Ocean color satellites have provided a synoptic view of global phytoplankton for over 25 years through near surface measurements of the concentration of chlorophyll a. While remote sensing of ocean color has revolutionized our understanding of phytoplankton and their role in the oceanic and freshwater ecosystems, it is important to consider both total phytoplankton biomass and changes in phytoplankton community composition in order to fully understand the dynamics of the aquatic ecosystems. With the upcoming launch of NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission, we will be entering into a new era of global hyperspectral data, and with it, increased capabilities to monitor phytoplankton diversity from space. In this paper, we analyze the needs of the user community, review existing approaches for detecting phytoplankton community composition in situ and from space, and highlight the benefits that the PACE mission will bring. Using this three-pronged approach, we highlight the challenges and gaps to be addressed by the community going forward, while offering a vision of what global phytoplankton community composition will look like through the “eyes” of PACE.

Wireless transmissions in water are mostly carried out via long-range (but low-rate) underwater acoustic communications, or short-range (but high-rate) underwater optical communications. In this paper, we are interested in finding out whether a statistical relationship exists between underwater acoustics and optics. Besides the theoretical interest of such a relationship, predicting the quality of the optical link through acoustics is also relevant in the context of a multimodal system with both acoustics and optics. Our study is based on a large data set acquired during the NATO ALOMEX 2015 expedition. During this experiment, we simultaneously measured several characteristics of the acoustic and optical links at multiple locations, reflecting a diversity of sea environments. Our results show a strong correlation between the properties of the acoustic link and the reliability of optical communications. This correlation makes it possible to predict the state of the underwater optical link at a certain depth and range. Due to the complexity of the acoustic and optical channels, we could not find the source of this correlation. This paper is, therefore, aimed to stimulate a theoretical study of the mutual properties of underwater acoustic and optical communication links. For reproducibility, we share the processed data from the experiment.

In this paper, we are interested in finding whether a statistical relationship exists between underwater acoustics and optics, despite the different physics that govern these technologies. Besides the theoretical interest of such a relationship, predicting the quality of the optical links through acoustics is relevant for multimodal communications. Our study is based on a large dataset acquired during the NATO ALOMEX 2015 expedition. The dataset consists of several acoustic and optical link characteristics measured at multiple locations, reflecting a diversity of sea environments. Our results, based on a systematic machine learning analysis, show a strong correlation between the properties of acoustic links and the reliability of optical communications. Potentially, this can be leveraged to predict the state of underwater optical links.

The increasing interest in the application of optical communications to underwater communications and networks calls for reliable simulation models, that allow a system designer to realistically assess the performance of optical systems before actual deployment in water. In this paper, we present an optical channel model that is based on samples of the total attenuation coefficient and of the solar light irradiance taken during the NATO STO CMRE ALOMEX'15 scientific cruise, in different water conditions. The data set includes both strong and mild solar illumination conditions, as well as a number of different settings for the light absorption and scattering coefficients. We implement the model as part of the DESERT Underwater network simulator, and employ it to show that the throughput of underwater optical links depends not only on the distance between the transmitter and the receiver, but also on the depth at which the devices are deployed. The corresponding insight is empirically validated in dry tests and lake experiments using a proof-of-concept optical modem, and helped drive the next stage of the modem development.

A bio-optical model has been developed for the Ligurian Sea which encompasses both deep, oceanic Case 1 waters and shallow, coastal Case 2 waters. The model builds on earlier Case 1 models for the region and uses field data collected on the BP09 research cruise to establish new relationships for non-biogenic particles and CDOM. The bio-optical model reproduces in situ IOPs accurately and is used to parameterize radiative transfer simulations which demonstrate its utility for modeling underwater light levels and above surface remote sensing reflectance. Prediction of euphotic depth is found to be accurate to within ∼3.2m (RMSE). Previously published light field models work well for deep oceanic parts of the Ligurian Sea that fit the Case 1 classification. However, they are found to significantly over-estimate euphotic depth in optically complex coastal waters where the influence of non-biogenic materials is strongest. For these coastal waters, the combination of the bio-optical model proposed here and full radiative transfer simulations provides significantly more accurate predictions of euphotic depth.

In this paper, we consider data muling over a network of fixed sensors by employing a mobile Autonomous Underwater Vehicle (AUV). We approach the problem using both acoustic and optical communications together in a multi-modal hybrid network: the most appropriate physical layer is chosen according to the quality of the transmissions that take place over time. We consider three distinct cases of water type: clear, coastal and turbid water, in order to test the system behavior under different conditions. The ambient light noise is realistically reproduced via the Hydrolight software and taken into account, due to its important contribution to the optical SNR in shallow waters. Finally, we simulate the performance of the system using the DESERT Underwater framework during missions of interest in different channel conditions and network depth. Our results show the effectiveness of a multi-modal underwater network in the cases of clear and coastal waters.

The BP09 experiment conducted by the Centre for Maritime Research and Experimentation in the Ligurian Sea in March 2009 provided paired vertical profiles of nadir-viewing radiances L(u)(z) and downward irradiances E(d)(z) and inherent optical properties (IOPs, absorption, scattering and backscattering coefficients). An inversion algorithm was implemented to retrieve IOPs from apparent optical properties (AOPs, radiance reflectance R(L), irradiance reflectance R(E) and diffuse attenuation coefficient K(d)) derived from the radiometric measurements. Then another inversion algorithm was developed to infer vertical profiles of water constituent concentrations, including chlorophyll-a concentration, non-algal particle concentration, and colored dissolved organic matter from the retrieved IOPs based on a bio-optical model. The algorithm was tested on a synthetic dataset and found to give reliable results with an accuracy better than 1%. When the algorithm was applied to the BP09 dataset it was found that good retrievals of IOPs could be obtained for sufficiently deep waters, i.e. for L(u)(z) and E(d)(z) measurements conducted to depths of 50 m or more. This requirement needs to be satisfied in order to obtain a good estimation of the backscattering coefficient. For such radiometric measurements a correlation of 0.88, 0.96 and 0.93 was found between retrieved and measured absorption, scattering and backscattering coefficients, respectively. A comparison between water constituent values derived from the measured IOPs and in-situ measured values, yielded a correlation of 0.80, 0.78, and 0.73 for chlorophyll-a concentration, non-algal particle concentration, and absorption coefficient of colored dissolved organic matter at 443 nm, respectively. This comparison indicates that adjustments to the bio-optical model are needed in order to obtain a better match between inferred and measured water constituent values in the Ligurian Sea using the methodology developed in this paper.

Understanding variability in the chlorophyll-specific absorption of marine phytoplankton, a(ph)(Chl)(*) (), is essential for primary production modelling, calculation of underwater light field characteristics, and development of algorithms for remote sensing of chlorophyll concentrations. Previous field and laboratory studies have demonstrated significant apparent variability in a(ph)(Chl)(*) () for natural samples and algal cultures. However, the potential impact of measurement uncertainties on derived values of a(ph)(Chl)(*) () has received insufficient study. This study presents an analysis of measurement uncertainties for a data set collected in the Ligurian Sea in Spring and assesses the impact on estimates of a(ph)(Chl)(*) (). It is found that a large proportion of apparent variability in this set of a(ph)(Chl)(*) () can be attributed to measurement errors. Application of the same analysis to the global NOMAD data set suggests that a significant fraction of variability in a(ph)(Chl)(*) () may also be due to measurement errors.

Modeling three‐dimensional coupled physical and biogeochemical processes in natural waters has developed over the past 2 decades to a stage where hindcasting and forecasting of ecosystem behavior are considered feasible. Constrained by physical variabilities such as wind, currents, density, and light, ecosystem models seek to reproduce and predict ocean biogeochemical cycles. The physical environment of marine ecosystems can generally be defined with confidence through ocean circulation models, which are largely independent of the ecosystem itself and represent the external forcing on ecosystem models. By contrast, the physical optics determining the distribution of light in the water column must be two‐way coupled with ecosystem models.

The particulate backscattering ratio (b(bp)/b(p)) is a useful indicator of the angular scattering characteristics of natural waters. Recent studies have shown evidence both for and against significant spectral variability in b(bp)/b(p) in the visible domain, but most show significant variability in its magnitude. We present results from a case study in which both backscattering and scattering coefficients were measured at nine wavelengths in a region of UK coastal waters where optical scattering is strongly influenced by inorganic particles and where a wide range of turbidities is found in a small geographic area. Using a new approach based on regression analysis of in situ signals, it is shown that, for this study site, most of the apparent variability in the magnitude of the backscattering ratio can be attributed to measurement uncertainties. Regression analysis suggests that b(bp)/b(p) is wavelength dependent for these mineral-rich waters. This conclusion can only be avoided by positing the existence of undocumented, systematic, wavelength-dependent errors in backscattering measurements made by two independently calibrated sensors. These results are important for radiative transfer simulations in mineral-dominated waters where the backscattering ratio has often been assumed to be spectrally flat. Furthermore, spectral dependence also has profound implications for our understanding of the relationship between b(bp)/b(p) and particle size distributions in coastal waters since the commonly assumed power-law distribution is associated with a spectrally flat particulate backscattering ratio for nonabsorbing particles.