Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms

Isada, Tomonori; Hirawake, Toru; Kobayashi, Tsukuru; Nosaka, Yuichi; Natsuike, Masafumi; Imai, Ichiro; Suzuki, Koji; Saitoh, Sei–Ichi

doi:10.1016/j.rse.2014.12.006

Cited by 28 publications

(34 citation statements)

References 104 publications

(150 reference statements)

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“…Planned capability will expand spectral, spatial, and temporal resolution, in addition to radiometric sensitivity (Mouw et al, 2015). Increased spectral resolution will provide the ability to exploit more spectral signatures of PFTs (Isada et al, 2015;Wolanin et al, 2016). In addition to increased spectral resolution, increased spatial resolution may lend clarity to coastal processes and phytoplankton response to finer scale physical features.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…However, the overall performance of using the inverted absorption spectra was restricted due to the limitations of the inversion model. A recent study in estimating the dominance of diatoms by Isada et al [27] using the derivative spectroscopy approach also suggested that the QAA-inverted absorption spectra is less precise than in situ measured absorption. The performance of directly using in situ Rrs(λ) spectra was, however, not assessed in their study.…”

Section: Phytoplankton Groups Differentiation Using Absorption and Rrmentioning

confidence: 99%

“…With recent advances in optical measurements, comprehensive understanding of the light field within the water, and improvements in satellite sensors, the possibility of taxonomic discrimination of phytoplankton groups has been investigated [23][24][25][26][27]. As satellite sensors expanded from multispectral to hyperspectral detection (e.g., Hyperion, HICO, and the future missions EnMAP [28], PACE, and HyspIRI), the consequently higher number of wavebands, narrower spectral bandwidths and fully-covered range of the visible light spectrum provide more comprehensive remote sensing data on spectral properties of the water reflectance.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Differentiation of Phytoplankton Taxonomic Groups: A Comparison between Using Remote Sensing Reflectance and Absorption Spectra

Hieronymi

Röttgers

et al. 2015

Remote Sensing

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Abstract:The emergence of hyperspectral optical satellite sensors for ocean observation provides potential for more detailed information from aquatic ecosystems. The German hyperspectral satellite mission EnMAP (enmap.org) currently in the production phase is supported by a project to explore the capability of using EnMAP data and other future hyperspectral data from space. One task is to identify phytoplankton taxonomic groups. To fulfill this objective, on the basis of laboratory-measured absorption coefficients of phytoplankton cultures (aph(λ)) and corresponding simulated remote sensing reflectance spectra (Rrs(λ)), we examined the performance of spectral fourth-derivative analysis and clustering techniques to differentiate six taxonomic groups. We compared different sources of input data, namely aph(λ), Rrs(λ), and the absorption of water compounds obtained from inversion of the Rrs(λ)) spectra using a quasi-analytical algorithm (QAA). Rrs(λ) was tested as it can be directly obtained from hyperspectral sensors. The last one was tested as expected influences of the spectral features of pure water absorption on Rrs(λ) could be avoided after subtracting it from the inverted total absorption. Results showed that derivative analysis of measured aph(λ) spectra performed best with only a few misclassified cultures. Based on Rrs(λ) spectra, the accuracy of this differentiation decreased but the OPEN ACCESSRemote Sens. 2015, 7 14782 performance was partly restored if wavelengths of strong water absorption were excluded and chlorophyll concentrations were higher than 1 mg•m −3 . When based on QAA-inverted absorption spectra, the differentiation was less precise due to loss of information at longer wavelengths. This analysis showed that, compared to inverted absorption spectra from restricted inversion models, hyperspectral Rrs(λ) is potentially suitable input data for the differentiation of phytoplankton taxonomic groups in prospective EnMAP applications, though still a challenge at low algal concentrations.

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“…For example, Hoepffner and Sathyendranath [3] decomposed absorption spectra into Gaussian absorption bands, while Lee et al [4] and Isada et al [5] performed derivative analysis on R rs (λ) and phytoplankton absorption spectra, respectively. In addition to different methods, different datasets were analyzed in the above-mentioned studies, ranging from cultures, through a spatially and temporally small-scale set of in situ samples, to a larger set of measurements covering various marine environments.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Spectral Band Requirements for Improving Retrievals of Phytoplankton Functional Types

2016

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Abstract:Studying phytoplankton functional types (PFTs) from space is possible due to recent advances in remote sensing. Though a variety of products are available, the limited number of wavelengths available compared to the number of model parameters needed to be retrieved is still a major problem in using ocean-color data for PFT retrievals. Here, we investigated which band placement could improve retrievals of three particular PFTs (diatoms, coccolithophores and cyanobacteria). In addition to analyzing dominant spectral features in the absorption spectra of the target PFTs, two previously-developed methods using measured spectra were applied to simulated data. Such a synthetic dataset allowed for significantly increasing the number of scenarios and enabled a full control over parameters causing spectral changes. We evaluated the chosen band placement by applying an adapted ocean reflectance inversion, as utilized in the generalized inherent optical properties (GIOP) retrieval. Results show that the optimal band settings depend on the method applied to determine the bands placement, as well as on the internal variability of the dataset investigated. Therefore, continuous hyperspectral instruments would be most beneficial for discriminating multiple PFTs, though a small improvement in spectral sampling and resolution does not significantly modify the results. Bands, which could be added to future instruments (e.g., Ocean and Land Colour Instrument (OLCI) instrument on the upcoming Sentinel-3B,-3C,-3D, etc., and further satellites) in order to enhance PFT retrieval capabilities, were also determined.

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Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms

Cited by 28 publications

References 104 publications

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

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

Hyperspectral Differentiation of Phytoplankton Taxonomic Groups: A Comparison between Using Remote Sensing Reflectance and Absorption Spectra

Investigation of Spectral Band Requirements for Improving Retrievals of Phytoplankton Functional Types

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