This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields.

High quality independent ground measurements that are traceable to metrology standards, with a full uncertainty budget, are required for validation over the lifetime of ocean-colour satellite missions. In this paper, we used radiometric Fiducial Reference Measurements (FRM) collected during four Atlantic Meridional Transect (AMT) field campaigns from 2016 to 2019 to assess the performance of radiometric products from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3A (S-3A) and 3B (S‐3B), the Moderate Resolution Imaging Spectroradiometer instrument aboard Aqua (MODIS-Aqua), and the Visible Infrared Imaging Radiometer Suite instrument aboard Suomi NPP and NOAA-20 (Suomi-VIIRS and NOAA-20 VIIRS). The AMT provides one of the few sampling platforms that make high-quality in situ radiometric measurements in oligotrophic, low chlorophyll-a oceanic waters for ocean colour satellite validation. In situ data were acquired and processed following established FRM protocols, calibrated to metrology standards, referenced to inter-comparison exercises and with a full uncertainty budget. From these we selected an uncertainty threshold, which we used as part of a matchup procedure that takes into account the temporal and spatial variability of both the in situ and satellite data. Three atmospheric correction models were compared for S-3A and S‐3B OLCI radiometric products; the standard OLCI IPF-OL-2, POLYMER and NASA SeaDAS l2gen. Based on the round-robin comparison, POLYMER provided the best performance in the retrieval of water-leaving radiances. The analysis showed that Suomi-VIIRS and MODIS-Aqua performed better than NOAA-20 VIIRS, and comparably with S‐3B OLCI standard products. The S-3A OLCI standard product outperformed the NASA products. The S-3A OLCI and S‐3B OLCI instruments were also compared during their tandem phase, which showed that S‐3B OLCI radiances were systematically higher than S-3A OLCI across the spectrum.

Water is an essential natural resource, but increasingly water also forms a threat to the human population, with floods being the most common natural disaster worldwide. Earth Observation has the potential for developing cost-effective methods to monitor risk, with free and open data available at the global scale. In this study, we present the application of remote sensing observations to map flooded areas, using the Vembanad-Kol-Wetland system in the southwest of India as a case study. In August 2018, this region experienced an extremely heavy monsoon season, which caused once-in-a-century floods that led to nearly 500 deaths and the displacement of over a million people. We review the use of existing algorithms to map flooded areas in the Lake Vembanad region using the spectral reflectances of the green, red and near-infrared bands from the MSI sensor on board Sentinel-2. Although the MSI sensor has no cloud-penetrating capability, we show that the Modified Normalised Difference Water Index and the Automated Water Extraction Index can be used to generate flood maps from multi-spectral visible remote sensing observations to complement commonly used SAR-based techniques to enhance temporal coverage (from 12 to 5 days). We also show that local knowledge of paddy cultivation practices can be used to map the manoeuvring of water levels and exclude inundated paddy fields to improve the accuracy of flood maps in the study region. The flood mapping addressed here has the potential to become part of a solution package based on multi-spectral visible remote sensing with capabilities to simultaneously monitor water quality and risk of human pathogens in the environment, providing additional important services during natural disasters.

Following the successes of the first phase of the European Commission Copernicus programme, EUMETSAT is continuing and expanding its offer of data access services, marine data products, as well as marine training activities and user support services, under phase two. EUMETSAT operates the Sentinel-3, Sentinel-6 and Jason-3 satellites, and provides level-1 and level-2 marine data products for ocean colour, sea surface temperature, and altimetry science and applications. User support services include data access, customisation, and visualisation platforms, web-based technical information about products, as well as a helpdesk available to answer a full range of user queries on the products and their use. Interactive training activities are designed to accommodate a diverse range of audiences, both research and operational, putting trainee needs and interests at the centre of learning objectives. A focus on co-development of resources and participant-led learning interventions allows participants to tailor their own experiences towards development of the skills and knowledge that will help them in their own applications and work tasks. Building on four years of successful general courses, EUMETSAT now seeks to develop further specialised training and advanced courses for the marine community. This presentation will showcase existing services and resources, and provide information on planned training events for 2022 and beyond. It will expand on our training approaches and provide further information on opportunities for collaboration with the wider marine community, during the UN ocean decade.

Satellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leaving radiance are affected by interactions with the intervening atmosphere, the efficacy of atmospheric correction algorithms is essential to derive accurate water-leaving radiometry. Modern ocean color satellite sensors such as the Ocean and Land Colour Instrument (OLCI) onboard the Copernicus Sentinel-3A and -3B satellites are providing unprecedented operational data at the higher spatial, spectral, and temporal resolution that is necessary to resolve optically complex coastal water quality. Validating these satellite-based radiance measurements with vicarious in situ radiometry, especially in optically complex coastal waters, is a critical step in not only evaluating atmospheric correction algorithm performance but ultimately providing accurate water quality metrics for stakeholders. In this study, a regional in situ dataset from the Chesapeake Bay was used to evaluate the performance of four atmospheric correction algorithms applied to OLCI Level-1 data. Images of the Chesapeake Bay are processed through a neural-net based algorithm (C2RCC), a spectral optimization-based algorithm (POLYMER), an iterative two-band bio-optical-based algorithm (L2gen), and compared to the standard Level-2 OLCI data (BAC). Performance was evaluated through a matchup analysis to in situ remote sensing reflectance data. Statistical metrics demonstrated that C2RCC had the best performance, particularly in the longer wavelengths (>560 nm) and POLYMER contained the most clear day coverage (fewest flagged data). This study provides a framework with associated uncertainties and recommendations to utilize OLCI ocean color data to monitor the water quality and biogeochemical dynamics in Chesapeake Bay.

Fiducial reference measurements are in-situ data traceable to metrology standards, with associated uncertainties. This paper presents the methodology used to derive the uncertainty budget for underway, above-water measurements from the Seabird Hyperspectral Surface Acquisition System deployed on an Atlantic Meridional Transect in 2018. The average uncertainty of remote sensing reflectance for clear sky days was ∼ 6% at wavelengths < 490 nm and ∼ 12% at wavelengths > 550 nm. The environmental variability such as sun position, wind speed and skylight distribution caused the greatest uncertainty. The different components of the uncertainty budget are critically assessed to indicate how the measurement procedure could be improved through reducing the principal uncertainty sources.

It is now generally accepted that climate variability and change, occurrences of extreme weather events, urbanisation and human pressures on the environment, and high mobility of human populations, all contribute to the spread of pathogens and to outbreaks of water-borne and vector-borne diseases such as cholera and malaria. The threats are heightened by natural disasters such as floods, droughts, earth-quakes that disrupt sanitation facilities. Aligned against these risks are the laudable Sustainable Development Goals of the United Nations dealing with health, climate, life below water, and reduced inequalities. Rising to the challenges posed by these goals requires an integrated approach bringing together various scientific disciplines that deal with parts of the problem, and also the various stakeholders including the populations at risk, local governing bodies, health workers, medical professionals, international organisations, charities, and non-governmental organisations. Satellite-based instruments capable of monitoring various properties of the aquatic ecosystems and the environs have important contributions to make in this context. In this chapter, we present two case studies—the Ganga Delta region and the Vembanad Lake region in south-western India—to illustrate some of the benefits that remote sensing can bring to address the problem of global health, and use these examples to identify the capacity building that is essential to maximise the exploitation of the remote sensing potential in this context.

EUMETSAT provides user support and training for all users of the Copernicus Marine Data Stream (CMDS). The CMDS refers to all the level 1 and level 2 marine data from sensors on the Sentinel-3 and Jason-3 satellites, including ocean colour, sea surface temperature, and surface topography data. Details on the products and processing methodologies are available through handbooks, product notices, and a number of services including a help desk, and online forum. The training service aims to support all users wishing to explore potential applications of the CMDS. The service is primarily based around the delivery of two week, blended courses with both an online and classroom component. The online component is hosted on a Moodle platform and uses a variety of prepared resources including short articles, videos, software installation, and basic software tutorials; supported by discussion forums, to prepare participants for the classroom phase. The classroom phase is focused on practical work, with no lectures given. Participants are led through examples of workflows using SNAP and Jupyter Notebooks/Python, and are then given one-on-one/small group trainer support to work for 3 days on personal projects that they defined during the online phase. These projects yield a diverse range of synergistic use cases of ocean remote sensing data for societally relevant applications The training service has also run a variety of collaborative courses with community led initiatives, and proposes to develop online courses and resources in response to community needs. Feedback and requests from the ocean remote sensing community are welcomed.

A large anticyclonic eddy formed in April 2019 in the Algero-Provencal basin between Mallorca and Sardinia, and lasted until November 2019. While mesoscale activity is usually high in this part of the Mediterranean basin, the formation of such large (about 150 km in diameter) and long-lived eddies is not common. The eddy formed from a filament originated in the Algerian coast and was visible in multiple sources of satellite data, including sea surface temperature and ocean colour from Sentinel-3, until summer. Because of the warming of the surface layer, during summer months the eddy remained as a subsurface structure, evidenced by the sea level anomaly derived from altimetry data. A surface signal developed again in November, and the eddy finally dissipated in December 2019. According to CMEMS model data, in its strongest period the eddy reached about 300 m in depth, and during its sub-surface period the center was located at about 100 m depth. While at the surface the temperature signal was very clear, model data suggest the salinity anomaly was stronger than temperature, especially at depth. Such large and long-lived eddies have an impact in the basin currents, specifically in the transport of cold water from the northern to the southern part of the western Mediterranean basin, influencing the ecosystem there. The impact of the presence of this eddy, its long duration and the additional mesoscale and submesoscale activity that originated in its surroundings are investigated using a combination of remote sensing data, in situ data and model data.

A hyperspectral inversion algorithm was used to distinguish between cyanobacteria and algal blooms in optically complex inland waters. A framework for the algorithm is presented that incorporates a bio-optical model, a solution for the radiative transfer equation using the EcoLight-S radiative transfer model, and a non-linear optimization procedure. The natural variability in the size of phytoplankton populations was simulated using a two-layered sphere model that generated size-specific inherent optical properties (IOPs). The algorithm effectively determined the type of high-biomass blooms in terms of the relative percentage species composition of cyanobacteria. It also provided statistically significant estimates of population size (as estimated by the effective diameter), chlorophyll-a (chl-a) and phycocyanin pigment concentrations, the phytoplankton absorption coefficient, and the non-algal absorption coefficient. The algorithm framework presented here can in principle be adapted for distinguishing between phytoplankton groups using satellite and in situ remotely sensed reflectance.

An extensive study collected in situ data along the Yellow Sea (YS) and East China Sea (ECS) to assess the radiometric properties and the concentration of the water constituents derived from Moderate Resolution Imaging Spectroradiometer (MODIS). Thirteen high quality match-ups were obtained for evaluating the MODIS estimates of Rrs(λ), chlorophyll a (Chi a) and concentrations of suspended particulate sediment matter (SPM). For MODIS Rrs(λ), the mean absolute percentage difference (APD) was in the range of 20%-36%, and the highest uncertainty appeared at 412 nm, whereas the band ratio of Rrs(λ) at 488 nm compared with that at 547 nm was highly consistent, with an APD of 7%. A combination of near-infrared bands and shortwave infrared wavelengths atmosphere correction algorithm (NIR-SWIR algorithm) was applied to the MODIS data, and the estimation accuracy of RIS were improved at most of the visible spectral bands except 645 nm, 667 nm and 678 nm. Two ocean-colour empirical algorithms for Chi a estimation were applied to the processed data, the results indicated that the accuracy of the derived Chi a values was obviously improved, the four-band algorithms outperformed the other algorithm for measured and simulated datasets, and the minimum APD was 35%. The SPM was also quantified. Two regional and two coastal SPM algorithms were modified according to the in situ data. By comparison, the modified Tassan model had a higher accuracy for the application along the YS and ECS with an APD of 21%. However, given the limited match-up dataset and the potential influence of the aerosol properties on atmosphere correction, further research is required to develop additional algorithms especially for the low Chi a coastal water.

Sustained global ocean observations are needed to recognise, understand, and manage changes in marine biodiversity, resources and habitats, and to implement wise conservation and sustainable development strategies. To meet this need, the Global Ocean Observing System (GOOS), a network of observing systems distributed around the world and coordinated by the Intergovernmental Oceanographic Commission (IOC) has proposed Essential Ocean Variables (EOVs) that are relevant to both the scientific and the broader community, including resource managers. Building a network that is truly global requires expanding participation beyond scientists from well-resourced countries to a far broader representation of the global community. New approaches are required to provide appropriate training, and resources and technology should follow to enable the application of this training to engage meaningfully in global observing networks and in the use of the data. Investments in technical capacity fulfil international reporting obligations under the UN Sustainable Development Goal 14A. Important opportunities are emerging now for countries to develop research partnerships with the IOC and GOOS to address these obligations. Implementing these partnerships requires new funding models and initiatives that support a sustained research capacity and marine technology transfer.

Microbiological contamination or elevated marine biotoxin concentrations within shellfish can result in temporary closure of shellfish aquaculture harvesting, leading to financial loss for the aquaculture business and a potential reduction in consumer confidence in shellfish products. We present a method for predicting short-term variations in shellfish concentrations of Escherichia coli and biotoxin (okadaic acid and its derivates dinophysistoxins and pectenotoxins). The approach was evaluated for 2 contrasting shellfish harvesting areas. Through a meta-data analysis and using environmental data (in situ, satellite observations and meteorological nowcasts and forecasts), key environmental drivers were identified and used to develop models to predict E. coli and biotoxin concentrations within shellfish. Models were trained and evaluated using independent datasets, and the best models were identified based on the model exhibiting the lowest root mean square error. The best biotoxin model was able to provide 1 wk forecasts with an accuracy of 86%, a 0% false positive rate and a 0% false discovery rate (n = 78 observations) when used to predict the closure of shellfish beds due to biotoxin. The best E. coli models were used to predict the European hygiene classification of the shellfish beds to an accuracy of 99% (n = 107 observations) and 98% (n = 63 observations) for a bay (St Austell Bay) and an estuary (Turnaware Bar), respectively. This generic approach enables high accuracy short-term farm-specific forecasts, based on readily accessible environmental data and observations.

Penryn, TR10 9FE, United Kingdom 6 2 Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH, United 7 Kingdom 8 3 Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, 9 Barrack Road, Weymouth, DT4 8UB, United Kingdom 10 4 Scottish Association for Marine Science, Oban, Argyll, PA37 1QA, United Kingdom 11 5 Renewable Energy Group, University of Exeter, Penryn Campus, Penryn, TR10 9FE, United 12 Kingdom 13 * Corresponding author: Tel.: +44 1326 259291; E-mail: schmidt.wiebke@gmail.com (W. 14 Schmidt) 15

The differences among phytoplankton carbon ($C_{phy}$) predictions from six ocean colour algorithms are investigated by comparison with \textit{in situ} estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Colour Climate Change Initiative merged product. The matching \textit{in situ} data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and \textit{in situ} data provides a relatively large matching dataset (N$>$500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in $C_{phy}$. Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions ($B$ \textless 0.15 mg\,Chl\,m$^{-3}$), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from -35\% to +150\% and RMSD (unbiased) from 5 to 10 mg\,C\,m$^{-3}$ among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscattering-based algorithms produce different reasults at the clearest waters and these differences are discussed in terms of the different algorithms used for $b_{bp}$ retrieval.

Particulate Organic Carbon (POC) plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-colour signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean colour data requires algorithms that are well validated, with uncertainties characterised. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean colour data provided by the Ocean Colour Climate Change Initiative (OC-CCI) and validated against the largest database of $\textit{in situ}$ POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of \cite{stramski2008} and \cite{loisel2002}) and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.

The Benguela, as a highly productive upwelling system, suffers from the occurrence of a variety of harmful algal blooms, most of which are associated with elevated biomass; a feature common to the shelf environment of upwelling systems. Most harmful blooms have in the past been attributed to one or another dinoflagellate species, but more recently harmful impacts have also been ascribed to other groups of phytoplankton, including diatom and autotrophic ciliate species. Typical bloom assemblages, forcing mechanisms and harmful impacts are outlined, and bloom types most amenable to detection with ocean colour radiometry are identified. Inherent and apparent optical properties of these algal assemblage types are described, and a preliminary evaluation is made of the suitability of available ocean colour data and algorithms. The evolution of several bloom events is described using various algorithms applied to ocean colour data from the Medium Resolution Imaging Spectrometer (MERIS), and recommendations are made about optimal ocean colour usage for high biomass algal blooms in coastal zones.

Synoptic scale knowledge of the size structure of phytoplankton communities can offer insight in to primary ecosystem diversity and biogeochemical variability from operational to the decadal scales. Accordingly, obtaining estimates of size and other phytoplankton functional type descriptors within known confidence limits from remotely sensed data has become a major objective to extend the use of ocean colour data beyond chlorophyll a retrievals. Here, a new forward and inverse modelling structure is proposed to determine information about the cell size of phytoplankton communities using Standard size distributions of two layered spheres to derive a full suite of algal inherent optical properties for a coupled radiative transfer model. This new capability allows explicit quantification of the remote sensing reflectance signal attributable to changes in phytoplankton cell size. Inversion of this model reveals regions within the parameter space where ambiguity may limit potential of inversion algorithms. Validation of the algorithm within the Benguela upwelling system using independent data shows promise for ecosystem applications and further investigation of the interaction between phytoplankton functional types and optical signals. The results here suggest that the utility of assemblage related signals in spectral reflectance is highly sensitive to algal biomass, the presence of other absorbing and scattering constituents and the resultant constituent-specific inherent optical property budget. As such, optimal methods for determining phytoplankton size from (in situ or satellite) ocean colour data will likely rely on appropriately spectrally dense and optimised sensors, well characterised measurement errors including those from atmospheric correction, and an ability to appropriately limit ambiguity within the context of regional inherent optical properties.