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The Ocean Colour Essential Climate Variable: Advances, Applications and Aspirations

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 12145

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Special Issue Editors

Dr. Thomas Jackson

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Guest Editor

Plymouth Marine Laboratory (PML), UK
Interests: ocean colour; remote sensing; climate data records; phytoplankton physiology; optical classification

Dr. Hayley Evers-King

E-Mail Website
Guest Editor

European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Germany
Interests: ocean colour; Sentinel-3; water quality; data synergy

Special Issue Information

Ocean colour (OC) is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). The global record of ocean colour is now continuous for more than 20 years and the most recent generation of ocean colour satellites provide enhanced and novel capabilities. Ocean colour is primarily utilised to observe and interpret spatiotemporal variability of phytoplankton, which support the marine food web, can act as sentinels of larger scale ecosystem change, and produce harmful algal blooms. Ocean colour can also be used in other oceanographic studies on topics such as suspended sediment, water quality, solar-induced heat in the upper layers of the ocean, oil spills, and sea ice. Recent increases in spectral, spatial, and temporal resolution, and a longer continuous record, mean that new insights and applications are possible.

This Special Issue is devoted to recent and ongoing advances in the development and utilisation of the ocean colour ECV. Example oceanographic topics of interest include but are not limited to:

OC ECV generation (inter-sensor calibration and bias correction, adaptation of algorithms for multi-sensor applications, etc.);
phytoplankton phenology;
primary productivity;
climate change;
harmful algal blooms
water quality;
synergistic use of OC and other remote sensing products and ECVs;
novel OC ECV downstream products and services;
contribution of data products to policy.

Dr. Thomas Jackson
Dr. Hayley Evers-King
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

ocean colour
phytoplankton
optical properties
ecosystem
ocean management
climate
ECV

Published Papers (4 papers)

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Research

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14 pages, 3523 KiB

Open AccessArticle

Chlorophyll-a and Sea Surface Temperature Changes in Relation to Paralytic Shellfish Toxin Production off the East Coast of Tasmania, Australia

by Lael Wakamatsu, Gregory L. Britten, Elliot J. Styles and Andrew M. Fischer

Remote Sens. 2022, 14(3), 665; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030665 - 30 Jan 2022

Cited by 2 | Viewed by 2897

Abstract

Toxic phytoplankton have been detrimental to the fishing and aquaculture industry on the east coast of Tasmania, causing millions of dollars in loss due to contaminated seafood. In 2012–2017, shellfish stocks were poisoned by Alexandrium catenella, a dinoflagellate species that produces paralytic shellfish toxins (PST). Remote sensing data may provide an environmental context for the drivers of PST events in Tasmania. We conducted spatial and temporal trend analyses of the Multi-Scale Ultra-High-Resolution Sea Surface Temperature (MUR SST) and Ocean Color Climate Change Initiative chlorophyll-a (OC-CCI chl-a) to determine if SST and chl-a correlated with the major toxin increases from 2012 to 2017. Along with the trends, we compare the remotely sensed oceanographic parameters of SST and chl-a to toxin events off the east coast of Tasmania to provide environmental context for the high-toxin period. Spatial and temporal changes for chl-a differ based on the north, central, and southeast coast of Tasmania. For sites in the north, chl-a was 5.3% higher from the pre-PST period relative to the PST period, 5.1% along the central part of the coast, and by 6.0% in the south based on deviations from the coastal study area time series. Overall, SST has slightly decreased from 2007 to 2020 (tau = −0.011, p = 0.827) and chl-a has significantly decreased for the east coast (tau = −0.164, p = 1.58 × 10⁻³). A negative relationship of SST and PST values occurred in the north (r = −0.530, p = 5.32 × 10⁻⁵) and central sites (r = −0.225, p = 0.157). The correlation between satellite chl-a (from OC-CCI, Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate-Resolution Imaging Spectrometer (MODIS) Aqua) and in situ data is weak, which makes it difficult to assess relationships present between chl-a and toxin concentrations. Moving forward, the development of a regional chl-a algorithm and increased in situ chl-a collection and plankton sampling at a species level will help to improve chl-a measurements and toxic phytoplankton production monitoring around Tasmania. Full article

(This article belongs to the Special Issue The Ocean Colour Essential Climate Variable: Advances, Applications and Aspirations)

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22 pages, 7256 KiB

Open AccessArticle

Hybrid Inversion Algorithms for Retrieval of Absorption Subcomponents from Ocean Colour Remote Sensing Reflectance

by Srinivas Kolluru, Surya Prakash Tiwari and Shirishkumar S. Gedam

Remote Sens. 2021, 13(9), 1726; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091726 - 29 Apr 2021

Cited by 1 | Viewed by 2476

Abstract

Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance

(R_{r s} (λ), s r^{- 1})

to Inherent Optical Properties (IOPs) of an aquatic medium (

λ

is the wavelength). Existing SAAs implement different methodologies with a [...] Read more.

Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance

(R_{r s} (λ), s r^{- 1})

to Inherent Optical Properties (IOPs) of an aquatic medium (

λ

is the wavelength). Existing SAAs implement different methodologies with a range of spectral IOP models and inversion methods producing concentrations of non-water constituents. Absorption spectrum decomposition algorithms (ADAs) are a set of algorithms developed to partition

a_{n w} (λ), m^{- 1}

(i.e., the light absorption coefficient without pure water absorption), into absorption subcomponents of phytoplankton

(a_{p h} (λ), m^{- 1})

and coloured detrital matter

(a_{d g} (λ), m^{- 1})

. Despite significant developments in ADAs, their applicability to remote sensing applications is rarely studied. The present study formulates hybrid inversion approaches that combine SAAs and ADAs to derive absorption subcomponents from

R_{r s} (λ)

and explores potential alternatives to operational SAAs. Using

R_{r s} (λ)

and concurrent absorption subcomponents from four datasets covering a wide range of optical properties, three operational SAAs, i.e., Garver–Siegel–Maritorena (GSM), Quasi-Analytical Algorithm (QAA), Generalized Inherent Optical Property (GIOP) model are evaluated in deriving

a_{n w} (λ)

from

R_{r s} (λ)

. Among these three models, QAA and GIOP models derived

a_{n w} (λ)

with lower errors. Among six distinctive ADAs tested in the study, the Generalized Stacked Constraints Model (GSCM) and Zhang’s model-derived absorption subcomponents achieved lower average spectral mean absolute percentage errors (MAPE) in the range of 8–38%. Four hybrid models, GIOP_GSCM, GIOP_Zhang, QAA_GSCM and QAA_Zhang, formulated using the SAAs and ADAs, are compared for their absorption subcomponent retrieval performance from

R_{r s} (λ)

. GIOP_GSCM and GIOP_Zhang models derived absorption subcomponents have lower errors than GIOP and QAA. Potential uncertainties associated with datasets and dependency of algorithm performance on datasets were discussed. Full article

(This article belongs to the Special Issue The Ocean Colour Essential Climate Variable: Advances, Applications and Aspirations)

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12 pages, 3605 KiB

Open AccessTechnical Note

High Chlorophyll-a Areas along the Western Coast of South Sulawesi-Indonesia during the Rainy Season Revealed by Satellite Data

by Anindya Wirasatriya, Raden Dwi Susanto, Joga Dharma Setiawan, Fatwa Ramdani, Iskhaq Iskandar, Abd. Rasyid Jalil, Ardiansyah Desmont Puryajati, Kunarso Kunarso and Lilik Maslukah

Remote Sens. 2021, 13(23), 4833; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234833 - 28 Nov 2021

Cited by 4 | Viewed by 2066

Abstract

The southern coast of South Sulawesi-Indonesia is known as an upwelling area occurring during dry season, which peaks in August. This upwelling area is indicated by high chlorophyll-a (Chl-a) concentrations due to a strong easterly wind-induced upwelling. However, the investigation of Chl-a variability is less studied along the western coast of South Sulawesi. By taking advantages of remote sensing data of Chl-a, sea surface temperature, surface wind, and precipitation, the present study firstly shows that along the western coast of South Sulawesi, there are two areas, which have high primary productivity occurring during the rainy season. The first area is at 119.0° E–119.5° E; 3.5° S–4.0° S, while the second area is at 119.0° E–119.5° E; 3.5° S–4.0° S. The maximum primary productivity in the first (second) area occurs in April (January). The generating mechanism of the high primary productivity along the western coast of South Sulawesi is different from its southern coast. The presence of river runoff in these two areas may bring anthropogenic organic compounds during the peak of rainy season, resulting in increased Chl-a concentration. Full article

(This article belongs to the Special Issue The Ocean Colour Essential Climate Variable: Advances, Applications and Aspirations)

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14 pages, 4628 KiB

Open AccessLetter

Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean

by Elodie Martinez, Anouar Brini, Thomas Gorgues, Lucas Drumetz, Joana Roussillon, Pierre Tandeo, Guillaume Maze and Ronan Fablet

Remote Sens. 2020, 12(24), 4156; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244156 - 18 Dec 2020

Cited by 7 | Viewed by 3849 | Correction

Abstract

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP, thanks to its ability to capture complex non-linear relationships, outperforms the SVR to capture satellite Chl spatial patterns (correlation of 0.75 vs. 0.65 on a global scale, respectively) along with its interannual variability and trend, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series. Full article

(This article belongs to the Special Issue The Ocean Colour Essential Climate Variable: Advances, Applications and Aspirations)

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