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Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition)

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 8674

Special Issue Editors

Prof. Dr. Zhe-Wen Zheng

E-Mail Website
Guest Editor
Institute of Marine Environmental Science and Technology, Department of Earth Science, National Taiwan Normal University, Taipei 106, Taiwan
Interests: remote sensing of oceanic environment; physical oceanography; typhoon-ocean Interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiayi Pan

E-Mail Website
Guest Editor
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
Interests: coastal and lake remote sensing; coastal ocean dynamics; marine remote sensing physics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing in a marine environment involves using sensors to collect non-contact ocean observations, providing crucial data and images related to various oceanic phenomena and processes. Recent advancements in remote sensing technology have significantly enhanced our ability to gather data at higher spatial and temporal resolutions, leveraging both passive and active sensor capabilities. These innovations present new opportunities for gaining groundbreaking insights and interpretations essential for practical implementations in marine environmental sciences.

This Special Issue, “Advanced Remote Sensing for Marine Environment Monitoring”, serves as a continuation of Volume 1, aiming to build upon previous contributions and further advance the field. We invite cutting-edge applications focusing on new observations, analytical methods, data, and modeling techniques that promise to enhance our understanding of marine environmental processes. We look forward to receiving your high-quality contributions, which will continue to push the boundaries of our understanding in this dynamic and critical area of research.

Prof. Dr. Zhe-Wen Zheng
Prof. Dr. Jiayi Pan
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 remote sensing
  • ocean environment
  • environment monitoring
  • remote sensing techniques
  • satellite data

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Related Special Issue

Published Papers (6 papers)

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Research

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25 pages, 14032 KiB  
Article
Sea Surface Temperature Forecasting Using Foundational Models: A Novel Approach Assessed in the Caribbean Sea
by David Francisco Bustos Usta, Lien Rodríguez-López, Rafael Ricardo Torres Parra and Luc Bourrel
Remote Sens. 2025, 17(3), 517; https://doi.org/10.3390/rs17030517 - 2 Feb 2025
Viewed by 1033
Abstract
Sea surface temperature (SST) plays a pivotal role in air–sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the Caribbean Sea, where upwelling and dynamic oceanographic processes significantly influence biodiversity and fisheries. This study evaluates the performance of [...] Read more.
Sea surface temperature (SST) plays a pivotal role in air–sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the Caribbean Sea, where upwelling and dynamic oceanographic processes significantly influence biodiversity and fisheries. This study evaluates the performance of foundational models, Chronos and Lag-Llama, in forecasting SST using 22 years (2002–2023) of high-resolution satellite-derived and in situ data. The Chronos model, leveraging zero-shot learning and tokenization methods, consistently outperformed Lag-Llama across all forecast horizons, demonstrating lower errors and greater stability, especially in regions of moderate SST variability. The Chronos model’s ability to forecast extreme upwelling events is assessed, and a description of such events is presented for two regions in the southern Caribbean upwelling system. The Chronos forecast resembles SST variability in upwelling regions for forecast horizons of up to 7 days, providing reliable short-term predictions. Beyond this, the model exhibits increased bias and error, particularly in regions with strong SST gradients and high variability associated with coastal upwelling processes. The findings highlight the advantages of foundational models, including reduced computational demands and adaptability across diverse tasks, while also underscoring their limitations in regions with complex physical oceanographic phenomena. This study establishes a benchmark for SST forecasting using foundational models and emphasizes the need for hybrid approaches integrating physical principles to improve accuracy in dynamic and ecologically critical regions. Full article
(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition))
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26 pages, 7571 KiB  
Article
A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending Techniques
by Farkhod Akhmedov, Halimjon Khujamatov, Mirjamol Abdullaev and Heung-Seok Jeon
Remote Sens. 2025, 17(2), 336; https://doi.org/10.3390/rs17020336 - 19 Jan 2025
Cited by 1 | Viewed by 897
Abstract
Oil spills pose significant threats to marine and coastal ecosystems, biodiversity and local economies, necessitating efficient and accurate detection systems. Traditional detection methods, such as manual inspection and satellite imaging, are often resource-intensive and time consuming. This study addresses these challenges by developing [...] Read more.
Oil spills pose significant threats to marine and coastal ecosystems, biodiversity and local economies, necessitating efficient and accurate detection systems. Traditional detection methods, such as manual inspection and satellite imaging, are often resource-intensive and time consuming. This study addresses these challenges by developing a novel approach to enhance the quality and diversity of oil spill datasets. Several studies have mentioned that the quality and size of a dataset is crucial for training robust vision-based deep learning models. The proposed methodology combines advanced object extraction techniques with traditional data augmentation strategies to generate high quality and realistic oil spill images under various oceanic conditions. A key innovation in this work is the application of image blending techniques, which ensure seamless integration of target oil spill features into diverse environmental ocean contexts. To facilitate accessibility and usability, a Gradio-based web application was developed, featuring a user-friendly interface that allows users to input target and source images, customize augmentation parameters, and execute the augmentation process effectively. By enriching oil spill datasets with realistic and varied scenarios, this research aimed to improve the generalizability and accuracy of deep learning models for oil spill detection. For this, we proposed three key approaches, including oil spill dataset creation from an internet source, labeled oil spill regions extracted for blending with a background image, and the creation of a Gradio web application for simplifying the oil spill dataset generation process. Full article
(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition))
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18 pages, 16650 KiB  
Article
Mapping Seagrass Distribution and Abundance: Comparing Areal Cover and Biomass Estimates Between Space-Based and Airborne Imagery
by Victoria J. Hill, Richard C. Zimmerman, Dorothy A. Byron and Kenneth L. Heck, Jr.
Remote Sens. 2024, 16(23), 4351; https://doi.org/10.3390/rs16234351 - 21 Nov 2024
Viewed by 1262
Abstract
This study evaluated the effectiveness of Planet satellite imagery in mapping seagrass coverage in Santa Rosa Sound, Florida. We compared very-high-resolution aerial imagery (0.3 m) collected in September 2022 with high-resolution Planet imagery (~3 m) captured during the same period. Using supervised classification [...] Read more.
This study evaluated the effectiveness of Planet satellite imagery in mapping seagrass coverage in Santa Rosa Sound, Florida. We compared very-high-resolution aerial imagery (0.3 m) collected in September 2022 with high-resolution Planet imagery (~3 m) captured during the same period. Using supervised classification techniques, we accurately identified expansive, continuous seagrass meadows in the satellite images, successfully classifying 95.5% of the 11.18 km2 of seagrass area delineated manually from the aerial imagery. Our analysis utilized an occurrence frequency (OF) product, which was generated by processing ten clear-sky images collected between 8 and 25 September 2022 to determine the frequency with which each pixel was classified as seagrass. Seagrass patches encompassing at least nine pixels (~200 m2) were almost always detected by our classification algorithm. Using an OF threshold equal to or greater than >60% provided a high level of confidence in seagrass presence while effectively reducing the impact of small misclassifications, often of individual pixels, that appeared sporadically in individual images. The image-to-image uncertainty in seagrass retrieval from the satellite images was 0.1 km2 or 2.3%, reflecting the robustness of our classification method and allowing confidence in the accuracy of the seagrass area estimate. The satellite-retrieved leaf area index (LAI) was consistent with previous in situ measurements, leading to the estimate that 2700 tons of carbon per year are produced by the Santa Rosa Sound seagrass ecosystem, equivalent to a drawdown of approximately 10,070 tons of CO2. This satellite-based approach offers a cost-effective, semi-automated, and scalable method of assessing the distribution and abundance of submerged aquatic vegetation that provides numerous ecosystem services. Full article
(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition))
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23 pages, 19437 KiB  
Article
Impact of Turbidity on Satellite-Derived Bathymetry: Comparative Analysis Across Seven Ports in the South China Sea
by Chunzhu Wei, Yaqi Xiao, Dongjie Fu and Tingting Zhou
Remote Sens. 2024, 16(23), 4349; https://doi.org/10.3390/rs16234349 - 21 Nov 2024
Cited by 1 | Viewed by 867
Abstract
This study investigates the uncertainty of satellite-derived bathymetry (SDB) in turbid port environments by integrating multi-temporal composites of Sentinel-2 and Landsat 8 satellite imagery with in situ bathymetry and turbidity data. The research aims to evaluate the effectiveness of SDB and its spatiotemporal [...] Read more.
This study investigates the uncertainty of satellite-derived bathymetry (SDB) in turbid port environments by integrating multi-temporal composites of Sentinel-2 and Landsat 8 satellite imagery with in situ bathymetry and turbidity data. The research aims to evaluate the effectiveness of SDB and its spatiotemporal correlation with satellite-based turbidity indicators across seven Chinese port areas. Results indicate that both Sentinel-2 and Landsat 8, using a three-band combination, achieved comparable performance in SDB estimation, with R2 values exceeding 0.85. However, turbidity showed a negative correlation with SDB accuracy, and higher turbidity levels limited the maximum retrievable water depth, resulting in SDB variances ranging from 0 to 15 m. Landsat 8 was more accurate in low to moderate turbidity environments (12–15), where SDB variance was lower, while higher turbidity (above 15) led to greater SDB variance and reduced accuracy. Sentinel-2 outperformed Landsat 8 in moderate to high turbidity environments (36–203), delivering higher R2 values and more consistent SDB estimates, making it a more reliable tool for areas with variable turbidity. These findings suggest that SDB is a viable method for bathymetric and turbidity mapping in diverse port settings, with the potential for broader application in coastal monitoring and marine management. Full article
(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition))
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Review

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29 pages, 2184 KiB  
Review
A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
by Andrea Mazzeo, Alfredo Renga and Maria Daniela Graziano
Remote Sens. 2024, 16(20), 3775; https://doi.org/10.3390/rs16203775 - 11 Oct 2024
Cited by 1 | Viewed by 2767
Abstract
The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the [...] Read more.
The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the automatic identification system (AIS). However, small vessels can be challenging targets for spaceborne sensors without relatively high resolution. Moreover, when faced with non-cooperative targets, hull detection alone is insufficient for obtaining critical information like target speed and heading. The wakes generated by the movement of ships can be used to solve both of these issues. Several interesting solutions have been developed over the years, based on both traditional and learning-based methodologies. This review aims to provide the first thorough overview of ship wake detection solutions, highlighting the key ideas behind traditional applications, then covering more innovative applications based on deep learning (DL), to serve as a solid starting point for present and future researchers interested in the field. Full article
(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition))
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Other

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15 pages, 7118 KiB  
Technical Note
Reconstruction of Sea Surface Chlorophyll-a Concentration in the Bohai and Yellow Seas Using LSTM Neural Network
by Qing Xu, Guiying Yang, Xiaobin Yin and Tong Sun
Remote Sens. 2025, 17(1), 174; https://doi.org/10.3390/rs17010174 - 6 Jan 2025
Cited by 1 | Viewed by 1124
Abstract
In order to improve the spatiotemporal coverage of satellite Chlorophyll-a (Chl-a) concentration products in marginal seas, a physically constrained deep learning model was established in this work to reconstruct sea surface Chl-a concentration in the Bohai and Yellow Seas using a Long Short-Term [...] Read more.
In order to improve the spatiotemporal coverage of satellite Chlorophyll-a (Chl-a) concentration products in marginal seas, a physically constrained deep learning model was established in this work to reconstruct sea surface Chl-a concentration in the Bohai and Yellow Seas using a Long Short-Term Memory (LSTM) neural network. Adopting the permutation feature importance method, time sequences of several geographical and physical variables, including longitude, latitude, time, sea surface temperature, salinity, sea level anomaly, wind field, etc., were selected and integrated to the reconstruction model as input parameters. Performance inter-comparisons between LSTM and other machine learning or deep learning models was conducted based on OC-CCI (Ocean Color Climate Change Initiative) Chl-a product. Compared with Gated Recurrent Unit, Random Forest, XGBoost, and Extra Trees models, the LSTM model exhibits the highest accuracy. The average unbiased percentage difference (UPD) of reconstructed Chl-a concentration is 11.7%, which is 2.9%, 7.6%, 10.6%, and 10.5% smaller than that of the other four models, respectively. Over the majority of the study area, the root mean square error is less than 0.05 mg/m3 and the UPD is below 10%, indicating that the LSTM model has considerable potential in accurately reconstructing sea surface Chl-a concentrations in shallow waters. Full article
(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition))
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