The potential for applying in situ multispectralRrsdata from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) to validate satellite derived ocean color hyperspectralRrsproducts was investigated in the 400-700 nm interval. The analysis was performed using a comprehensive data set of simulated hyperspectralRrsin combination with an algorithm designed to re-construct hyperspectralRrsfrom multispectral ones. Results were assessed using in situ hyperspectralRrsrepresentative of diverse water types. Excluding waters dominated by a high concentration of colored dissolved organic matter, results indicate the capability of determining hyperspectralRrsfrom AERONET-OC multispectral data with mean relative and absolute uncertainties generally lower than 2% and 5 × 10−5sr−1, respectively, at a number of the key center-wavelengths of the Ocean Color Instrument (OCI) onboard the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE).

Marine data products from the Ocean and Land Color Instrument (OLCI-A), operated onboard the Copernicus Sentinel 3A satellite, were assessed using in situ reference measurements representative of various European Seas. Results from the evaluation of inherent optical properties indicated substantial accuracy for the satellite derived absorption coefficient of pigmented particlesaOLCI-Aph(λ), the diffuse attenuation coefficient of downward irradianceKOLCI-Ad(λ) and the back-scattering coefficient of marine particlesbOLCI-Abp(λ). Conversely, large underestimates were shown for the absorption coefficient of non-water constituentsaOLCI-Anw(λ) and the cumulative absorption coefficient of colored dissolved organic matter and of non-pigmented particlesaOLCI-Adg(λ).

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.

Two regional bio-optical algorithms are combined to retrieve the Chlorophyll-a($\text {Chl-}\textit {a}$) concentration in the Black Sea. The first is a band-ratio algorithm that computes$\text {Chl-}\textit {a}$as a function of the slope of Remote Sensing Reflectance ($\mathit {R_{\text {RS}}}$) values at two wavelengths using a polynomial regression that captures the overall data trend, enhancing extrapolation results. The second algorithm is a Multilayer Perceptron neural net based on$\mathit {R_{\text {RS}}}$values at three individual wavelengths that features interpolation capabilities helpful to fit data non-linearities. A new merging scheme is then designed to benefit from the complementarity of the two approaches. Remote sensing data employed to demonstrate the merging of regional results for the Black Sea are those acquired by the Ocean and Land Color Instrument on board Sentinel-3A to acknowledge the need for data products of higher accuracy within the long-term Copernicus program.

The seasonal impact of adjacency effects (AE) on satellite ocean color data at visible and near-infrared (NIR) wavelengths by the Sea-Viewing Wide Field-of-View Sensor, the Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODISA), the Medium Resolution Imaging Spectrometer, the Ocean and Land Color Instrument, the Operational Land Imager (OLI), and the MultiSpectral Imagery (MSI) was theoretically evaluated at a validation site in the northern Adriatic Sea. The analysis made use of comprehensive simulations accounting for multiple scattering, sea surface roughness, sensor viewing geometry, actual coastline, typical and extreme atmospheric conditions, and the seasonal variability of solar illumination and, land and water optical properties. Results, obtained by relying on the normalization of the radiometric sensitivity of each sensor to the same input radiance, show that the spectral and seasonal impacts of AE considerably vary among sensors. AE significantly exceed the radiometric sensitivity of MSI at its sole blue band in winter, whereas they significantly outdo the noise threshold of OLI and MODISA high-resolution data exclusively in the NIR in summer. Conversely, for all other sensors and for MODISA low-resolution data, AE are particularly significant at NIR bands between March and October and at the blue-green bands in winter.

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.

This letter discusses the normalized water-leaving reflectance obtained from two atmospheric corrections built in the MEGS 8.0 processor applied to the Medium Resolution Imaging Spectrometer (MERIS) marine data. In situ reference data for intercomparisons with satellite-derived reflectance products are from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC). Case of study is the northern Adriatic Sea AERONET-OC site characterized by both Case-1 and Case-2 waters. The accuracy of MERIS pigment indices is also discussed in relation to uncertainties and biases affecting atmospherically corrected data.

Advances in satellite ocean color technologies and methodologies are expected to lead to the generation of coastal water bio-optical products with accuracies close to those targeted for oceanic regions. In view of contributing to such a progress, multilayer perceptron neural networks complying with standard Medium Resolution Imaging Spectrometer (MERIS) pigment indices were developed relying on regional highly accurate in situ data. This work illustrates and discusses the application to sample MERIS imagery of those neural networks trained to produce pigment indices in seas characterized by increased levels of bio-optical complexity: the Baltic, the Northern Adriatic, and the Western Black Seas.

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 Color CAL/VAL team is evaluating the VIIRS bio-optical products for real-time operations. VIIRS ocean data are being processed using standard government algorithms, and channel calibration and product validation evaluation activities are ongoing. A network of 27 global “Golden Regions” has been established to evaluate and validate bio-optical products. Satellite inter-comparison for data consistency with current ocean color products, and real time vicarious adjustment calculation are performed using in situ water leaving radiance propagated to Top of Atmosphere in coastal and open ocean regions. In addition, routine matchups with VIIRS and MODIS-Aqua are done with in situ data collection from ships and real time coastal AERONET-OC sites. The above activities, product evaluation and tracking of channel stability, are being contributed to the JPSS Team to evaluate the overall mission, including calibration and inter-satellite product consistency. Initial NPP VIIRS ocean bio-optical products are demonstrated with other ocean color satellites.

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%).

MERIS Level 1 and Level 2 water products will be improved in the 3rdMERIS reprocessing which is planned to take place before the end of 2009. The instrument radiometric degradation model will be updated. Improvements to the atmospheric correction in both case 1 and case 2 waters will be implemented. A vicarious adjustment strategy to remove residual biases in the Level 2 marine signals will be put in place. In addition, a cloud screening scheme with improved detection capabilities will improve the cirrus detection capability. In parallel, long term algorithmic improvements are being pursued and are partially covered by three exploratory ongoing studies. The first study addresses the limitation of the current MERIS atmospheric correction scheme in sun glint conditions. The second aims at defining an operational adjacency effect correction. The third study makes use of the ability of MERIS to measure transmission in the O2-A oxygen band to better identify and characterize clouds and aerosols.

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.

Water-type (WT) definition as a function of bio-optical properties has relevance to the exploitation of satellite ocean color data because it may provide a mean to optimize the atmospheric correction process or select the most appropriate bio-optical algorithm for the determination of optically significant constituents in seawater. Within such a framework, this paper defines a WT index assuming the existence of two extreme conditions. On one side, there are waters with optical properties totally depending on the phytoplankton component. On the other side, waters characterized by fully unrelated bio-optical quantities. The index of intermediate WTs is then defined on a statistical basis, and the resulting scheme is applied to a set of experimental data collected in an optically complex coastal region. Finally, in view of supporting remote sensing applications, a neural network algorithm is implemented to identify WTs using the normalized water leaving radiance.

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.

In situ autonomous systems are commonly used for the collection of measurements for the vicarious calibration of satellite data and the successive validation of derived products. However, the use of autonomous systems creates the need of assessing the quality of the large volume of collected data. Within the framework of ocean color activities, this work investigates the consistency of normalized water leaving radiances spectra produced from measurements taken with an above-water autonomous system installed on an oceanographic tower. The study has shown the need of addressing the problem under two different levels of inference. The first level, so-called self-consistency, has demonstrated the capability of identifying spectra with a low statististical representativeness within the dataset itself. The second level, so-called relative-consistency, has provided the possibility of evaluating whether a spectrum is relatively consistent to a reference set of quality-assured data.

Multilayer perceptron (MLP) neural network algorithms were developed to retrieve the absorption coefficient spectra of the colored dissolved organic matter (CDOM) and nonpigmented particulate matter (NPPM) from the remote sensing reflectance R/sub rs/ of optically complex waters. The two MLP algorithms, consisting of one hidden layer with ten neurons and requiring R/sub rs/ at 412, 490, and 665 nm as inputs, were trained with a comprehensive experimental dataset of the Northern Adriatic Sea coastal waters. The products of the proposed regional MLP algorithms showed higher accuracies than regional band-ratio algorithms, and exhibited average uncertainties of 20% and 25% in the determination of CDOM and NPPM absorption coefficients at 412 nm, respectively.

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.

The determination of phytoplankton in seawater, quantified as chlorophyll-a concentration (Chl-a) or absorption of pigmented matter (a/sub ph/), is a major objective of optical remote sensing. The accuracy of multilayer perceptron (MLP) neural network algorithms in determining Chl-a and a/sub ph/ at 443 nm as a function of the multispectral remote sensing reflectance (R/sub rs/) was investigated for optically complex waters. The implementation of the MLP algorithms was carried out relying on an experimental dataset collected in a coastal region of the northern Adriatic Sea. The performance of the algorithms was assessed on both separate and combined Case 1 and Case 2 water types. The proposed MLP algorithms showed a better accuracy both with respect to other algorithms developed on the basis of the same dataset as well as with respect to independent algorithms operationally used for the processing of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The study also showed a high accuracy in determining a/sub ph/(443) and, thus, further confirmed the possibility of computing the inherent optical properties of seawater significant components from the R/sub rs/ spectra.

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.