A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

The global ocean biogeochemical models that are used in order to assess the ocean role in the global carbon cycle and estimate the impact of the climate change on marine ecosystems are getting more and more sophisticated.They now often account for several phytoplankton functional types that play particular roles in marine food webs and the ocean carbon cycle.These phytoplankton functional types have specific physiological characteristics, which are usually poorly known and therefore add uncertainties to model results.Indeed, this evolution in model complexity is not accompanied by a similar increase in the number and diversity of in situ data sets necessary for model calibration and evaluation.Thus, it is of primary importance to develop new methods to improve model performance using existing biogeochemical data sets, despite their current limitations.In this paper, we have optimized 45 physiological parameters of the PISCES global model, using a variational optimal control method.In order to bypass a global 3-D ocean variational assimilation, which would require enormous computation and memory storage, we have simplified the estimation procedure by assimilating monthly climatological in situ observations at five contrasted oceanographic stations of the JGOFS program in a 1-D version of the PISCES model.We began by estimating the weight matrix in the cost function by using heuristic considerations.Then we used this matrix to estimate the 45 parameters of the 1-D version of the PISCES model by assimilating the different monthly profiles (observed profiles at the five stations) in the same variational procedure on a time window of 1 year.This set of optimized parameters was then used in the standard 3-D global PISCES version to perform a 500 year global simulation.The results of both the standard and the optimized versions of the model were compared to satellite-derived chlorophyll-a images, which are an independent and global data set, showing that our approach leads to significant improvements in simulated surface chlorophyll-a in most of the regions of the world ocean.Besides demonstrating that we have improved the accuracy of the PISCES model, this study proposes a sound methodology that could be used to efficiently account for in situ data in biogeochemical ocean models.

The El Niño Southern Oscillation (ENSO) drives important changes in the marine productivity of the Equatorial Pacific, in particular during major El Niño/La Niña transitions. Changes in environmental conditions associated with these climatic events also likely impact phytoplankton composition. In this work, the distribution of four major phytoplankton groups (nanoeucaryotes, Prochlorococcus, Synechococcus, and diatoms) was examined between 1996 and 2007 by applying the PHYSAT algorithm to the ocean color data archive from the Ocean Color and Temperature Sensor (OCTS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Coincident with the decrease in chlorophyll concentrations, a large-scale shift in the phytoplankton composition of the Equatorial Pacific, that was characterized by a decrease in Synechococcus and an increase in nanoeucaryote dominance, was observed during the early stages of both the strong El Niño of 1997 and the moderate El Niño of 2006. A significant increase in diatoms dominance was observed in the Equatorial Pacific during the 1998 La Niña and was associated with elevated marine productivity. An analysis of the environmental variables using a coupled physical-biogeochemical model (NEMO-PISCES) suggests that the Synechococcus dominance decrease during the two El Niño events was associated with an abrupt decline in nutrient availability (−0.9 to −2.5 μM NO3 month−1). Alternatively, increased nutrient availability (3 μM NO3 month−1) during the 1998 La Niña resulted in Equatorial Pacific dominance diatom increase. Despite these phytoplankton community shifts, the mean composition is restored after a few months, which suggests resilience in community structure.

Abstract. Dimethylsulfide (DMS) is biologically produced in the surface ocean and is the dominant natural source of sulfur to the atmosphere. Although DMS is an algal by-product, the ratio of DMS to chlorophyll (DMS:Chl) varies widely in the surface ocean. This is presumably because dimethylsulfoniopropionate (DMSP), the major precursor of DMS, DMSP-lyases, which catalyze the conversion of DMSP to DMS, and Chl vary as well with taxonomic composition than with the physiological state of the algal assemblage. Here we use remote sensing of Chl and phytoplankton dominance from PHYSAT with in-situ measured DMS concentrations to assess on an unprecedented spatial scale the affect of species composition on the DMS:Chl ratio in the surface ocean. Meridional distributions at 22° W in the Atlantic, and 95° W and 110° W in the Pacific, showed the same marked drop in DMS:Chl ratios near the equator, down to few mmol g−1, yet the basins exhibited different species dominance signatures. Hence, our results suggest that species composition was of secondary importance in controlling DMS and DMS:Chl variations in equatorial upwellings as well as physiological shifts in algal DMS production since mixed layer growth conditions (i.e., nutrient stress, temperature and light) were relatively homogeneous over the eastern equatorial Pacific. In the Indian sector of the Southern Ocean, warm core eddies with contrasting PHYSAT signatures displayed similar DMS levels. However, DMS:Chl ratios in eddies dominated by Synechococcus (SYN) were about 50% lower than that found in eddies showing nanoeucayotes or Phaeocystis-like signatures. DMS:Chl ratios varied with latitude in SYN dominated regions with ratios at low latitudes (away from equatorial upwellings) about twice that found at high northern and southern latitudes. This is the sole piece of coherent observations which indicates that species composition and growth conditions affect the large-scale dynamics of the DMS:Chl ratio. Overall, it appears that the DMS:Chl ratio is not consistent within specific phytoplankton groups determined from space. So DMS concentrations can not be derived from water-leaving radiance spectra obtained simultaneously from ocean color sensor measurements of Chl concentrations and dominant phytoplankton functional types. To proceed with the global investigation and better discriminate between factors affecting DMS:Chl ratios in the surface ocean, we recommend the use of PHYSAT records with higher spatial resolution in conjunction with other satellite products (e.g. particulate backscattering coefficients and indices of phytoplankton physiology and bloom status).

A biogeochemical model of the tropical Pacific has been used to assess the impact of interannual variability in a western Pacific iron source on the iron-limited ecosystem of the central and eastern Pacific during the 1997–1998 El Niño. A reference simulation and two simulations with an iron source in the western Pacific have been performed. The two “source” simulations differed only in the temporal variability of the iron source. In the variable source simulation, the iron concentration in the source region was proportional to the velocity of the New Guinea Coastal Undercurrent (NGCUC). In the constant source simulation, the same time-averaged concentration of iron was imposed with no temporal variability. The variable source was designed to mimic variations of iron flux from the northeast slope of New Guinea to the NGCUC due to modulation of sedimentary iron resuspension as previously hypothesized. Through the comparison of these simulations, it appeared that: (i) an iron source in the NGCUC, regardless of its source variability, increases biomass in the eastern equatorial Pacific because of the greater eastward iron flux by the Equatorial Undercurrent and (ii) a variable NGCUC iron source does not change the temporal variability of eastern Pacific chlorophyll, and in particular the timing and intensity of the June 1998 bloom. To explain eastern Pacific biological variability, local rather than remote processes are needed, such as wind-driven upwelling, the local depth of the thermocline, tropical instability waves and biological processes such as high grazing pressure. Therefore, while the western Pacific sources of dissolved iron are important in our model to sustain annually integrated equatorial Pacific production, they are unlikely to strongly constrain the timing of blooms in the central and eastern Pacific such as during the 1998 La Niña.

Dimethylsulfoniopropionate (DMSP) is produced in surface seawater by phytoplankton. Phytoplankton culture experiments have shown that nanoeucaryotes (NANO) display much higher mean DMSP-to-Carbon or DMSP-to-Chlorophyll (Chl) ratios than Prochlorococcus (PRO), Synechococcus (SYN) or diatoms (DIAT). Moreover, the DMSP-lyase activity of algae which cleaves DMSP into dimethylsulfide (DMS) is even more group specific than DMSP itself. Ship-based observations have shown at limited spatial scales, that sea surface DMS-to-Chl ratios (DMS:Chl) are dependent on the composition of phytoplankton groups. Here we use satellite remote sensing of Chl (from SeaWiFS) and of Phytoplankton Group Dominance (PGD from PHYSAT) with ship-based sea surface DMS concentrations (8 cruises in total) to assess this dependence on an unprecedented spatial scale. PHYSAT provides PGD (either NANO, PRO, SYN, DIAT, Phaeocystis (PHAEO) or coccolithophores (COC)) in each satellite pixel (1/4° horizontal resolution). While there are identification errors in the PHYSAT method, it is important to note that these errors are lowest for NANO PGD which we typify by high DMSP:Chl. In summer, in the Indian sector of the Southern Ocean, we find that mean DMS:Chl associated with NANO + PHAEO and PRO + SYN + DIAT are 13.6±8.4 mmol g−1 (n=34) and 7.3±4.8 mmol g−1 (n=24), respectively. That is a statistically significant difference (P<0.001) that is consistent with NANO and PHAEO being relatively high DMSP producers. However, in the western North Atlantic between 40° N and 60° N, we find no significant difference between the same PGD. This is most likely because coccolithophores account for the non-dominant part of the summer phytoplankton assemblages. Meridional distributions at 22° W in the Atlantic, and 95° W and 110° W in the Pacific, both show a marked drop in DMS:Chl near the equator, down to few mmol g−1, yet the basins exhibit different PGD (NANO in the Atlantic, PRO and SYN in the Pacific). In tropical and subtropical Atlantic and Pacific waters away from the equatorial and coastal upwelling, mean DMS:Chl associated with high and low DMSP producers are statistically significantly different, but the difference is opposite of that expected from culture experiments. Hence, in a majority of cases PGD is not of primary importance in controlling DMS:Chl variations. We therefore conclude that water-leaving radiance spectra obtained simultaneously from ocean color sensor measurements of Chl concentrations and dominant phytoplankton groups can not be used to predict global fields of DMS.

The predictability of Northern Africa dust events is assessed using daily numerical forecast simulations for the next three days. The dust concentration fields, modeled with the CHIMERE-DUST model, were first evaluated by comparison with both AERONET surface data and OMI and SEVIRI satellite measurements. The accuracy and spread between measurements and simulations are discussed for the first short observation period of the AMMA experiment in Western Africa, between January and March 2006. The predictability of dust events was then estimated by comparing model results for different leads in a forecast mode. The model performance was evaluated with respect to its capability to forecast the surface wind speed, which is the key process for dust emission, and the transport of mineral dust near source regions and towards remote areas. It is shown that emissions forecast can vary up to 80% (close to the sources) but that the variability on forecasted dust concentrations and optical thicknesses do not exceed 40% and 20%.

We have combined the first satellite maps of the global distribution of phytoplankton functional type and new measurements of phytoplankton-specific isoprene productivities, with available remote marine isoprene observations and a global model, to evaluate our understanding of the marine isoprene source and its impacts on organic aerosol abundances. Using satellite products to scale up data on phytoplankton-specific isoprene productivity to the global oceans, we infer a mean "bottom-up" oceanic isoprene emission of 0.31±0.08 (1σ) Tg/yr. By minimising the mean bias between the model and isoprene observations in the marine atmosphere remote from the continents, we produce a "top-down" oceanic isoprene source estimate of 1.9 Tg/yr. We suggest our reliance on limited atmospheric isoprene data, difficulties in simulating in-situ isoprene production rates in laboratory phytoplankton cultures, and limited knowledge of isoprene production mechanisms across the broad range of phytoplankton communities in the oceans under different environmental conditions as contributors to this difference between the two estimates. Inclusion of secondary organic aerosol (SOA) production from oceanic isoprene in the model with a 2% yield produces small contributions (0.01–1.4%) to observed organic carbon (OC) aerosol mass at three remote marine sites in the Northern and Southern Hemispheres. Based on these findings we suggest an insignificant role for isoprene in modulating remote marine aerosol abundances, giving further support to a recently postulated primary OC source in the remote marine atmosphere.

Phytoplankton plays an important role in the global carbon cycle via the fixation of inorganic carbon during photosynthesis. However, the efficiency of this “biological pump of carbon” strongly depends on the nature of the phytoplankton. Monitoring spatial and temporal variations of the distribution of dominant phytoplankton groups at the global scale is thus of critical importance. Recently, an algorithm has been developed to detect the major dominant phytoplankton groups from anomalies of the marine signal measured by ocean color satellites. This method, called PHYSAT, allows to identify nanoeucaryotes, Prochlorococcus, Synechococcus and diatoms. In this paper, PHYSAT has been improved to detect an additional group, named phaeocystis‐like, by analyzing specific signal anomalies in the Southern Ocean during winter months. This new version of PHYSAT was then used to process daily global SeaWiFS GAC data between 1998 and 2006. The global distribution of major phytoplankton groups is presented in this study as a monthly climatology of the most frequent phytoplankton group. The contribution of nanoeucaryotes‐dominated waters to the global ocean varies from 45 to 70% depending on the season, whereas both diatoms and phaeocystis‐like contributions exhibit a stronger seasonal variability mostly due to the large blooms that occur during winter in the Southern Ocean. Three regions of particular interest are also studied in more details: the Southern Ocean, the North Atlantic, and the Equatorial Pacific. The North Atlantic diatom bloom shows a large interannual variability. Large blooms of both diatoms and phaeocystis‐like are observed during winter in the Southern Ocean, with a larger contribution from diatoms. Their respective geographical distribution is shown to be tightly related to the depth of the mixed‐layer, with diatoms prevailing in stratified waters. Synechococcus and Prochloroccocus prevail in the Equatorial Pacific, but our data show also sporadic diatoms contributions in this region during La Niña. The observed seasonal cycle and interannual variability of phytoplankton groups in the global ocean suggest that the PHYSAT archive is suitable to study the impact of climate variability on the structure of marine ecosystems.

This paper presents a comparison of the atmospheric correction accuracy between the standard sea‐viewing wide field‐of‐view sensor (SeaWiFS) algorithm and the NeuroVaria algorithm for the ocean off the Indian coast in March 1999. NeuroVaria is a general method developed to retrieve aerosol optical properties and water‐leaving reflectances for all types of aerosols, including absorbing ones. It has been applied to SeaWiFS images of March 1999, during an episode of transport of absorbing aerosols coming from pollutant sources in India. Water‐leaving reflectances and aerosol optical thickness estimated by the two methods were extracted along a transect across the aerosol plume for three days. The comparison showed that NeuroVaria allows the retrieval of oceanic properties in the presence of absorbing aerosols with a better spatial and temporal stability than the standard SeaWiFS algorithm. NeuroVaria was then applied to the available SeaWiFS images over a two‐week period. NeuroVaria algorithm retrieves ocean products for a larger number of pixels than the standard one and eliminates most of the discontinuities and artifacts associated with the standard algorithm in presence of absorbing aerosols.

Automated instruments on board Volunteer Observing Ships (VOS) have provided high-frequency pCO(2) measurements over basin-wide regions for a decade or so. In order to estimate regional air-sea CO2 fluxes, it is necessary to interpolate between in-situ measurements to obtain maps of the marine pCO(2). Such an interpolation remains, however, a difficult task because VOS lines are too distant from each other to capture the high pCO(2) variability. Relevant physical parameters available at large scale are thus necessary to serve as a guide to estimate the pCO(2) values between the VOS lines. Satellites do not measure pCO(2) but they give access to parameters related to the processes that control its variability, such as sea surface temperature (SST). In this paper we developed a method to compute pCO(2) maps using satellite data (SST and CHL, the chlorophyll concentration), combined with a climatology of the mixed-layer depth (MLD). Using 15 401 measurements of surface pCO(2) acquired in the North Atlantic between UK and Jamaica, between June 1994 and August 1995, we show that the parameterization of pCO(2) as a function of SST, CHL and MLD yields more realistic pCO(2) values than parameterizations that have been widely used in the past, based on SST, latitude, longitude or SST only. This parameterization was then used to generate seasonal maps of pCO(2) over the North Atlantic. Results show that our approach yields the best marine pCO(2) estimates, both in terms of absolute accuracy, when compared with an independent data set, and of geographical patterns, when compared to the climatology of Takahashi et al. (2002). This suggests that monitoring the seasonal variability of pCO(2) over basin-wide regions is possible, provided that sufficient VOS lines are available.

The PHYSAT method, which enables identification of four different phytoplankton groups from their impact on the normalized water-leaving radiance (nLw) spectra, is applied to coincident in situ measurements of both chlorophyll a concentration (Chl a) and nLw. Observations show that measurements acquired in waters dominated by haptophytes, diatoms and Synechococcus-like cyanobacteria have optical properties that deviate significantly from the mean OC4V4 bio-optical model, which is currently used as the standard for global ocean color processing. A specific OC4v4-like relationship, i.e., Chl a as a 4th-order polynomial of the “maximum band ratio”, was fitted for each dominant phytoplankton group identified by PHYSAT. The resulting OC4-SD bio-optical model thus starts from a classification of the dominant phytoplankton group, followed by a species-dependent estimate of Chl a. It has been applied to global daily SEAWIFS data of the year 2001. Monthly mean maps of Chl a derived from OC4-SD or OC4v4 show large regional differences that can reach 50% at high latitudes. The new algorithm leads to lower concentrations in regions where the standard model retrievals are known to be too high, such as the Mediterranean Sea. Conversely, higher concentrations are retrieved in regions dominated by diatom blooms, such as the northern North Atlantic in summer, where previous studies have demonstrated a low bias in standard SEAWIFS Chl a.

Ocean colour sensors on board satellite measure the solar radiation reflected by the ocean and the atmosphere. This information, denoted reflectance, is affected for about 90% by air molecules and aerosols in the atmosphere and only for about 10% by water molecules and phytoplankton cells in the ocean. Our method focuses on the chlorophyll-a concentration (chl-a) retrieval, which is commonly used as a proxy for phytoplankton concentration.Our algorithm, denoted NeuroVaria, computes relevant atmospheric (Angstrom coefficient, optical thickness, single-scattering albedo) and oceanic parameters (chl-a, oceanic particulate scattering) by minimizing the difference over the whole spectrum (visible + near-infrared) between the observed reflectance and the reflectance computed from artificial neural networks that have been trained with a radiative transfer model. This algorithm has been presented in [Jamet, C., Thiria, S., Moulin, C., Crepon, M. Use of a neuro-variational inversion for retrieving oceanic and atmospheric constituents from ocean colour imagery. a feasibility study. J. Atmo. Ocean. Tech. 22 (4), 460-475, doi: 10.1175/JTECH1688.1, 2005]. NeuroVaria has been applied to SeaWiFS imagery in the Mediterranean sea. A comparison in the Mediterranean with in-situ measurements of the water-leaving reflectance, optical thickness and chl-a shows that NeuroVaria is consistent to process accurate atmospheric corrections and chl-a estimation for case-I waters and weakly absorbing aerosols. It validates the first step of this new approach of ocean colour images processing. (C) 2006 COSPAR. Published by Elsevier Ltd. All rights reserved.

Ocean color sensors on board satellite measure the solar radiation reflected by the ocean and the atmosphere. This information, denoted reflectance, is affected for 90% by air molecules and aerosols in the atmosphere and for only 10% by water molecules and phytoplankton cells in the ocean. Our method focuses on the chlorophyll-a concentration (chl-a) retrieval, which is commonly used as a proxy for phytoplankton concentration. Our algorithm, denoted NeuroVaria, computes relevant atmospheric (Angstrom coefficient, optical thickness, single-scattering albedo) and oceanic parameters (chl-a, oceanic particulate scattering) by minimizing the difference over the whole spectrum (visible + near infrared) between the observed reflectance and the reflectance computed from artificial neural networks that have been learned with a radiative transfer model. NeuroVaria has been applied to SeaWiFS (sea-viewing wide field-of-view sensor) imagery in the Mediterranean sea. A comparison with in-situ measurements of the water-leaving reflectance shows that NeuroVaria enables to better reconstruct this component at 443 nm than the standard SeaWiFS processing. This leads to an improvement of the retrieval of the chl-a for the oligotrophic sea. This result is generalized to the entire Mediterranean sea through weekly maps of chl-a.

Ocean color sensors enable a quasi-permanent monitoring of the chlorophyll a concentration (Chl a) in surface waters. This ubiquitous photosynthetic pigment cannot, however, be used to distinguish between phytoplankton species. Distinguishing phytoplankton groups from space is nevertheless necessary to better study some biochemical processes such as carbon fixation at the global scale, and is thus one of the major challenges of ocean color research. In situ data have shown that the water-leaving radiances (nLw), measured by ocean color sensors at different wavelengths in the visible spectrum, vary significantly for a given Chl a. This natural variability is due partly to differences in optical properties of phytoplankton species. Here, we derive relationships between nLw and phytoplankton species by using a large set of quantitative inventories of phytoplankton pigments collected during nine cruises from Le Havre (France) to Nouméa (New Caledonia) in the framework of the GeP&CO program. Coincident SeaWiFS nLw data between 412 and 555nm are extracted and normalized to remove the effect of Chl a. These normalized spectra vary significantly with in situ pigment composition, so that four major phytoplankton groups, i.e., haptophytes, Prochlorococcus, Synechococcus-like cyanobacteria and diatoms, can be distinguished. This classification (PHYSAT) is applied to the global SeaWiFS dataset for year 2001, and global maps of phytoplankton groups are presented. Haptophytes and diatoms are found mostly in high latitudes and in eutrophic regions. Diatoms show a strong seasonal cycle with large-scale blooms during spring and summer. These results, obtained with only five channels in the visible spectrum, demonstrate that ocean color measurements can be used to discriminate between dominant phytoplankton groups provided that sufficient data are available to establish the necessary empirical relationships.

The SEVIRI instrument on board Meteosat Second Generation (MSG) offers new capabilities to monitor aerosol transport over the Atlantic and the Mediterranean at high temporal and spatial resolutions, in particular, Saharan dust from North Africa, biomass-burning aerosols from subtropical Africa and pollution from Europe. An inversion technique was developed to estimate both aerosol optical thickness and Angström coefficients from SEVIRI measurements at 0.63 and 0.81 µm. This method relies on an optimized set of aerosol models to ensure a fast processing of full-resolution MSG images and to allow the processing of long time series. SEVIRI images for slots 45, 49 and 53 (11:15, 12:15, 13:15 UT) were processed for June 2003. The retrieved optical thicknesses and Angström coefficients are in good agreement with AERONET in-situ measurements in the Atlantic and in the Mediterranean. Monthly mean maps of both parameters are compared to that obtained with the polar orbiting sensor POLDER for June 2003. There is a good consistency between the two monthly means in terms of optical thickness, but the Angström coefficients show significant differences in the Atlantic zone which is affected by dust transport. These differences may be explained by the lack of specific non-spherical dust models within the inversion. The preliminary results presented in this paper demonstrate, nevertheless, the potential of MSG/SEVIRI for the monitoring of aerosol optical properties at high frequencies over the Atlantic and the Mediterranean.

We present an automatic classification method based on topological neural network algorithms to retrieve aerosol optical properties from multi-spectral ocean-color satellite imagery. The first step of the method consisted in an unsupervised classification of a large set of clear-sky top of the atmosphere reflectance spectra measured by the sensor. We used the so-called Kohonen map which aggregates similar spectra into a reduced set of pertinent groups. The second step consisted in labeling these groups by clustering them with synthetic TOA reflectance spectra whose optical properties (i.e., aerosol type or optical thickness) are known. These synthetic spectra have been computed using a radiative transfer model. In the present study, we dealt with five aerosol types (maritime, coastal, tropospheric, oceanic and mineral) and several aerosol optical thickness values ranging from 0.05 to 0.8. These simulated spectra were then projected onto the Kohonen map to label each group of the map. The last step consisted in applying this method to the SeaWiFS imagery of the Mediterranean region for the years 1999 and 2000. The Kohonen map was “educated” from pixels randomly extracted during the year 1999 in this region. We accounted for the viewing geometry of the sensor by clustering the simulated spectra into ten groups of similar geometries, as defined by both scattering and sun zenith angles. The analysis of SeaWiFS images was performed pixel-by-pixel by selecting the suitable labeling (in terms of viewing geometry), then by identifying the closest spectrum in the Kohonen map, which finally gives the aerosol optical properties. This method led to accurate and coherent results, as shown by the comparison with in situ aerosol measurements provided by the AERONET station at Lampedusa and by the study of two aerosol events over the Mediterranean. One of the major advantages of this method is that it enables us to automatically identify the aerosol type and to retrieve the aerosol optical properties with a better accuracy than classical methods such as those used by SeaWifs. It gives accurate results for optical thickness values larger than 0.35 and is able to retrieve dust aerosols such as African dust aerosol (absorbing aerosol). These should ensure a more precise inversion of ocean-color imagery where the knowledge of atmospheric optical parameters is essential. Moreover the method is able to give probabilities for the estimate values of aerosol properties.