greatly influenced by the Amazon River plume size and direction?

It is possible that the effects of Climate Change caused a ‘tipping point’ that shifted Sargassum from the Sargasso Sea in the North Atlantic to the NERR. As the GASB is vulnerable to a range of physical and biogeochemical processes, further understanding of the GASB involves looking at the ecosystem of the Equatorial Atlantic which is made accessible due to Earth observation data.

Macroalgal beds provide important ecosystem functions and services, such as contributing to carbon sequestration and biodiversity. Macroalgal abundance along polar coastlines, including those in Greenland, the world’s largest island, may be changing due to ocean warming. The remote and harsh environment makes in situ monitoring difficult and costly. Therefore, a lack of baseline data in Greenland complicates efforts to assess changes in macroalgal abundance. Satellite remote sensing has proven to be an effective large-scale monitoring tool for submerged vegetation in shallow and relatively clear coastal waters. Here, we provide the first fjord-scale distribution estimates for intertidal and shallow, submerged macrophytes (primarily macroalgae but also some seagrass) in the Nuup Kangerlua, Kobbefjord, and Ameralik fjord systems using 10-m resolution Sentinel-2 MSI images. Macroalgal abundance is estimated by applying commonly used vegetation indices (FAI, NDVI, EVI) and supervised classification algorithms. The Sentinel-2 images are also used to quantify the spatiotemporal variability of ice and turbidity to examine possible relationships between the impacts of light availability and the distribution of macroalgae. Classification of coastal macroalgae in this region is complicated by large seasonal changes in solar zenith angle, sea ice cover, icebergs, and turbidity. Other challenges include frequent cloud/fog cover, large tidal range, proximity of exposed intertidal macroalgae to terrestrial vegetation, and topographic shading. Furthermore, submerged macroalgae in these fjord systems do not form canopies that float on the surface. Our classification targeted submerged, sub-tidal macroalgae and intertidal macroalgae whose degree of exposure varies with the tidal stage. We minimized the seasonal influences of solar zenith angle and sea ice by limiting the analysis to the months of May-September. Centimeter-scale drone orthomosaics and drop-camera images were used to validate the satellite-derived macroalgae distributions. As topographic shading and turbidity limit detection in some areas, and macroalgae growing at water depths deeper than 10 m are not detected, the Sentinel-2-derived macroalgae distribution presented here should be considered a conservative estimate. Our approach to classify coastal marine macrophytes in complex fjord environments in Greenland form the basis for expanding the mapping along in coastal Greenland and elsewhere in the Arctic.

Tropical regions harbour some of Earth’s most productive and diverse marine ecosystems, which provide important services for human populations. Under future scenarios of global climate change, potential alterations to phytoplankton ecological indicators are predicted to occur in the tropical oceans. Therefore, there is an increasing requirement for the synoptic monitoring of such indicators in marine ecosystems. Phytoplankton phenology and size structure are key ecological indicators that influence the survival and recruitment of higher trophic levels, marine food web structure, and biogeochemical cycling. For example, the presence of larger phytoplankton cells within marine ecosystems supports food chains that ultimately contribute to fisheries resources. In addition, the timing of food availability in tropical marine ecosystems has far-reaching impacts on higher trophic levels, reef-dwelling organisms, and coastal fisheries that are an invaluable economic resource in tropical regions. Monitoring these ecological indicators can thus provide important information to help understand the response of marine ecosystems to environmental change. Here, we use an interdisciplinary approach to investigate the interannual variability of phytoplankton ecological indicators in response to climate warming within tropical sub-regions of the global oceans. Remotely-sensed datasets are used to derive and investigate the mechanistic linkages between phytoplankton indicators and the physical environment, and where possible, we extend our analysis using available in situ datasets (e.g., BGC-Argo) that describe concurrent biophysical changes within the deeper layers of the water column. Furthermore, we attempt to bridge the gap between long-term trends in phytoplankton dynamics, regional warming, and stocks of commonly-fished, planktivorous pelagic species (e.g., anchovies and sardines) over the oceanic regions of interest. We note that the results of this research are preliminary and are currently ongoing as part of a recently-funded European Space Agency Living Planet Fellowship. This project aims towards understanding the trophic linkages between phytoplankton indicators and fisheries under a changing climate.

Overexploitation of marine fishing grounds is a worldwide problem that is increasing through the last decades analogous to the introduction of new fishing technologies and the reduction of fish stocks. As the fish population in an area decreases, professional fishermen must put more effort to sustain their production numbers and in turn increase the pressures on the ecosystem creating a perpetual loop that inevitably leads to economic and environmental collapse. Therefore, sustainable development in the fisheries industry requires a balance between viable production rates and fish stock monitoring by the competent authorities to intervene if and where it is necessary.

The present study aims to establish a processing chain for monitoring the large-scale distribution of small pelagic fish using remote sensing data. Copernicus Sentinel-3 data are used to calculate the oceanic parameters such as chlorophyll-a, which drives the primary production, and Sea Surface Temperature (SST) from which many species depend. Daily Sentinel-3 data are used for the calculation of chlorophyll-a concentration and SST, from the sensors OLCI and SLSTR. To compensate for missing pixels due to cloud cover and invalid measurements, a spatiotemporal interpolation method has been developed to produce Level-3 information. The identification and localization of mesoscale oceanic fronts are performed by a gradient-based technique. For this process, the original, interpolated data are used for each parameter to calculate the ocean color and thermal fronts for the chlorophyll-a and SST parameters respectively.

Another variable that is related to the spatial distribution of small pelagic fish is the oceanic circulation and specifically the formations of permanent and seasonal mesoscale oceanic fronts. For this purpose, data for the daily circulation characteristics are computed from an in-house developed gradient-based methodology. These variables, with the addition of bathymetry data, are used in a Random Forest regression model to identify the daily Potential Fishing Zones (PFZs). The model is calibrated and validated with the use of field acoustic measurements of fish biomass.

Final PFZ daily data are produced with the co-assessment of all the variables with a dedicated trained Random Forest regression model. For the training of the model real, four years of acoustic data of sardines (Sardina pilchardus) and anchovies (Engraulis encrasicolus) are used as collected in the framework of the Mediterranean Acoustic Survey (MEDIAS) carried out in the North Aegean Sea (Eastern Mediterranean). In correlation to the field measurements, the model results present an accuracy of 81% on identifying areas with high biomass, showcasing the great potential for detecting near real-time PFZs. In terms of variable importance, the higher score has Chlorophyll-a concentration and after it, in descending order, the bathymetry, the SST followed by the thermal and ocean color fronts. Even though oceanic fronts hold a vital role in small pelagic fish distribution the correlation depends on various factors, such as seasonality, the width and scale of the fronts, and the unique biological and oceanographic characteristics of each surrounding area. As such, further study improvement must examine the field data's spatial relation to the observed permanent or seasonal fronts and classify them in order of importance for the local fish populations.

The proposed methodology can equip the fishery management authorities with the necessary tools for accurate and continuous monitoring of the fishing fields. This information can lead to sustainable development in the fishing sector with a better understanding of the spatial and temporal system dynamics. Furthermore, the results of the model consist of ready-to-use daily data layers that can easily be incorporated into other scientific fields, such as biodiversity studies, without the need for GIS and spatial analysis expertise.

With the aim of safeguarding species diversity of our oceans, it is fundamental to investigate and track the pressure of fishing fleets. Poaching is a global problem that can damage the biodiversity of both terrestrial and marine protected areas. The unsustainable exploitation of fish stocks by overfishing in protected areas during a time when the fishing is not allowed is a contributing cause for fish population decline and grave environmental damages. Indeed, pirate fishers often use prohibited fishing gears and unregulated practices potentially fatal for entire marine habitats and ecosystems.

Transceivers equipped onboard ships, including Vessel Monitoring Systems (VMS) and Automatic Identification System (AIS), can be used for identifying ships and tracing their routes. However, the IMO (International Maritime Organisation) regularizes the usage of such monitoring systems only for specific classes of ships (> 300 tons). Besides, this collaborative system can be intentionally turned off during unlawful activities, which according to Frontex occur most of the time night-time.

On the other hand, ship detection methodologies based on spaceborne Synthetic Aperture Radar (SAR) images promote extensive applications to achieve effective maritime security, thanks to the big amount of available data and cloud penetrating capabilities. In addition, the synergetic exploitation of multifrequency/multimission data can provide better treats at sea by using multiple images gathered with short time intervals (below 15 minutes) and with different radar backscattering properties. In this context, the COastal Area monitoring with SAR data and multimission/multifrequency Techniques (COAST) project aims at


1) identification of non-cooperating and visible ships in SAR images by means of pre-selection and discrimination approaches applied to multi-frequency data acquired from different missions, selected on the basis of revisit time analysis;
2) identification of non-cooperating and non-visible ships in SAR images, by means of identification and study of wakes in multi-frequency SAR images;
3) classification and 3D reconstruction of ships by means of SAR tomographic techniques;
4) joint use of SAR and AIS data for estimation of motion components (direction, heading, speed) and characterization of ships.

To achieve these purposes, a ship detection pipeline is constructed for multimission data analysis including Sentinel-1, Cosmo-Skymed, and SAOCOM data. The study is carried out by implementing a cascade approach that employs a sub-look analysis (SLA) for the removal of false alarms after the pre-screening. Tomographic SAR processing of fully polarimetric data sets acquired in multi-baseline (MB) interferometric SAR configuration, instead, is used to analyze and characterize the scattering mechanisms of complex targets, as ships. A polarimetric approach to SAR tomography, indeed, is a useful tool to characterize different components of complex volumetric media, in terms of height positions but also by their physical properties.
All these techniques are used for maritime applications including illegal fishing, maritime trafficking, and management of protected coastal areas.