Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters

Knowing that coastal areas concentrate about 60% of the world's population (within 100 km from the coast), that 75-90% of the global sink of suspended river load takes place in coastal waters in which about 15% of the primary production occurs, the ecological, societal and economical value of these areas are obvious (fish resources, aquaculture, water quality information, recreation areas management, global carbon budget, etc). In that context, precise assessment of suspended particulate matter (SPM) concentrations and of the phenomena controlling its temporal variability is a key objective for many research fields in coastal areas. SPM which encompasses organic (living and non-living) and inorganic matter controls the penetration of light into the water and brings new nutrients into the system, both key parameters influencing phytoplankton primary production. Concentrations and availability of SPM are also known to control rates of food intake, growth and reproduction for various filter feeder organisms. Phytoplankton is highly sensitive to environmental perturbations (such as nutrient inputs, light, and turbulence). The abundance, biomass and dynamics of phytoplankton in coastal areas therefore reflect the prevailing environmental conditions and represent key parameters for assessing information on the ecological conditions, as well as on the coastal water quality. Because phytoplankton is highly sensitive to environmental perturbations [1], its distribution patterns and temporal variability represent good indicators of the ecological conditions of a defined region [2, 3]. Coastal waters also host complex ecosystems and represent important fishery areas that support industry and provide livelihood to coastal settlements. The food chain in the coastal ocean is generally short (especially in upwelling systems, having as low as three trophic levels) whereas the open ocean food web presents up to six trophic levels [4]. As a result, when compared to the open ocean, a relative lower fraction of the primary production gets respired in the coastal ocean while a higher fraction reaches the uppermost trophic level (fish) [5] or is exported to adjacent areas (coastal or open sea)..

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Inversion neuro-variationnelle des images de la couleur de l'ocean - Restitution des proprietes optiques des aerosols et de la concentration en chlorophylle-a pour les eaux du cas I

The optical instruments on board satellite measure the solar radiation reflected by the sea and the atmosphere. This radiation is strongly contaminated by its interaction with the atmosphere in the wavelengths which interest the ocean color. The preliminary removal of this contamination to observe the true ocean color is called atmospheric correction. The work of this thesis is focused on the SeaWiFS atmospheric correction algorithm. It shows the contribution of mathematical methods which are the networks of artificial neurons and the variational inversion for the atmospheric correction algorithms. A first neural networks inversion in the infra-red allow to retrieve the optical properties of the aerosols. These restitutions are compared, on the Mediterranean Sea, with the SeaWiFS products and are validated with ground-based measurements, showing a better estimate of the Angstrom coefficient and an equal estimate of the optical thickness. The second inversion is done on all the spectrum visible and near infra-red by combining neural networks and variational inversion. The chlorophyll-a maps are compared, on the same region, with in-situ data showing a better estimate of weak values than the SeaWIFS algorithm.Les instruments optiques a bord de satellite mesurent le rayonnement solaire reflechi par la mer et l'atmosphere. Ce rayonnement est fortement contamine par son interaction avec l'atmosphere dans les longueurs d'ondes qui interessent la couleur de l'ocean. L'elimination prealable de cette contanimation pour observer la veritable couleur de l'eau est appele correction atmospherique. Ce travail se focalise sur l'algorithme de correction atmospherique du capteur SeaWiFS. Il montre l'apport des methodes mathematiques que sont les reseaux de neurones artificiels et l'inversion variationnelle pour les algorithmes de correction atmospherique. Une premiere inversion par reseaux de neurones dans le proche infra-rouge permet de restituer les proprietes optiques des aerosols. Ces restitutions sont comparees, sur la mer Mediterranee, avec les produits SeaWiFS et validees avec des mesures au sol, montrant une meilleure estimation du coefficient d'Angstrom et une estimation egale de l'epaisseur optique. La deuxieme inversion se fait sur tout le spectre visible et proche infra-rouge en combinant reseaux de neurones et inversion variationnelle. Les cartes de chlorophylle-a sont comparees, sur la meme zone, a des donnees in-situ montrant une meilleure estimation des faibles valeurs que l'algorithme SeaWIFS

Inversion neuro-variationnelle des images de la couleur de l'océan (restitution des paramètres optiques des aérosols et de la concentration en chlorophylle-a pour les eaux du cas 1)

PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Validation protocol for the evaluation of space-borne lidar particulate back-scattering coefficient bbp

Introduction: Space-borne lidar measurements from sensors such as CALIOP were recently used to retrieve the particulate back-scattering coefficient, b bp , in the upper ocean layers at a global scale and those observations have a strong potential for the future of ocean color with depth-resolved observations thereby complementing the conventional ocean color remote sensed observations as well as overcoming for some of its limitations. It is critical to evaluate and validate the space-borne lidar measurements for ocean applications as CALIOP was not originally designed for ocean applications. Few validation exercises of CALIOP were published and each exercise designed its own validation protocol. We propose here an objective validation protocol that could be applied to any current and future space-borne lidars for ocean applications. Methods: We, first, evaluated published validation protocols for CALIOP b bp product. Two published validation schemes were evaluated in our study, by using in-situ measurements from the BGC-Argo floats. These studies were either limited to day- or nighttime, or by the years used or by the geographical extent. We extended the match-up exercise to day-and nighttime observations and for the period 2010–2017 globally. We studied the impact of the time and distance differences between the in-situ measurements and the CALIOP footprint through a sensitivities study. Twenty combinations of distance (from 9-km to 50-km) and time (from 9 h to 16 days) differences were tested. Results & Discussion: A statistical score was used to objectively selecting the best optimal timedistance windows, leading to the best compromise in term of number of matchups and low errors in the CALIOP product. We propose to use either a 24 h/9 km or 24 h/15 km window for the evaluation of space-borne lidar oceanic products

Retrieval of the spectral diffuse attenuation coefficient Kd(λ) in open and coastal ocean waters using a neural network inversion,

The diffuse attenuation coefficient, Kd(λ) is a fundamental radiometric parameter that is used to assess the light availability in the water column. A neural network approach is developed to assess Kd(λ) at any visible wavelengths from the remote sensing reflectances as measured by the SeaWiFS satellite sensor. The neural network (NN) inversion is trained using a combination of simulated and in-situ data sets covering a broad range ofKd(λ), between 0.0073 m-1 at 412 nm and 12.41 m-1at 510 nm. The performance of the retrieval is evaluated against two data sets, one consisting of mainly synthetic data while the other one contains in-situ data only and is compared to those obtained with previous published empirical (NASA, Morel and Maritorena (2001) and Zhang and Fell (2007)) and semi-analytical (Lee et al., 2005b) algorithms. On the in-situ data set from the COASTLOOC campaign, the retrieval accuracy of the present algorithm is quite similar to published algorithms for oligotrophic and mesotrophic ocean waters. But for Kd(490) > 0.25 m-1, the NN approach allows to retrieve Kd(490) with a much better accuracy than the four other methods. The results are consistent when compared with other SeaWiFS wavelengths. This new inversion is as suitable in the open ocean waters as in the turbid waters. The work here is straightforwardly applicable to the MERIS sensor and with few changes to the MODIS-AQUA sensor. The algorithm in matlab and C code is provided as auxiliary material