Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

• Autor(a) principal: Bracher, Astrid
• Data de Publicação: 2017
• Outros Autores: Bouman, Heather A., Brewin, Robert J. W., Bricaud, Annick, Brotas, Vanda, Ciotti, Aurea M., Clementson, Lesley, Devred, Emmanuel, Di Cicco, Annalisa, Dutkiewicz, Stephanie, Hardman-Mountford, Nick J., Hickman, Anna E., Hieronymi, Martin, Hirata, Takafumi, Losa, Svetlana N., Mouw, Colleen B., Organelli, Emanuele, Raitsos, Dionysios E., Uitz, Julia, Vogt, Meike, Wolanin, Aleksandra
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10451/41134
• topic: ocean color; phytoplankton functional types; algorithms; satellite sensors; roadmap

To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition.