Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water

Yijun Yan; Yixin Zhang; Jinchang Ren; Madjid Hadjal; David Mckee; Fu-jen Kao; Tariq Durrani

doi:10.1007/978-981-19-0390-8_71

Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water

Yijun Yan, Yixin Zhang, Jinchang Ren (Lead / Corresponding author), Madjid Hadjal, David Mckee, Fu-jen Kao, Tariq Durrani

Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Marine Scotland is tasked with reporting on the environmental status of Scottish marine waters, an enormous area of water extending from the shoreline to deep oceanic waters. As one of the most important variables, chlorophyll concentration (Chl) plays an important role in the seawater quality monitoring. Currently, the Chl observation is mostly done by expensive ship-based surveys that have very limited spatio-temporal coverage. Satellite based ocean colour remote sensing has the potential to significantly enhance monitoring capabilities but this opportunity has not been widely adopted by statutory reporting bodies across Europe due to concerns over satellite data quality. To break through this bottleneck, in this paper, we explore to implement advanced machine learning techniques to automatically estimate the Chl via the historic time series of ocean colour remote sensing data during from July 2002 to September 2019.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems
Subtitle of host publication	Proceedings of the 10th International Conference on Communications, Signal Processing, and Systems
Editors	Qilian Liang, Wei Wang, Xin Liu, Zhenyu Na, Baoju Zhang
Place of Publication	Singapore
Publisher	Springer
Pages	582-587
Number of pages	6
Edition	1
ISBN (Electronic)	9789811903908
ISBN (Print)	9789811903892 (hbk), 9789811903922 (pbk)
DOIs	https://doi.org/10.1007/978-981-19-0390-8_71
Publication status	Published - 2022
Event	10th International Conference on Communications, Signal Processing, and Systems, CSPS 2021 - Changbaishan, China Duration: 24 Jul 2021 → 25 Jul 2021

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	878
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	10th International Conference on Communications, Signal Processing, and Systems, CSPS 2021
Country/Territory	China
City	Changbaishan
Period	24/07/21 → 25/07/21

Keywords

Chlorophyll
Environment monitoring
Multispectral remote sensing
Scottish marine water

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-19-0390-8_71

Cite this

Yan, Y., Zhang, Y., Ren, J., Hadjal, M., Mckee, D., Kao, F., & Durrani, T. (2022). Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water. In Q. Liang, W. Wang, X. Liu, Z. Na, & B. Zhang (Eds.), Communications, Signal Processing, and Systems: Proceedings of the 10th International Conference on Communications, Signal Processing, and Systems (1 ed., pp. 582-587). (Lecture Notes in Electrical Engineering; Vol. 878). Springer . https://doi.org/10.1007/978-981-19-0390-8_71

Yan, Yijun ; Zhang, Yixin ; Ren, Jinchang et al. / Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water. Communications, Signal Processing, and Systems: Proceedings of the 10th International Conference on Communications, Signal Processing, and Systems. editor / Qilian Liang ; Wei Wang ; Xin Liu ; Zhenyu Na ; Baoju Zhang. 1. ed. Singapore : Springer , 2022. pp. 582-587 (Lecture Notes in Electrical Engineering).

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title = "Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water",

abstract = "Marine Scotland is tasked with reporting on the environmental status of Scottish marine waters, an enormous area of water extending from the shoreline to deep oceanic waters. As one of the most important variables, chlorophyll concentration (Chl) plays an important role in the seawater quality monitoring. Currently, the Chl observation is mostly done by expensive ship-based surveys that have very limited spatio-temporal coverage. Satellite based ocean colour remote sensing has the potential to significantly enhance monitoring capabilities but this opportunity has not been widely adopted by statutory reporting bodies across Europe due to concerns over satellite data quality. To break through this bottleneck, in this paper, we explore to implement advanced machine learning techniques to automatically estimate the Chl via the historic time series of ocean colour remote sensing data during from July 2002 to September 2019.",

keywords = "Chlorophyll, Environment monitoring, Multispectral remote sensing, Scottish marine water",

author = "Yijun Yan and Yixin Zhang and Jinchang Ren and Madjid Hadjal and David Mckee and Fu-jen Kao and Tariq Durrani",

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doi = "10.1007/978-981-19-0390-8_71",

language = "English",

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Yan, Y, Zhang, Y, Ren, J, Hadjal, M, Mckee, D, Kao, F & Durrani, T 2022, Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water. in Q Liang, W Wang, X Liu, Z Na & B Zhang (eds), Communications, Signal Processing, and Systems: Proceedings of the 10th International Conference on Communications, Signal Processing, and Systems. 1 edn, Lecture Notes in Electrical Engineering, vol. 878, Springer , Singapore, pp. 582-587, 10th International Conference on Communications, Signal Processing, and Systems, CSPS 2021, Changbaishan, China, 24/07/21. https://doi.org/10.1007/978-981-19-0390-8_71

Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water. / Yan, Yijun; Zhang, Yixin; Ren, Jinchang (Lead / Corresponding author) et al.
Communications, Signal Processing, and Systems: Proceedings of the 10th International Conference on Communications, Signal Processing, and Systems. ed. / Qilian Liang; Wei Wang; Xin Liu; Zhenyu Na; Baoju Zhang. 1. ed. Singapore: Springer , 2022. p. 582-587 (Lecture Notes in Electrical Engineering; Vol. 878).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water

AU - Yan, Yijun

AU - Zhang, Yixin

AU - Ren, Jinchang

AU - Hadjal, Madjid

AU - Mckee, David

AU - Kao, Fu-jen

AU - Durrani, Tariq

PY - 2022

Y1 - 2022

N2 - Marine Scotland is tasked with reporting on the environmental status of Scottish marine waters, an enormous area of water extending from the shoreline to deep oceanic waters. As one of the most important variables, chlorophyll concentration (Chl) plays an important role in the seawater quality monitoring. Currently, the Chl observation is mostly done by expensive ship-based surveys that have very limited spatio-temporal coverage. Satellite based ocean colour remote sensing has the potential to significantly enhance monitoring capabilities but this opportunity has not been widely adopted by statutory reporting bodies across Europe due to concerns over satellite data quality. To break through this bottleneck, in this paper, we explore to implement advanced machine learning techniques to automatically estimate the Chl via the historic time series of ocean colour remote sensing data during from July 2002 to September 2019.

AB - Marine Scotland is tasked with reporting on the environmental status of Scottish marine waters, an enormous area of water extending from the shoreline to deep oceanic waters. As one of the most important variables, chlorophyll concentration (Chl) plays an important role in the seawater quality monitoring. Currently, the Chl observation is mostly done by expensive ship-based surveys that have very limited spatio-temporal coverage. Satellite based ocean colour remote sensing has the potential to significantly enhance monitoring capabilities but this opportunity has not been widely adopted by statutory reporting bodies across Europe due to concerns over satellite data quality. To break through this bottleneck, in this paper, we explore to implement advanced machine learning techniques to automatically estimate the Chl via the historic time series of ocean colour remote sensing data during from July 2002 to September 2019.

KW - Chlorophyll

KW - Environment monitoring

KW - Multispectral remote sensing

KW - Scottish marine water

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DO - 10.1007/978-981-19-0390-8_71

M3 - Conference contribution

AN - SCOPUS:85128777253

SN - 9789811903892 (hbk)

SN - 9789811903922 (pbk)

T3 - Lecture Notes in Electrical Engineering

SP - 582

EP - 587

BT - Communications, Signal Processing, and Systems

A2 - Liang, Qilian

A2 - Wang, Wei

A2 - Liu, Xin

A2 - Na, Zhenyu

A2 - Zhang, Baoju

PB - Springer

CY - Singapore

T2 - 10th International Conference on Communications, Signal Processing, and Systems, CSPS 2021

Y2 - 24 July 2021 through 25 July 2021

ER -

Yan Y, Zhang Y, Ren J, Hadjal M, Mckee D, Kao F et al. Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water. In Liang Q, Wang W, Liu X, Na Z, Zhang B, editors, Communications, Signal Processing, and Systems: Proceedings of the 10th International Conference on Communications, Signal Processing, and Systems. 1 ed. Singapore: Springer . 2022. p. 582-587. (Lecture Notes in Electrical Engineering). Epub 2022 Mar 30. doi: 10.1007/978-981-19-0390-8_71

Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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