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Sentinel-3 Satellites: A Three-Sensor Mission to Observe the Physical, Bio-Optical and Biogeochemical Properties of Marine/Water Bodies

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 July 2023) | Viewed by 24844

Special Issue Editors

Consiglio Nazionale delle Ricerche (CNR), Area della Ricerca CNR S. Cataldo, Via Moruzzi 1, 56100 Pisa, Italy
Interests: ocean and land remote sensing; satellite radar altimetry; water level; coastal zone; inland waters
Special Issues, Collections and Topics in MDPI journals
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Interests: validation of remote sensing data; application of remote sensing to coastal regions; development of new remote sensing for high resolution; validation of remote sensing data sets in challenging areas, including the arctic and coastal regions
Special Issues, Collections and Topics in MDPI journals
Bedford Institute of Oceanography, Fisheries and Oceans Canada, Dartmouth, NS B2Y 4A2, Canada
Interests: phytoplankton ecology; bio-optics; satellite ocean colour; climate change; coastal oceanography; ecosystem dynamics
Special Issues, Collections and Topics in MDPI journals
Univ. Littoral Cote d’Opale, Univ. Lille, CNRS, UMR 8187, LOG, Laboratoire d’Océanologie et de Géosciences, 62930 Wimereux, France
Interests: active and passive remote sensing of ocean color; atmospheric correction; inversion techniques for the estimation of biogeochemical parameters
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Shirshov Institute of Oceanology, Russian Academy of Sciences, 117997 Moscow, Russia
Interests: seawater optical properties; satellite ocean color; field studies; regional algorithms; climatic factors
* Former Guest Editor. He deceased on 28 December 2020.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Sentinel-3 satellite, as part of the European Copernicus program, is primarily an ocean mission. The Sentinel-3A satellite was launched in February 2016 and has been in routine phase since October 2017. The twin Sentinel-3B satellite was launched in April 2018. During the commissioning phase, the two satellites have been positioned in tandem configuration, separated by 30 seconds. Once Sentinel-3B is operational, it will fly in the same orbit of Sentinel-3A, but 140° ahead. Each satellite carries three main sensors, specifically an SAR radar altimeter, SST radiometer and ocean colour imager, revisiting the same place every 2 days with the two satellites. In this Special Issue we invite contributions highlighting how Sentinel-3 data are improved (technologies, algorithms, etc.) and used (also in combination/synergy with in situ and other satellite missions and/or modelling tools) to contribute to the study/research/monitoring (also operationally) of the ocean from the global to the coastal scale. Of particular interest are also studies addressing synergies between the three Sentinel-3 sensors. Comparative studies made possible by the Sentinel-3A/B tandem phase are also encouraged. Work that seeks to build on the previous records of SST, Ocean Color, and altimetry are also encouraged (especially with ENVISAT). This includes improvements in quality and consistency with applications to interannual and climate scale variability.

Examples include:

  1. Development of the OLCI regional algorithms for retrieval of the bio-optical parameters in Case 2 waters and their validation using in situ data.
  2. Joining the satellite data products from OLCI, MODIS and MERIS sensors to build long-term series of bio-optical data and sea surface temperature data. For SST built on the results of the SST special collection, see https://www.mdpi.com/journal/remotesensing/special_issues/SST_RS
  3. Using OLCI and SLSTR data to estimate the changes in the amount of solar radiation entering the waters of the Arctic sea and analyze the causes and consequences. Use of these products for the validation of SST and Ocean Colour in the Arctic is encouraged.
  4. Use of the parameters derived from OLCI and SLSTR data as essential climate variables.
  5. Sentinel-3 radar altimetry for studies on ocean circulation variability, sea level changes, extreme events (storm surges and hurricanes), ocean wave field, assimilation of data in models, etc.
  6. Sentinel-3 radar altimetry in the coastal zone: progress on waveform modelling and retracking, improvements in corrections (SSB, wet troposphere, tides, etc.); assessment of coastal altimetry; calibration and validation of coastal altimetry data; intercalibration against various missions; applications of coastal altimetry data, including the usage of data from the various providers (e.g., SARvatore, SAR Versatile Altimetric Toolkit for Ocean Research and Exploitation for Sentinel-3).
  7. Synergy between S3A and S3B for following high-spatial and frequency events over coastal waters and inland seas.

Dr. Stefano Vignudelli
Dr. Jorge Vazquez
Dr. Emmanuel Devred
Dr. Cédric Jamet
Dr. Oleg Kopelevich
Guest Editors

Manuscript Submission Information

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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 color
  • inland and coastal waters
  • synergy
  • radar altimetry
  • sea level
  • wave
  • currents
  • SST
  • regional algorithms
  • long-term series
  • solar radiation
  • essential climate variables

Published Papers (7 papers)

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Research

20 pages, 12203 KiB  
Article
The Seasonality of Eddy-Induced Chlorophyll-a Anomalies in the Kuroshio Extension System
by Tongyu Wang, Shuwen Zhang, Fajin Chen and Luxing Xiao
Remote Sens. 2023, 15(15), 3865; https://doi.org/10.3390/rs15153865 - 03 Aug 2023
Viewed by 735
Abstract
The Kuroshio Extension (KE) System exhibits highly energetic mesoscale phenomena, but the impact of mesoscale eddies on marine ecosystems and biogeochemical cycling is not well understood. This study utilizes remote sensing and Argo floats to investigate how eddies modify surface and subsurface chlorophyll-a [...] Read more.
The Kuroshio Extension (KE) System exhibits highly energetic mesoscale phenomena, but the impact of mesoscale eddies on marine ecosystems and biogeochemical cycling is not well understood. This study utilizes remote sensing and Argo floats to investigate how eddies modify surface and subsurface chlorophyll-a (Chl-a) concentrations. On average, cyclones (anticyclones) induce positive (negative) surface Chl-a anomalies, particularly in winter. This occurs because cyclones (anticyclones) lift (deepen) isopycnals and nitrate into (out of) the euphotic zone, stimulating (depressing) the growth of phytoplankton. Consequently, cyclones (anticyclones) result in greater (smaller) subsurface Chl-a maximum (SCM), depth-integrated Chl-a, and depth-integrated nitrate. The positive (negative) surface Chl-a anomalies induced by cyclones (anticyclones) are mainly located near (north of) the main axis of the KE. The second and third mode represent monopole Chl-a patterns within eddy centers corresponding to either positive or negative anomalies, depending on the sign of the principal component. Chl-a concentrations in cyclones (anticyclones) above the SCM layer are higher (lower) than the edge values, while those below are lower (higher), regardless of winter variations. The vertical distributions and displacements of Chl-a and SCM depth are associated with eddy pumping. In terms of frequency, negative (positive) Chl-a anomalies account for approximately 26% (18%) of the total cyclones (anticyclones) across all four seasons. The opposite phase suggests that nutrient supply resulting from stratification differences under convective mixing may contribute to negative (positive) Chl-a anomalies in cyclone (anticyclone) cores. Additionally, the opposite phase can also be attributed to eddy stirring, trapping high and low Chl-a, and/or eddy Ekman pumping. Based on OFES outputs, the seasonal variation of nitrate from winter to summer primarily depends on the effect of vertical mixing, indicating that convective mixing processes contribute to an increase (decrease) in nutrients during winter (summer) over the KE. Full article
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20 pages, 7829 KiB  
Article
Quantification of Underwater Sargassum Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic Ocean
by Léa Schamberger, Audrey Minghelli and Malik Chami
Remote Sens. 2022, 14(20), 5230; https://doi.org/10.3390/rs14205230 - 19 Oct 2022
Cited by 6 | Viewed by 1715
Abstract
Sargassum” is a pelagic species of algae that drifts and aggregates in the tropical Atlantic Ocean. The number of Sargassum aggregations increased in the Caribbean Sea during the last decade. The aggregations eventually wash up on shores thus leading to a [...] Read more.
Sargassum” is a pelagic species of algae that drifts and aggregates in the tropical Atlantic Ocean. The number of Sargassum aggregations increased in the Caribbean Sea during the last decade. The aggregations eventually wash up on shores thus leading to a socio-economic issue for the population and the coastal ecosystem. Satellite ocean color data, such as those provided by the Sentinel-3/OLCI satellite sensor (Copernicus), can be used to detect the occurrences of Sargassum and to estimate their abundance per pixel using the Maximum Chlorophyll Index (noted MCI). Such an index is, however, ineffective if the algae are located beneath the sea surface, which frequently happens, considering the rough Caribbean oceanic waters. The objective of this study is to propose a relevant methodology that enables the detection of underwater Sargassum aggregations. The methodology relies on the inversion of the radiative transfer equation in the water column. The inverted model provides the immersion depth of the Sargassum aggregations (per pixel) and their fractional coverage from the above water reflectances. The overall methodology has been applied to Sentinel-3/OLCI data. The comparison with the MCI method, which is solely devoted to the sea surface retrieval of Sargassum aggregations, shows that the proposed methodology allows retrieving about twice more Sargassum aggregation occurrences than the MCI estimates. A relative increase of 31% of the fractional coverage over the entire study area is observed when using the proposed method in comparison to MCI method. For the satellite scenes considered here, the rate of Sargassum aggregations immersed between 2 m and 5 m depth ranges between 30% and 51% over the total amount (i.e., surface + in-water), which clearly demonstrates the importance of considering the retrieval of in-water aggregations to gain understanding on Sargassum spatial variability in the oceanic and coastal ecosystems. Full article
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16 pages, 31022 KiB  
Article
Combined Use of Space Borne Optical and SAR Data to Improve Knowledge about Sea Ice for Shipping
by Christine König, Thomas König, Suman Singha, Anja Frost and Sven Jacobsen
Remote Sens. 2021, 13(23), 4842; https://doi.org/10.3390/rs13234842 - 29 Nov 2021
Cited by 2 | Viewed by 1606
Abstract
As a first step towards a new combined product for sea ice classification based on optical/thermal data collected by Sentinel-3 satellites and SAR data from Sentinel-1 satellites, which can be used as an appropriate support for navigation in Arctic and sub-Arctic waters, two [...] Read more.
As a first step towards a new combined product for sea ice classification based on optical/thermal data collected by Sentinel-3 satellites and SAR data from Sentinel-1 satellites, which can be used as an appropriate support for navigation in Arctic and sub-Arctic waters, two existing classification algorithms are adapted to these data. The classification based on optical data has improved, so it is expected that the results will be ideally suited to be processed together with SAR data into significantly improved sea ice information products to support marine navigation. The usefulness of the combined processing is demonstrated by means of two simple algorithms and a more sophisticated approach is outlined, which will be realized in the future in order to form the basis for an integration into an operational service with the involvement of further partners and users. Full article
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27 pages, 7129 KiB  
Article
Evaluation of the CDOM Absorption Coefficient in the Arctic Seas Based on Sentinel-3 OLCI Data
by Dmitry Glukhovets, Oleg Kopelevich, Anna Yushmanova, Svetlana Vazyulya, Sergey Sheberstov, Polina Karalli and Inna Sahling
Remote Sens. 2020, 12(19), 3210; https://doi.org/10.3390/rs12193210 - 01 Oct 2020
Cited by 19 | Viewed by 3247
Abstract
Our work’s primary goal is to reveal the problematic issues related to estimates of the colored organic matter absorption coefficient in the northern seas from data of the Ocean and Land Color Instrument (OLCI) installed on the Sentinel-3 satellites, e.g., a comparison of [...] Read more.
Our work’s primary goal is to reveal the problematic issues related to estimates of the colored organic matter absorption coefficient in the northern seas from data of the Ocean and Land Color Instrument (OLCI) installed on the Sentinel-3 satellites, e.g., a comparison of the OLCI standard error assessment ADG443_NN_err relating to the measurement and the retrieval of the geophysical products and the uncertainties in the northern seas’ real situation. The natural conditions are incredibly unfavorable there, mainly due to frequent cloudiness and low sun heights. We conducted a comprehensive multi-sensor study of the uncertainties using various approaches. We directly compared the data from satellites (OLCI Sentinel-3 and four other ocean color sensors) and field measurements in five sea expeditions (2016–2019) using the different processing algorithms. Our analysis has shown that the final product’s real uncertainties are significantly (≥100%) higher than the calculated errors of the ADG443_NN_err (~10%). The main reason is the unsatisfactory atmospheric correction. We present the analysis of the various influential factors (satellite sensors, processing algorithms, and other parameters) and formulate future work goals. Full article
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20 pages, 20446 KiB  
Article
Sentinel-3A SRAL Global Statistical Assessment and Cross-Calibration with Jason-3
by Jungang Yang, Jie Zhang and Changying Wang
Remote Sens. 2019, 11(13), 1573; https://doi.org/10.3390/rs11131573 - 03 Jul 2019
Cited by 17 | Viewed by 2956
Abstract
The Sentinel-3A satellite, equipped with Synthetic Aperture Radar (SAR) Altimeter (SRAL) instrument to derive sea surface height, significant wave height and surface wind speed over the global ocean, was launched on 16 February 2016. The assessment of data quality and the system performance [...] Read more.
The Sentinel-3A satellite, equipped with Synthetic Aperture Radar (SAR) Altimeter (SRAL) instrument to derive sea surface height, significant wave height and surface wind speed over the global ocean, was launched on 16 February 2016. The assessment of data quality and the system performance of the altimeter are very important to data application. In this article, Sentinel-3A SRAL data quality is assessed and altimetry system performance is estimated by verifying data availability and monitoring the parameters of altimeter and radiometer through the global statistical analyses of Sentinel-3A Non-Time-Critical (NTC) Marine Level 2 products during the period from 13 March 2016 to 25 February 2019, in comparison with self-crossovers and cross-calibration with the Jason-3 mission. The global statistical analyses and the comparisons at self-crossovers show that Sentinel-3A SRAL data and performance are stable and have no trend over time, and the total cycle average root mean square errors (RMSEs) of sea surface height (SSH) differences at self-crossovers is about 5.4 cm. The comparisons at the dual-crossovers show the consistency of the observation between Sentinel-3A SRAL and Jason-3, and indicate that the systemic bias of SSH is about 2.96 cm. In general, it can be concluded that Sentinel-3A SRAL has good and stable data quality and system performance for operational ocean forecasting and scientific research. Full article
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25 pages, 1056 KiB  
Article
Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor
by Mohamed Abdelillah Mograne, Cédric Jamet, Hubert Loisel, Vincent Vantrepotte, Xavier Mériaux and Arnaud Cauvin
Remote Sens. 2019, 11(6), 668; https://doi.org/10.3390/rs11060668 - 19 Mar 2019
Cited by 69 | Viewed by 6124
Abstract
The Sentinel-3A satellite was launched on 16 February 2016 with the Ocean and Land Colour Instrument (OLCI-A) on-board for the study of ocean color. The accuracy of ocean color parameters depends on the atmospheric correction algorithm (AC). This processing consists of removing the [...] Read more.
The Sentinel-3A satellite was launched on 16 February 2016 with the Ocean and Land Colour Instrument (OLCI-A) on-board for the study of ocean color. The accuracy of ocean color parameters depends on the atmospheric correction algorithm (AC). This processing consists of removing the contribution of the atmosphere from the total measured signal by the remote sensor at the top of the atmosphere. Five ACs: the baseline AC, the Case 2 regional coast color neural network AC, its alternative version, the Polymer AC, and the standard NASA AC, are inter-compared over two bio-optical contrasted French coastal waters. The retrieved water-leaving reflectances are compared with in situ ocean color radiometric measurements collected using an ASD FielSpec4 spectrometer. Statistical and spectral analysis were performed to assess the best-performing AC through individual (relative error (RE) at 412 nm ranging between 23.43 and 57.31%; root mean squared error (RMSE) at 412 nm ranging between 0.0077 and 0.0188) and common (RE(412 nm) = 24.15–50.07%; RMSE(412 nm) = 0.0081–0.0132) match-ups. The results suggest that the most efficient schemes are the alternative version of the Case 2 regional coast color neural network AC with RE(412 nm) = 33.52% and RMSE(412 nm) = 0.0101 for the individual and Polymer with RE(412 nm) = 24.15% and RMSE(412 nm) = 0.0081 for the common ACs match-ups. Sensitivity studies were performed to assess the limitations of the AC, and the errors of retrievals showed no trends when compared to the turbidity and CDOM. Full article
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20 pages, 11184 KiB  
Article
Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image
by Xia Wang, Feng Ling, Huaiying Yao, Yaolin Liu and Shuna Xu
Remote Sens. 2019, 11(3), 327; https://doi.org/10.3390/rs11030327 - 07 Feb 2019
Cited by 29 | Viewed by 6011
Abstract
Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible [...] Read more.
Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image. Full article
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