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The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal ((w)) tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i) an assessment of existing atmospheric correction (AC) algorithms developed for turbid coastal waters; and (ii) a switching method that automatically selects the most sensitive SPM vs. (w) relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer) to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR) spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low- to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE) with a method based on multi-temporal analyses of atmospheric constituents (MACCS). For the selected scenes, the ACOLITE-MACCS difference was lower than 7%. Despite some inaccuracies in (w) retrieval, we demonstrate that the SPM concentration can be reliably estimated using OLI, MODIS and VIIRS, regardless of their differences in spatial and spectral resolutions. Match-ups between the OLI-derived SPM concentration and autonomous field measurements from the Loire and Gironde estuaries' monitoring networks provided satisfactory results. The multi-sensor approach together with the multi-conditional algorithm presented here can be applied to the latest generation of ocean color sensors (namely Sentinel2/MSI and Sentinel3/OLCI) to study SPM dynamics in the coastal ocean at higher spatial and temporal resolutions. International audience

Since 2011, the distribution extent of pelagic Sargassum algae has substantially increased and now covers the whole Tropical North Atlantic Ocean, with significant inter-annual variability. The ocean colour imagery has been used as the only way to monitor regularly such a vast area. However, the detection is hampered by cloud masking, sunglint, coastal contamination and other phenomena. All together, they lead to false detections that can hardly be discriminated by classic radiometric analysis, but may be overcome by considering the shape and the context of the detections. Here, we built a machine learning model base exclusively on spatial features to filter out false detections after the detection process. Moderate-Resolution Imaging Spectroradiometer (MODIS, 1 km) data from Aqua and Terra satellites were used to generate daily map of Alternative Floating Algae Index (AFAI). Based on this radiometric index, Sargassum presence in the Tropical Atlantic North Ocean was inferred. For every Sargassum aggregations, five contextual indices were extracted (number of neighbours, surface of neighbours, temporal persistence, distance to the coast and aggregation texture) then used by a random forest binary classifier. Contextual features at large-scale were most important in the classifier. Trained with a multi-annual (2016-2020) learning set, the model performs the filtering of daily false detections with an accuracy of ~ 90%. This leads to a reduction of detected Sargassum pixels of ~ 50% over the domain. The method provides reliable data while preserving high spatial and temporal resolutions (1 km, daily). The resulting distribution is consistent with the literature for seasonal and inter-annual fluctuations, with maximum coverage in 2018 and minimum in 2016. This dataset will be useful for understanding the drivers of Sargassum dynamics at fine and large scale and validate future models. The methodology used here demonstrates the usefulness of contextual features for complementing classical remote sensing approaches. Our model could easily be adapted to other datasets containing erroneous detections. International audience

We report the retrieval of sea surface directional mean square slope from Global Navigation Satellite System Reflections (GNSS-R) Delay-Doppler-Map (DDM) data collected during an experimental flight at 1 km altitude. We emphasize the utilization of the entire DDM to more precisely infer ocean roughness directional parameters, and argue that the DDM exhibits the impact of both roughness and scatterer velocity. Obtained estimates are analyzed and compared with Jason-1 measurements, ECMWF numerical weather model outputs and optical data.

We observed the wastage of ice masses on the Kerguelen Islands ( Indian Ocean, 49 degrees S, 69 degrees E) using historical information and recent satellite data. Overall, the total ice-covered area on the islands declined from 703 to 552 km(2) between 1963 and 2001, a reduction of 21%. The area of Cook ice cap ( the main ice body) decreased asymmetrically from 501 to 403 km(2). West flowing glaciers lost 11% of their area, while east flowing glaciers lost 28%. After 1991, the retreat rate accelerated from 1.9 km(2)/a ( 1963-1991) to 3.8 km(2)/a ( 1991-2003). Between 1963 and 2000, the ice volume loss was 25-30 km(3), equivalent to an area-average ice-thinning rate of 1.4-1.7 m/a. The glacial retreat took place in the climatic context of a relatively low level of precipitation ( compared to the 1950s) and a similar to 1 degrees C warming that occurred between 1964 and 1982. The acceleration of the ice losses since at least the 1990s indicates that the state of the ice bodies on the Kerguelen Islands is still far from balanced. Together with other studies in Patagonia, South Georgia, and Heard Island, our analysis is consistent with a pattern of strong and accelerated wastage of ice masses influenced by the Southern Ocean.

Land use (LU) and land cover (LC) are two complementary pieces of cartographic information used for urban planning and environmental monitoring. In the context of New Caledonia, a biodiversity hotspot, the availability of up-to-date LULC maps is essential to monitor the impact of extreme events such as cyclones and human activities on the environment. With the democratization of satellite data and the development of high-performance deep learning techniques, it is possible to create these data automatically. This work aims at determining the best current deep learning configuration (pixel-wise vs. semantic labelling architectures, data augmentation, image prepossessing, …), to perform LULC mapping in a complex, subtropical environment. For this purpose, a specific data set based on SPOT6 satellite data was created and made available for the scientific community as an LULC benchmark in a tropical, complex environment using five representative areas of New Caledonia labelled by a human operator: four used as training sets, and the fifth as a test set. Several architectures were trained and the resulting classification was compared with a state-of-the-art machine learning technique: XGboost. We also assessed the relevance of popular neo-channels derived from the raw observations in the context of deep learning. The deep learning approach showed comparable results to XGboost for LC detection and over-performed it on the LU detection task (61.45% vs. 51.56% of overall accuracy). Finally, adding LC classification output of the dedicated deep learning architecture to the raw channels input significantly improved the overall accuracy of the deep learning LU classification task (63.61% of overall accuracy). All the data used in this study are available on line for the remote sensing community and for assessing other LULC detection techniques. Remote Sensing, 13 (12) ISSN:2072-4292

La production primaire (PP) élevée des vasières intertidales aux latitudes tempérées est principalement assurée par le microphytobenthos (MPB), qui soutient les réseaux trophiques benthiques et pélagiques. Dans cette thèse, nous utilisons un modèle couplé physique-biologie pour étudier la variabilité spatio-temporelle de la dynamique du MPB sur une vasière intertidale de la côte Atlantique française. Le modèle simule explicitement la biomasse du MPB et du brouteur Peringia ulvae. Les résultats fournissent des conclusions clés sur la dynamique du MPB. À l’hiver et au printemps, une lumière et une température de surface de la vase (TSV) optimales pour la croissance du MPB donnent lieu à une efflorescence printanière du MPB. La lumière est le facteur le plus limitant annuellement. Cependant, la TSV limite la croissance du MPB 40 % du temps en été. La photoinhibition pourrait se superposer à la thermoinhibition au printemps et en été. Le broutage et la remise en suspension (RES) du MPB façonnent également la dynamique du MPB. La bioturbation par P. ulvae contribue à une RES chronique du MPB du sédiment vers la colonne d’eau au printemps et en été. Les vagues contribuent à la RES du MPB par le biais d’événements de RES massive en hiver, au printemps et en automne. 50 % de la PP annuelle du MPB est exporté vers la colonne d’eau par le biais de RES chroniques et massives. Nous avons également développé une méthode qui combine les données de télédétection et les résultats du modèle couplé physique-biologique en un algorithme capable de prédire la PP à partir de données satellitales. En plus d’apporter de nouvelles perspectives sur la dynamique du MPB, ce travail propose de nouveaux outils numériques pour surveiller et prédire la PP du MPB et son devenir dans les eaux côtières dans un contexte de changement climatique. The high primary production (PP) of intertidal mudflats at temperate latitudes is mostly supported by microphytobenthos (MPB), which support both benthic and pelagic food webs. In the present thesis, we use a physical-biological coupled model to investigate the spatial and temporal variability of MPB dynamics on a large temperate intertidal mudflat of the French Atlantic coast. The model explicitly simulates the MPB biomass and the grazer (Peringia ulvae) biomass and density. The outputs provide key findings on MPB dynamics. In winter-spring, optimal light and mud surface temperature (MST) conditions for MPB growth lead to a MPB spring bloom. Light is the most limiting driver over the year. However, a high MST limits the MPB growth 40% of the time during summer. The photoinhibition of MPB photosynthesis can potentially superimpose on thermoinhibition in spring-summer. Grazing and resuspension of MPB biomass also shape the dynamics of the MPB biomass. Bioturbation by P. ulvae contributes to a chronic export of MPB biomass from the sediment to the water column in spring-summer. Waves contribute to the MPB resuspension through massive resuspension events in winter, spring and fall. 50% of the annual MPB PP is exported to the water column through chronic and massive resuspension events. We also developed a new method that combine remote sensing data with outputs of the physical-biological coupled model into a single algorithm that can predict PP from satellite data. In addition to bring new insights on the MPB dynamics, this work proposes new numerical tools to monitor and predict MPB PP and its fate in coastal waters in a context of climate change.

Systematic map protocol Within the EU, the approval of pesticides is regulated by Regulation (EC) No 1107/2009. An amendment to this Regulation (Regulation (EU) No 2018/605), which establishes the scientific criteria for the identification of pesticide active substances with endocrine disrupting (ED) properties, was published in April 2018. In June 2018, EFSA, the European Chemicals Agency (ECHA) and the Joint Research Centre (JRC) published a guidance document on the implementation of the scientific criteria, this publication primarily addresses the Estrogen, Androgen, Steroidogenesis and Thyroid, known as ‘EATS’, modalities in vertebrates. It specifically included recommendations on how to perform hormonal measurements in mammalians. However, when it comes to non-mammalian vertebrate test guidelines (TG), there are a number of ‘gaps’ which need to be filled to fully address assessment of ‘EATS’ modalities and mirror mammalian TGs. To harmonise vertebrate OECD TGs, these additional endpoints need to be investigated and therefore some guidance on how to perform, report and evaluate hormonal measurements in fish, birds and amphibians needs to be prepared. The overall aim of this systematic evidence map is therefore to collect and analyse data in support of the ED assessment (Estrogen, Androgen, Steroidogenesis and Thyroid) for non-target vertebrates in order to develop recommendations on how to perform, report and interpret hormonal measurements in fish, amphibians and birds in toxicity studies. This draft protocol is now being published on the open repository Zenodo as part of an 'open peer review' process, inviting the wider scientific community to comment on the systematic mapping methodology aiming to collate literature that addresses the development, optimisation and/or validation hormone measurement methods in fish, amphibian and bird species. Comments and feedback via this form Feedback until Monday 20th May 2019 23:59 (CET).

Maintaining good water quality for farm ponds is critical for having a productive fish population. Many farm ponds lack minimal water quality parameter necessity for having a productive pond. Adding agricultural lime to a farm pond improves this by increasing the hardness, alkalinity and providing a stable pH.

In 2016, the French government adopted a law for biodiversity, setting an objective of protecting 55,000 hectares of mangroves. This objective is particularly important to French Guiana, which shelters almost 60% of French mangrove ecosystems, and where mangroves occupy three quarters of the coastline. The coast of French Guiana is also where issues associated with demographic and economic dynamics concentrate. There is thus a need to plan for an economic development that is compatible with the objective of protecting mangrove ecosystems. Ecosystem services (ES) assessment can support such decision-making, informing on the costs and benefits associated with alternative mangrove conservation strategies. While the many services provided by mangrove ecosystems are well documented worldwide, the extent to which these can be encountered in the specific case of French Guiana is currently only very partially known. Relying on the Fuzzy cognitive mapping (FCM) approach, we collected and compared the perception of multiple and heterogeneous groups of stakeholders, of the functioning of the mangrove social-ecological system at the scale of French Guiana. Results, allow to identify mangroves ES and threats particularly influenced by the high sedimentary dynamism of the shoreline. This generates two distinct components of the mangrove social-ecological system: mud banks where ecosystem services are spatially and temporally unstable, and associated with perceived constraints for key coastal activities, and estuarine mangroves where the ecosystem services usually described in the literature on mangroves can be found. Disservices associated with mangrove ecosystems were also identified as a key interaction. This can inform the research needs that should support sustainable development trajectories, fully accounting for the protection of French Guianese mangrove ecosystems.

This document provides an overview of all aspects of the CryoSat-2 Arctic sea-ice thickness data product (version 2.6) generated at the Alfred Wegener Institute Helmholtz Center for Polar and Marine Research (AWI). It contains information on the primary and auxiliary data sets used in the processing, the description of the algorithm used to derive geophysical information along orbit segments and on space-time grids, the technical specifications of the product files, information on data access, as well as the known issues of the data record.