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Editorial Board Members’ Collection Series: Recent Progress in Ocean Colour...
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Editorial Board Members’ Collection Series: Recent Progress in Ocean Colour Remote Sensing

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 924

Special Issue Editors

Dr. Andrew Clive Banks

E-Mail Website
Guest Editor
Satellite Oceanography and Marine Optics, Institute of Oceanography, Hellenic Centre for Marine Research, Heraklion 71003, Crete, Greece
Interests: validation and vicarious calibration of satellite data; accuracy of satellite and in situ data (uncertainty and SI traceability); fiducial reference measurements; open ocean and coastal remote sensing of the Eastern Mediterranean; ocean color; sea surface temperature; albedo; BRDF; coastal zone; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Jason Keith Jolliff

E-Mail Website
Guest Editor
Naval Research Laboratory, Stennis Space Center, Hancock County, MS 39529, USA
Interests: ocean color; ocean remote sensing; sensor fusion; hyperspectral sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Chen

E-Mail Website
Guest Editor
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 36 Baochubei Road, Hangzhou 310012, China
Interests: ocean color; ocean lidar; ocean optics; ocean ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ocean colour remote sensing utilizes the intensity and spectral variation of visible light scattered upward from beneath the ocean surface (water leaving radiance) to derive concentrations of biogeochemical constituents and inherent optical properties. Satellite ocean colour has revealed decadal-scale changes in the ocean biosphere and has been designated an essential climate variable by the World Meteorological Organisation, with water leaving radiance and derived chlorophyll estimates, a proxy for phytoplankton, as its main components. Ocean colour satellite sensors not only estimate phytoplankton variables (including chlorophyll, phytoplankton, harmful algal blooms, etc.), but can also be used to map other living and abiotic products across the globe, including suspended sediment loads and light absorption of coloured dissolved organic matter. Recent advances in satellite technology and algorithm development have made it possible to detect water quality, ocean ecology, and many other aspects of the ocean environment using remote sensing technology. A project entitled “Editorial Members’ Collection Series—Recent Advances in Ocean Colour Observations” has been dedicated to the journal Remote Sensing to address the current status, challenges, and future research priorities for remote sensing of ocean colour. This Special Issue is devoted to the most recent advances in the studies of remote sensing technology and its applications in ocean colour studies, with an emphasis on the following topics:

  • Uncertainty in satellite ocean colour measurements;
  • Satellite ocean colour validation measurements and their uncertainties, including fiducial reference measurements (FRM);
  • Assessment and monitoring of water quality;
  • Bio-optic models and atmospheric correction;
  • Lidar and polarimetry remote sensing;
  • Assimilation of ocean colour and other applications of ocean-colour products in modeling;
  • Climate change monitoring and climate data record improvements using satellite ocean colour;
  • Interactions between ocean colour observations and other factors, including phytoplankton and fisheries;
  • Ocean colour with deep learning;
  • Hypersepectral remote sensing, for example, applications for the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission (NASA);
  • New applications for small satellites (cubesats and nanosastellites);
  • Linkages between ocean colour data and ocean physics: submesoscale eddies and filaments, frontal dynamics and coastal ocean circulation.

Dr. Andrew Clive Banks
Dr. Jason Keith Jolliff
Dr. Peng Chen
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 and optics
  • phytoplankton
  • harmful algal blooms
  • water quality
  • assimilation and modeling

Published Papers (2 papers)

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Research

18 pages, 5010 KiB  
Article
Enhancing Subsurface Phytoplankton Layer Detection in LiDAR Data through Supervised Machine Learning Techniques
by Chunyi Zhong, Peng Chen and Siqi Zhang
Remote Sens. 2024, 16(11), 1953; https://doi.org/10.3390/rs16111953 - 29 May 2024
Viewed by 167
Abstract
Phytoplankton are the foundation of marine ecosystems and play a crucial role in determining the optical properties of seawater, which are critical for remote sensing applications. However, passive remote sensing techniques are limited to obtaining data from the near surface, and cannot provide [...] Read more.
Phytoplankton are the foundation of marine ecosystems and play a crucial role in determining the optical properties of seawater, which are critical for remote sensing applications. However, passive remote sensing techniques are limited to obtaining data from the near surface, and cannot provide information on the vertical distribution of the subsurface phytoplankton. In contrast, active LiDAR technology can provide detailed profiles of the subsurface phytoplankton layer (SPL). Nevertheless, the large amount of data generated by LiDAR brought a challenge, as traditional methods for SPL detection often require manual inspection. In this study, we investigated the application of supervised machine learning algorithms for the automatic recognition of SPL, with the aim of reducing the workload of manual detection. We evaluated five machine learning models—support vector machine (SVM), linear discriminant analysis (LDA), a neural network, decision trees, and RUSBoost—and measured their performance using metrics such as precision, recall, and F3 score. The study results suggest that RUSBoost outperforms the other algorithms, consistently achieving the highest F3 score in most of the test cases, with the neural network coming in second. To improve accuracy, RUSBoost is preferred, while the neural network is more advantageous due to its faster processing time. Additionally, we explored the spatial patterns and diurnal fluctuations of SPL captured by LiDAR. This study revealed a more pronounced presence of SPL at night during this experiment, thereby demonstrating the efficacy of LiDAR technology in the monitoring of the daily dynamics of subsurface phytoplankton layers. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Recent Progress in Ocean Colour Remote Sensing)
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24 pages, 12245 KiB  
Article
How Representative Are European AERONET-OC Sites of European Marine Waters?
by Ilaria Cazzaniga and Frédéric Mélin
Remote Sens. 2024, 16(10), 1793; https://doi.org/10.3390/rs16101793 - 18 May 2024
Viewed by 316
Abstract
Data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) have been extensively used to assess Ocean Color radiometric products from various satellite sensors. This study, focusing on Ocean Color radiometric operational products from the Sentinel-3 Ocean and Land Colour Instrument [...] Read more.
Data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) have been extensively used to assess Ocean Color radiometric products from various satellite sensors. This study, focusing on Ocean Color radiometric operational products from the Sentinel-3 Ocean and Land Colour Instrument (OLCI), aims at investigating where in the European seas the results of match-up analyses at the European marine AERONET-OC sites could be applicable. Data clustering is applied to OLCI remote sensing reflectance RRS(λ) from the various sites to define different sets of optical classes, which are later used to identify class-based uncertainties. A set of fifteen classes grants medium-to-high classification levels to most European seas, with exceptions in the South-East Mediterranean Sea, the Atlantic Ocean, or the Gulf of Bothnia. In these areas, RRS(λ) spectra are very often identified as novel with respect to the generated set of classes, suggesting their under-representation in AERONET-OC data. Uncertainties are finally mapped onto European seas according to class membership. The largest uncertainty values are obtained in the blue spectral region for almost all classes. In clear waters, larger values are obtained in the blue bands. Conversely, larger values are shown in the green and red bands in coastal and turbid waters. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Recent Progress in Ocean Colour Remote Sensing)
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