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Remote Sensing Applications in Marine Environmental Monitoring
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Remote Sensing Applications in Marine Environmental Monitoring

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Marine Environmental Science".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 9110

Special Issue Editors

Dr. Virginia Zamparelli

E-Mail Website
Guest Editor
Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy (CNR), Neaples, Italy
Interests: SAR; SAR processing; sea-surface parameters; sea-surface radial velocity; doppler centroid anomaly
Special Issues, Collections and Topics in MDPI journals
Dr. Simona Verde

E-Mail Website
Guest Editor
Institute for the Electromagnetic Sensing of the Environment (IREA), National Research Council (CNR), Naples, Italy
Interests: remote sensing; synthetic aperture radar; SAR tomography; SAR interferometry
Special Issues, Collections and Topics in MDPI journals
Dr. Pietro Mastro

E-Mail Website
Guest Editor
Institute for the Electromagnetic Sensing of the Environment (IREA), National Research Council (CNR), Naples, Italy
Interests: remote sensing; machine learning; SAR Interferometry; change detection

Special Issue Information

Dear Colleagues,

We are pleased to invite you to the Special Issue on “Remote Sensing Applications in Marine Environmental Monitoring”.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Detection and biogeophysical property determination of marine litters and floating materials (such as sea ice, algal blooms, and spilled oil), through the use of remote sensing methods;
  • PolSAR and InSAR methods for maritime surveillance, ocean waves, and sea state measurement;
  • Remote sensing of the ocean water color;
  • Maritime surveillance case studies such as oil-spill monitoring, navigation in sea-ice-infested waters, ship detection, and ship traffic;
  • Mapping of the marine environment, including high-resolution wind fields, coastal wave fields, shoreline changes, upwelling phenomena, roll vortices, currents, fronts, gravity waves, internal waves, rain cells, salinity, and shallow-water bathymetry;
  • Innovative SAR concepts for optimal sensing of the marine environment;
  • Remote sensing concepts and advanced sensors for the marine/ocean environment.

We look forward to receiving your contributions.  

Dr. Virginia Zamparelli
Dr. Simona Verde
Dr. Pietro Mastro
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. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly 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 2600 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 winds, wave, currents, bathimetry
  • ecological applications: water quality, oil spill, algal blooms, etc.
  • radar
  • remote sensing
  • ocean and coastal monitoring
  • coastal areas safety and protection
  • sea ice
  • syntetic aperture radar
  • optical data

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

19 pages, 40083 KiB  
Article
A Comparative Analysis Between the ENVISAT and ICEYE SAR Systems for the Estimation of Sea Surface Current Velocity
by Virginia Zamparelli, Pietro Mastro, Antonio Pepe and Simona Verde
J. Mar. Sci. Eng. 2025, 13(1), 164; https://doi.org/10.3390/jmse13010164 - 18 Jan 2025
Viewed by 466
Abstract
In this work, we present the results of a comparative analysis between the first-generation Advanced Synthetic Aperture Radar (ASAR) sensor mounted on board the ENVISAT platform and the novel ICEYE micro-satellite synthetic aperture radar (SAR) sensor in measuring the radial velocity of ocean [...] Read more.
In this work, we present the results of a comparative analysis between the first-generation Advanced Synthetic Aperture Radar (ASAR) sensor mounted on board the ENVISAT platform and the novel ICEYE micro-satellite synthetic aperture radar (SAR) sensor in measuring the radial velocity of ocean currents through the Doppler Centroid Anomaly (DCA) technique. First, the basic principles of DCA and the theoretical precision of the Doppler Centroid (DC) estimates are introduced. Subsequently, the role of the DC measurements in retrieving the sea surface current velocity is addressed. To achieve this goal, two sets of SAR data gathered by ASAR (C-band) and from the X-band ICEYE instruments, respectively, are exploited. The standard deviation of DCA measurements is derived and tested against what is expected by theory. The presented analysis results are beneficial to evaluate the pros and cons of the new-generation X-band to the first-generation ASAR/ENVISAT system, which has been extensively exploited for ocean currents monitoring applications. As an outcome, we find that with inherently selected methods for DC estimates, the performance offered by ICEYE is comparable to, or even better than (with specific parameters selection), the consolidated approaches based on the ASAR sensor. Nonetheless, new SAR constellations offer an undoubted advantage regarding improved spatial resolution and time repeatability. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Marine Environmental Monitoring)
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21 pages, 12271 KiB  
Article
Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
by Jonggu Kang, Chansu Yang, Jonghyuk Yi and Yangwon Lee
J. Mar. Sci. Eng. 2024, 12(11), 2095; https://doi.org/10.3390/jmse12112095 - 19 Nov 2024
Viewed by 1002
Abstract
The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) [...] Read more.
The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) in oil spill detection due to their wide-area monitoring capabilities. While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. In this study, we conducted a comprehensive and objective comparison to evaluate the suitability of CNN and Transformer models for marine oil spill detection. High-resolution optical satellite images were used to optimize DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2Former, which employs a Transformer-based architecture for both encoding and decoding. The results of cross-validation demonstrate a mean Intersection over Union (mIoU) of 0.740, 0.840 and 0.804 for all the models, respectively, indicating their potential for detecting oil spills in the ocean. Additionally, we performed a histogram analysis on the predicted oil spill pixels, which allowed us to classify the types of oil. These findings highlight the considerable promise of the Swin Transformer models for oil spill detection in the context of future marine disaster monitoring. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Marine Environmental Monitoring)
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18 pages, 7440 KiB  
Article
A Novel Method for the Estimation of Sea Surface Wind Speed from SAR Imagery
by Zahra Jafari, Pradeep Bobby, Ebrahim Karami and Rocky Taylor
J. Mar. Sci. Eng. 2024, 12(10), 1881; https://doi.org/10.3390/jmse12101881 - 20 Oct 2024
Cited by 1 | Viewed by 1076
Abstract
Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. The accurate estimation of wind speed is crucial for developing an efficient machine learning (ML) model [...] Read more.
Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. The accurate estimation of wind speed is crucial for developing an efficient machine learning (ML) model for target detection. For example, high wind speeds make it more likely to mistakenly detect clutter as a marine target. This paper presents a novel approach for the estimation of sea surface wind speed (SSWS) and direction utilizing satellite imagery through innovative ML algorithms. Unlike existing methods, our proposed technique does not require wind direction information and normalized radar cross-section (NRCS) values and therefore can be used for a wide range of satellite images when the initial calibrated data are not available. In the proposed method, we extract features from co-polarized (HH) and cross-polarized (HV) satellite images and then fuse advanced regression techniques with SSWS estimation. The comparison between the proposed model and three well-known C-band models (CMODs)—CMOD-IFR2, CMOD5N, and CMOD7—further indicates the superior performance of the proposed model. The proposed model achieved the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), with values of 0.97 m/s and 0.62 m/s for calibrated images, and 1.37 and 0.97 for uncalibrated images, respectively, on the RCM dataset. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Marine Environmental Monitoring)
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16 pages, 7297 KiB  
Article
A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data
by Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri and Gianluca Gennarelli
J. Mar. Sci. Eng. 2024, 12(9), 1609; https://doi.org/10.3390/jmse12091609 - 10 Sep 2024
Viewed by 911
Abstract
In the context of sea state monitoring, reconstructing the wave field and estimating the sea state parameters from radar data is a challenging problem. To reach this goal, this paper proposes a fully data-driven, deep learning approach based on a convolutional neural network. [...] Read more.
In the context of sea state monitoring, reconstructing the wave field and estimating the sea state parameters from radar data is a challenging problem. To reach this goal, this paper proposes a fully data-driven, deep learning approach based on a convolutional neural network. The network takes as input the radar image spectrum and outputs the sea wave directional spectrum. After a 2D fast Fourier transform, the wave elevation field is reconstructed, and accordingly, the sea state parameters are estimated. The reconstruction strategy, herein presented, is tested using numerical data generated from a synthetic sea wave simulator, considering the spectral proprieties of the Joint North Sea Wave Observation Project model. A performance analysis of the proposed deep-learning estimation strategy is carried out, along with a comparison to the classical modulation transfer function approach. The results demonstrate that the proposed approach is effective in reconstructing the directional wave spectrum across different sea states. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Marine Environmental Monitoring)
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16 pages, 5125 KiB  
Article
Regional Sea Level Changes in the East China Sea from 1993 to 2020 Based on Satellite Altimetry
by Lujie Xiong, Fengwei Wang and Yanping Jiao
J. Mar. Sci. Eng. 2024, 12(9), 1552; https://doi.org/10.3390/jmse12091552 - 5 Sep 2024
Viewed by 832
Abstract
A comprehensive analysis was carried out to investigate the driving factors and influencing mechanisms of spatiotemporal variation of sea level at multiple scales in the East China Sea (ECS) via satellite altimetry datasets from 1993 to 2020. Based on the altimetry grid data [...] Read more.
A comprehensive analysis was carried out to investigate the driving factors and influencing mechanisms of spatiotemporal variation of sea level at multiple scales in the East China Sea (ECS) via satellite altimetry datasets from 1993 to 2020. Based on the altimetry grid data processed by the local mean decomposition method, the spatiotemporal changes of ECS sea level are analyzed from the multi-scale perspective in terms of multi-year, seasonal, interannual, and multi-modal scales. The results revealed that the ECS regional mean sea level change rate is 3.41 ± 0.58 mm/year over the 28-year period. On the seasonal scale, the regional mean sea level change rates are 3.45 ± 0.66 mm/year, 3.35 ± 0.60 mm/year, 3.39 ± 0.71 mm/year, and 3.57 ± 0.75 mm/year, for the four seasons (i.e., spring, summer, autumn, and winter) respectively. The spatial distribution analysis showed that ECS sea level changes are most pronounced in coastal areas. The northeast sea area of Taiwan and the edge of the East China Sea shelf are important areas of mesoscale eddy activity, which have an important impact on regional sea level change. The ECS seasonal sea level change is mainly affected by monsoons, precipitation, and temperature changes. The spatial distribution analysis indicated that the impact factors, including seawater thermal expansion, monsoons, ENSO, and the Kuroshio Current, dominated the ECS seasonal sea level change. Additionally, the ENSO and Kuroshio Current collectively affect the spatial distribution characteristics. Additionally, the empirical orthogonal function was employed to analyze the three modes of ECS regional sea level change, with the first three modes contributing 26.37%, 12.32%, and 10.47%, respectively. Spatially, the first mode mainly corresponds to ENSO index, whereas the second and third modes are linked to seasonal factors, and exhibit antiphase effects. The analyzed correlations between the ECS sea level change and southern oscillation index (SOI), revealed the consistent spatial characteristics between the regions affected by ENSO and those by the Kuroshio Current. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Marine Environmental Monitoring)
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18 pages, 5173 KiB  
Article
Research on the Inversion of Chlorophyll-a Concentration in the Hong Kong Coastal Area Based on Convolutional Neural Networks
by Weidong Zhu, Shuai Liu, Kuifeng Luan, Yuelin Xu, Zitao Liu, Tiantian Cao and Piao Wang
J. Mar. Sci. Eng. 2024, 12(7), 1119; https://doi.org/10.3390/jmse12071119 - 3 Jul 2024
Viewed by 1452
Abstract
Chlorophyll-a (Chl-a) concentration is a key indicator for assessing the eutrophication level in water bodies. However, accurately inverting Chl-a concentrations in optically complex coastal waters presents a significant challenge for traditional models. To address this, we employed Sentinel-2 MSI sensor data and leveraged [...] Read more.
Chlorophyll-a (Chl-a) concentration is a key indicator for assessing the eutrophication level in water bodies. However, accurately inverting Chl-a concentrations in optically complex coastal waters presents a significant challenge for traditional models. To address this, we employed Sentinel-2 MSI sensor data and leveraged the power of five machine learning models, including a convolutional neural network (CNN), to enhance the inversion process in the coastal waters near Hong Kong. The CNN model demonstrated superior performance with on-site data validation, outperforming the other four models (R2 = 0.810, RMSE = 1.165 μg/L, MRE = 35.578%). The CNN model was employed to estimate Chl-a concentrations from images captured over the study area in April and October 2022, resulting in the creation of a thematic map illustrating the spatial distribution of Chl-a levels. The map indicated high Chl-a concentrations in the northeast and southwest areas of Hong Kong Island and low Chl-a concentrations in the southeast facing the open sea. Analysis of patch size effects on CNN model accuracy indicated that 7 × 7 and 9 × 9 patches yielded the most optimal results across the tested sizes. Shapley additive explanations were employed to provide post-hoc interpretations for the best-performing CNN model, highlighting that features B6, B12, and B8 were the most important during the inversion process. This study can serve as a reference for developing machine learning models to invert water quality parameters. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Marine Environmental Monitoring)
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29 pages, 27799 KiB  
Article
Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data
by Polina Lemenkova
J. Mar. Sci. Eng. 2024, 12(5), 709; https://doi.org/10.3390/jmse12050709 - 25 Apr 2024
Viewed by 1761
Abstract
This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in [...] Read more.
This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Marine Environmental Monitoring)
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