Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories

Combining Lagrangian trajectories and satellite observations provides a novel basis for monitoring changes in water properties with high temporal and spatial resolution. In this study, a prediction scheme was developed for synthesizing satellite observations and Lagrangian model data for better inte...

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Autores principales:	Junfang Lin, Peter I. Miller, Bror F. Jönsson, Michael Bedington
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	early warning harmful algal bloom remote sensing Lagrangian particle tracking Science Q General. Including nature conservation, geographical distribution QH1-199.5
Acceso en línea:	https://doaj.org/article/6c92b20b55ae423b894b02bdaceadd3d
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spelling	oai:doaj.org-article:6c92b20b55ae423b894b02bdaceadd3d2021-11-15T04:43:26ZEarly Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories2296-774510.3389/fmars.2021.736262https://doaj.org/article/6c92b20b55ae423b894b02bdaceadd3d2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmars.2021.736262/fullhttps://doaj.org/toc/2296-7745Combining Lagrangian trajectories and satellite observations provides a novel basis for monitoring changes in water properties with high temporal and spatial resolution. In this study, a prediction scheme was developed for synthesizing satellite observations and Lagrangian model data for better interpretation of harmful algal bloom (HAB) risk. The algorithm can not only predict variations in chlorophyll-a concentration but also changes in spectral properties of the water, which are important for discrimination of different algal species from satellite ocean color. The prediction scheme was applied to regions along the coast of England to verify its applicability. It was shown that the Lagrangian methodology can significantly improve the coverage of satellite products, and the unique animations are effective for interpretation of the development of HABs. A comparison between chlorophyll-a predictions and satellite observations further demonstrated the effectiveness of this approach: r2 = 0.81 and a low mean absolute percentage error of 36.9%. Although uncertainties from modeling and the methodology affect the accuracy of predictions, this approach offers a powerful tool for monitoring the marine ecosystem and for supporting the aquaculture industry with improved early warning of potential HABs.Junfang LinPeter I. MillerBror F. JönssonMichael BedingtonFrontiers Media S.A.articleearly warningharmful algal bloomremote sensingLagrangianparticle trackingScienceQGeneral. Including nature conservation, geographical distributionQH1-199.5ENFrontiers in Marine Science, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	early warning harmful algal bloom remote sensing Lagrangian particle tracking Science Q General. Including nature conservation, geographical distribution QH1-199.5
spellingShingle	early warning harmful algal bloom remote sensing Lagrangian particle tracking Science Q General. Including nature conservation, geographical distribution QH1-199.5 Junfang Lin Peter I. Miller Bror F. Jönsson Michael Bedington Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories
description	Combining Lagrangian trajectories and satellite observations provides a novel basis for monitoring changes in water properties with high temporal and spatial resolution. In this study, a prediction scheme was developed for synthesizing satellite observations and Lagrangian model data for better interpretation of harmful algal bloom (HAB) risk. The algorithm can not only predict variations in chlorophyll-a concentration but also changes in spectral properties of the water, which are important for discrimination of different algal species from satellite ocean color. The prediction scheme was applied to regions along the coast of England to verify its applicability. It was shown that the Lagrangian methodology can significantly improve the coverage of satellite products, and the unique animations are effective for interpretation of the development of HABs. A comparison between chlorophyll-a predictions and satellite observations further demonstrated the effectiveness of this approach: r2 = 0.81 and a low mean absolute percentage error of 36.9%. Although uncertainties from modeling and the methodology affect the accuracy of predictions, this approach offers a powerful tool for monitoring the marine ecosystem and for supporting the aquaculture industry with improved early warning of potential HABs.
format	article
author	Junfang Lin Peter I. Miller Bror F. Jönsson Michael Bedington
author_facet	Junfang Lin Peter I. Miller Bror F. Jönsson Michael Bedington
author_sort	Junfang Lin
title	Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories
title_short	Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories
title_full	Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories
title_fullStr	Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories
title_full_unstemmed	Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories
title_sort	early warning of harmful algal bloom risk using satellite ocean color and lagrangian particle trajectories
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/6c92b20b55ae423b894b02bdaceadd3d
work_keys_str_mv	AT junfanglin earlywarningofharmfulalgalbloomriskusingsatelliteoceancolorandlagrangianparticletrajectories AT peterimiller earlywarningofharmfulalgalbloomriskusingsatelliteoceancolorandlagrangianparticletrajectories AT brorfjonsson earlywarningofharmfulalgalbloomriskusingsatelliteoceancolorandlagrangianparticletrajectories AT michaelbedington earlywarningofharmfulalgalbloomriskusingsatelliteoceancolorandlagrangianparticletrajectories
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Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories

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