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Linking Remote sensing data with Essential Biodiversity Variables in coastal areas

A global in-situ data set for validation of ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in-situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient and total suspended matter. Data were obtained from multi-project archives acquired via open internet services, or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 151,673 rows, with each row representing a unique station in space and time (cf 136,250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68,641 (cf 59,781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.pangaea.de/10.1594/PANGAEA.941318 (Valente et al., 2022)., SI

The particle size distribution (PSD) of suspended particles in near-surface seawater is a key property linking biogeochemical and ecosystem characteristics with optical properties that affect ocean color remote sensing. Phytoplankton size affects their physiological characteristics and ecosystem and biogeochemical roles, e.g. in the biological carbon pump, which has an important role in the global carbon cycle and thus climate. It is thus important to develop capabilities for measurement and predictive understanding of the structure and function of oceanic ecosystems, including the PSD, phytoplankton size classes (PSCs) and phytoplankton functional types (PFTs). Here, we present an ocean color satellite algorithm for the retrieval of the parameters of an assumed power-law PSD. The forward optical model considers two distinct particle populations (particle assemblage categories) — phytoplankton and non-algal particles (NAP). Phytoplankton are modeled as coated spheres following the Equivalent Algal Populations (EAP) framework, and NAP are modeled as homogeneous spheres. The forward model uses Mie and Aden-Kerker scattering computations, for homogeneous and coated spheres (for phytoplankton and NAP, respectively) to model the total particulate spectral backscattering coefficient as the sum of phytoplankton and NAP backscattering. The PSD retrieval is achieved via Spectral Angle Mapping (SAM) which uses backscattering end-members created by the forward model. The PSD is used to retrieve size-partitioned absolute and fractional phytoplankton carbon concentrations (i.e. carbon-based PSCs), as well as particulate organic carbon (POC), using allometric coefficients. The EAP-based formulation allows for the estimation of chlorophyll-a concentration via the retrieved PSD, as well as the estimation of the percent of backscattering due to NAP vs. phytoplankton. The PSD algorithm is operationally applied to the merged Ocean Colour Climate Change Initiative (OC-CCI) v5.0 ocean color data set. Results of an initial validation effort are also presented, using PSD, POC, and pico-phytoplankton carbon in-situ measurements. Validation results indicate the need for an empirical tuning for the absolute phytoplankton carbon concentrations; however these results and comparison with other phytoplankton carbon algorithms are ambiguous as to the need for the tuning. The latter finding illustrates the continued need for high-quality, consistent, large global data sets of phytoplankton carbon and related variables to facilitate future algorithm improvements.

The element carbon plays a central role in climate and life on Earth. It is capable of moving among the geosphere, cryosphere, atmosphere, biosphere and hydrosphere. This flow of carbon is referred to as the Earth's carbon cycle. It is also intimately linked to the cycling of other elements and compounds. The ocean plays a fundamental role in Earth's carbon cycle, helping to regulate atmospheric CO2 concentration. The ocean biological carbon pump (OBCP), defined as a set of processes that transfer organic carbon from the surface to the deep ocean, is at the heart of the ocean carbon cycle. Monitoring the OBCP is critical to understanding how the Earth's carbon cycle is changing. At present, satellite remote sensing is the only tool available for viewing the entire surface ocean at high temporal and spatial scales. In this paper, we review methods for monitoring the OBCP with a focus on satellites. We begin by providing an overview of the OBCP, defining and describing the pools of carbon in the ocean, and the processes controlling fluxes of carbon between the pools, from the surface to the deep ocean, and among ocean, land and atmosphere. We then examine how field measurements, from ship and autonomous platforms, complement satellite observations, provide validation points for satellite products and lead to a more complete view of the OBCP than would be possible from satellite observations alone. A thorough analysis is then provided on methods used for monitoring the OBCP from satellite platforms, covering current capabilities, concepts and gaps, and the requirement for uncertainties in satellite products. We finish by discussing the potential for producing a satellite-based carbon budget for the oceans, the advantages of integrating satellite-based observations with ecosystem models and field measurements, and future opportunities in space, all with a view towards bringing satellite observations into the limelight of ocean carbon research.

Early detection of dense harmful algal blooms (HABs) is possible using ocean colour remote sensing. Some algorithms require a training dataset, usually constructed from satellite images with a priori knowledge of the existence of the bloom. This approach can be limited if there is a lack of in situ observations, coincident with satellite images. A laboratory experiment collected biological and bio-optical data from a culture of Karenia mikimotoi, a harmful phytoplankton dinoflagellate. These data showed characteristic signals in chlorophyll-specific absorption and backscattering coefficients. The bio-optical data from the culture and a bio-optical model were used to construct a training dataset for an existing statistical classifier. MERIS imagery over the European continental shelf were processed with the classifier using different training datasets. The differences in positive rates of detection of K. mikimotoi between using an algorithm trained with purely manually selected areas on satellite images and using laboratory data as training was overall in situ observations of non-harmful high chlorophyll blooms in the area would improve testing of the ability to distinguish harmful from non-harmful high chlorophyll blooms. This approach can be expanded to use additional wavelengths, different satellite sensors and different phytoplankton genera.

Phytoplankton is at the base of the marine food web and plays a fundamental role in the global carbon cycle. Ongoing climate change significantly impacts phytoplankton distribution in the ocean. Monitoring phytoplankton is crucial for a full understanding of changes in the marine ecosystem. To observe phytoplankton from space, chlorophyll-a concentration (Chl) has been widely used as a proxy of algal biomass, although it can be impacted by physiology. Therefore, there has been an increasing focus towards estimating phytoplankton biomass in units of carbon (Cphyto). Here, we developed an algorithm to quantify Cphyto from space-based observations that accounts for the spatio-temporal variations of the backscattering coefficient associated with the fraction of detrital particles that do not covary with Chl. The main findings are: (i) a spatial and temporal variation of the detritus component must be accounted for in the Cphyto algorithm; (ii) the refined Cphyto algorithm performs better (relative bias of 23.7%) than any previously existing model; and (iii) our algorithm shows the lowest error in Cphyto across areas where picophytoplankton dominates (relative bias of 14%). In other areas, it is currently not possible to accurately assess the performance of the refined algorithm due to the paucity of in situ carbon data associated with nano- and micro-phytoplankton size classes.

Satellites collecting optical data offer a unique perspective from which to observe the problem of plastic litter in the marine environment, but few studies have successfully demonstrated their use for this purpose. For the first time, we show that patches of floating macroplastics are detectable in optical data acquired by the European Space Agency (ESA) Sentinel-2 satellites and, furthermore, are distinguishable from naturally occurring materials such as seaweed. We present case studies from four countries where suspected macroplastics were detected in Sentinel-2 Earth Observation data. Patches of materials on the ocean surface were highlighted using a novel Floating Debris Index (FDI) developed for the Sentinel-2 Multi-Spectral Instrument (MSI). In all cases, floating aggregations were detectable on sub-pixel scales, and appeared to be composed of a mix of seaweed, sea foam, and macroplastics. Building first steps toward a future monitoring system, we leveraged spectral shape to identify macroplastics, and a Naïve Bayes algorithm to classify mixed materials. Suspected plastics were successfully classified as plastics with an accuracy of 86%.

Satellite remote sensing is an invaluable tool for observing our earth systems. However, few studies have succeeded in applying this for detection of floating litter in the marine environment. We demonstrate that plastic debris aggregated on the ocean surface is detectable in optical data acquired by the European Space Agency (ESA) Sentinel-2 satellites. Furthermore, using an automated classification approach, we show that floating macroplastics are distinguishable from seawater, seaweed, sea foam, pumice, and driftwood.Sentinel-2 was used to detect floating aggregations likely to include macroplastics across four study sites, namely: coastal waters of Accra (Ghana), Da Nang (Vietnam), the east coast of Scotland (UK), and the San Juan Islands (BC, Canada). Aggregations were detectable on sub-pixel scales using a Floating Debris Index (FDI), and were composed of a mix of materials including sea foam and seaweed. A probabilistic machine learning approach was then applied to assess if detected plastics could be discriminated from the natural sources of marine debris. Our automated Naïve Bayes classifier was trained using a library of pumice, seaweed, timber, sea foam and seawater detections, as well as validated macroplastics from Durban Harbour (South Africa). Across the four study sites, suspected marine plastics were classified as such with an accuracy approaching 90%. The ‘misclassified’ plastics were mostly identified as seawater, suggesting an insufficient amount of pixel was filled with materials.Results from this study show that plastic debris aggregated on the ocean surface can be detected in optical data collected by Sentinel-2, and identified. With the aim of generating global ‘hotspot’ maps of floating plastics in coastal waters, automating this two-stage process across the Sentinel-2 archive is being progressed; however, the method would also be applicable to drones and other remote sensing platforms with similar band characteristics. To extend remote detection methods to river systems and optically complex and/or tidal coastal waters, in situ data collection across optical water types is the next key step.

This deliverable aims at defining the details of the products and service to be developed in close cooperation with large user groups, including the DANUBIUS European research infrastructure, GEO AquaWatch/Blue Planet, Lagoons for Life and end-users in regional case studies. The deliverable also includes a detailed description of the case studies that will be considered as exemplars in the project

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

The relatively high spatial resolution, short revisit time and red-edge spectral band (705 nm) of the ESA Sentinel-2 Multi Spectral Imager makes this sensor attractive for monitoring water quality of coastal and inland waters. Reliable atmospheric correction is essential to support routine retrieval of optically active substance concentration from water-leaving reflectance. In this study, six publicly available atmospheric correction algorithms (Acolite, C2RCC, iCOR, l2gen, Polymer and Sen2Cor) are evaluated against above-water optical in situ measurements, within a robust methodology, in two optically diverse coastal regions (Baltic Sea, Western Channel) and from 13 inland waterbodies from 5 European countries with a range of optical properties. The total number of match-ups identified for each algorithm ranged from 1059 to 1668 with 521 match-ups common to all algorithms. These in situ and MSI match-ups were used to generate statistics describing the performance of each algorithm for each respective region and a combined dataset. All ACs tested showed high uncertainties, in many cases >100% in the red and >1000% in the near-infra red bands. Polymer and C2RCC achieved the lowest root mean square differences (~0.0016 sr−1) and mean absolute differences (~40–60% in blue/green bands) across the different datasets. Retrieval of blue-green and NIR-red band ratios indicate that further work on AC algorithms is required to reproduce the spectral shape in the red and NIR bands needed to accurately retrieve the chlorophyll-a concentration in turbid waters.

Satellites collecting optical data offer a unique perspective from which to observe the problem of plastic litter in the marine environment, but few studies have successfully demonstrated their use for this purpose. For the first time, we show that patches of floating macroplastics are detectable in optical data acquired by the European Space Agency (ESA) Sentinel-2 satellites and, furthermore, are distinguishable from naturally occurring materials such as seaweed. We present case studies from four countries where suspected macroplastics were detected in Sentinel-2 Earth Observation data. Patches of materials on the ocean surface were highlighted using a novel Floating Debris Index (FDI) developed for the Sentinel-2 Multi-Spectral Instrument (MSI). In all cases, floating aggregations were detectable on sub-pixel scales, and appeared to be composed of a mix of seaweed, sea foam, and macroplastics. Building first steps toward a future monitoring system, we leveraged spectral shape to identify macroplastics, and a Naïve Bayes algorithm to classify mixed materials. Suspected plastics were successfully classified as plastics with an accuracy of 86%.

Phytoplankton total chlorophyll concentration (TCHLa) and phytoplankton size structure are two important ecological indicators in biological oceanography. Using high performance liquid chromatography (HPLC) pigment data, collected from surface waters along the Atlantic Meridional Transect (AMT), we examine temporal changes in TCHLa and phytoplankton size class (PSC: micro-, nano- and pico-phytoplankton) between 2003 and 2010 (September to November cruises only), in three ecological provinces of the Atlantic Ocean. The HPLC data indicate no significant change in TCHLa in northern and equatorial provinces, and an increase in the southern province. These changes were not significantly different to changes in TCHLa derived using satellite ocean-colour data over the same study period. Despite no change in AMT TCHLa in northern and equatorial provinces, significant differences in PSC were observed, related to changes in key diagnostic pigments (fucoxanthin, peridinin, 19′-hexanoyloxyfucoxanthin and zeaxanthin), with an increase in small cells (nano- and pico-phytoplankton) and a decrease in larger cells (micro-phytoplankton). When fitting a three-component model of phytoplankton size structure — designed to quantify the relationship between PSC and TCHLa to each AMT cruise, model parameters varied over the study period. Changes in the relationship between PSC and TCHLa have wide implications in ecology and marine biogeochemistry, and provide key information for the development and use of empirical ocean-colour algorithms. Results illustrate the importance of maintaining a time-series of in-situ observations in remote regions of the ocean, such as that acquired in the AMT programme.

© 2015 Published by Elsevier Ltd. Primary productivity and subsequent carbon cycling in the coastal zone have a significant impact on the global carbon budget. It is currently unclear how anthropogenic activity could alter these budgets but long term coastal time series of hydrological, biogeochemical and biological measurements represent a key means to better understand past drivers, and hence to predicting future seasonal and inter-annual variability in carbon fixation in coastal ecosystems. An 8-year time series of primary production from 2003 to 2010, estimated using a recently developed absorption-based algorithm, was used to determine the nature and extent of change in primary production at a coastal station (L4) in the Western English Channel (WEC). Analysis of the seasonal and inter-annual variability in production demonstrated that on average, nano- and pico-phytoplankton account for 48% of the total carbon fixation and micro-phytoplankton for 52%. A recent decline in the primary production of nano- and pico-phytoplankton from 2005 to 2010 was observed, corresponding with a decrease in winter nutrient concentrations and a decrease in the biomass of Phaeocystis sp. Micro-phytoplankton primary production (PPM) remained relatively constant over the time series and was enhanced in summer during periods of high precipitation. Increases in sea surface temperature, and decreases in wind speeds and salinity were associated with later spring maxima in PPM. Together these trends indicate that predicted increases in temperature and decrease in wind speeds in future would drive later spring production whilst predicted increases in precipitation would also continue these blooms throughout the summer at this site.

Racault, Marie-Fanny LP/0000-0002-7584-2515; Agirbas, Ertugrul/0000-0001-7987-9668; Martinez-Vicente, Victor/0000-0003-3492-583X; Brewin, Robert/0000-0001-5134-8291 WOS: 000336490000003 Ecological indicators are used extensively as tools to manage environmental resources. in the oceans, indicators of plankton can be measured using a variety of observing systems including: mooring stations, ships, autonomous floats and ocean colour remote sensing. Given the broad range of temporal and spatial sampling resolutions of these different observing systems, as well as discrepancies in measurements obtained from different sensors, the estimation and interpretation of plankton indicators can present significant challenges. To provide support to the assessment of the state of the marine ecosystem, we propose a suite of plankton indicators and subsequently classify them in an ecological framework that characterizes key attributes of the ecosystem. We present two case studies dealing with plankton indicators of biomass, size structure and phenology, estimated using the most spatially extensive and longest in situ and remote-sensing observations. Discussion of these studies illustrates how some of the challenges in estimating and interpreting plankton indicators may be addressed by using for example relative measurement thresholds, interpolation procedures and delineation of biogeochemical provinces. We demonstrate that one of the benefits attained, when analyzing a suite of plankton indicators classified in an ecological framework, is the elucidation of non-trivial changes in composition, structure and functioning of the marine ecosystem. EC FP7 GreenSeas project [265294 FP7-ENV-2010]; Natural Environment Research CouncilNERC Natural Environment Research Council [pml010008] Funding Source: researchfish; Directorate For GeosciencesNational Science Foundation (NSF)NSF - Directorate for Geosciences (GEO) [1154661] Funding Source: National Science Foundation; Division of Ocean SciencesNational Science Foundation (NSF)NSF - Directorate for Geosciences (GEO) [1154661] Funding Source: National Science Foundation The work was funded by the EC FP7 GreenSeas project (265294 FP7-ENV-2010).

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS), and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales, their role in marine biogeochemistry, the global carbon cycle, the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean, and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration, since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency, the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA), and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

We used a numerical model to investigate if and to what extent cellular photoprotective capacity accounts for succession and vertical distribution of marine phytoplankton species/groups. A model describing xanthophyll photoprotective activity in phytoplankton has been implemented in the European Regional Sea Ecosystem Model and applied at the station L4 in the Western English Channel. Primary producers were subdivided into three phytoplankton functional types defined in terms of their capacity to acclimate to different light-specific environments: low light (LL-type), high light (HL-type) and variable light (VL-type) adapted species. The LL-type is assumed to have low cellular level of xanthophyll-cycling pigments (PX) relative to the modelled photosynthetically active pigments (chlorophyll and fucoxanthin (FUCO) = PSP). The HL-type has high PX content relative to PSP while VL-type presents an intermediate PX to PSP ratio. Furthermore, the VL-type is capable of reversibly converting FUCO to PX and synthesizing new PX under high-light stress. In order to reproduce phytoplankton community succession with each of the three groups being dominant in different periods of the year, we had also to assume reduced grazing pressure on HL-adapted species. Model simulations realistically reproduce the observed seasonal patterns of pigments and nutrients highlighting the reasonability of the underpinning assumptions. Our model suggests that pigment-mediated photophysiology plays a primary role in determining the evolution of marine phytoplankton communities in the winter-spring period corresponding to the shoaling of the mixed layer and the increase of light intensity. Grazing selectivity however contributes to the phytoplankton community composition in summer.

[1] Phytoplankton are an important component of the oceanic carbon cycle. Yet, due to methodological constraints, the carbon biomass of phytoplankton is poorly characterized. To address this limitation, we have explored the bio-optical relationship between in situ measurements of the particle backscattering coefficient at 470 nm, bbp(470), and the phytoplankton carbon concentration for cells with diameter less than 20 µm (Cf). We found a significant relationship between bbp(470) and Cf for Atlantic oceanic waters with chlorophyll-a concentrations less than 0.4 mg m−3 (or bbp(470)

We present over 900 carbonate system observations collected over four years (2007–2010) in the Western English Channel (WEC). We determined CO2 partial pressure (pCO2), Total Alkalinity (TA) and Dissolved Inorganic Carbon (DIC) along a series of 40 km transects, including two oceanographic stations (L4 and E1) within a sustained coastal observatory. Our data follow a seasonal pattern of CO2 undersaturation from January to August, followed by supersaturation in September–October and a return to near-equilibrium thereafter. This pattern is explained by the interplay of thermal and biological sinks in winter and spring–summer, respectively, followed by the breakdown of stratification and mixing with deeper, high-CO2 water in autumn. The drawdown of DIC and inorganic N between March and June with a C:N ratio of 8.7–9.5 was consistent with carbon over-consumption during phytoplankton growth. Monthly mean surface pCO2 was strongly correlated with depth integrated chlorophyll a highlighting the importance of subsurface chlorophyll a maxima in controlling C-fluxes in shelf seas. Mixing of seawater with riverine freshwater in near-shore samples caused a reduction in TA and the saturation state of calcite minerals, particularly in winter. Our data show that the L4 and E1 oceanographic stations were small, net sinks for atmospheric CO2 over an annual cycle (−0.52±0.66 mol C m−2 y−1 and −0.62±0.49 mol C m−2 y−1, respectively).

Understanding the sources of the optical backscattering coefficient from particles (bbp) aids interpretation of ocean colour measured from satellites. The hypothesis tested is that phytoplankton make a significant contribution to the bbp in the open ocean. Using phytoplankton pigment, flow cytometry and microscopy data, measured concurrently with in situ bbp in the cen- tral North Atlantic Ocean, we separated the phytoplankton signal from that of bacteria. Three dis- tinct pelagic communities were detected, all associated with the sub-polar front. A significant per- centage of variance in the bbp signal (r 2 = 0.68) was explained by phytoplankton between 2 and 20 μm in diameter (nanoeukaryotes). To further test the hypothesis, bbp was calculated using liter- ature values of the backscattering properties and cell size of phytoplankton and flow-cytometric cell counts. In agreement with previous modelling studies, bacteria, due to their great abundance, dominate the biological bbp signal. However, the variations in bbp were related to changes in phytoplankton abundance (particularly of nanoeukaryotes), and the use of high backscattering efficiency factors per cell for nanoeukaryotes yielded computed values close to the observed bbp. We found no evidence that the detrital component played a significant role in bbp at this site. How- ever, given the limited methods available to test this result, such lack of evidence is inconclusive. To be able to explain the remaining proportion of the variation in bbp, a significant methodological advance is required for the better quantification and characterisation of the organogenic detritus.

Mycosporine-like amino acids (MAAs), phytoplankton pigments, and inherent optical properties were analyzed in sea surface microlayer (SSM), near-surface (0-2 m), and subsurface (0–110 m) samples from stations off the Iberian Peninsula in June-July 2005. During a visible surface slick, MAA concentrations reached 290 µg L−1 in the SSM, which correlated with an increase in abundance of the dinoflagellate, Prorocentrum micans (> 4000 cells mL−1) and covaried with low Ekman transport. High levels of colored dissolved organic material (CDOM) were also found in the SSM, and a prominent absorption shoulder between 300 and 340 nm was evident in the CDOM absorption spectra, indicative of MAAs in the dissolved fraction. Low CDOM slope ratios (SCDOM(275-295) : (350-400)) during the development of the surface slick suggest microbial production of CDOM, possibly exuded from the sudden proliferation in P. micans. In the absence of a well-defined SSM, higher SCDOM(275–295) : (350–400) ratios were found, suggesting photobleaching of CDOM at the surface. The particulate absorption spectra of MAAs (aPMAA(λ)) were effective at absorbing ultraviolet (UV) radiation in a narrow spectral band between 310 and 350 nm. Since aCDOM(λ) exhibits an exponential rise in absorption in the UV and was high in the SSM and the subsurface compared with aPMAA (λ), CDOM would be the principal mechanism of UVA and B attenuation in the water column, providing an additional sunscreen to phytoplankton.

The seasonality of the mass specific scattering and backscattering by different components of the suspended particulate matter (SPM) was investigated at a coastal station in the Western English Channel. This study considered different components of the SPM, including inorganic and organic suspended matter, chlorophyll a and six phytoplankton groups. The data set comprised a 9-year time series of observations at station L4. Scattering variability was best explained by the particulate organic matter content, with significant differences between spring‐summer and autumn‐winter. Particulate backscattering variability was best explained by the inorganic fraction, with no clear seasonal variation. The temporal variations of the spectral slopes of these optical properties were related to the particle composition and size distribution slope.

The marine laboratories in Plymouth have sampled at two principle sites in the western English Channel for over a century in open-shelf (station E1; 508 020N, 48 220W) and coastal (station L4; 508 150N, 48 130W) waters. These stations are seasonally stratified from late-April until September, and the variable biological response is regulated by subtle variations in temperature, light, nutrients and meteorology. Station L4 is characterized by summer nutrient depletion, although intense summer precipitation, increasing riverine input to the system, results in pulses of increased nitrate concentration and surface freshening. The winter nutrient concentrations at E1 are consistent with an open-shelf site. Both stations have a spring and autumn phytoplankton bloom; at station E1, the autumn bloom tends to dominate in terms of chlorophyll concentration. The last two decades have seen a warming of around 0.68C per decade, and this is superimposed on several periods of warming and cooling over the past century. In general, over the western English Channel domain, the end of the 20th century was around 0.58C warmer than the first half of the century. The warming magnitude and trend is consistent with other stations across the north-west European Shelf and occurred during a period of reduced wind stress and increased levels of insolation (þ20%); these are both correlated with the larger scale climatic forcing of the North Atlantic Oscillation.

This paper presents an optical classification of station L4 south of Plymouth in the western English Channel based on satellite and in situ observations. Weekly-sampled in situ data from 2003 and 2004 are presented on temperature and salinity with depth, together with surface measurements of absorption by phytoplankton, coloured dissolved organic matter (CDOM) and non-algal particles (NAP), phytoplankton counts, HPLC chl-a concentrations and suspended particulates. Measured absorption values are compared with a published optical model used to estimate the magnitude and natural variability of absorption by in-water optically active constituents in case 1 waters. Satellite retrievals of remote sensing reflectance, R rs , are compared with model-based limits. L4 is found to be classed as turbid for ~ 35% of the year (in winter) related to the vertical stability and suspended particulate matter. In situ absorption data reveal an excess of CDOM absorption over that expected in modelled case 1 waters, while phytoplankton and NAP absorption are more consistent with case 1. Time series of satellite and in situ surface chl-a and vertical diffuse attenuation coefficient at 490 nm agree well statistically, probably as a result of the range of chl-a encountered and the use of R rs ratio based satellite algorithms. There was no evidence of coastal adjacency effects upon the chl-a retrievals. L4 can be considered as case 1 or case 2 depending upon the time period, the optical parameter or even the wavelength. This suggests that simple labelling of the site as case 1 or 2 is misleading and instead the station should be characterised in terms of the individual inherent optical property of interest.

In order to improve robustness of remote sensing algorithms for lakes, it is vital to understand the variability of inherent optical properties (IOPs) and their mass-specific representations (SIOPs). In this study, absorption coefficients for particulate and dissolved constituents were measured at 38 stations distributed over a biogeochemical gradient in Lake Balaton, Hungary. There was a large range of phytoplankton absorption (aph(λ)) over blue and red wavelengths (aph(440) = 0.11–4.39 m−1, aph(675) = 0.048–2.52 m−1), while there was less variability in chlorophyll-specific phytoplankton absorption (a*ph(λ)) in the lake (a*ph(440) = 0.022 ± 0.0046 m2mg−1, a*ph(675) = 0.010 ± 0.0020 m2mg−1) and adjoining wetland system, Kis-Balaton (a*ph(440) = 0.017 ± 0.0015 m2mg−1, a*ph(675) = 0.0088 ± 0.0017 m2mg−1). However, in the UV, a*ph(350) significantly increased with increasing distance from the main inflow (Zala River). This was likely due to variable production of photoprotective pigments (e.g., MAAs) in response to the decreasing gradient of colored dissolved organic matter (CDOM). The slope of CDOM absorption (SCDOM) also increased from west to east due to larger terrestrial CDOM input in the western basins. Absorption by nonalgal particles (aNAP(λ)) was highly influenced by inorganic particulates, as a result of the largely mineral sediments in Balaton. The relative contributions to the absorption budget varied more widely than oceans with a greater contribution from NAP (up to 30%), and wind speed affected the proportion attributed to NAP, phytoplankton, or CDOM. Ultimately, these data provide knowledge of the heterogeneity of (S)IOPs in Lake Balaton, suggesting the full range of variability must be considered for future improvement of analytical algorithms for constituent retrieval in inland waters.

Transient micronutrient enrichment of the surface ocean can enhance phytoplankton growth rates and alter microbial community structure with an ensuing spectrum of biogeochemical feedbacks. Strong phytoplankton responses to micronutrients supplied by volcanic ash have been reported recently. Here we: (i) synthesize findings from these recent studies; (ii) report the results of a new remote sensing study of ash fertilization; and (iii) calculate theoretical bounds of ash-fertilized carbon export. Our synthesis highlights that phytoplankton responses to ash do not always simply mimic that of iron amendment; the exact mechanisms for this are likely biogeochemically important but are not yet well understood. Inherent optical properties of ash-loaded seawater suggest rhyolitic ash biases routine satellite chlorophyll-a estimation upwards by more than an order of magnitude for waters with 0.5 mg chlorophyll-a m-3. For this reason post-ash-deposition chlorophyll-a changes in oligotrophic waters detected via standard Case 1 (open ocean) algorithms should be interpreted with caution. Remote sensing analysis of historic events with a bias less than a factor of 2 provided limited stand-alone evidence for ash-fertilization. Confounding factors were poor coverage, incoherent ash dispersal, and ambiguity ascribing biomass changes to ash supply over other potential drivers. Using current estimates of iron release and carbon export efficiencies, uncertainty bounds of ash-fertilized carbon export for 3 events are presented. Patagonian iron supply to the Southern Ocean from volcanic eruptions is less than that of windblown dust on thousand year timescales but can dominate supply at shorter timescales. Reducing uncertainties in remote sensing of phytoplankton response and nutrient release from ash are avenues for enabling assessment of the oceanic response to large-scale transient nutrient enrichment.

The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive intercomparison due to the availability of quality checked in situ databases. The CoastColour Round Robin (CCRR) project, funded by the European Space Agency (ESA), was designed to bring together three reference data sets using these to test algorithms and to assess their accuracy for retrieving water quality parameters. This paper provides a detailed description of these reference data sets, which include the Medium Resolution Imaging Spectrometer (MERIS) level 2 match-ups, in situ reflectance measurements, and synthetic data generated by a radiative transfer model (HydroLight). These data sets, representing mainly coastal waters, are available from doi:10.1594/PANGAEA.841950., The data sets mainly consist of 6484 marine reflectance (either multispectral or hyperspectral) associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: total suspended matter (TSM) and chlorophyll a (CHL) concentrations, and the absorption of coloured dissolved organic matter (CDOM). Inherent optical properties are also provided in the simulated data sets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three data sets are compared. Match-up and in situ sites where deviations occur are identified. The distributions of the three reflectance data sets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

Three cruises were conducted in the Irish Sea during May, June and July 2001 to determine the variability in inherent optical properties (IOP), photo physiological parameters and primary production (PP) and to assess the effect of IOP on satellite PP algorithms. The absorption coefficients of phytoplankton (a ph ), coloured dissolved organic material (a CDOM ) and nonalgal particles (a NAP ) were higher during May than June and July. A radiative transfer model was used to model the in-water light field based on a ph (case 1) and a ph , a CDOM and a NAP (case 2). When PP was compared using these light fields, there was a 46% difference in estimates. The case 2 in-water light field was coupled to a wavelength resolving satellite model of PP (PP case2 ) and had a low root mean square error (RMS) (0.27 log 10 PP) compared with in situ PP case2 . IOP absorption, especially a CDOM , had a significant effect on the performance of this algorithm, but scattering of light by suspended particulate material had a small effect. A look-up table was generated from the in situ a ph , a CDOM and a NAP measurements, which can be used in conjunction with satellite products to produce satellite maps of PP. There was case2 and the satellite PP maps, which suggests that they could be produced routinely and accurately to monitor PP in the Irish Sea and other coastal and estuarine areas.

In this paper, we compare bio-physical marine products from SeaWiFS, MODIS and a novel bio-optical absorption model with in situ measurements of chlorophyll-a (Chla) concentrations, total suspended material (TSM) concentrations, normalized water-leaving radiances (nLw) and absorption coefficients of coloured dissolved organic matter (aCDOM), total particulate (atotal) and phytoplankton (aphy) for 26 satellite match-ups in three Northern European seas. Cruises were undertaken in 2002 and 2003 in phytoplankton dominated open ocean waters of the Celtic Sea and optically complex waters of the Western English Channel (WEC) and North Sea. For all environments, Chla concentrations varied from 0.4 to 7.8 mg m−3, TSM from 0.2 to 6.0 mg l−1 and aCDOM at 440 nm from 0.02 to 0.30 m−1. SeaWiFS OC4v4, with the Remote Sensing Data Analysis Service (RSDAS) atmospheric correction for turbid waters, showed the most accurate retrieval of in situ Chla (RMS = 0.24; n = 26), followed by MODIS chlor_a_3 (RMS = 0.40; n = 26). This suggested that improving the atmospheric correction over optically complex waters results in more accurate Chla concentrations compared to those obtained using more complicated Chla algorithms. We found that the SeaWiFS OC4v4 and the MODIS chlor_a_2 switching band ratio algorithms, which mainly use longer wavebands than 443 nm, were less affected by CDOM. They were both more accurate than chlor_MODIS in the higher CDOM waters of the North Sea. Compared to MODIS the absorption model was better at retrieving atotal (RMS = 0.39; n = 78) and aCDOM (RMS = 0.79; n = 12) in all study areas and TSM in the WEC (RMS = 0.04; n = 10) but it underestimated Chla concentrations (RMS = 0.45; n = 26). The results are discussed in terms of atmospheric correction, sensor characteristics and the functioning and performance of Chla algorithms.

Un-supervised hyperspectral remote-sensing reflectance data (

In contrast to generally sparse biological communities in open-ocean settings, seamounts and ridges are perceived as areas of elevated productivity and biodiversity capable of supporting commercial fisheries. We investigated the origin of this apparent biological enhancement over a segment of the North Mid-Atlantic Ridge (MAR) using sonar, corers, trawls, traps, and a remotely operated vehicle to survey habitat, biomass, and biodiversity. Satellite remote sensing provided information on flow patterns, thermal fronts, and primary production, while sediment traps measured export flux during 2007-2010. The MAR, 3,704,404 km 2 in area, accounts for 44.7% lower bathyal habitat (800-3500 m depth) in the North Atlantic and is dominated by fine soft sediment substrate (95% of area) on a series of flat terraces with intervening slopes either side of the ridge axis contributing to habitat heterogeneity. The MAR fauna comprises mainly species known from continental margins with no evidence of greater biodiversity. Primary production and export flux over the MAR were not enhanced compared with a nearby reference station over the Porcupine Abyssal Plain. Biomasses of benthic macrofauna and megafauna were similar to global averages at the same depths totalling an estimated 258.9 kt C over the entire lower bathyal north MAR. A hypothetical flat plain at 3500 m depth in place of the MAR would contain 85.6 kt C, implying an increase of 173.3 kt C attributable to the presence of the Ridge. This is approximately equal to 167 kt C of estimated pelagic biomass displaced by the volume of the MAR. There is no enhancement of biological productivity over the MAR; oceanic bathypelagic species are replaced by benthic fauna otherwise unable to survive in the mid ocean. We propose that globally sea floor elevation has no effect on deep sea biomass; pelagic plus benthic biomass is constant within a given surface productivity regime. Publisher PDF

Coastal areas of the North Sea are commercially important for fishing and tourism, and are subject to the increasingly adverse effects of harmful algal blooms, eutrophication and climate change. Monitoring phytoplankton in these areas using Ocean Colour Remote Sensing is hampered by the high spatial and temporal variations in absorption and scattering properties. In this paper we demonstrate a clustering method based on specific-absorption properties that gives accurate water quality products from the Medium Resolution Imaging Spectrometer (MERIS). A total of 468 measurements of Chlorophyll a (Chla), Total Suspended Material (TSM), specific- (sIOP) and inherent optical properties (IOP) were measured in the North Sea between April 1999 and September 2004. Chla varied from 0.2 to 35 mg m− 3, TSM from 0.2 to 75 g m− 3 and absorption properties of coloured dissolved organic material at 442 nm (aCDOM(442)) was 0.02 to 0.26 m− 1. The variation in absorption properties of phytoplankton (aph) and non-algal particles (aNAP) were an order of magnitude greater than that for aph normalized to Chla (aph*) and aNAP normalized to TSM (aNAP*). Hierarchical cluster analysis on aph*, aNAP* and aCDOM reduced this large data set to three groups of high aNAP*–aCDOM, low aph* situated close to the coast, medium values further offshore and low aNAP*–aCDOM, high aph* in open ocean and Dutch coastal waters. The median sIOP of each cluster were used to parameterize a semi-analytical algorithm to retrieve concentrations of Chla, TSM and aCDOM(442) from MERIS data. A further 60 measurements of normalized water leaving radiance (nLw), Chla, TSM, aCDOM(442) and aNAP(442) collected between 2003 and 2006 were used to assess the accuracy of the satellite products. The regionalized MERIS algorithm showed improved performance in Chla and aCDOM(442) estimates with relative percentage differences of 29 and 8% compared to 34 and 134% for standard MERIS Chla and adg(442) products, and similar retrieval for TSM at concentrations > 1 g− 3.