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1.
Philos Trans A Math Phys Eng Sci ; 378(2181): 20190357, 2020 Oct 02.
Artigo em Inglês | MEDLINE | ID: mdl-32862820

RESUMO

Increasing contributions of prymnesiophytes such as Phaeocystis pouchetii and Emiliania huxleyi to Barents Sea (BS) phytoplankton production have been suggested based on in situ observations of phytoplankton community composition, but the scattered and discontinuous nature of these records confounds simple inference of community change or its relationship to salient environmental variables. However, provided that meaningful assessments of phytoplankton community composition can be inferred based on their optical characteristics, ocean-colour records offer a potential means to develop a synthesis between sporadic in situ observations. Existing remote-sensing algorithms to retrieve phytoplankton functional types based on chlorophyll-a (chl-a) concentration or indices of pigment packaging may, however, fail to distinguish Phaeocystis from other blooms of phytoplankton with high pigment packaging, such as diatoms. We develop a novel algorithm to distinguish major phytoplankton functional types in the BS and apply it to the MODIS-Aqua ocean-colour record, to study changes in the composition of BS phytoplankton blooms in July, between 2002 and 2018, creating time series of the spatial distribution and intensity of coccolithophore, diatom and Phaeocystis blooms. We confirm a north-eastward expansion in coccolithophore bloom distribution, identified in previous studies, and suggest an inferred increase in chl-a concentrations, reported by previous researchers, may be partly explained by increasing frequencies of Phaeocystis blooms. This article is part of the theme issue 'The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning'.


Assuntos
Haptófitas/isolamento & purificação , Oceanos e Mares , Tecnologia de Sensoriamento Remoto/métodos , Água do Mar/microbiologia , Algoritmos , Regiões Árticas , Clorofila A/metabolismo , Cor , Diatomáceas/crescimento & desenvolvimento , Diatomáceas/isolamento & purificação , Diatomáceas/metabolismo , Ecossistema , Eutrofização , Aquecimento Global , Haptófitas/crescimento & desenvolvimento , Haptófitas/metabolismo , Modelos Biológicos , Noruega , Fenômenos Ópticos , Fitoplâncton/crescimento & desenvolvimento , Fitoplâncton/isolamento & purificação , Fitoplâncton/metabolismo , Tecnologia de Sensoriamento Remoto/estatística & dados numéricos , Estações do Ano
2.
Philos Trans A Math Phys Eng Sci ; 378(2181): 20190367, 2020 Oct 02.
Artigo em Inglês | MEDLINE | ID: mdl-32862821

RESUMO

A bio-optical model for the Barents Sea is determined from a set of in situ observations of inherent optical properties (IOPs) and associated biogeochemical analyses. The bio-optical model provides a pathway to convert commonly measured parameters from glider-borne sensors (CTD, optical triplet sensor-chlorophyll and CDOM fluorescence, backscattering coefficients) to bulk spectral IOPs (absorption, attenuation and backscattering). IOPs derived from glider observations are subsequently used to estimate remote sensing reflectance spectra that compare well with coincident satellite observations, providing independent validation of the general applicability of the bio-optical model. Various challenges in the generation of a robust bio-optical model involving dealing with partial and limited quantity datasets and the interpretation of data from the optical triplet sensor are discussed. Establishing this quantitative link between glider-borne and satellite-borne data sources is an important step in integrating these data streams and has wide applicability for current and future integrated autonomous observation systems. This article is part of the theme issue 'The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning'.


Assuntos
Ecossistema , Monitoramento Ambiental/métodos , Imagens de Satélites/métodos , Água do Mar/análise , Regiões Árticas , Ciclo do Carbono , Clorofila/análise , Monitoramento Ambiental/instrumentação , Aquecimento Global , Camada de Gelo/química , Modelos Teóricos , Noruega , Oceanos e Mares , Fenômenos Ópticos , Tecnologia de Sensoriamento Remoto/instrumentação , Tecnologia de Sensoriamento Remoto/métodos , Imagens de Satélites/instrumentação , Espectrofotometria/instrumentação , Espectrofotometria/métodos
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