Assessment of ocean color atmospheric correction methods and development of a regional ocean color operational dataset for the Baltic Sea based on Sentinel-3 OLCI

The Baltic Sea is characterized by large gradients in salinity, high concentrations of colored dissolved organic matter, and a phytoplankton phenology with two seasonal blooms. Satellite retrievals of chlorophyll-a concentration (chl-a) are hindered by the optical complexity of this basin and the reduced performance of the atmospheric correction in its highly absorbing waters. Within the development of a regional ocean color operational processing chain for the Baltic Sea based on Sentinel-3 Ocean and Land Colour Instrument (OLCI) full-resolution data, the performance of four atmospheric correction processors for the retrieval of remote-sensing reflectance (Rrs) was analyzed. Assessments based on three Aerosol Robotic Network-Ocean Color (AERONET-OC) sites and shipborne hyperspectral radiometers show that POLYMER was the best-performing processor in the visible spectral range, also providing a better spatial coverage compared with the other processors. Hence, OLCI Rrs spectra retrieved with POLYMER were chosen as input for a bio-optical ensemble scheme that computes chl-a as a weighted sum of different regional multilayer perceptron neural nets. This study also evaluated the operational Rrs and chl-a datasets for the Baltic Sea based on OC-CCI v.6. The chl-a retrievals based on OC-CCI v.6 and OLCI Rrs, assessed against in-situ chl-a measurements, yielded similar results (OC-CCI v.6: R2 = 0.11, bias = −0.22; OLCI: R2 = 0.16, bias = −0.03) using a common set of match-ups for the same period. Finally, an overall good agreement was found between chl-a retrievals from OLCI and OC-CCI v.6 although differences between Rrs were amplified in terms of chl-a estimates

CoastColour Round Robin data sets: A database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

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

A Virtual Geostationary Ocean Color Sensor to Analyze the Coastal Optical Variability

In the coastal environment the optical properties can vary on temporal scales that are shorter than the near-polar orbiting satellite temporal resolution (~1 image per day), which does not allow capturing most of the coastal optical variability. The objective of this work is to fill the gap between the near-polar orbiting and geostationary sensor temporal resolutions, as the latter sensors provide multiple images of the same basin during the same day. To do that, a Level 3 hyper-temporal analysis-ready Ocean Color (OC) dataset, named Virtual Geostationary Ocean Color Sensor (VGOCS), has been created. This dataset contains the observations acquired over the North Adriatic Sea by the currently functioning near-polar orbiting sensors, allowing approaching the geostationary sensor temporal resolution. The problem in using data from different sensors is that they are characterized by different uncertainty sources that can introduce artifacts between different satellite images. Hence, the sensors have different spatial and spectral resolutions, their calibration procedures can have different accuracies, and their Level 2 data can be retrieved using different processing chains. Such differences were reduced here by adjusting the satellite data with a multi-linear regression algorithm that exploits the Fiducial Reference Measurements data stream of the AERONET-OC water-leaving radiance acquired at the Acqua Alta Oceanographic Tower, located in the Gulf of Venice. This work aims to prove the suitability of VGOCS in analyzing the coastal optical variability, presenting the improvement brought by the adjustment on the quality of the satellite data, the VGOCS spatial and temporal coverage, and the inter-sensor differences. Hence, the adjustment will strongly increase the agreement between the satellite and in situ data and between data from different near-polar orbiting OC imagers; moreover, the adjustment will make available data traditionally masked in the standard processing chains, increasing the VGOCS spatial and temporal coverage, fundamental to analyze the coastal optical variability. Finally, the fulfillment by VGOCS of the three conditions for a hyper-temporal dataset will be demonstrated in this work

Phenology of Trichodesmium spp. blooms in the Great Barrier Reef lagoon, Australia, from the ESA-MERIS 10-year mission.

Trichodesmium, a filamentous bloom-forming marine cyanobacterium, plays a key role in the biogeochemistry of oligotrophic ocean regions because of the ability to fix nitrogen. Naturally occurring in the Great Barrier Reef (GBR), the contribution of Trichodesmium to the nutrient budget may be of the same order as that entering the system via catchment runoff. However, the cyclicity of Trichodesmium in the GBR is poorly understood and sparsely documented because of the lack of sufficient observations. This study provides the first systematic analysis of Trichodesmium spatial and temporal occurrences in the GBR over the decade-long MERIS ocean color mission (2002-2012). Trichodesmium surface expressions were detected using the Maximum Chlorophyll Index (MCI) applied to MERIS satellite imagery of the GBR lagoonal waters. The MCI performed well (76%), albeit tested on a limited set of images (N = 25) coincident with field measurements. A north (Cape York) to south (Fitzroy) increase in the extent, frequency and timing of the surface expressions characterized the GBR, with surface expressions extending over several hundreds of kilometers. The two southernmost subregions Mackay and Fitzroy accounted for the most (70%) bloom events. The bloom timing of Trichodesmium varied from May in the north to November in the south, with wet season conditions less favorable to Trichodesmium aggregations. MODIS-Aqua Sea Surface Temperature (SST) datasets, wind speed and field measurements of nutrient concentrations were used in combination with MCI positive instances to assess the blooms' driving factors. Low wind speed ( 24°C were associated with the largest surface aggregations. Generalized additive models (GAM) indicated an increase in bloom occurrences over the 10-year period with seasonal bloom patterns regionally distinct. Interannual variability in SST partially (14%) explained bloom occurrences, and other drivers, such as the subregion and the nutrient budget, likely regulate Trichodesmium surface aggregations in the GBR

PANTHYR hyperspectral water reflectance - VEIT

<p><strong>Introduction</strong> </p><p>This dataset contains water-leaving radiance reflectance (, variable names reflectance and reflectance_nosc) measurements made by an autonomous Pan and Tilt Hyperspectral Radiometer (PANTHYR, Vansteenwegen et al. 2019) installed at site VEIT. Data are provided in NetCDF format with information on processing settings provided in the NetCDF global attributes.  This dataset contains measurements from the first two deployments (Oct. 2019—Oct. 2020 and Nov. 2020—Mar. 2022) that pass quality control, and have bounding calibration information, and ancillary wind speed available. For this site, the use of reflectance with the Similarity Spectrum offset correction (Ruddick et al. 2005, 2006) is recommended.</p><p><strong>Methods</strong>  </p><p>A PANTHYR was deployed at Acqua Alta Oceanographic Tower, Adriatic Sea, VEIT, located at 45.3139°N, 12.5083°E for two deployments Oct. 2019—Oct. 2020 and Nov. 2020—Mar. 2022. </p><p>PANTHYR consists of a pair of TriOS RAMSES radiometers, one for measurement of radiance, and one with a cosine collector for measurement of irradiance, with custom control hard- and software, mounted on a pan and tilt head.  The RAMSES spectral range is about 350—950 nm in 190 channels. The pan and tilt head allows the orientation of each radiometer in a specific direction. Using the standard protocol, a PANTHYR cycle consists of sequential measurements of downwelling irradiance (, 6 replicates), downwelling (sky) radiance (, 6 replicates), and upwelling radiance (, 11 replicates). Three and measurements are performed each before and after the measurements. Measurement cycles are performed every 20 minutes during daytime, at 90, 135, 225, and/or 270 degrees relative azimuth to the sun to minimize air-water interface reflectance (Mobley 1999, Ruddick et al. 2006). Platform pointing conditions are skipped by the definition of an absolute pointing azimuth keep-out zone. Each cycle takes between several minutes (first deployment) and less than a minute (second and later deployments). </p><p>Measurements are converted from digital counts to (ir)radiance using two laboratory instrument characterisations performed by Tartu Observatory (Estonia) before and after each deployment period. Calibration data for a specific scan are obtained from linear interpolation in time between pre-deployment and post-deployment instrument characterisation. The calibrated scan data are linearly interpolated from the instrument specific wavelengths to a common wavelength grid (355—900 nm, every 2.5 nm). Individual calibrated scans are subjected to quality control as in Ruddick et al. (2006), i.e. scans differing > 25% at 550 nm from their neighbouring scans are rejected. For the Ed measurements, this quality control step takes the change in sun zenith angle between the measurements into account.</p><p>If sufficient calibrated scans are available in the cycle, i.e. >=5/6 , >=5/6 , >=9/11 , the scans are mean averaged and the standard deviation is computed. The water-leaving radiance reflectance (, variable name reflectance_nosc) is then computed according to:</p><p>reflectance_nosc = / × ( - × )</p><p>where , , and are the mean averaged values, and the effective Fresnel correction factor as determined from lookup tables provided by Mobley (1999). Ancillary wind speed is obtained fromthe GDAS1 0.25 degree global model 6 hourly nowcast archive, by interpolation of the model grid in time and space to the measurement average time, and site position. The used wind speed and are provided in the global attributes of each file.</p><p>In the present dataset, reflectance data are provided with and without a "nosc" suffix, indicating whether a residual correction for the air-water interface reflectance (Ruddick et al. 2005) is performed. The reflectance without the "nosc" suffix uses the Similarity Spectrum (Ruddick et al. 2006) to estimate a spectrally flat residual air-water interface reflectance error () using the 720 and 780 nm combination:</p><p>reflectance = / × ( - × ) - </p><p> = ( × 780 – 720) / ( -1),</p><p>where is the Similarity Spectrum ratio between the two used wavelengths, i.e. 2.35 for 720:780 nm. The value is provided in the global attributes of each file. For this VEIT dataset, the use of reflectance with Similarity Spectrum correction is recommended.</p><p>The reflectance products are further quality controlled using the following criteria:</p><p>1) / at 750 nm < 5%, removing non-clear sky conditions</p><p>2) Variability (coefficient of variation) of water reflectance at 780 nm < 10%, removing highly variable water conditions</p><p>3) Water reflectance > 0 for 350—900 nm, removing spectra with negative reflectance retrievals</p><p>4) NIR water reflectance (840—900 nm) is assumed to be decreasing with wavelength, removing potentially contaminated spectra</p><p>5) Bright water spectra (average VIS reflectance 400—700 nm > 0.07 or average NIR reflectance 780—950 nm > 0.01) have a local maximum at around 810 nm (805—815 nm) due to the local minimum in pure water absorption, removing potentially contaminated spectra</p><p>6) Irradiance measurements in the range 860—885 nm are within 20% of the Gregg and Carder (1990) clear sky model with an aerosol optical depth of 0.1 at normal pressure, removing cloudy, shadowed, or very hazy conditions</p><p><strong>Acknowledgements</strong></p><p>The installation and maintenance of the PANTHYR at AAOT was carried out within the context of the HYPERNETS project funded by the European Union's Horizon 2020 research and innovation programme (Grant agreement n◦ 775983) and of the HYPERNETS-POP project funded by the European Space Agency (contract n◦ 4000139081/22/I-EF). The skipper and crew of the AAOT and R/V Litus and are also acknowledged.</p><p><strong>References</strong></p><p>Gregg, W.W. and Carder, K.L., 1990. A simple spectral solar irradiance model for cloudless maritime atmospheres. Limnology and oceanography, 35(8), pp.1657-1675.</p><p>Mobley, C.D., 1999. Estimation of the remote-sensing reflectance from above-surface measurements. Applied optics, 38(36), pp.7442-7455.</p><p>Ruddick, K., De Cauwer, V. and Van Mol, B., 2005, August. Use of the near infrared similarity reflectance spectrum for the quality control of remote sensing data. In Remote Sensing of the Coastal Oceanic Environment (Vol. 5885, p. 588501). SPIE.</p><p>Ruddick, K.G., De Cauwer, V., Park, Y.J. and Moore, G., 2006. Seaborne measurements of near infrared water‐leaving reflectance: The similarity spectrum for turbid waters. Limnology and Oceanography, 51(2), pp.1167-1179.</p><p>Vansteenwegen, D., Ruddick, K., Cattrijsse, A., Vanhellemont, Q. and Beck, M., 2019. The pan-and-tilt hyperspectral radiometer system (PANTHYR) for autonomous satellite validation measurements—Prototype design and testing. Remote Sensing, 11(11), p.1360.</p&gt

“Flex 2018” Cruise: an opportunity to assess phytoplankton chlorophyll fluorescence retrieval at different observative scales

In frame of the European Space Agency’s (ESA) “FLEXSense Campaign 2018” and the Copernicus Marine Environment Monitoring Service (CMEMS) project, the Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR) organized the oceanographic cruise “FLEX 2018”. The CNR research vessel “Dallaporta” provided a ground station for several bio-optical instruments to investigate the coastal waters of the Tyrrhenian Sea (central Italy) in June 2018. The field measurements were performed in time synchrony with spaceborne (i.e. Sentinel 3A and Sentinel 3B satellites) and airborne (i.e. HyPlant airborne imaging spectrometer) observations, with the intent to contribute to calibration/validation activities for existing and future space mission developments. Particularly, active and passive fluorescence were investigated at different scales in aquatic ecosystems, to support preparatory activities of the FLuorescence EXplorer (FLEX) satellite mission to be launched in 2022. Results provide new insight on the sensitivity of Solar Induced Fluorescence (SIF) retrievals for atmospheric disturbances and other scale related aspects, and will eventually facilitate the implementation of robust retrieval schemes for the FLEX mission products. In addition, active fluorescence signals acquired from a LIDAR fluorosensor show a good agreement with SIF pattern retrieved by HyPlant and Sentinel-3 Ocean and Land Colour Instrument (OLCI). Our results demonstrate that the combination of active and passive fluorescence, together with the synergistic measurements from integrated platforms, is a promising approach to support the retrieval and interpretation of SIF in aquatic environments

Assessing the Accuracy of PRISMA Standard Reflectance Products in Globally Distributed Aquatic Sites

PRISMA is the Italian Space Agency’s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA’s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications

Review of constituent retrieval in optically deep and complex waters from satellite imagery

We provide a comprehensive overview of water constituent retrieval algorithms and underlying definitions and models for optically deep and complex (i.e. case 2) waters using earth observation data. The performance of constituent retrieval algorithms is assessed based on matchup validation experiments published between January 2006 and May 2011. Validation practices range from singular vicarious calibration experiments to comparisons using extensive in situ time series. Band arithmetic and spectral inversion algorithms for all water types are classified using a method based scheme that supports the interpretation of algorithm validity ranges. Based on these ranges we discuss groups of similar algorithms in view of their strengths and weaknesses. Such quantitative literature analysis reveals clear application boundaries. With regard to chlorophyll retrieval, validation of blue–green band ratios in coastal waters is limited to oligotrophic, predominantly ocean waters, while red-NIR ratios apply only at more than 10 mg/m3. Spectral inversion techniques — although not validated to the same extent — are necessary to cover all other conditions. Suspended matter retrieval is the least critical, as long as the wavelengths used in empirical models are increased with concentrations. The retrieval of dissolved organic matter however remains relatively inaccurate and inconsistent, with large differences in the accuracy of comparable methods in similar validation experiments. We conclude that substantial progress has been made in understanding and improving retrieval of constituents in optically deep and complex waters, enabling specific solutions to almost any type of optically complex water. Further validation and intercomparison of spectral inversion procedures are however needed to learn if solutions with a larger validity range are feasible

From Observation to Information and Users: The Copernicus Marine Service Perspective

The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical and biogeochemical ocean and sea-ice state for the global ocean and the European regional seas. CMEMS serves a wide range of users (more than 15,000 users are now registered to the service) and applications. Observations are a fundamental pillar of the CMEMS value-added chain that goes from observation to information and users. Observations are used by CMEMS Thematic Assembly Centres (TACs) to derive high-level data products and by CMEMS Monitoring and Forecasting Centres (MFCs) to validate and constrain their global and regional ocean analysis and forecasting systems. This paper presents an overview of CMEMS, its evolution, and how the value of in situ and satellite observations is increased through the generation of high-level products ready to be used by downstream applications and services. The complementary nature of satellite and in situ observations is highlighted. Long-term perspectives for the development of CMEMS are described and implications for the evolution of the in situ and satellite observing systems are outlined. Results from Observing System Evaluations (OSEs) and Observing System Simulation Experiments (OSSEs) illustrate the high dependencies of CMEMS systems on observations. Finally future CMEMS requirements for both satellite and in situ observations are detailed