Radiometric water products from the neural network (NNv2) in the alternative atmospheric correction (AAC) processing chain of Ocean and Land Colour Instrument (OLCI) data were assessed over different marine regions. These products, not included among the operational ones, were custom-produced from Copernicus Sentinel-3 OLCI Baseline Collection 3. The assessment benefitted ofin situreference data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) from sites representative of different water types. These included clear waters in the Western Mediterranean Sea, optically complex waters characterized by varying concentrations of total suspended matter and chromophoric dissolved organic matter (CDOM) in the northern Adriatic Sea, and optically complex waters characterized by very high concentrations of CDOM in the Baltic Sea. The comparison of the water-leaving radiances$L_{\text {WN}}(\lambda)$derived from OLCI data on board Sentinel-3A and Sentinel-3B with those from AERONET-OC confirmed consistency between the products from the two satellite sensors. However, the accuracy of satellite data products exhibited dependence on the water type. A general underestimate of${L}_{\text {WN}}(\lambda)$was observed for clear waters. Conversely, overestimates were observed for data products from optically complex waters with the worst results obtained for CDOM-dominated waters. These findings suggest caution in exploiting NNv2 radiometric products, especially for highly absorbing and clear waters.

The quantification of uncertainties affecting satellite ocean color products is a fundamental step to ensure their compliance with mission and science requirements. This work investigated a methodology relying on the use ofin situradiometric data with known uncertainties to determine those affecting matching satellite data. By exploitingin situradiometric data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC), an advanced method was applied to radiometric data products from the Ocean and Land Color Instruments onboard the Sentinel-3A satellite (OLCI-A) and the Visible Infrared Imager Radiometer Suite onboard the Suomi National-Polar Orbiting Partnership satellite (VIIRS-S). The results from the analysis support the relevance of the method proposed.

Atmospheric correction (AC) algorithms for ocean color (OC) data processing usually rely on ancillary data documenting the atmosphere and the sea state to help the calculation of the remote sensing reflectance$R_{\text {RS}}$from the radiance measured by a space sensor. This study aims at assessing the impact that the uncertainties associated with these ancillary data have on the AC outputs. For this objective, a full year of global Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery is processed with the standard AC algorithml2genof the National Aeronautics and Space Administration with different sets of ancillary data, the reference case with National Centers for Environmental Prediction (NCEP) Reanalysis-2 meteorological data and satellite ozone products, as well as with ten ensemble members from the European Centre for Medium-Range Weather Forecast (ECMWF) CERA-20C data. The spread within the ensemble data and the differences with respect to the reference case are taken as a measure of the uncertainties associated with ancillary data. The impact on$R_{\text {RS}}$of perturbations in ancillary variables vary in space, the variables having the largest effects being wind speed and relative humidity, and ozone at bands where ozone absorption is largest, while sea-level pressure and precipitable water have the smallest effect. Sensitivity coefficients quantifying the relationship between perturbations in ancillary variables and effects on$R_{\text {RS}}$change with variable and wavelength. At the global scale, the variations found on$R_{\text {RS}}$when ancillary data are perturbed are usually small but not negligible and should be considered in the ocean color (OC) data uncertainty budget.

Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper we have for the first time analysed the uncertainties in the atmospheric correction for OC arising from the uncertainties in the ancillary data (such as wind speed and column densities of gases and aerosols). The spread in the ERA5 ensemble is used as estimate for the uncertainty in the ancillary parameters, which is then propagated to uncertainties in remote sensing reflectances using the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where first we illustrate the complete uncertainty budget using an uncertainty tree diagram. The uncertainties from the ancillary data are then propagated using a Monte Carlo approach. Wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties. In total, the ancillary data add about 1 % uncertainty at 412nm, increasing to 2% for 555nm with significant variability as a function of observation conditions.

This letter summarizes a regional assessment of radiometric data products from the Ocean and Land Colour Instrument operated onboard Sentinel-3A. The assessment is supported byin situreference measurements from the Ocean Colour component of the Aerosol Robotic Network and the Bio-optical mapping of Marine Properties Program. Results indicate a systematic underestimate of the water-leaving radiance at the blue and red spectral bands. Conversely, the aerosol optical depth at 865 nm exhibits overestimate, while the Ångström exponent shows a narrow distribution of values confined below a maximum of approximately 1.7. These findings suggest difficulty in separating water and atmospheric radiance contributions, which results in a poor determination of aerosol load and type and, consequently, an overestimate of atmospheric effects.

In a context of climate change, extreme conditions may be becoming more frequent. For instance, ecologically disruptive or harmful algal blooms in the marine environment show signs of increased frequencies. Some of these blooms are also optical extremes with intense manifestations in the satellite ocean colour record but often without correct derived metrics related to their occurrence, in some cases they are even omitted completely due to failures in atmospheric correction or cloud masking. Considering the ecological importance of these conditions on marine ecosystems and even human activities, they should be described quantitatively by remote sensing to be properly integrated into the statistical treatment of extreme events. Therefore, a spectral library was created containing the two main types of extreme blooms in European waters, namely intense cyanobacteria blooms in the Baltic Sea and coccolithophore blooms in the North-East Atlantic and Black Sea, including data previously excluded by operational satellite ocean colour processing. This library is a unique resource that can facilitate the use of novel spectral analysis and classification techniques for extreme marine optical events. Demonstration applications of such techniques gave results indicating single spectral classes may be sufficient to describe each case and that this kind of spectral library, analysis and classification could serve as important tools in the monitoring of these extreme events in terms of their occurrence, size, extent and evolution.

The aerosol products derived from the ocean-color missions Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate-Resolution Spectroradiometer (MODIS) Aqua and Terra are compared with field measurements from globally distributed Aerosol Robotic Network (AERONET) sites. Validation statistics are found consistent for the three missions. The median absolute relative difference between SeaWiFS and AERONET aerosol optical thickness τais approximately 20% at all bands while it is slightly higher for both MODIS products (between 20% and 28%). This is associated with a larger relative bias (median of relative differences between satellite and AERONET τa) on the order of +15% for these missions. With respect to previous processing versions, a noticeable improvement is seen in the representation of the spectral dependence of τa. The bias found for the Ångström exponent varies from -0.08 to +0.13 for the three missions.

The Ocean-Colour Climate-Change Initiative (OC-CCI) aims to create a long-term, consistent, error-characterised time series of ocean-colour products, for use in climate change studies. Climate Change Initiative is a programme of the European Space Agency devoted to using satellites to generate climate quality time series data of Essential Climate Variables (ECVs) identified by the Global Climate Observing System (GCOS). Within the ocean colour CCI project, a user consultation was undertaken, targeting both the climate modelling community and the Earth Observation community. Taking the user requirements into account, a set of criteria was developed, for selecting the best ocean-colour algorithms for climate research. Candidate atmospheric correction algorithms and in water algorithms have been submitted to a round robin comparison. The overall best performers are being used to generate test products, to be evaluated further.

A validation analysis of remote sensing reflectance (RRS) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard Terra (MODIS-T) is conducted with fieldRRSdata obtained at three fixed sites equipped with autonomous radiometers in the coastal northern Adriatic Sea and at two locations in the Baltic Sea, and during oceanographic campaigns in the Baltic, Black, and Mediterranean Seas and the English Channel. Validation results show mean relative differences |ψ| between satellite and fieldRRSvalues of 10%-13% at 531 and 547 nm and 23%-36% at 667 nm. There are much larger deviations in statistics for shorter wavelengths with large |ψ| and bias values in the Baltic Sea. The root-mean-square differences tend to decrease with wavelength from 0.0011-0.0014 sr-1at 412 nm to 0.0002-0.0005 sr-1at 667 nm. Overall, the uncertainties shown for MODIS-T are very consistent with those associated with MODIS onboard Aqua.

Trends in the difference between primary satellite ocean-color products and in situ reference data at a coastal site are analyzed and presented. Investigated products are the normalized water-leaving radiance LWNand aerosol optical thickness τafrom the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Moderate-Resolution Imaging Spectroradiometer onboard the Aqua and Terra platforms (MODIS-A and MODIS-T, respectively). In situ reference data are from the Ocean-Color component of the Aerosol Robotic Network (AERONET-OC). Restricting the period of investigation to May 2002-April 2010 for SeaWiFS and MODIS-T and July 2002-June 2008 for MODIS-A, results do not indicate any statistically significant trend in the bias of LWN. Biases, determined at around the middle of the considered period, exhibit values from -0.4% to +7.7% for SeaWiFS and from -1.9% to +4.6% for MODIS-T in the 412-555-nm spectral interval. Higher and systematically negative biases from -15.4% to -6.2% are observed for MODIS-A in the same spectral interval. Statistically appreciable trends are observed for τafrom SeaWiFS at 443 and 490 nm (approximately +1% per year) and from MODIS-A at 667 nm (+4.7% per year). The biases are very high for both MODIS-A and MODIS-T τaproducts in the 412-555-nm spectral interval (on average, +21% and +16%, respectively) when compared to SeaWiFS (exhibiting values between +1% and +4%).

The comparison of coincident daily records of the concentration of chlorophylla(Chla) derived from the Sea-viewing Wide Field-of-view Sensor and the Moderate Resolution Imaging Spectroradiometer can provide the distribution of the standard deviation ¿ associated with the zero-mean random component of the uncertainty budget. The proposed approach does not quantify the bias characterizing the Chlasatellite product, but it is spatially and temporally resolved and thus appears as a useful complement to validation exercises within situdata. The global median of ¿ is equal to 0.074 for log-transformed Chla. The values of ¿ that are around or larger than 0.1 are found in a significant part of the ocean, including the centers of the subtropical gyres, some Arctic and Antarctic areas, or eastern boundary upwelling regions. In general, ¿ increases at the low and high ends of the Chlarange, whereas it is lowest in the interval 0.1-0.3 mg ·m-3.

The accurate merging of primary radiometric ocean color products such as the normalized water-leaving radiance requires combining data from various space missions, which may be affected by different uncertainties as resulting from absolute calibration and minimization of the atmospheric effects. A statisticalcorrectionschemebased on a multilinear regression algorithm is used here to remove systematic differences betweeninsituand remote-sensing measurements. The application of thecorrectionschemeto Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) primary radiometric products improves the convergence between remote-sensing andinsitumeasurements, with the largest effects at 412 and 443 nm. Specifically, the scatter and bias of MODIS derived with respect toinsituLwnat 412 nm have shown values of 12% and 3% forcorrectedwith respect to values of 34% and -28% foruncorrecteddata, respectively. Similarly, the scatter and bias for SeaWiFS-derivedLwnat 412 nm have shown values of 14% and 4% forcorrectedwith respect to 32% and -20% foruncorrecteddata. Results at 667 nm for MODIS and at 670 nm for SeaWiFS, although displaying a reduction in the scatter of data, have shown a significant residual bias of about 11% and 17% with respect toinsituvalues. Finally, it was shown the need for restricting the application of thecorrectionschemeto data with atmospheric and marine optical features represented within the reference data set used to define the correction coefficients.

This paper discusses the validation of the operational Medium Resolution Imaging Spectrometer (MERIS) land product which corresponds to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). This biophysical variable acts as an indicator of the presence and state of the vegetation and it is currently estimated from MERIS data at both reduced and full resolution using a physically-based approach. The quality of the MERIS FAPAR products, derived from the MERIS Global Vegetation Index (MGVI) algorithm, capitalizes on the availability of MERIS data since June 2002. The validation protocol to assess the accuracy of FAPAR product includes (1) the estimates of theoretical uncertainties (versus the algorithm formulae and instrument calibration performance), (2) the assessment of the performance for detecting various events (verisimilitude) against the time-series of well-known land surfaces and (3) the direct comparisons of the FAPAR MERIS values to similar products generated by other independent sensors, like the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the MODerate Resolution Imaging Spectroradiometer (MODIS), and against ground-estimates of FAPAR performed over various vegetation types.

A three-year time-series of radiometric data collected with an autonomous above-water system at the Acqua Alta Oceanographic Tower in the northern Adriatic Sea has shown its applicability for monitoring the trophic state of marine waters and its suitability for the validation of remote sensing products in coastal areas. Specifically, the radiometric data have been used to produce surface chlorophyll a concentration (Chla) by applying a regional algorithm proposed for the northern Adriatic Sea coastal waters. A comparison based on 41 match-ups between these Chla and reference values from high-performance liquid chromatography, has shown an average absolute difference of 32%. The comparison of Chla derived from remote sensing SeaWiFS and in situ above-water radiances has shown an average absolute difference of 21% for 183 match-ups, when the same regional algorithm is applied to both types of radiometric data.

An operational system for autonomous above-water radiance measurements, called the SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM), was deployed at the Acqua Alta Oceanographic Tower in the northern Adriatic Sea and used for the validation of remote sensing radiometric products in coastal waters. The SeaPRISM data were compared with simultaneous data collected from an independent in-water system for a wide variety of sun elevations along with different atmospheric, seawater, and sea state conditions. The average absolute differences between the above- and in-water determinations of water-leaving radiances (computed linearly) were less than 4.5% in the 412-555-nm spectral interval. A similar comparison for normalized water-leaving radiances showed average absolute differences less than 5.1%. The comparison between normalized water-leaving radiances computed from remote sensing and SeaPRISM matchup data, showed absolute spectral average (linear) differences of 17.0%, 22.1%, and 20.8% for SeaWiFS, MODIS, and MERIS, respectively. The results, in keeping with those produced by independent in-water systems, suggest the feasibility of operational coastal networks of autonomous above-water radiometers deployed on fixed platforms (towers, lighthouses, navigation aids, etc.) to support ocean color validation activities.

Novelty detection is used to identify the range of applicability of empirical ocean color algorithms. This method is based on the assumption that the level of accuracy of the algorithm output depends on the representativeness of inputs in the training dataset. The effectiveness of the novelty detection method is assessed using two datasets: one representative of the northern Adriatic Sea coastal waters and the other representative of open sea waters. The two datasets are independently used to develop neural network algorithms for the retrieval of chlorophyll-a concentration (Chl-a). The range of applicability of the individual algorithms is presented using remote sensing data derived from the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS) for three selected regions: the central Mediterranean Sea, the North Sea, and the Baltic Sea. An extension of the novelty detection technique is also proposed to blend the individual algorithms and to avoid discontinuities in the resulting Chl-a maps.

An evaluation of the accuracy of atmospheric and marine satellite-derived products is presented and discussed for the northern Adriatic Sea coastal region using match-ups of in situ and Sea-Viewing Wide-Field-of-View Sensor (SeaWiFS) data for the period September 1997-September 2001. The study, making use of a simple atmospheric correction scheme including a near-infrared (NIR) turbid-water correction, has shown mean relative percentage differences between in situ and satellite-derived aerosol optical thickness lower than 23% in the spectral range between 443 and 865 nm. By applying regional empirical bio-optical algorithms for chlorophyll a concentration (Chla), total suspended matter concentration (TSM), and diffuse attenuation coefficient at 490 nm (K/sub d/(490)), match-ups analysis has shown mean relative percentage differences of 40% for Chla, 28% for TSM, and 30% for K/sub d/(490). The analysis is supported by comparison of in situ and satellite-derived normalized water leaving radiances to highlight the importance of the NIR turbid-water correction and to discuss the intrinsic uncertainties due to the use of empirical algorithms.