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Quality control (QC) practices are a fundamental requirement for any measurement program targeting the delivery of high-quality data. In agreement with such a need, the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) includes a number of QC steps ensuring the delivery of normalized water-leaving radiance LWN spectra at incremental accuracy levels identified as level 1.0, level 1.5, and level 2.0. Currently, the final QC step allowing for rising level 1.5 LWN spectra to level 2.0 implies the execution of an expert-based procedure, which is extremely time consuming and naturally undergoes subjective decisions on dubious cases. These limitations solicited the development of an automated procedure, so-called , mimicking the steps supporting an expert analyst during the final QC of AERONET-OC LWN spectra. applies hierarchical tests to check (i) the relative consistency of level 1.5 LWN spectra (called candidates) with respect to LWN reference spectra (called prototypes) constructed using LWN spectra formerly and independently quality controlled; (ii) the absence of any pronounced spectral feature in portions of each LWN candidate spectrum expected to exhibit a regular shape; and additionally, when applicable, (iii) the temporal consistency of the LWN candidate spectrum with respect to close-in-time spectra as a criterion to further strengthen the quality of data. performance has been verified using LWN spectra from AERONET-OC measurement sites representative of various water types embracing oligotrophic/mesotrophic waters dominated by chlorophyll-a concentration and coastal waters exhibiting increasing levels of optical complexity. has shown an acceptance rate of AERONET-OC level 1.5 LWN candidate spectra varying between approximately 89% and 93% with agreement in the range of 88%–93% with respect to the LWN spectra independently quality controlled through the expert-based procedure. The additional capability of to rank the fully quality-controlled LWN spectra combining weights depending on the various tests, anticipates the possibility to best support applications with diverse accuracy needs. Finally, acceptance rates of for LWN prototype spectra built using level 1.5 data, an alternative to fully quality-controlled level 2.0, have shown values generally increased by less than 1%. This indicates the possibility to lessen the constraint implying the existence of reference level 2.0 LWN data for the relative-consistency test at the expense of a fairly low reduction in accuracy.

During the last decade, new applications exploiting data from satellite borne lidar measurements demonstrated that these sensors can give valuable information about ocean optical properties [1,2,3]. Within this framework, COLOR (CDOM-proxy retrieval from aeOLus ObseRvations) consisted in a 18-month feasibility study approved by ESA within the Aeolus+ Innovation program. COLOR had the objective to evaluate and document the feasibility of deriving an in-water prototype product from the analysis of the signal acquired by the ESA Earth Explorer Wind Mission ADM-Aeolus (Atmospheric Dynamics Mission). In particular, COLOR project focused on the AEOLUS potential retrieval of the diffuse attenuation coefficient for downwelling irradiance (Kd [m-1]) from the ocean sub-surface backscattered component of the 355 nm received lidar signal.The core activity of the project was the characterization of the signal from the AEOLUS ground bin through two parallel and strongly interacting activities: a) Radiative transfer numerical modelling; b) AEOLUS data analysis. The main result of the project will be presented together with the discussion of the perspectives of the satellite lidar missions dedicated to ocean color. [1] M. J. Behrenfeld et al. (2019). Global satellite-observed daily vertical migrations of ocean animals», Nature, vol. 576, n. 7786, Art. n. 7786, dic. 2019, doi: 10.1038/s41586-019-1796-9.[2] Jamet, C., Ibrahim, A., Ahmad, Z., Angelini, F., Babin, M., Behrenfeld, M. J., et al. (2019). Going beyond standard ocean color observations: lidar and polarimetry. Front. Mar. Sci. 6:251. doi: 10.3389/fmars.2019.00251[3] D. Dionisi, V. E. Brando, G. Volpe, S. Colella, e R. Santoleri (2020). Seasonal distributions of ocean particulate optical properties from spaceborne lidar measurements in Mediterranean and Black sea», Remote Sens. Environ., vol. 247, pag. 111889, set. 2020, doi: 10.1016/j.rse.2020.111889.

Many scientific publications that affect machine learning have set the basis for pattern recognition and symmetry. In this paper, we revisit the concept of “Mind-life continuity” published by the authors, testing the symmetry between cognitive and electrophoretic strata. We opted for machine learning to analyze and understand the total protein profile of neurotypical subjects acquired by capillary electrophoresis. Capillary electrophoresis permits a cost-wise solution but lacks modern proteomic techniques’ discriminative and quantification power. To compensate for this problem, we developed tools for better data visualization and exploration in this work. These tools permitted us to examine better the total protein profile of 92 young adults, from 19 to 25 years old, healthy university students at the University of Lisbon, with no serious, uncontrolled, or chronic diseases affecting the nervous system. As a result, we created a graphical user interface toolbox named MODeLING.Vis, which showed specific expected protein profiles present in saliva in our neurotypical sample. The developed toolbox permitted data exploration and hypothesis testing of the biomolecular data. In conclusion, this analysis offered the data mining of the acquired neuroproteomics data in the molecular weight range from 9.1 to 30 kDa. This molecular weight range, obtained by pattern recognition of our dataset, is characteristic of the small neuroimmune molecules and neuropeptides. Consequently, MODeLING.Vis offers a machine-learning solution for probing into the neurocognitive response.

The sea-surface reflectance factor ρ required for the determination of the water- leaving radiance from above-water radiometric measurements is derived from radiative transfer simulations relying on models of the sky-radiance distribution and sea-surface statistics. This work primarily investigates the impact on ρ of various sky-radiance and sea-surface models. A specific replicability analysis, restricted to the 550 nm wavelength, has been performed with the Monte Carlo code for Ocean Color Simulations (so-called MOX) accounting for the measurement geometry recommended in protocols for the validation of satellite ocean color data and commonly applied for operational measurements. Results indicate that the variability of ρ increases with wind speed and reaches the largest values for sun elevations close to the zenith or approaching the horizon. In particular, a variability up to about 2% is observed for wind speeds below 4 ms−1 and sun zenith angles larger than 20°. Finally, the benchmark of the ρ values from this study with those formally determined with the Hydrolight code and widely utilized by the ocean color community, exhibits systematic differences. The source of these differences is discussed and the implications for field measurements are addressed.

22 pages, 14 figures, 5 tables, 2 appendixes.-- Data availability statement: AERONET-OC data are available from NASA (https://aeronet.gsfc.nasa.gov/new_web/ocean_color.html), The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) supports activities related to ocean color such as validation of satellite data products, assessment of atmospheric correction schemes, and evaluation of bio-optical models through globally distributed standardized measurements of water-leaving radiance and aerosol optical depth. In view of duly assisting the AERONET-OC data user community, this work (i) summarizes the latest investigations on a number of scientific issues related to above-water radiometry, (ii) emphasizes the network expansion that from 2002 until the end of 2020 integrated 31 effective measurement sites, (iii) shows the equivalence of data product accuracy across sites and time for measurements performed with different instrument series, (iv) illustrates the variety of water types represented by the network sites ensuring validation activities across a diversity of observation conditions, and (v) documents the availability of water-leaving radiance data corrected for bidirectional effects by applying a method specifically developed for chlorophyll-a-dominated waters and an alternative one that is likely suitable for any water type, With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

A field intercomparison was conducted at the Acqua Alta Oceanographic Tower (AAOT) in the northern Adriatic Sea, from 9 to 19 July 2018 to assess differences in the accuracy of in- and above-water radiometer measurements used for the validation of ocean colour products. Ten measurement systems were compared. Prior to the intercomparison, the absolute radiometric calibration of all sensors was carried out using the same standards and methods at the same reference laboratory. Measurements were performed under clear sky conditions, relatively low sun zenith angles, moderately low sea state and on the same deployment platform and frame (except in-water systems). The weighted average of five above-water measurements was used as baseline reference for comparisons. For downwelling irradiance ( E d ), there was generally good agreement between sensors with differences of

A relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration (Chl-a). Alas, ocean color remote sensing applications to estimate Chl-a in this brackish basin, characterized by large gradients in salinity and dissolved organic matter, are hampered by its optical complexity and atmospheric correction limits. This study presents Chl-a retrieval improvements for a fully reprocessed multi-sensor time series of remote-sensing reflectances (Rrs) at ~1 km spatial resolution for the Baltic Sea. A new ensemble scheme based on multilayer perceptron neural net (MLP) bio-optical algorithms has been implemented to this end. The study documents that this approach outperforms band-ratio algorithms when compared to in situ datasets, reducing the gross overestimates of Chl-a observed in the literature for this basin. The Rrs and Chl-a time series were then exploited for eutrophication monitoring, providing a quantitative description of spring and summer phytoplankton blooms in the Baltic Sea over 1998–2019. The analysis of the phytoplankton dynamics enabled the identification of the latitudinal variations in the spring bloom phenology across the basin, the early blooming in spring in the last two decades, and the description of the spatiotemporal coverage of summer cyanobacterial blooms in the central and southern Baltic Sea.

In the context of the Copernicus Program, EUMETSAT prioritizes the creation of an ocean color infrastructure for system vicarious calibration (OC-SVC). This work aims to reply to this need by proposing the European Radiometry Buoy and Infrastructure (EURYBIA). EURYBIA is designed as an autonomous European infrastructure operating within the Marine Optical Network (MarONet) established by University of Miami (Miami, FL, USA) based on the Marine Optical Buoy (MOBY) experience and NASA support. MarONet addresses SVC requirements in different sites, consistently and in a traceable way. The selected EURYBIA installation is close to the Lampedusa Island in the central Mediterranean Sea. This area is widely studied and hosts an Atmospheric and Oceanographic Observatory for long-term climate monitoring. The EURYBIA field segment comprises off-shore and on-shore infrastructures to manage the observation system and perform routine sensors calibrations. The ground segment includes the telemetry center for data communication and the processing center to compute data products and uncertainty budgets. The study shows that the overall uncertainty of EURYBIA SVC gains computed for the Sentinel-3 OLCI mission under EUMETSAT protocols is of about 0.05% in the blue-green wavelengths after a decade of measurements, similar to that of the reference site in Hawaii and in compliance with requirements for climate studies.

An intercomparison of radiance and irradiance ocean color radiometers (The Second Laboratory Comparison Exercise—LCE-2) was organized within the frame of the European Space Agency funded project Fiducial Reference Measurements for Satellite Ocean Color (FRM4SOC) May 8−13, 2017 at Tartu Observatory, Estonia. LCE-2 consisted of three sub-tasks: 1) SI-traceable radiometric calibration of all the participating radiance and irradiance radiometers at the Tartu Observatory just before the comparisons; 2) Indoor intercomparison using stable radiance and irradiance sources in controlled environment; and 3) Outdoor intercomparison of natural radiation sources over terrestrial water surface. The aim of the experiment was to provide one link in the chain of traceability from field measurements of water reflectance to the uniform SI-traceable calibration, and after calibration to verify whether different instruments measuring the same object provide results consistent within the expected uncertainty limits. This paper describes the activities and results of the first two phases of LCE-2: the SI-traceable radiometric calibration and indoor intercomparison, the results of outdoor experiment are presented in a related paper of the same journal issue. The indoor experiment of the LCE-2 has proven that uniform calibration just before the use of radiometers is highly effective. Distinct radiometers from different manufacturers operated by different scientists can yield quite close radiance and irradiance results (standard deviation s < 1%) under defined conditions. This holds when measuring stable lamp-based targets under stationary laboratory conditions with all the radiometers uniformly calibrated against the same standards just prior to the experiment. In addition, some unification of measurement and data processing must be settled. Uncertainty of radiance and irradiance measurement under these conditions largely consists of the sensor’s calibration uncertainty and of the spread of results obtained by individual sensors measuring the same object.

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.

An intercomparison of radiance and irradiance ocean color radiometers (the second laboratory comparison exercise—LCE-2) was organized within the frame of the European Space Agency funded project Fiducial Reference Measurements for Satellite Ocean Color (FRM4SOC) May 8–13, 2017 at Tartu Observatory, Estonia. LCE-2 consisted of three sub-tasks: (1) SI-traceable radiometric calibration of all the participating radiance and irradiance radiometers at the Tartu Observatory just before the comparisons; (2) indoor, laboratory intercomparison using stable radiance and irradiance sources in a controlled environment; (3) outdoor, field intercomparison of natural radiation sources over a natural water surface. The aim of the experiment was to provide a link in the chain of traceability from field measurements of water reflectance to the uniform SI-traceable calibration, and after calibration to verify whether different instruments measuring the same object provide results consistent within the expected uncertainty limits. This paper describes the third phase of LCE-2: The results of the field experiment. The calibration of radiometers and laboratory comparison experiment are presented in a related paper of the same journal issue. Compared to the laboratory comparison, the field intercomparison has demonstrated substantially larger variability between freshly calibrated sensors, because the targets and environmental conditions during radiometric calibration were different, both spectrally and spatially. Major differences were found for radiance sensors measuring a sunlit water target at viewing zenith angle of 139° because of the different fields of view. Major differences were found for irradiance sensors because of imperfect cosine response of diffusers. Variability between individual radiometers did depend significantly also on the type of the sensor and on the specific measurement target. Uniform SI traceable radiometric calibration ensuring fairly good consistency for indoor, laboratory measurements is insufficient for outdoor, field measurements, mainly due to the different angular variability of illumination. More stringent specifications and individual testing of radiometers for all relevant systematic effects (temperature, nonlinearity, spectral stray light, etc.) are needed to reduce biases between instruments and better quantify measurement uncertainties.

This study investigates how the number of data collected for algorithm development in typical case-1 waters and optically complex cases can affect the remote sensing of ocean color (OC) products. Marine conditions dominated by the colored dissolved organic matter (CDOM) and nonalgal particles are considered. The applied OC inversion schemes are based on multilayer perceptron (MLP) neural nets for data classification and regression. Simulated data for MLP training are generated with a forward OC model. Results show that a disproportion of samples representing different marine optical cases influences the MLP learning and hence also the data product retrieval, mostly in mixed case-1 and CDOM-dominated environments. A postclassification correction is then employed for performance improvements. Methodological developments are presented, acknowledging the coastal water monitoring prioritized by the Copernicus Earth Observation program.

This study investigates standard and regional algal pigment index 1 (API1) estimates in the Atlantic off the southwestern Iberian Peninsula. Standard API1 data are those delivered by the Medium Resolution Imaging Spectrometer (MERIS) orbiting sensor. Equivalent quantities are computed by applying a regional inversion scheme using as input both MERIS and in situ remote sensing reflectances $(R_{\mathrm{rs}})$ . Reference data for the development of the regional algorithm and for the analysis of tested products include field measurements of total concentration of chlorophyll $a$ (TChl a ) and coincident $R_{\mathrm{rs}}$ values collected at different distances from the coast. Validation results, based on matchup analysis, identifies a systematic overestimation of standard API1 versus the reference TChl a values. The additional comparison of product maps in selected regions of interest confirms this tendency and demonstrates the feasibility and relevance of using regional algorithms for investigating spaceborne products. Analogous applications are hence devised for the early-stage evaluation of the forthcoming Sentinel-3/OLCI data products.

An intercomparison of radiance and irradiance ocean color radiometers (the second laboratory comparison exercise--LCE-2) was organized within the frame of the European Space Agency funded project Fiducial Reference Measurements for Satellite Ocean Color (FRM4SOC) May 8-13, 2017 at Tartu Observatory, Estonia. LCE-2 consisted of three sub-tasks: (1) SI-traceable radiometric calibration of all the participating radiance and irradiance radiometers at the Tartu Observatory just before the comparisons; (2) indoor, laboratory intercomparison using stable radiance and irradiance sources in a controlled environment; (3) outdoor, field intercomparison of natural radiation sources over a natural water surface. The aim of the experiment was to provide a link in the chain of traceability from field measurements of water reflectance to the uniform SI-traceable calibration, and after calibration to verify whether different instruments measuring the same object provide results consistent within the expected uncertainty limits. This paper describes the third phase of LCE-2: The results of the field experiment. The calibration of radiometers and laboratory comparison experiment are presented in a related paper of the same journal issue [1]. Compared to the laboratory comparison, the field intercomparison has demonstrated substantially larger variability between freshly calibrated sensors, because the targets and environmental conditions during radiometric calibration were different, both spectrally and spatially. Major differences were found for radiance sensors measuring a sunlit water target at viewing zenith angle of 139° because of the different fields of view. Major differences were found for irradiance sensors because of imperfect cosine response of diffusers. Variability between individual radiometers did depend significantly also on the type of the sensor and on the specific measurement target. Uniform SI traceable radiometric calibration ensuring fairly good consistency for indoor, laboratory measurements is insufficient for outdoor, field measurements, mainly due to the different angular variability of illumination. More stringent specifications and individual testing of radiometers for all relevant systematic effects (temperature, nonlinearity, spectral stray light, etc.) are needed to reduce biases between instruments and better quantify measurement uncertainties.

This document concerns the collection, analysis and on-line experimentation of ocean color data off the Western Iberian Peninsula. Field measurements have been acquired during the BIOMETORE field campaign in summer 2016 to evaluate and enhance Earth observation capabilities of the Copernicus program. Deliverables of the Ocean and Land Colour Instrument on board of the Sentinel-3 satellite of the European Space Agency are of specific interest. Preliminary evaluations confirm the quality of the in situ measurements to address the match-up future analysis of radiometric values and derived data products. On-line experimentation undertaken with the WebEnhanced Service To Ocean Color demonstrates the feasibility of enabling in a transparent way the user's access to complex functionalities such as neural network applications. FCT [IF/00541/2013/CP1181] info:eu-repo/semantics/publishedVersion

The authors are grateful to Dr. Giuseppe Zibordi (E.C. Joint Research Centre, Italy) for his support from in situ marine radiometry perspectives. This study was supported by the Portuguese Foundation for Science and Technology (FCT/MEC) through the grant PEst-OE/EEI/UI0527/2011. Access to the Milipeia cluster (University of Coimbra, Portugal) was granted through the project 'Large-scale parallel Monte Carlo simulations for ocean colour applications.' Additional funding was granted through the ESA contract No. 22576/09/I-OL, ARG/003-025/1406/CIMA, and NOVA LINCS Ref. UID/CEC/04516/2013. This paper presents a high-performance computing (HPC) framework for Monte Carlo (MC) simulations in the ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer code named MOX, developed by the authors to create a virtual marine environment for investigating the quality of OC data products derived from in situ measurements of in-water radiometric quantities. A consolidated set of solutions for performance modeling, prediction, and optimization is implemented to enhance the efficiency of MC OC simulations on HPC run-time infrastructures. HPC, machine learning, and adaptive computing techniques are applied taking into account a clear separation and systematic treatment of accuracy and precision requirements for large-scale MC OC simulations. The added value of the work is the integration of computational methods and tools for MC OC simulations in the form of an HPC-oriented problem-solving environment specifically tailored to investigate data acquisition and reduction methods for OC field measurements. Study results highlight the benefit of close collaboration between HPC and application domain researchers to improve the efficiency and flexibility of computer simulations in the marine optics application domain. publishersversion published

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.

Standard ocean color data products from the Medium Resolution Imaging Spectrometer (MERIS) are compared with equivalent regional products in European seas exhibiting different bio-optical properties: the northern Adriatic Sea, the Baltic Sea and the Western Black Sea (ADRS, BLTS and BLKS, respectively). Investigated quantities are: 1) the algal-2 pigment index, alg2; 2) the composite absorption coefficient of yellow substance and non-pigmented particles at 442 nm, adg; and 3) the concentration of the total suspended matter, TSM. Regional data products are created using ocean color inversion schemes based on MultiLayer Perceptron (MLP) neural nets trained with field measurements from the Coastal Atmosphere and Sea Time Series (CoASTS) and Bio-Optical mapping of Marine Properties (BiOMaP) programs. MLP input is the remote sensing reflectance RRS at MERIS center-wavelengths specifically selected for different water types in view of minimizing the perturbing effects of inaccurate atmospheric correction on the retrieval of regional data products. A new method is also proposed to define the applicability of regional MLPs to input RRS. Results indicate that MERIS alg2 values tend to overestimate the equivalent quantity computed with MLP regional algorithms. The agreement between MERIS and regional TSM data products is significantly better than that reported for alg2 and adg, especially for BLKS. Findings highlight the relevance of using regional inversion schemes to evaluate standard products over extended oceanographic regions as a complement to the analysis of match-ups between marine products measured in situ and derived from space-born data.

The objective of this study is to verify whether accurate and continuous estimates of freshwater discharge at the mouth of a narrow estuary with a single channel can be obtained from a bottom-mounted current profiler (ADCP). The focus is on moderate- to high-discharge events that significantly affect the water circulation corresponding to low river flow conditions. Observations at the Guadiana Estuary (southern Iberia) indicate lateral subtidal flow variability, constant cross-channel area, and quasi-steady response of the axial velocity to discharge events. Based on the concept of maximum entropy, the mean and maximum channel velocities were related by a constant ratio, Ω, using data from three cross-channel surveys. This relationship was then used to estimate the freshwater discharge at the mouth based on the maximum velocity obtained from the detided ADCP velocity profiles. This approach was possible because the ADCP was deployed near the position of maximum current velocity, that is, over the deepest part of the channel. The results show good correspondence with observations, indicating that the entropy model can complete or substitute the records from upstream gauged stations that do not include the contribution from downstream tributaries. A Multilayer Perceptron neural net (MLP) based on the entropy approach was then implemented with the purpose of estimating the discharge when Ω is unknown. This latter analysis showsthat the relationship between maximum velocity and discharge is quasi-stationary. Consequently, the MLP can successfully estimate freshwater runoff if the training data represent all statistical properties of the river discharge dynamics. The results also indicate that Ω may vary not only with concomitant hydrographic conditions, but also with the recent (i.e., several days prior) discharge magnitude. info:eu-repo/semantics/publishedVersion

Above-water radiometry depends on estimates of the reflectance factor rho of the sea surface to compute the in situ water-leaving radiance. The Monte Carlo code for ocean color simulations MOX is used in this study to analyze the effect of different environmental components on r values. A first aspect is examining the reflectance factor without and by accounting for the sky-radiance polarization. The influence of the sea-surface statistics at discrete grid points is then considered by presenting a new scheme to define the variance of the waves slope. Results at different sun elevations and sensor orientations indicate that the light polarization effect on r simulations reduces from similar to 17 to similar to 10% when the wind speed increases from 0 to 14ms(-1). An opposite tendency characterizes the modeling of the sea-surface slope variance, with r differences up to similar to 12% at a wind speed of 10ms(-1). The joint effect of polarization and the the sea-surface statistics displays a less systematic dependence on the wind speed, with differences in the range similar to 13 to similar to 18%. The r changes due to the light polarization and the variance of the waves slope become more relevant at sky-viewing geometries respectively lower and higher than 40 degrees with respect to the zenith. An overall compensation of positive and negative offsets due to light polarization is finally documented when considering different sun elevations. These results address additional investigations which, by combining the modeling and experimental components of marine optics, better evaluate specific measurement protocols for collecting above-water radiometric data in the field. (C) 2016 Optical Society of America

Multilayer perceptron (MLP) neural networks for regional satellite ocean color applications have been developed and assessed using in situ data from various European seas. Considered MLP products are chlorophyll a concentration ( Chl–a ), absorption by yellow substance at 412 nm ( a ys (412)) and concentration of total suspended matter (TSM), all determined from spectral remote sensing reflectance R RS . Investigated oceanographic regions comprise the Eastern Mediterranean Sea, the northern Adriatic Sea, the Western Black Sea and the Baltic Sea. The in situ measurements applied in the study were produced within the framework of the Coastal Atmosphere and Sea Time Series (CoASTS) and Bio-Optical Mapping of Marine Properties (BiOMaP) programs contributing to a unique dataset that represents different water types including chlorophyll a , yellow substance and sediment dominated waters. Performance analysis of the proposed regional MLPs indicates that Chl–a can be quantified with the highest accuracy in the Eastern Mediterranean Sea (with absolute percent difference of 14% with respect to in situ measurements). In the case of a ys (412), the most accurate determination is observed for the Baltic Sea waters (13%). Instead, TSM retrieval is the most accurate in the Black Sea (14%). The study demonstrated the limited generalization capability of regional algorithms. Within this context, saturation of MLP output occurring with input data not statistically represented in the training set has been investigated through cross-basin product analysis in view of proposing a practical solution to the problem.

Advanced remote sensing techniques and regional algorithms are combined to produce and analyze a 12-year multi-sensor time series of bio-optical properties for the Adriatic Sea. First, a multi-sensor series of normalized water-leaving radiance LWN is created with an optically-based merging technique applied to SeaWiFS and MODIS data, then distributions of inherent optical properties (IOPs) are obtained with a semi-analytical bio-optical model. Additionally, Chla is computed by two regional empirical algorithms associated with two classes of optical properties representative of open ocean waters and Adriatic coastal regions (Classes 1 and 2, respectively). The domain of applicability of each algorithm is dynamically determined on the basis of the LWN input spectrum by a novelty detection approach. The various satellite products are validated using a comprehensive bio-optical time series collected in the northern Adriatic. At the basin level, the frequency of occurrence of Classes 1 and 2 are 61% and 10%, respectively, with Class 2 waters particularly located in the northern part and characterized by higher levels of Chla and IOPs. The annual cycle of Chla over the 12-year period exhibits significant variations in amplitude and phase; moreover, the annual cycle shows a reduced amplitude during a period centered on 2004 for a time interval that varies with the region. In the northern Adriatic, the overall level of Chla appears lower between 2003 and 2008 compared to the beginning of the series.

The development and assessment of satellite ocean color products require quality assured in situ data representative of the variety of bio-optical regimes encountered in the different seas. The measurement program named Bio-Optical mapping of Marine Properties (BiOMaP) fulfills this requirement by using identical instruments and applying cross-site consistent methods for the characterization of seawater inherent and apparent optical properties in the various European seas. This work introduces the BiOMaP radiometric data and describes their application to the validation of primary ocean color products. Within this framework, the radiometric data are discussed through the spectral shape and amplitude of normalized water-leaving radiances ( L WN ). Specifically, the spectral shape is expressed through the Principal Component Analysis of L WN (λ)/ L WN (555) while the amplitude is represented by L WN (555). The resulting distribution of BiOMaP data in a three dimensional feature space demonstrates a continuity of cases across the investigated marine regions confirming a wide representativity of bio-optical regimes. The application of BiOMaP data to the validation of remote sensing reflectance from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), indicates improved performance of the SeaWiFS Data Analysis System (SeaDAS, version 6.1) atmospheric correction. In particular, the comparison of satellite and in situ matchups in the blue spectral region shows biases of a few percent with respect to the much larger reported in studies relying on earlier SeaDAS versions. Matchup analyses, restricted to the Eastern Mediterranean, Black and Baltic Seas, indicate marked regional differences likely explained by the diversity of water and aerosol types.

A performance modeling method is presented to predict the execution time of a parallel Monte Carlo (MC) radiative transfer simulation code for ocean color applications. The execution time of MC simulations is predicted using a multi-layer perceptron (MLP) neural network regression model trained with past execution time measurements in different execution environments and simulation cases. On the basis of the MLP performance model, a complementary job-environment mapping algorithm enables an efficient utilization of available high-performance computing resources minimizing the total execution time of the simulation jobs distributed in multiple environments.

article i nfo The study presents and discusses the application of in situ data from the ocean color component of the Aerosol Robotic Network (AERONET-OC) to assess primary remote sensing products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the AQUA platform and from the Sea-viewing Wide- Field-of-view Sensor (SeaWiFS) on the OrbView-2 spacecraft. Three AERONET-OC European coastal sites exhibiting different atmospheric and marine optical properties were considered for the study: the Acqua Alta Oceanographic Tower (AAOT) in the northern Adriatic Sea representing Case-1 and Case-2 moderately sediment dominated waters; and, the Gustaf Dalen Lighthouse Tower (GDLT) in the northern Baltic Proper and the Helsinki Lighthouse Tower (HLT) in the Gulf of Finland, both characterized by Case-2 waters dominated by colored dissolved organic matter (CDOM). The analysis of MODIS derived normalized water- leaving radiance at 551 nm, LWN(551), has shown relatively good results for all sites with uncertainties of the order of 10% and biases ranging from − 1t o−4%. Larger uncertainty and bias have been observed at 443 nm for the AAOT (i.e., 18 and −7%, respectively). At the same center wavelength, results for GDLT and HLT have exhibited much larger uncertainties (i.e., 56 and 67%, respectively) and biases (i.e., 18 and 25%, respectively), which undermine the possibility of presently using remote sensing LWN data at the blue center wavelengths for bio-optical investigations in the Baltic Sea. An evaluation of satellite derived aerosol optical thickness, τa, has shown uncertainties and biases of the order of tens of percent increasing with wavelength at all sites. Specifically, MODIS derived τa at 869 nm has shown an overestimate of 71% at the AAOT, 101% at GDLT and 91% at HLT, respectively. This result highlights the effects of a limited number of aerosol models for the atmospheric correction process, and might also indicate the need of applying a vicarious calibration factor to the remote sensing data at the 869 nm center wavelength to remove the effects of uncertainties in the atmospheric optical model and the space sensor radiometric calibration. Similar results have been obtained from the analysis of SeaWiFS data. Finally, in view of illustrating the possibility of increasing the accuracy of satellite regional radiometric products, AERONET-OC data have been applied to reduce systematic errors in MODIS and Medium Resolution Imaging Spectrometer (MERIS) LWN data likely due to the atmospheric correction process. Results relying on MODIS match-ups for the Baltic Sites (i.e., GDLT and HLT) and MERIS matchups for the AAOT, have indicated a substantial reduction of both uncertainty and bias in the blue and red center wavelengths.

The ocean color component of the Aerosol Robotic Network (AERONET-OC) has been implemented to support long-term satellite ocean color investigations through cross-site consistent and accurate measurements collected by autonomous radiometer systems deployed on offshore fixed platforms. The AERONET-OC data products are the normalized water-leaving radiances determined at various center wavelengths in the visible and near-infrared spectral regions. These data complement atmospheric AERONET aerosol products, such as optical thickness, size distribution, single scattering albedo, and phase function. This work describes in detail this new AERONET component and its specific elements including measurement method, instrument calibration, processing scheme, quality assurance, uncertainties, data archive, and products accessibility. Additionally, the atmospheric and bio-optical features of the sites currently included in AERONET-OC are briefly summarized. After illustrating the application of AERONET-OC data to the validation of primary satellite products over a variety of complex coastal waters, recommendations are then provided for the identification of new deployment sites most suitable to support satellite ocean color missions.

Radiometric products determined from fixed-depth and continuous in-water profile data collected at a coastal site characterized by moderately complex waters were compared to investigate differences and limitations between the two measurement methods. The analysis focused on measurements performed with the same radiometer system sequentially deployed at discrete depths (i.e., 1 and 3 m) and successively used to profile the water column. Within the 412–683-nm spectral interval, comparisons show uncertainties of 2%–4%, 3%–5%, and 2% for the subsurface values of upwelling radiance, Lun, upward irradiance, Eun, and downward irradiance, Edn, all normalized with respect to the above-water downward irradiance. The related spectral biases vary from −2% to 1% for Lun, are in the range of 2%–3% for Eun, and are lower than 0.5% for Edn. Derived products like the irradiance reflectance, R, Q factor at nadir, Q, and normalized water leaving radiance, LWN, exhibit spectral uncertainties of 4%–6%, 2%–3%, and 2%–4%. The related spectral biases vary from 1% to 3%, 2% to 3%, and −2% to 1%, respectively. An analysis of these results indicates a general diminishing of uncertainties and biases with a decrease of the diffuse attenuation coefficient, Kd, determined at 490 nm, for most of the quantities investigated. Exceptions are Edn and Kd because an increase of Kd reduces the perturbations due to wave effects on downward irradiance measurements. An evaluation of the perturbing effects due to the presence of optical stratifications, which lead to a nonlinear decrease with depth of log-transformed radiometric measurements, shows an expected increase in uncertainty and bias specifically evident for Ku, Eun, Kl, and Lun, and derived quantities like R, Q, and LWN. Overall results, supported by a t-test analysis, indicate the possibility of using moorings in moderately complex coastal waters to determine LWN with a slightly higher uncertainty with respect to that achievable with continuous profiling systems.

Accurate merging of primary radiometric ocean color products like 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 statistical correction scheme based on a multi-linear regression algorithm is here used to remove systematic differences between in situ and remote sensing measurements. The application of the correction scheme to SeaWiFS and MODIS primary radiometric products has shown an increased convergence between remote sensing and in situ measurements, with the largest effects at 412 and 443 nm. Specifically, the scatter and bias of MODIS derived with respect to in situ Lwn at 412 nm has shown values of 12% and 2% for corrected with respect to values of 29% and -20% for uncorrected data, respectively. Similarly, the scatter and bias for SeaWiFS derived Lwn at 412 nm has shown values of 11% and 2% for corrected with respect to 27% and -11% for uncorrected data. 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 8% with respect to in situ values. Differently, the correction scheme does not improve Lwn values at the other center-wavelengths were the agreement between remote sensing and in situ data is already comparable to the intrinsic uncertainty of the in situ data. Finally, the study has shown the need of restricting the application of the correction scheme to data with spectral features represented within the reference dataset used for defining the correction parameters., JRC.H.3-Global environement monitoring

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.

This paper, addresses the problem of novelty detection in the case that the observed data is a mixture of a known 'background' process contaminated with an unknown other process, which generates the outliers, or novel observations. The framework we describe here is quite general, employing univariate classification with incomplete information, based on knowledge of the distribution (the 'probability density function', 'pdf') of the data generated by the 'background' process. The relative proportion of this 'background' component (the 'prior' 'background' 'probability), the 'pdf' and the 'prior' probabilities of all other components are all assumed unknown. The main contribution is a new classification scheme that identifies the maximum proportion of observed data following the known 'background' distribution. The method exploits the Kolmogorov-Smirnov test to estimate the proportions, and afterwards data are Bayes optimally separated. Results, demonstrated with synthetic data, show that this approach can produce more reliable results than a standard novelty detection scheme. The classification algorithm is then applied to the problem of identifying outliers in the SIC2004 data set, in order to detect the radioactive release simulated in the 'oker' data set. We propose this method as a reliable means of novelty detection in the emergency situation which can also be used to identify outliers prior to the application of a more general automatic mapping algorithm. © Springer-Verlag 2007.

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.

Motivation: Despite theoretical arguments that so-called ‘loop designs’ for two-channel DNA microarray experiments are more efficient, biologists continue to use ‘reference designs’. We describe two sets of microarray experiments with RNA from two different biological systems (TPA-stimulated mammalian cells and Streptomyces coelicolor). In each case, both a loop and a reference design were used with the same RNA preparations with the aim of studying their relative efficiency. Results: The results of these experiments show that (1) the loop design attains a much higher precision than the reference design, (2) multiplicative spot effects are a large source of variability, and if they are not accounted for in the mathematical model, for example, by taking log-ratios or including spot effects, then the model will perform poorly. The first result is reinforced by a simulation study. Practical recommendations are given on how simple loop designs can be extended to more realistic experimental designs and how standard statistical methods allow the experimentalist to use and interpret the results from loop designs in practice. Availability: The data and R code are available at http://exgen.ma.umist.ac.uk Contact: veronica.vinciotti@brunel.ac.uk

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.

Wave perturbations induce uncertainties in subsurface quantities determined from the extrapolation of optical measurements taken at different depths. An analysis of these uncertainties was made using data collected in the northern Adriatic Sea coastal waters over a wide range of environmental conditions with a profiling system having a 6-Hz acquisition rate, ∼0.1 m s−1 deployment speed, radiance sensors with 20° full angle field of view, and irradiance collectors of ∼1-cm diameter. The uncertainties were quantified as a function of the depth resolution of radiance and irradiance profiles through the percent differences between the subsurface values computed from full and reduced resolution profiles (the latter synthetically created by removing data from the former). The applied method made the analysis independent from instrument calibration; from perturbations induced by instrument self-shading, deployment structure, and bottom effects; and from environmental variability caused by seawater and i...

The development and validation of ocean colour products require the availability of in situ atmospheric and marine data. Since 1995, the Coastal Atmosphere and Sea Time Series (CoASTS) Program ensures the collection of an in situ comprehensive set of measurements taken from an oceanographic tower in the northern Adriatic Sea. An example of a validation exercise is presented through the comparison of in situ and satellite-derived aerosol optical thickness at various wavelengths in the 443–865 nm spectral interval.

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.

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.

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.

Chlorophyll a concentration (Chl) product validation off theWestern Iberian coast is here undertaken by directly comparing remote sensing data with in situ surface reference values. Both standard and recently developed alternative algorithms are considered for match-up data analysis. The investigated standard products are those produced by the MERIS (algal 1 and algal 2) and MODIS (OC3M) algorithms. The alternative data products include those generatedwithin the CoastColour Project and Ocean Color Climate Change Initiative (OC-CCI) funded by ESA, as well as a neural net model trained with field measurements collected in the Atlantic off Portugal (MLPATLP). Statistical analyses showed that satellite Chl estimates tend to be larger than in situ reference values. The study also revealed that a non-uniform Chl distribution in the water column can be a concurring factor to the documented overestimation tendency when considering larger optical depth match-up stations. Among standard remote sensing products, MODIS OC3M and MERIS algal 2 yield the best agreement with in situ data. The performance of MLPATLP highlights the capability of regional solutions to further improve Chl retrieval by accounting for environmental specificities. Results also demonstrate the relevance of oceanographic regions such as the Nazaré area to evaluate how complex hydrodynamic conditions can influence the quality of Chl products. This studywas performed in the framework of HabSpot FCT Project, PTDC/MAR/100348/2008 and European Space Agency projects DUE CoastColour (ESRIN/AO/1-6141/09/l-EC) and Climate Change Iniciative — Ocean Color (AO-1/6207/09/I-LG). The work has been also partially supported by the European Space Agency within the framework of the MERIS Validation Activities under contract n. 12595/09/I-OL, and sampling activities benefited from European projects HERMES (GOCE-CT-2005-511234) and Hermione (EC contract 226354) support. We would like to thank NASA OBPG for the MODIS data and ESA Project AOPT-2423 for providing MERIS full resolution images. Ana C. Brito was funded by a Portuguese Post-doc grant from FCT (BPD/63017/2009) and by the Investigador FCT Program (IF/00331/2013). Davide D'Alimonte was funded by Investigador FCT Program (IF/00541/2013). info:eu-repo/semantics/publishedVersion

An autonomous system for making above-water radiance measurements has been produced by adding a new measurement scenario to a CIMEL CE-318 sun photometer. The new system, called the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Photometer Revision for Incident Surface Measurement (SeaPRISM), combines the normal CE-318 capability for measuring direct sun irradiance and sky radiance, with a new capability for measuring above-water radiance for the retrieval of water-leaving radiance. The system has been extensively tested during several measurement periods over a 1-yr time frame from August 1999 to July 2000 under various sun elevations along with different atmospheric, seawater, and sea-state conditions. The field assessment of the new instrument was conducted at an oceanographic tower located in the northern Adriatic Sea within the framework of measurement campaigns aimed at supporting ocean color calibration and validation activities. Sample data at 440, 500, 670, 870, and 1020 nm were collect...

The collection of high-quality in situ measurements is a key component for the calibration and validation of space-borne radiometric data and the development of inversion schemes for retrieving higher level product maps underpinning climate-change studies. In situ radiometric measurements to compute quantities such as the remote sensing reflectance are, however, subject to different perturbing factors. Scope of this chapter is to discuss perturbations due to large deployment structures, such as ships and oceanographic towers, as well as instrument self-shading and sea-surface light focusing. The radiance distribution induced by these perturbing elements cannot be evaluated analytically by directly solving the radiative transfer equation. Among numerical simulations, the use of deterministic approaches is partially limited by the absence of translational invariance in the perturbed light field. On the other hand, results show that Monte Carlo simulations of radiometric perturbations represent an effective mean for enhancing the quality of field measurements by offering the possibility to define correction schemes and refine measurement protocols.

Statistical performance tuning of a parallel Monte Carlo (MC) radiative transfer code for ocean color (OC) applications is presented. A low observed-to-peak performance ratio due to highly sparse computations is compensated by online and offline tuning techniques based on a statistical indicator of products accuracy. Run-time adaptive control employs the accuracy indicator to set up two complementary tuning criteria: one general to MC computations and the other specific to OC applications. The same accuracy indicator is also used for pre-execution tuning of a threshold parameter. Numerical simulations of real case scenarios showed that the proposed methods consistently led to faster runs, while satisfying application accuracy requirements. Specifically, speed-ups range from 2.17 to 7.44 times when compared with the un-optimized version of the MC code. The applied techniques are orthogonal to parallelization, so that the reported performance gains are further amplified by parallel speed-ups.

The Geo-Info project aims to provide Geoscience experts with software toolkits tailored for selected target application domains. Within this framework, high-performance computing (HPC) solutions are here applied to optimize a Monte Carlo (MC) code to support Ocean Color (OC) investigations. The present paper introduces the Geo-Info project, describes the HPC solutions applied for the OC MC case study, and gives early performance results focusing on runtime, speedup, and parallel efficiency.

Motivation: Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods. Results: A newly developed software tool called MILVA (microarray latent visualization and analysis) is presented here to investigate microarray data without separating gene expression profiles into discrete classes. The underpinning of the MILVA software is the two-dimensional topographic representation of multidimensional microarray data. On this basis, the interactive MILVA functions allow a continuous exploration of microarray data driven by the direct supervision of the biologist in detecting activity patterns of co-regulated genes. Availability: The MILVA software is freely available. The software and the related documentation can be downloaded from http://www.ncrg.aston.ac.uk/Projects/milva. User 'surrey' as username and '3245' as password to login. The software is currently available for Windows platform only. Contact: d.lowe@aston.ac.uk

This study addresses the regression of in-water radiometric profile data with the objective of investigating solutions to minimize uncertainties of derived products like subsurface radiance and irradiance (L(u0) and E(d0)) and diffuse attenuation coefficients. Analyses are conducted using radiometric profiles generated through Monte Carlo simulations and field measurements. A nonlinear NL approach is presented as an alternative to the standard linear method LN. Results indicate that the LN method, relying on log-transformed data, tends to underestimate regression results with respect to NL operating on non-transformed data. The log-transformation is thus identified as the source of biases in data products. Observed differences between LN and NL regression results for L(u0) are of the order of 1-2%, that is well below the target uncertainty for data products from in situ measurements (i.e., 5%). For E(d0), instead, differences can easily exceed 5% as a result of more pronounced light focusing and defocusing effects due to wave perturbations. This work also remarks the importance of applying the multi-cast measurement scheme as a mean to increase the precision of data products.

A Monte Carlo code for ocean color simulations has been developed to model in-water radiometric fields of downward and upward irradiance (E(d) and E(u)), and upwelling radiance (L(u)) in a two-dimensional domain with a high spatial resolution. The efficiency of the code has been optimized by applying state-of-the-art computing solutions, while the accuracy of simulation results has been quantified through benchmark with the widely used Hydrolight code for various values of seawater inherent optical properties and different illumination conditions. Considering a seawater single scattering albedo of 0.9, as well as surface waves of 5 m width and 0.5 m height, the study has shown that the number of photons required to quantify uncertainties induced by wave focusing effects on E(d), E(u), and L(u) data products is of the order of 10(6), 10(9), and 10(10), respectively. On this basis, the effects of sea-surface geometries on radiometric quantities have been investigated for different surface gravity waves. Data products from simulated radiometric profiles have finally been analyzed as a function of the deployment speed and sampling frequency of current free-fall systems in view of providing recommendations to improve measurement protocols.