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

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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 (31 December 2024) | Viewed by 9956

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Special Issue Editors

Dr. Andrew Clive Banks

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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
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Dr. Jason Keith Jolliff

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Guest Editor

Naval Research Laboratory, Stennis Space Center, Hancock County, MS 39529, USA
Interests: ocean color; ocean remote sensing; sensor fusion; hyperspectral sensing
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Dr. Peng Chen

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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

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Keywords

ocean colour and optics
phytoplankton
harmful algal blooms
water quality
assimilation and modeling

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Published Papers (9 papers)

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Research

20 pages, 31619 KiB

Open AccessArticle

Impact of the Uncertainties of Polarized Water-Leaving Radiance on the Retrieval of Oceanic Constituents and Inherent Optical Properties in Global Oceans via Multiangle Polarimetric Observations

by Jia Liu, Chunxia Li, Xianqiang He, Tieqiao Chen, Xinyin Jia, Yan Bai, Dong Liu, Bo Qu, Yihao Wang, Xiangpeng Feng, Yupeng Liu, Geng Zhang, Siyuan Li, Bingliang Hu and Delu Pan

Remote Sens. 2025, 17(7), 1148; https://doi.org/10.3390/rs17071148 - 24 Mar 2025

Viewed by 201

Abstract

Compared with traditional single-view and radiometric-only observations, multiangle polarimetric observations of water-leaving radiation play a crucial role in enhancing the retrieval of ocean constituents and aerosol microphysical properties. In this study, the impacts of uncertainties in the degree of polarization (DOP) of water-leaving radiance (L_w) on the retrieval of oceanic constituents and inherent optical properties (IOPs) were investigated via global radiative transfer (RT) simulations and the fully connected U-Net (FCUN) model. The uncertainties in the retrieval of oceanic constituents and IOPs were further investigated with various sensor azimuth angles. The results indicated that the global mean absolute percentage errors (MAPEs) for differing oceanic constituents and IOPs significantly decreased as the number of observation angles increased. Taking the retrieval of Chla as an example, the global MAPEs between the FCUN predictions and RT simulation inputs for Chla concentrations under differing observation angles were 7.41%, 3.76%, 2.70%, 2.44%, 2.62%, and 1.82%. Moreover, the MAPEs at sensor azimuth angles of 0° and 30° were significantly lower than those at other azimuth angles for the single-view observations. As the number of observation angles increased, the variation in MAPEs with the sensor azimuth angle gradually weakened. Furthermore, the impact of errors in the L_w DOP on the retrieval uncertainties decreased as the number of observation angles increased, and the global MAPEs of Chla after adding the various random instrument noises were 46.56% (46.91%), 6.59% (7.21%), 5.21% (5.79%), 4.72% (4.98%), 3.99% (4.52%), and 3.64% (4.03%). Overall, the multiangle polarimetric observations can suppress or balance the impact of uncertainties in the L_w DOP on the retrieval of oceanic constituents and IOPs. 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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19 pages, 7401 KiB

Open AccessArticle

A New Algorithm Based on the Phytoplankton Absorption Coefficient for Red Tide Monitoring in the East China Sea via a Geostationary Ocean Color Imager (GOCI)

by Xiaohui Xu, Yaqin Huang, Jian Chen and Zhi Zeng

Remote Sens. 2025, 17(5), 750; https://doi.org/10.3390/rs17050750 - 21 Feb 2025

Viewed by 390

Abstract

Rapid and accurate dynamic monitoring and quantitative analysis of red tide disasters are of significant practical importance to national economic development. Remote sensing technology is an effective means for monitoring red tides. This paper utilizes GOCI satellite data and employs a quasi-analytical algorithm (QAA) to retrieve the spectral curves of phytoplankton absorption coefficients. On the basis of a detailed analysis of the differences in the spectral curves of the phytoplankton absorption coefficients between red tide and non-red tide waters, we establish a red tide identification algorithm for the East China Sea on the basis of phytoplankton absorption coefficients. The algorithm is applied to multiple red tide events in the East China Sea. The results indicate that this algorithm can effectively determine the occurrence locations of red tides and extract relevant information about them. 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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19 pages, 22497 KiB

Open AccessArticle

Water Quality Monitoring Using Landsat 8 OLI in Pleasant Bay, Massachusetts, USA

by Haley E. Synan, Brian L. Howes, Sara Sampieri and Steven E. Lohrenz

Remote Sens. 2025, 17(4), 638; https://doi.org/10.3390/rs17040638 - 13 Feb 2025

Viewed by 977

Abstract

Water quality monitoring is essential to assess and manage anthropogenic eutrophication, especially for coastal estuaries in heavily populated areas. Current monitoring techniques rely on in situ sampling, which can be expensive and limited in spatial and temporal coverage. Satellite remote sensing, using the Landsat 8 (Operational Land Imager, OLI) platform, has the potential to provide more extensive coverage than traditional methods. Coastal waters are optically more complex and often shallower and more enclosed than the open ocean, presenting conditions that pose challenges to remote sensing approaches. Here, we compared in situ data from 18 stations around Pleasant Bay, Massachusetts, USA from the years 2014–2021 to contemporaneous observations with Landsat 8 OLI. Satellite-derived estimates of chlorophyll-a and Secchi depth were acquired using various algorithms including the “Case-2 Regional/Coast Color” (C2RCC), “Case-2 Extreme” (C2X), l2gen processor, and a random forest machine learning algorithm. Based on our results, predictions of water quality indices from both C2RCC and random forest techniques can be a useful addition to existing water quality monitoring efforts, potentially expanding both spatial and temporal coverage of monitoring efforts. 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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21 pages, 5879 KiB

Open AccessArticle

Accelerating CO₂ Outgassing in the Equatorial Pacific from Satellite Remote Sensing

by Yiwu Shang, Jingyuan Xi, Yi Yu, Wentao Ma and Shuangling Chen

Remote Sens. 2025, 17(2), 247; https://doi.org/10.3390/rs17020247 - 12 Jan 2025

Viewed by 785

Abstract

The equatorial Pacific serves as the world’s largest oceanic source of CO₂. The contrasting ocean environment in the eastern (i.e., upwelling) and western (i.e., warm pool) regions makes it difficult to fully characterize its CO₂ dynamics with limited in situ observations. In this study, we addressed this challenge using monthly surface partial pressure of CO₂ (pCO₂sw) and air-sea CO₂ fluxes (FCO₂) data products reconstructed from satellite and reanalysis data at a spatial resolution of 1° × 1° in the period of 1982–2021. We found that during the very strong El Niño events (1997/1998, 2015/2016), both pCO₂sw and FCO₂ showed a significant decrease of 41–58 μatm and 0.5–0.8 mol·m⁻²·yr⁻¹ in the eastern equatorial Pacific, yet they remained at normal levels in the western equatorial Pacific. In contrast, during the very strong La Niña events (1999/2000, 2007/2008, and 2010/2011), both pCO₂sw and FCO₂ showed a strong increase of 40–48 μatm and 1.0–1.4 mol·m⁻²·yr⁻¹ in the western equatorial Pacific, yet with little change in the eastern equatorial Pacific. In the past 40 years, pCO₂sw in the eastern equatorial Pacific was increasing at a higher rate (2.32–2.51 μatm·yr⁻¹) than that in the western equatorial Pacific (1.75 μatm·yr⁻¹), resulting in an accelerating CO₂ outgassing (at a rate of 0.03 mol·m⁻²·yr⁻²) in the eastern equatorial Pacific. We comprehensively analyzed the potential effects of different factors, such as sea surface temperature, sea surface wind speed, and ΔpCO₂ in driving CO₂ fluxes in the equatorial Pacific, and found that ΔpCO₂ had the highest correlation (R ≥ 0.80, at p ≤ 0.05), highlighting the importance of accurate estimates of pCO₂sw from satellites. Further studies are needed to constrain the retrieval accuracy of pCO₂sw in the equatorial Pacific from satellite remote sensing. 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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16 pages, 41766 KiB

Open AccessArticle

Methodology for Removing Striping Artifacts Encountered in Planet SuperDove Ocean-Color Products

by Brittney Slocum, Sherwin Ladner, Adam Lawson, Mark David Lewis and Sean McCarthy

Remote Sens. 2024, 16(24), 4707; https://doi.org/10.3390/rs16244707 - 17 Dec 2024

Viewed by 855

Abstract

The Planet SuperDove sensors produce eight-band, three-meter resolution images covering the blue, green, red, red-edge, and NIR spectral bands. Variations in spectral response in the data used to perform atmospheric correction combined with low signal-to-noise over ocean waters can lead to visible striping artifacts in the downstream ocean-color products. It was determined that the striping artifacts could be removed from these products by filtering the top of the atmosphere radiance in the red and NIR bands prior to selecting the aerosol models, without sacrificing high-resolution features in the imagery. This paper examines an approach that applies this filtering to the respective bands as a preprocessing step. The outcome and performance of this filtering technique are examined to assess the success of removing the striping effect in atmospherically corrected Planet SuperDove data. 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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20 pages, 3134 KiB

Open AccessArticle

Evaluating MULTIOBS Chlorophyll-a with Ground-Truth Observations in the Eastern Mediterranean Sea

by Eleni Livanou, Raphaëlle Sauzède, Stella Psarra, Manolis Mandalakis, Giorgio Dall’Olmo, Robert J. W. Brewin and Dionysios E. Raitsos

Remote Sens. 2024, 16(24), 4705; https://doi.org/10.3390/rs16244705 - 17 Dec 2024

Viewed by 1303

Abstract

Satellite-derived observations of ocean colour provide continuous data on chlorophyll-a concentration (Chl-a) at global scales but are limited to the ocean’s surface. So far, biogeochemical models have been the only means of generating continuous vertically resolved Chl-a profiles on a regular grid. MULTIOBS is a multi-observations oceanographic dataset that provides depth-resolved biological data based on merged satellite- and Argo-derived in situ hydrological data. This product is distributed by the European Union’s Copernicus Marine Service and offers global multiyear, gridded Chl-a profiles within the ocean’s productive zone at a weekly temporal resolution. MULTIOBS addresses the scarcity of observation-based vertically resolved Chl-a datasets, particularly in less sampled regions like the Eastern Mediterranean Sea (EMS). Here, we conduct an independent evaluation of the MULTIOBS dataset in the oligotrophic waters of the EMS using in situ Chl-a profiles. Our analysis shows that this product accurately and precisely retrieves Chl-a across depths, with a slight 1% overestimation and an observed 1.5-fold average deviation between in situ data and MULTIOBS estimates. The deep chlorophyll maximum (DCM) is adequately estimated by MULTIOBS both in terms of positioning (root mean square error, RMSE = 13 m) and in terms of Chl-a (RMSE = 0.09 mg m⁻³). The product accurately reproduces the seasonal variability of Chl-a and it performs reasonably well in reflecting its interannual variability across various depths within the productive layer (0–120 m) of the EMS. We conclude that MULTIOBS is a valuable dataset providing vertically resolved Chl-a data, enabling a holistic understanding of euphotic zone-integrated Chl-a with an unprecedented spatiotemporal resolution spanning 25 years, which is essential for elucidating long-term trends and variability in oceanic primary productivity. 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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23 pages, 5452 KiB

Open AccessArticle

Bio-Optical Properties and Ocean Colour Satellite Retrieval along the Coastal Waters of the Western Iberian Coast (WIC)

by Luciane Favareto, Natalia Rudorff, Vanda Brotas, Andreia Tracana, Carolina Sá, Carla Palma and Ana C. Brito

Remote Sens. 2024, 16(18), 3440; https://doi.org/10.3390/rs16183440 - 16 Sep 2024

Viewed by 1945

Abstract

Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data. 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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18 pages, 5010 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1250

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1179

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 (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

R_{R S} (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,

R_{R S} (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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