Long-term observing systems for ocean knowledge
Ocean-observation technologies are constantly evolving, as are the way we process and exploit the data. We are currently facing a two or three order of magnitude increase in data flow coming from AUV missions or satellites (e.g. the data hub of the Copernicus Sentinel Constellation has delivered 2.5 Petabytes of data in just one year of service) and biological and genetic data are increasing exponentially. To ensure rapid scientific benefits, it is vital to realize the full potential of these capabilities as well as to refine and anticipate new sensor and processing developments with advanced experimental strategies.
From the scale of the global ocean to coastal domains, a major goal is to reduce sampling gaps and better reveal interactions between physical, biological and ecological components. This theme relies on established leadership in long-term observation of the ocean floor (EMSO), global high sea (ARGO/NAOS), coastal ocean at high frequency (COAST-HF), coastline dynamics (IR ILICO) and benthic ecosystems. It also benefits from expertise in marine data management (national oceanographic data centre and portal operated by Ifremer), technology for marine energy harvesting (ITE France energies Marine), innovative data-driven techniques for ocean remote sensing data (ANR MN EMOCEAN 2013-2017).
This theme will address five specific objectives:
• Develop new monitoring technologies and strategies for under-resolved ocean components, especially chemical, biological and ecological variables,
through the development of novel integrated sensors (e.g. taxonomics,
genomics, acoustics…) and cross-sensor cueing methodologies;
• Develop new monitoring technologies and strategies for the study of changes of land cover /land use of coastal zones under different environmental conditions in a context of climate change. Assess existing and new approaches in satellite data analysis (e.g. heterogeneous data fusion and “coastal zones analytics”) for classification, clustering, regression, and machine learning, using specific topics and examples;
• Design and implement integrated observatories (e.g. water quality; physical, chemical and geological environment; biodiversity; human pressures), focusing on the inference of climate trends and examination of extreme events;
• Extract, reconstruct, forecast and emulate physical, chemical, biological,
geological and ecological essential ocean variables (EOVs) from multi-source ocean data streams to uncover local and remote interactions at both shortterm and climate time scales, with a focus on data-driven and model-datacoupled strategies, for operational oceanography applications and research purposes;
• Design and implement thematically-relevant data management and dissemination facilities to favour the access and exploitation of multi-source marine data for the dissemination of knowledge and the creation of novel high added-value services.