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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 by in situ reference 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 Angstrom 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.

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 LWN and aerosol optical thickness τa from 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 τa from 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 τa products in the 412-555-nm spectral interval (on average, +21% and +16%, respectively) when compared to SeaWiFS (exhibiting values between +1% and +4%).

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.

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.

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.