A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications.

Light emerging from natural water bodies and measured by radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities for characterizing aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; the NASA Surface Biology and Geology designated observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP-AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries, and oceans. Specific in situ measurements include remote-sensing reflectance, irradiance reflectance, and coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.902230 (Casey et al., 2019).

The open-ocean missing backscattering is in the structural complexity of particles.

Marine microscopic particles profoundly impact global biogeochemical cycles, but our understanding of their dynamics is hindered by lack of observations. To fill this gap, optical backscattering measured by satellite sensors and in-situ autonomous platforms can be exploited. Unfortunately, these observations remain critically limited by an incomplete mechanistic understanding of what particles generate the backscattering signal. To achieve this understanding, optical models are employed. The simplest of these models-the homogeneous sphere-severely underestimates the measured backscattering and the missing signal has been attributed to submicron particles. This issue is known as the missing backscattering enigma. Here we show that a slightly more complex optical model-the coated sphere-can predict the measured backscattering and suggests that most of the signal comes from particles >1 µm. These findings were confirmed by independent size-fractionation experiments. Our results demonstrate that the structural complexity of particles is critical to understand open-ocean backscattering and contribute to solving the enigma.

Determination of the absorption coefficient of chromophoric dissolved organic matter from underway spectrophotometry.

Measurements of the absorption coefficient of chromophoric dissolved organic matter (ay) are needed to validate existing ocean-color algorithms. In the surface open ocean, these measurements are challenging because of low ay values. Yet, existing global datasets demonstrate that ay could contribute between 30% to 50% of the total absorption budget in the 400-450 nm spectral range, thus making accurate measurement of ay essential to constrain these uncertainties. In this study, we present a simple way of determining ay using a commercially-available in-situ spectrophotometer operated in underway mode. The obtained ay values were validated using independent collocated measurements. The method is simple to implement, can provide measurements with very high spatio-temporal resolution, and has an accuracy of about 0.0004 m-1 and a precision of about 0.0025 m-1 when compared to independent data (at 440 nm). The only limitation for using this method at sea is that it relies on the availability of relatively large volumes of ultrapure water. Despite this limitation, the method can deliver the ay data needed for validating and assessing uncertainties in ocean-colour algorithms.

On the discrimination of multiple phytoplankton groups from light absorption spectra of assemblages with mixed taxonomic composition and variable light conditions.

According to recommendations of the international community of phytoplankton functional type algorithm developers, a set of experiments on marine algal cultures was conducted to (1) investigate uncertainties and limits in phytoplankton group discrimination from hyperspectral light absorption properties of assemblages with mixed taxonomic composition, and (2) evaluate the extent to which modifications of the absorption spectral features due to variable light conditions affect the optical discrimination of phytoplankton. Results showed that spectral absorption signatures of multiple species can be extracted from mixed assemblages, even at low relative contributions. Errors in retrieved pigment abundances are, however, influenced by the co-occurrence of species with similar spectral features. Plasticity of absorption spectra due to changes in light conditions weakly affects interspecific differences, with errors <21% for retrievals of pigment concentrations from mixed assemblages.

Inversion of spectral absorption coefficients to infer phytoplankton size classes, chlorophyll concentration, and detrital matter.

Measured spectral absorption coefficients were inverted to infer phytoplankton concentration in three size classes (picoplankton, nanoplankton, and microplankton), chlorophyll concentration [Chl], and both magnitude and spectral shape of absorption by colored detrital matter (CDM). Our algorithm allowed us to solve for the nonlinear factor of CDM absorption slope separately from the other linear factors, thus fully utilizing the additive characteristic inherent in absorption coefficients. We validated the inversion with three datasets: two spatially distributed global datasets, the Laboratoire d'Océanographie de Villefranche dataset and the NASA bio-Optical Marine Algorithm Dataset, and a time series coastal dataset, the Martha's Vineyard Coastal Observatory dataset. Comparison with high performance liquid chromatography analyses showed that the phytoplankton size classes can be retrieved with correlation coefficients (r)>0.7, root mean square errors of 0.2, and median relative errors of 20% in oceanic waters and with similar performance in coastal waters. Much improved agreement was found for the entire phytoplankton population, with r>0.90 for [Chl] and absorption coefficients (aph) for all three datasets. The inferred aCDM(400) and CDM spectral slope agree within ±4% of measurements in both oceanic and coastal waters. The results indicate that the chlorophyll-a specific absorption spectra used as an inversion kernel represent well the global mean states for each of the three phytoplankton size classes. The method can be applied to either bulk or particulate absorption data and is spectrally flexible.

Shedding light on the sea: André Morel's legacy to optical oceanography.

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.

Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site).

Models based on the multivariate partial least squares (PLS) regression technique are developed for the retrieval of phytoplankton size structure from measured light absorption spectra (BOUSSOLE site, northwestern Mediterranean Sea). PLS-models trained with data from the Mediterranean Sea showed good accuracy in retrieving, over the nine-year BOUSSOLE time series, the concentrations of total chlorophyll a [Tchl a], of the sum of seven diagnostic pigments and of pigments associated with micro, nano, and picophytoplankton size classes separately. PLS-models trained using either total particle or phytoplankton absorption spectra performed similarly, and both reproduced seasonal variations of biomass and size classes derived by high performance liquid chromatography. Satisfactory retrievals were also obtained using PLS-models trained with a data set including various locations of the world's oceans, with however a lower accuracy. These results open the way to an application of this method to absorption spectra derived from hyperspectral and field satellite radiance measurements.

Statistical analysis of absorption spectra of phytoplankton and of pigment concentrations observed during three POMME cruises using a neural network clustering method.

We present a neural network methodology for clustering large data sets into pertinent groups. We applied this methodology to analyze the phytoplankton absorption spectra data gathered by the Laboratoire d'Océanographie de Villefranche. We first partitioned the data into 100 classes by means of a self-organizing map (SOM) and then we clustered these classes into 6 significant groups. We focused our analysis on three POMME campaigns. We were able to interpret the absorption spectra of the samples taken in the first oceanic optical layer during these campaigns, in terms of seasonal variability. We showed that spectra from the PROSOPE Mediterranean campaign, which was conducted in a different region, were strongly similar to those of the POMME-3 campaign. This analysis led us to propose regional empirical relationships, linking phytoplankton absorption spectra to pigment concentrations, that perform better than the previously derived overall relation.

Retrieval of pigment concentrations and size structure of algal populations from their absorption spectra using multilayered perceptrons.

Spectral absorption coefficients of phytoplankton can now be derived, under some assumptions, from hyperspectral ocean color measurements and thus become accessible from space. In this study, multilayer perceptrons have been developed to retrieve information on the pigment composition and size structure of phytoplankton from these absorption spectra. The retrieved variables are the main pigment groups (chlorophylls a, b, c, and photosynthetic and nonphotosynthetic carotenoids) and the relative contributions of three algal size classes (pico-, nano-, and microphytoplankton) to total chlorophyll a. The networks have been trained, tested, and validated using more than 3,700 simultaneous absorption and pigment measurements collected in the world ocean. Among pigment groups, chlorophyll a is the most accurately retrieved (average relative errors of 17% and 16% for the test and validation data subsets, respectively), while the poorest performances are found for chlorophyll b (average relative errors of 51% and 40%). Relative contributions of algal size classes to total chlorophyll a are retrieved with average relative errors of 19% to 33% for the test subset and of 18% to 47% for the validation subset. The performances obtained for the validation data, showing no strong degradation with respect to test data, suggest that these neural networks might be operated with similar performances for a large variety of marine areas.

Statistical analysis of a database of absorption spectra of phytoplankton and pigment concentrations using self-organizing maps.

We present a statistical analysis of a large set of absorption spectra of phytoplankton, measured in natural samples collected from ocean water, in conjunction with detailed pigment concentrations. We processed the absorption spectra with a sophisticated neural network method suitable for classifying complex phenomena, the so-called self-organizing maps (SOM) proposed by Kohonen [Kohonen, Self Organizing Maps (Springer-Verlag, 1984)]. The aim was to compress the information embedded in the data set into a reduced number of classes characterizing the data set, which facilitates the analysis. By processing the absorption spectra, we were able to retrieve well-known relationships among pigment concentrations and to display them on maps to facilitate their interpretation. We then showed that the SOM enabled us to extract pertinent information about pigment concentrations normalized to chlorophyll a. We were able to propose new relationships between the fucoxanthin/Tchl-a ratio and the derivative of the absorption spectrum at 510 nm and between the Tchl-b/Tchl-a ratio and the derivative at 640 nm. Finally, we demonstrate the possibility of inverting the absorption spectrum to retrieve the pigment concentrations with better accuracy than a regression analysis using the Tchl-a concentration derived from the absorption at 440 nm. We also discuss the data coding used to build the self-organizing map. This methodology is very general and can be used to analyze a large class of complex data.