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A global in-situ data set for validation of ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in-situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient and total suspended matter. Data were obtained from multi-project archives acquired via open internet services, or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 151,673 rows, with each row representing a unique station in space and time (cf 136,250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68,641 (cf 59,781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.pangaea.de/10.1594/PANGAEA.941318 (Valente et al., 2022)., SI

The upwelling spectral radiance distribution is polarized, and this polarization varies with the optical properties of the water body. Knowledge of the polarized, upwelling, bidirectional radiance distribution function (BRDF) is important for generating consistent, long-term data records for ocean color because the satellite sensors from which the data are derived are sensitive to polarization. In addition, various studies have indicated that measurement of the polarization of the radiance leaving the ocean can used to determine particle characteristics (Tonizzo et al., 2007, Ibrahim et al., 2016, Chami et al., 2001). Models for the unpolarized BRDF (Morel et al., 2002, Lee et al., 2011) have been validated (Voss et al., 2007, Gleason et al., 2012), but variations in the polarization of the upwelling radiance due to the sun angle, viewing geometry, dissolved material, and suspended particles have not been systematically documented. In this work, we simulated the upwelling radiance distribution using a Monte Carlo-based radiative transfer code and measured it using a set of fish-eye cameras with linear polarizing filters. The results of model-data comparisons from three field experiments in clear and turbid coastal conditions showed that the degree of linear polarization (DOLP) of the upwelling light field could be determined by the model with an absolute error of &plusmn, 0.05 (or 5% when the DOLP was expressed in %). This agreement was achieved even with a fixed scattering Mueller matrix, but did require in situ measurements of the other inherent optical properties, e.g., scattering coefficient, absorption coefficient, etc. This underscores the difficulty that is likely to be encountered using the particle scattering Mueller matrix (as indicated through the remote measurement of the polarized radiance) to provide a signature relating to the properties of marine particles beyond the attenuation/absorption coefficient.

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594/PANGAEA.854832 (Valente et al., 2015).

International audience; André Morel (1933-2012) was a prominent pioneer of modern optical oceanography, enabling significant advances in this field. Through his forward thinking and research over more than 40 years, he made key contributions that this field needed to grow and to reach its current status. This article first summarizes his career and then successively covers different aspects of optical oceanography where he made significant contributions, from fundamental work on optical properties of water and particles to global oceanographic applications using satellite ocean color observations. At the end, we share our views on André's legacy to our research field and scientific community. Expected final online publication date for the Annual Review of Marine Science Volume 6 is January 03, 2014. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

Simulated bidirectional reflectance distribution functions (BRDF) were compared with measurements made just beneath the water’s surface. In Case I water, the set of simulations that varied the particle scattering phase function depending on chlorophyll concentration agreed more closely with the data than other models. In Case II water, however, the simulations using fixed phase functions agreed well with the data and were nearly indistinguishable from each other, on average. The results suggest that BRDF corrections in Case II water are feasible using single, average, particle scattering phase functions, but that the existing approach using variable particle scattering phase functions is still warranted in Case I water., JRC.H.1-Water Resources

The contribution of phytoplankton groups to total chlorophyll a (chl a) was derived using CHEMTAX from pigment measurements by HPLC and compared with the carbon (C) biomass estimations obtained from microscopy in the southern Baltic Sea in April 2005. Five different matrices of pigment:chl a input ratios, derived from the literature, were tested. Successive runs of CHEMTAX showed peridinin:chl a for dinoflagellates and fucoxanthin:chl a for diatoms to converge at 0.452 ±0.02 (mean ± SD) and 0.489 ± 0.03, respectively, with initial ratios varying by a factor of 2 to 3 across matrices. The 2 techniques were in relatively good agreement for the dominant phytoplankton groups. Peridinin, diadinoxanthin, chlorophylls c1 and c2 (here grouped together as chl c1+c2), fucoxanthin and alloxanthin were the principal accessory pigments; dinoflagellates, diatoms and cryptophytes were the groups forming the majority of the C biomass. Diadinoxanthin and chl c1+c2 were mainly associated with the dominant dinoflagellates rather than with other phytoplankton classes. Excluding cyanophytes, the correlation between carbon biomass of other minor phytoplankton groups and their chl a was either poor or not significant due to uncertainties in either microscopic counts or CHEMTAX classification. There was a good correlation between carbon biomass of phytoplankton and chl a. The estimated C:chl a ratio of total phytoplankton varied between 8 and 40 (in average 20 ± 7), with a higher value for dinoflagellates (30 ± 17) than for diatoms (9 ± 7). Fucoxanthin- containing small flagellates might have led to the overestimation of the diatom contribution by CHEMTAX at a few stations., JRC.H.3-Global environement monitoring

International audience; Whether for biogeochemical studies or ocean color validation activities, high-performance liquid chromatography (HPLC) is an established reference technique for the analysis of chlorophyll a and associated phytoplankton pigments. The results of an intercomparison exercise of HPLC pigment determination, performed for the first time on natural samples and involving four laboratories (each using a different HPLC procedure), are used to address three main objectives: (a) estimate (and explain) the level of agreement or discrepancy in the methods used, (b) establish whether or not the accuracy requirements for ocean color validation activities can be met, and (c) establish how higher order associations in individual pigments (i.e., sums and ratios) influence the uncertainty budget while also determining how this information can be used to minimize the variance within larger pigment databases. The round-robin test samples (11 different samples received in duplicate by each laboratory) covered a range of total chlorophyll a concentration, [TChl a], representative of open ocean conditions from 0.045 mg m(-3), typical of the highly oligotrophic surface waters of the Ionian Sea, to 2.2 mg m(-3), characteristic of the upwelling regime off Morocco. Despite the diversity in trophic conditions and HPLC methods, the agreement between laboratories, defined here as the absolute percent difference (APD), was approximately 7.0% for [TChl a], which is well within the 25% accuracy objective for remote sensing validation purposes. For other pigments (mainly chemotaxinomic carotenoids), the agreement between methods was 21.5% on average (ranging from 11.5% for fucoxanthin to 32.5% for peridinin), and inversely depended on pigment concentration (with large disagreements for pigments close to the detection limits). It is shown that better agreement between methods can be achieved if some simple procedures are employed: (a) disregarding results less than the effective limit of quantitation (LOQ, an alternative to the method detection limit, MDL), (b) standardizing the manner in which the concentration of pigment standards are determined, and (c) accurately accounting for divinyl chlorophyll a when computing [TChl a] for those methods which do not chromatographically separate it from monovinyl chlorophyll a. The use of these quality-assurance procedures improved the agreement between methods, with average APD values dropping from 7.0% to 5.5% for [TChl a] and from 21.5% to 13.9% for the principal carotenoids. Additionally, it is shown that subsequent grouping of individual pigment concentrations into sums and ratios significantly reduced the variance and, thus, improved the agreement between laboratories. This grouping, therefore, provides a simple mechanism for decreasing the variance within databases composed of merged data from different origins. Among the recommendations for improving database consistency in the future, it is suggested that submissions to a database should include the relevant information related to the limit of detection for the HPLC method. (C) 2003 Elsevier B.V. All rights reserved.

The predictions of a spectral light-photosynthesis model are compared with field data. The model calculations are based on pigment (chlorophyll and pheophytin) and temperature profiles and, when available, on irradiance recorded on deck. The agreement between computed and measured production values is satisfying over the full range (10(-4)-1 g C m-3 d-1 or 0.03-10 g C m-2 d-1). It is better when C-14 fixation has been measured via the in situ method; a small bias appears when production was measured on deck (simulated in situ method). In both cases however the standard deviation remains similar and computed and measured column production agrees within a factor of approximately-3. The same data set is also used to predict column production from pigment concentration within only the top layer, as supposedly remotely sensed. The model is run in combination with pigment profiles, which are ``reconstructed'' (in magnitude and shape) as a function of the upper layer concentration with statistical relationships previously established. The agreement between computed and measured production (within a factor 3.3 at 1 SD) is encouraging. The model uses mean and constant physiological parameters, which actually vary in the natural environment. Among these parameters, the Chl-specific absorption by phytoplankton algae and, to a lesser extent, the maximum quantum yield for growth are crucial. Very likely their variations are the main causes of divergence between predicted and field values.

Maps of surface chlorophyllous pigment (Chl a + Pheo a) are currently produced from ocean color sensors. Transforming such maps into maps of primary production can be reliably done only by using light-production models in conjuction with additional information about the column-integrated pigment content and its vertical distribution. As a preliminary effort in this direction. $\ticksim 4,000$ vertical profiles pigment (Chl a + Pheo a) determined only in oceanic Case 1 waters have been statistically analyzed. They were scaled according to dimensionless depths (actual depth divided by the depth of the euphotic layer, $Z_e$) and expressed as dimensionless concentrations (actual concentration divided by the mean concentration within the euphotic layer). The depth $Z_e$ generally unknown, was computed with a previously develop bio-optical model. Highly sifnificant relationships were found allowing $\langle C \rangle_tot$, the pigment content of the euphotic layer, to be inferred from the surface concentration, $\bar C_pd$, observed within the layer of one penetration depth. According to their $\bar C_pd$ values (ranging from $0.01 to > 10 mg m^-3$), we categorized the profiles into seven trophic situations and computed a mean vertical profile for each. Between a quasi-uniform profile in eutrophic waters and a profile with a strong deep maximum in oligotrophic waters, the shape evolves rather regularly. The wellmixed cold waters, essentially in the Antarctic zone, have been separately examined. On average, their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values of $ρ$, the ratio of Chl a tp (Chl a + Pheo a), have also been obtained for each trophic category. The energy stored by photosynthesizing algae, once normalized with respect to the integrated chlorophyll biomass $\langle C \rangle _tot $ is proportional to the available photosythetic energy at the surface via a parameter $ψ∗$ which is the cross-section for photosynthesis per unit of areal chlorophyll. By tanking advantage of the relative stability of $ψ∗.$ we can compute primary production from ocean color data acquired from space. For such a computation, inputs are the irradiance field at the ocean surface, the "surface" pigment from which $\langle C \rangle _tot$ can be derived, the mean $ρ value pertinent to the trophic situation as depicted by the $\bar C_pd or $\langle C \rangle _tot$ values, and the cross-section $ψ∗$. Instead of a contant $ψ∗.$ value, the mean profiles can be used; they allow the climatological field of the $ψ∗.$ parameter to be adjusted through the parallel use of a spectral light-production model.