Abstract
Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with in-situ High Performance Liquid Chromatography (HPLC) data for the South China Sea (SCS), collected from August 2006 to September 2011. Four algorithms were evaluated to determine their ability to detect three phytoplankton size classes. Chlorophyll-a (Chl-a) and absorption spectra of phytoplankton (aph(λ)) were also measured to help understand PSC’s algorithm performance. Results show that the three abundance-based approaches performed better than the inherent optical property (IOP)-based approach in the SCS. The size detection of microplankton and picoplankton was generally better than that of nanoplankton. A three-component model was recommended to produce maps of surface PSCs in the SCS. For the IOP-based approach, satellite retrievals of inherent optical properties and the PSCs algorithm both have impacts on inversion accuracy. However, for abundance-based approaches, the selection of the PSCs algorithm seems to be more critical, owing to low uncertainty in satellite Chl-a input data
Highlights
Marine phytoplankton communities play an important role in the Earth’s carbon cycle [1,2,3], and they often consist of hundreds of species, making their identification and understanding difficult [4]
Throughout the inter-comparisons of the four algorithms, we found that the abundance-based approaches provided better spatial retrieval of phytoplankton size classes (PSCs) than inherent optical property (IOP)-based ones
Four algorithms designed to detect phytoplankton size structure were assessed using in situ data and satellite data (MERIS, Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS)) in this study
Summary
Marine phytoplankton communities play an important role in the Earth’s carbon cycle [1,2,3], and they often consist of hundreds of species, making their identification and understanding difficult [4]. In terms of primary production and the global carbon cycle, cell size, referred to here as phytoplankton size classes (PSCs), has been introduced to describe phytoplankton communities, because phytoplankton size is easier to determine and has significant links to the marine ecosystem [4,5,6]. Satellite sensors routinely provide synoptic and frequent global ocean-colour products for the ocean surface. High-quality global ocean-colour products can be extensively used in PSCs studies [8,9]. A number of satellite algorithms have been developed for estimating the phytoplankton community structure, some of which provide size structure estimations of phytoplankton. The algorithms for assessing PSCs from remote sensing data can be mainly categorized into abundance-based and inherent optical property (IOP)-based approaches [10,11,12]
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