Diatom Phenology in the Southern Ocean: Mean Patterns, Trends and the Role of Climate Oscillations

Soppa, Mariana Altenburg; Völker, Christoph; Bracher, Astrid

doi:10.3390/rs8050420

Cited by 49 publications

(72 citation statements)

References 70 publications

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“…For the Southen Ocean region 1-Sf shows rather an inverse seasonality as opposed to Chla given by the other three products. The former peaks at 40 • S to 60 • S in late winter to spring (Aug to Oct), while the others show highest Chla in summer (Dec-Feb) which is , e.g., in agreement with the diatom phenology studied by Soppa et al (2016). It is recalled here that (1-Sf) represents a relative contribution of microplankton to total biomass, therefore its seasonality can be different from that observed for the other products which consider the magnitude of biomass in terms of Chla.…”

Section: Match-ups With In Situ Observationssupporting

confidence: 70%

Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT)

et al. 2017

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We derive the chlorophyll a concentration (Chla) for three main phytoplankton functional types (PFTs) -diatoms, coccolithophores and cyanobacteria -by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 March 2012 and evaluated against PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi-and hyperspectral instruments are highlighted.

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Section: Match-ups With In Situ Observationssupporting

confidence: 70%

Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT)

et al. 2017

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“…, IOCCG , Soppa et al. ), tracking of harmful algal blooms (e.g., Soto et al. ), and testing of algorithms for phytoplankton size distribution and functional group detection (Uitz et al.…”

Section: Essential Biodiversity Variables In the Coastal Zonementioning

confidence: 99%

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

Muller‐Karger

Hestir

Ade

et al. 2018

Ecological Applications

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The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

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“…This finding of the necessity to account for physiological plasticity also directly impacts the PFT methods described here, most acutely for abundance-, radiance-, and absorption-based methods. While satellite PFT time series data have already been used to assess regional PFT variability and trends Sadeghi et al, 2012b;Alvain et al, 2013;Soppa et al, 2016). there is a need to characterize physiological plasticity in PFT retrievals to more accurately quantify phytoplankton compositional response to a changing ocean.…”

Section: Algorithm Selectionmentioning

confidence: 99%

A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean

et al. 2017

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Phytoplankton are composed of diverse taxonomical groups, which are manifested as distinct morphology, size, and pigment composition. These characteristics, modulated by their physiological state, impact their light absorption and scattering, allowing them to be detected with ocean color satellite radiometry. There is a growing volume of literature describing satellite algorithms to retrieve information on phytoplankton composition in the ocean. This synthesis provides a review of current methods and a simplified comparison of approaches. The aim is to provide an easily comprehensible resource for non-algorithm developers, who desire to use these products, thereby raising the level of awareness and use of these products and reducing the boundary of expert knowledge needed to make a pragmatic selection of output products with confidence. The satellite input and output products, their associated validation metrics, as well as assumptions, strengths, and limitations of the various algorithm types are described, providing a framework for algorithm organization to assist users and inspire new aspects of algorithm development capable of exploiting the higher spectral, spatial and temporal resolutions from the next generation of ocean color satellites.

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Diatom Phenology in the Southern Ocean: Mean Patterns, Trends and the Role of Climate Oscillations

Cited by 49 publications

References 70 publications

Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT)

Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT)

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean

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