| Robert J W Brewin,Samantha J Lavender,Nick J Hardman-Mountford,Takafumi Hirata.A spectral response approach for detecting dominant phytoplankton size class from satellite remote sensing[J].Acta Oceanologica Sinica,2010,(2):14-32 |
| A spectral response approach for detecting dominant phytoplankton size class from satellite remote sensing |
| A spectral response approach for detecting dominant phytoplankton size class from satellite remote sensing |
| 投稿时间：2008-12-11 修订日期：2009-04-07 |
| DOI：10.1007/s13131-010-0018-y |
| 中文关键词: phytoplankton size remote sensing absorption ocean colour SeaWiFS |
| 英文关键词:phytoplankton size remote sensing absorption ocean colour SeaWiFS |
| 基金项目:This work is funded by the National Environmental Research Council, UK, through a PhD studentship at the Centre for observation of Air-Sea Interactions & fluXes (CASIX), the National Centre for Earth Observation and NERC Oceans 2025 programme (Themes 6 and 10). |
| 作者 | 单位 | E-mail | | Robert J W Brewin | School of Marine Science and Engineering, University of Plymouth, UK | robert.brewin@plymouth.ac.uk | | Samantha J Lavender | School of Marine Science and Engineering, University of Plymouth, UK
ARGANS Ltd, Plymouth, UK | | | Nick J Hardman-Mountford | Plymouth Marine Laboratory(PML), Plymouth, UK
National Centre for Earth Observation, PML, Plymouth, UK | | | Takafumi Hirata | Plymouth Marine Laboratory(PML), Plymouth, UK
National Centre for Earth Observation, PML, Plymouth, UK | |
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| 中文摘要: |
| An important goal in ocean colour remote sensing is to accurately detect different phytoplankton groups with the potential uses including the validation of multi-phytoplankton carbon cycle models; synoptically monitoring the health of our oceans, and improving our understanding of the bio-geochemical interactions between phytoplankton and their environment. In this paper a new algorithm is developed for detecting three dominant phytoplankton size classes based on distinct differences in their optical signatures. The technique is validated against an independent coupled satellite reflectance and in situ pigment dataset and run on the 10-year NASA Sea viewing Wide Field of view Sensor (SeaWiFS) data series. Results indicate that on average 3.6% of the global oceanic surface layer is dominated by microplankton, 18.0% by nanoplankton and 78.4% by picoplankton. Results, however, are seen to vary depending on season and ocean basin. |
| 英文摘要: |
| An important goal in ocean colour remote sensing is to accurately detect different phytoplankton groups with the potential uses including the validation of multi-phytoplankton carbon cycle models; synoptically monitoring the health of our oceans, and improving our understanding of the bio-geochemical interactions between phytoplankton and their environment. In this paper a new algorithm is developed for detecting three dominant phytoplankton size classes based on distinct differences in their optical signatures. The technique is validated against an independent coupled satellite reflectance and in situ pigment dataset and run on the 10-year NASA Sea viewing Wide Field of view Sensor (SeaWiFS) data series. Results indicate that on average 3.6% of the global oceanic surface layer is dominated by microplankton, 18.0% by nanoplankton and 78.4% by picoplankton. Results, however, are seen to vary depending on season and ocean basin. |
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