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New Advances in Marine Remote Sensing Applications
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New Advances in Marine Remote Sensing Applications

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4268

Special Issue Editors

Prof. Dr. Yuanzhi Zhang

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Guest Editor
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China
Interests: coastal environment; carbon neutrality; subsidence; erosion; coastline change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Po Hu

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Guest Editor
Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Interests: data modelling; upwelling, storms and waves; oceanic dynamics; climate change; typhoon and its impact
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongmei Chen

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Guest Editor
Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: spatial model; land use change; coastal environments; wetland and mangrove
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lin Li

E-Mail Website
Guest Editor
Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Interests: remote sensing; lunar and planetary geology; environmental sciences; data processing; radiative transfer models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing data are widely utilized to assess marine environments that have increasingly attracted public attention at global, regional and local scales. Water pollution caused by rapid urban development, sea level rise caused by the greenhouse effect, the melting of sea ice, storm surge, etc., have exacerbated issues induced by regional and global climate changes in the Earth sphere interactions, and these effects are often destructive to human development. Remote sensing, armed with recently emerging cloud computing, machine learning and AI technologies, is expected to play an unprecedent, yet important, role in assessing marine environments.

This Special Issue invites original research articles, as well as review articles that focus on ongoing efforts in using satellite or airborne remote sensing to understand the marine environment, land–ocean interactions, their response to global climate change and their interaction with human activities. The suggested topics are relevant, but not limited to, ocean data acquisition and pre-processing, data analysis and modeling, physical ocean parameters, sea level change, ocean–atmosphere interactions, coastal disasters, coastal ecosystem, water pollution, coastal (marine) engineering, coastal urbanization, as well as other coastal resilience themes.

Prof. Dr. Yuanzhi Zhang
Prof. Dr. Po Hu
Prof. Dr. Dongmei Chen
Prof. Dr. Lin Li
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

  • application of machine learning methods in oceanography
  • data acquisition and pre-processing
  • coastal–ocean environments and ecosystems
  • coastal erosion and coastline change
  • estuarine engineering and coastal infrastructure
  • sea level rise and climate change
  • ocean-atmosphere interactions
  • typhoon impact and disaster
  • water pollution and red tide
  • wind field and wave estimation

Published Papers (6 papers)

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Research

18 pages, 3451 KiB  
Article
The Characteristics of Submesoscale Eddies Near the Coastal Regions of Eastern Japan: Insights from Sentinel-1 Imagery
by Gang Li, Yijun He, Jinghan Wen, Guoqiang Liu, Vladimir Kudryavtsev, Xiaojie Lu and William Perrie
J. Mar. Sci. Eng. 2024, 12(5), 761; https://doi.org/10.3390/jmse12050761 - 30 Apr 2024
Viewed by 303
Abstract
A long-term time series of 319 Sentinel-1 SAR Imagery with Interferometric Wide Swath (IW) mode was used to study the characteristics of submesoscale eddies over Japanese coastal regions from 2015 to 2021, including spatiotemporal eddy properties and possible mechanisms of their formation. The [...] Read more.
A long-term time series of 319 Sentinel-1 SAR Imagery with Interferometric Wide Swath (IW) mode was used to study the characteristics of submesoscale eddies over Japanese coastal regions from 2015 to 2021, including spatiotemporal eddy properties and possible mechanisms of their formation. The results showed that around 98% of the 1499 eddies identified from the SAR snapshots were submesoscale eddies (horizontal scales of O1–20 km) with a ratio of around 78% cyclones to around 22% anticyclones. Around 8% of the submesoscale eddies were found in these SAR images in winter since the submesoscale current-induced signals are masked by the stronger wind speed, compared with other seasons. Typical features of submesoscale eddies are summarized, providing a preliminary qualitative analysis of potential generation mechanisms specific to the eddy characteristics in this region. This study suggests that Sentinel-1 images are capable of providing insights into the observed submesoscale eddies near the coastal regions of eastern Japan, thereby contributing to the improved understanding of the generation of submesoscale eddies. Full article
(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)
17 pages, 32322 KiB  
Article
Automatic Detection of Floating Ulva prolifera Bloom from Optical Satellite Imagery
by Hailong Zhang, Quan Qin, Deyong Sun, Xiaomin Ye, Shengqiang Wang and Zhixin Zong
J. Mar. Sci. Eng. 2024, 12(4), 680; https://doi.org/10.3390/jmse12040680 - 19 Apr 2024
Viewed by 502
Abstract
Annual outbreaks of floating Ulva prolifera blooms in the Yellow Sea have caused serious local environmental and economic problems. Rapid and effective monitoring of Ulva blooms from satellite observations with wide spatial-temporal coverage can greatly enhance disaster response efforts. Various satellite sensors and [...] Read more.
Annual outbreaks of floating Ulva prolifera blooms in the Yellow Sea have caused serious local environmental and economic problems. Rapid and effective monitoring of Ulva blooms from satellite observations with wide spatial-temporal coverage can greatly enhance disaster response efforts. Various satellite sensors and remote sensing methods have been employed for Ulva detection, yet automatic and rapid Ulva detection remains challenging mainly due to complex observation scenarios present in different satellite images, and even within a single satellite image. Here, a reliable and fully automatic method was proposed for the rapid extraction of Ulva features using the Tasseled-Cap Greenness (TCG) index from satellite top-of-atmosphere reflectance (RTOA) data. Based on the TCG characteristics of Ulva and Ulva-free targets, a local adaptive threshold (LAT) approach was utilized to automatically select a TCG threshold for moving pixel windows. When tested on HY1C/D-Coastal Zone Imager (CZI) images, the proposed method, termed the TCG-LAT method, achieved over 95% Ulva detection accuracy though cross-comparison with the TCG and VBFAH indexes with a visually determined threshold. It exhibited robust performance even against complex water backgrounds and under non-optimal observing conditions with sun glint and cloud cover. The TCG-LAT method was further applied to multiple HY1C/D-CZI images for automatic Ulva bloom monitoring in the Yellow Sea in 2023. Moreover, promising results were obtained by applying the TCG-LAT method to multiple optical satellite sensors, including GF-Wide Field View Camera (GF-WFV), HJ-Charge Coupled Device (HJ-CCD), Sentinel2B-Multispectral Imager (S2B-MSI), and the Geostationary Ocean Color Imager (GOCI-II). The TCG-LAT method is poised for integration into operational systems for disaster monitoring to enable the rapid monitoring of Ulva blooms in nearshore waters, facilitated by the availability of near-real-time satellite images. Full article
(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)
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14 pages, 3865 KiB  
Article
An Improved Method for Retrieving Subsurface Temperature Using the ConvLSTM Model in the Western Pacific Ocean
by Yuyuan Zhang, Yahao Liu, Yuan Kong and Po Hu
J. Mar. Sci. Eng. 2024, 12(4), 620; https://doi.org/10.3390/jmse12040620 - 4 Apr 2024
Viewed by 608
Abstract
In the era of marine big data, making full use of multi-source satellite observations to accurately retrieve and predict the temperature structure of the ocean subsurface layer is very significant in advancing the understanding of oceanic processes and their dynamics. Considering the time [...] Read more.
In the era of marine big data, making full use of multi-source satellite observations to accurately retrieve and predict the temperature structure of the ocean subsurface layer is very significant in advancing the understanding of oceanic processes and their dynamics. Considering the time dependence and spatial correlation of marine characteristics, this study employed the convolutional long short-term memory (ConvLSTM) method to retrieve the subsurface temperature in the Western Pacific Ocean from several types of satellite observations. Furthermore, considering the temperature’s vertical distribution, the retrieved results for the upper layer were iteratively used in the calculation for the deeper layer as input data to improve the algorithm. The results show that the retrieved results for the 100 to 500 m depth temperature using the 50 m layer in the calculation resulted in higher accuracy than those retrieved from the standard ConvLSTM method. The largest improvement was in the calculation for the 100 m layer, where the thermocline was located. The results indicate that our improved ConvLSTM method can increase the accuracy of subsurface temperature retrieval without additional input data. Full article
(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)
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13 pages, 1938 KiB  
Article
Global Investigation of Wind–Wave Interaction Using Spaceborne SAR Measurements
by Huimin Li and Yijun He
J. Mar. Sci. Eng. 2024, 12(3), 433; https://doi.org/10.3390/jmse12030433 - 28 Feb 2024
Viewed by 687
Abstract
Spaceborne synthetic aperture radar (SAR) has been widely acknowledged for its advantages in collecting ocean surface measurements under all weather conditions during day and night. Despite the strongly nonlinear imaging process, SAR measurements of ocean waves provide an invaluable resource for studies into [...] Read more.
Spaceborne synthetic aperture radar (SAR) has been widely acknowledged for its advantages in collecting ocean surface measurements under all weather conditions during day and night. Despite the strongly nonlinear imaging process, SAR measurements of ocean waves provide an invaluable resource for studies into wave dynamics at the global scale. In this study, we take advantage of a newly defined parameter, the mean cross-spectrum (MACS) at a discrete wavenumber along the sensor line-of-sight axis, to further investigate the ocean wave properties. With the range peak wavenumber extracted from the MACS profile, together with the collocated model winds, the inverse wave age (iwa) is estimated. As an indicator of local wind–wave coupling, the global map of the iwa depicts a distinct pattern, with larger iwa values observed in the storm tracks. In addition to the mean, stronger variability in the iwa is also found in the storm tracks, while the iwa remains relatively steady in the trade winds with lower variability. This makes the SAR-derived iwa a significant parameter in reflecting the varying degrees of wind–wave coupling in variable geographical locations across the ocean basins. It will help to promote the practical application of SAR measurements, as well as advancing our understanding of ocean wave dynamics. Full article
(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)
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21 pages, 11292 KiB  
Article
Estimating Total Suspended Matter and Analyzing Influencing Factors in the Pearl River Estuary (China)
by Zhaoyue Ma, Yong Zhao, Wenjing Zhao, Jiajun Feng, Yingying Liu, Jin Yeu Tsou and Yuanzhi Zhang
J. Mar. Sci. Eng. 2024, 12(1), 167; https://doi.org/10.3390/jmse12010167 - 15 Jan 2024
Viewed by 808
Abstract
This study on total suspended matter (TSM) in the Pearl River Estuary established a regression analysis model using Landsat 8 reflectance and measured TSM data, crucial for environmental management and engineering projects. High coefficients of determination (>0.6) were reported for the selected models. [...] Read more.
This study on total suspended matter (TSM) in the Pearl River Estuary established a regression analysis model using Landsat 8 reflectance and measured TSM data, crucial for environmental management and engineering projects. High coefficients of determination (>0.6) were reported for the selected models. TSM concentration was notably high in 2013, peaking at 180 mg/L during the flood season and 80 mg/L in the dry season. In contrast, 2020 saw lower concentrations. Similar spatial distribution patterns were observed during dry and flood seasons, with high nearshore and low offshore TSM concentrations. Statistical analyses revealed natural factors (precipitation and runoff) as major influencers of the TSM distribution, with human activities presenting localized, limited impacts, except under long-term and large-scale conditions. Over time, the influence of large-scale water-based construction, such as the Hong Kong–Zhuhai–Macao Bridge, on TSM distribution became significant. Full article
(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)
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17 pages, 3502 KiB  
Article
Remote Sensing Monitoring of Green Tide Disaster Using MODIS and GF-1 Data: A Case Study in the Yellow Sea
by Yanzhuo Men, Yingying Liu, Yufei Ma, Ka Po Wong, Jin Yeu Tsou and Yuanzhi Zhang
J. Mar. Sci. Eng. 2023, 11(12), 2212; https://doi.org/10.3390/jmse11122212 - 22 Nov 2023
Viewed by 788
Abstract
Satellites with low-to-medium spatial resolution face challenges in monitoring the early and receding stages of green tides, while those with high spatial resolution tend to reduce the monitoring frequency of such phenomena. This study aimed to observe the emergence, evolution, and migratory patterns [...] Read more.
Satellites with low-to-medium spatial resolution face challenges in monitoring the early and receding stages of green tides, while those with high spatial resolution tend to reduce the monitoring frequency of such phenomena. This study aimed to observe the emergence, evolution, and migratory patterns of green tides. We integrated GF-1 and MODIS imagery to collaboratively monitor the green tide disaster in the Yellow Sea during 2021. Initially, a linear regression model was employed to adjust the green tide coverage area as captured using MODIS imagery. We jointly observed the distribution range, drift path, and coverage area of the green tide and analyzed the drift path in coordination with offshore wind field and flow field data. Furthermore, we investigated the influence of SST, SSS, and rainfall on the 2021 green tide outbreak. The correlations calculated between SST, SSS, and precipitation with the changes in the area of the green tide were 0.43, 0.76, and 0.48, respectively. Our findings indicate that the large-scale green tide outbreak in 2021 may be associated with several factors. An increase in SST and SSS during the initial phase of the green tide established the essential conditions, while substantial rainfall during its developmental stage provided favorable conditions. Notably, the SSS exhibited a close association with the outbreak of the green tide. Full article
(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)
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