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SAR Monitoring of Marine and Coastal Environments

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

Deadline for manuscript submissions: 30 December 2024 | Viewed by 2025

Special Issue Editors


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Guest Editor
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, China
Interests: synthetic aperture radar; arctic sea ice remote sensing; oil spilling detection; modeling of electromagnetic backscattering from ocean surface; ocean surface parameters retrieval from synthetic aperture radar (SAR) imageries

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Guest Editor
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Interests: SAR; internal waves

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Guest Editor
National Ocean Technology Center, Tianjin 300112, China
Interests: SAR; microwave remote sensing; remote sensing; waves; synthetic aperture radar; radar satellite; earth observation; satellite image processing

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Guest Editor
School of Marine Science and Engineering, South China University of Technology, 777 Xingyedadao East Rd, Guangzhou 511400, China
Interests: remote sensing; synthetic aperture radar; ocean remote sensing; image processing; machine learnig; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR) technology has revolutionized the monitoring of marine and coastal environments by providing high-resolution, all-weather, day-and-night imaging capabilities from space. This technology plays a crucial role in assessing various aspects of coastal zones and remote oceans, including shoreline changes, coastal erosion, habitat mapping, pollution monitoring, and polar sea ice observations.

The aim of this Special Issue is to gather cutting-edge research on SAR applications in marine and coastal environments. This issue will explore advancements in SAR data processing and applications specific to coastal and marine studies. Contributions will highlight novel methodologies (e.g., deep-learning-based approaches) and insightful case studies that enhance our understanding and management of these dynamic ecosystems. The scope of the issue aligns with the journal's commitment to publishing interdisciplinary research that integrates remote sensing with environmental sciences.

Potential topics for submissions include, but are not limited to, the applications of SAR for:

  • Coastal erosion monitoring and shoreline dynamics;
  • Mapping of marine habitats and biodiversity;
  • Marine pollution and oil spill monitoring;
  • The monitoring of harsh and polar marine environments;
  • The development of deep learning techniques in coastal and ocean remote sensing
  • Fusion of SAR and multi-source data;
  • Mapping coastal infrastructure and assessing vulnerability to natural hazards;
  • Case studies demonstrating the utility of SAR in coastal and marine resource management.

We invite original research articles, reviews, and technical notes that contribute significant advancements or novel applications of SAR technology in marine and coastal environments. Submissions should emphasize practical insights, methodological innovations, and their implications for environmental policy and sustainable development.

Prof. Dr. Tao Xie
Prof. Dr. Junmin Meng
Prof. Dr. He Wang
Dr. Xinwei Chen
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

  • synthetic aperture radar (SAR)
  • marine environments
  • coastal hazards
  • remote sensing
  • deep learning
  • harsh environments
  • polar oceans
  • marine pollution
  • habitat mapping
  • climate change

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Published Papers (1 paper)

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Research

17 pages, 9836 KiB  
Article
An Algorithm to Retrieve Range Ocean Current Speed under Tropical Cyclone Conditions from Sentinel-1 Synthetic Aperture Radar Measurements Based on XGBoost
by Yuhang Zhou, Weizeng Shao, Ferdinando Nunziata, Weili Wang and Cheng Li
Remote Sens. 2024, 16(17), 3271; https://doi.org/10.3390/rs16173271 - 3 Sep 2024
Viewed by 579
Abstract
In this study, a novel algorithm to retrieve the current speed along the range direction under extreme sea states is developed from C-band synthetic aperture radar imagery. To this aim, a Sentinel-1 (S-1) dual-polarized synthetic aperture radar (SAR) dataset consisting of 2300 images [...] Read more.
In this study, a novel algorithm to retrieve the current speed along the range direction under extreme sea states is developed from C-band synthetic aperture radar imagery. To this aim, a Sentinel-1 (S-1) dual-polarized synthetic aperture radar (SAR) dataset consisting of 2300 images is collected during 200 tropical cyclones (TCs). The dataset is complemented with collocated wave simulations from the Wavewatch-III (WW3) model and reanalysis currents from the HYbrid Coordinate Ocean Model (HYCOM). The corresponding TC winds are officially released by IFRMER, while the Stokes drift following the wave propagation direction is estimated from the waves simulated by WW3. In this study, first the dependence of wind, Stokes drift, and range current on the Doppler centroid anomaly is investigated, and then the extreme gradient boosting (XGBoost) machine learning model is trained on 87% of the S-1 dataset for range current retrieval purposes. The rest of the dataset is used for testing the retrieval algorithm, showing a root mean square error (RMSE) and a correlation coefficient (r) of 0.11 m/s and 0.97, respectively, with the HYCOM outputs. A validation against measurements collected from two high-frequency (HF) phased-array radars is also performed, resulting in an RMSE and r of 0.12 m/s and 0.75, respectively. Those validation results are better than the 0.22 m/s RMSE and 0.28 r achieved by the empirical CDOP model. Hence, the experimental results confirm the soundness of the XGBoost, exhibiting a certain improvement over the empirical model. Full article
(This article belongs to the Special Issue SAR Monitoring of Marine and Coastal Environments)
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