{"id":145755,"date":"2026-07-01T08:55:55","date_gmt":"2026-07-01T00:55:55","guid":{"rendered":"https:\/\/www.curtin.edu.au\/research\/?post_type=hdr-r-projects&p=145755"},"modified":"2026-07-01T08:55:55","modified_gmt":"2026-07-01T00:55:55","slug":"remote-sensing-of-phytoplankton-diversity-using-cutting-edge-satellite-ocean-colour-techniques","status":"publish","type":"hdr-r-projects","link":"https:\/\/www.curtin.edu.au\/research\/hdr-r-projects\/remote-sensing-of-phytoplankton-diversity-using-cutting-edge-satellite-ocean-colour-techniques\/","title":{"rendered":"Remote Sensing of phytoplankton diversity using cutting-edge satellite ocean colour techniques"},"content":{"rendered":"\n
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Phytoplankton \u2013 the microscopic photosynthesizing algae inhabiting the surface sunlit layers of the oceans, are responsible for the production of around 50 billion tons of organic carbon per year in the World oceans, which is about half the total Planet primary production. Phytoplankton are highly diverse, and different species have specific roles in the ecosystem and different carbon sequestration efficiencies. As a result, identifying phytoplankton functional types (PFTs) from space became a major endeavour of the international satellite ocean colour community for over two decades, with the development of various models to derive synoptic maps of PFTs from satellite observations. However, there is still a strong need for the validation of these approaches across a large range of marine ecosystems, and in particular for their implementation in coastal waters.<\/p>\n\n\n\n

Aim\u00a0<\/p>\n\n\n\n

The availability of a 20-year record of concurrent in situ bio-optical datasets and multi-spectral satellite observations in coastal and oceanic waters around Australia makes this goal now accessible through the Australian Ocean Data Network (AODN) portal and the IMOS Satellite Remote Sensing facility (SRS) archives. The SRS facility provides satellite remote sensed marine products from NASA\u2019s MODIS sensor and ESA\u2019s MERIS and OLCI sensors. AODN and the SRS facility are part of the Integrated Marine Observing System (IMOS), a national collaborative research infrastructure operated by a consortium of institutions and funded by the Australian government. Through IMOS, we can now monitor and understand the long-term impacts of human activities and climate change on coastal environments, marine ecosystems and biodiversity.<\/p>\n\n\n\n

Objectives<\/p>\n\n\n\n

For this research project, the PhD candidate will use the IMOS in situ bio-optical database and the SRS facility remote sensing data to<\/p>\n\n\n\n

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  1. Examine existing numerical inversion algorithms, and develop new ones when required, for the detection and mapping of PFTs from space. This first phase will rely on observations at two established sites on the West and East coasts of Australia:\n
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    1. In tropical waters at the CSIRO-led Lucinda Jetty Coastal Observatory (LJCO, in the Northeast of Australia; started in 2014). LJCO is the only long-term permanent validation site in the Southern Hemisphere.<\/li>\n\n\n\n
    2. In temperate waters off Perth (Western Australia), where the Curtin remote sensing team has deployed a Thetis profiling mooring off Rottnest Island in 2017\/2018, and now performs monthly sampling operations since February 2024. The collected data are used to quantify changes in water quality and support the validation of satellite ocean colour products from ESA\u2019s Sentinel 3A OLCI sensor.
      The seasonality and phytoplankton types at these two sites differ widely, providing a large range of conditions for algorithms validation and development.<\/li>\n<\/ol>\n<\/li>\n\n\n\n
    3. Validate the methods developed in phase 1 across a wide range of Australian ecosystems characterised by diverse phytoplankton communities, from coastal waters to the open ocean, and from temperate regions to the tropics. This second phase will give access to detailed maps of phytoplankton diversity around Australia using state-of-the-art ocean colour remote sensing techniques. If interested, the candidate will explore the use of machine-learning techniques and their potential for application to the Ocean Colour Instrument (OCI) on NASA\u2019s PACE mission (Plankton, Aerosol, Cloud, ocean Ecosystem mission). OCI is a hyper-spectral sensor (5 nm spectral resolution) with a broad spectral range, from ultraviolet to shortwave infrared light, and thus providing access to unprecedented, detailed information on coastal and open ocean ecosystems.<\/li>\n\n\n\n
    4. Build an almost 20-year climatology of phytoplankton abundance and diversity for key selected regions in order to:\n
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      1. Explore the spatial and seasonal trends in the phytoplankton community structure in contrasted systems around Australia.<\/li>\n\n\n\n
      2. Examine the environmental and anthropogenic factors driving changes in phytoplankton communities across a wide range of scales.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n\n\n\n

        Significance <\/p>\n\n\n\n

        This cross-disciplinary research project, combining cutting-edge remote sensing and machine-learning techniques, will support ecosystem models and provide assessment tools of Australian marine ecosystems functioning and their carbon sequestration efficiency.
        Wider applications relevant for the Australian community include coastal water quality and ecosystem health monitoring, and water resources and fisheries management programs.<\/p>\n\n\n\n

        Ideal Candidate <\/p>\n\n\n\n

        We are seeking a graduate from environment physics \/ environmental sciences. Ideally some background in oceanography and satellite remote sensing.
        The applicant would be interested in (and motivated by) current questions about how ocean environments and ecosystems are currently changing in response to climate change, and the impact on the global carbon cycle and marine food webs.
        Plus:<\/p>\n\n\n\n