Special Issue "Synthetic Aperture Radar Observations of Marine Coastal Environments-II"

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

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editors

Dr. Martin Gade
E-Mail Website
Guest Editor
Institut für Meereskunde, Universität Hamburg, Bundesstraße 53, 20146 Hamburg, Germany
Interests: coastal remote sensing and air–sea interactions
Special Issues and Collections in MDPI journals
Prof. Dr. XiaoMing Li
E-Mail Website
Guest Editor
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: SAR oceanography; retrieval of marine–meteor parameters by SAR; observation of multi-scale processes of ocean dynamics by satellite remote sensing
Special Issues and Collections in MDPI journals
Dr. Konstantinos Topouzelis
E-Mail Website
Guest Editor
Head, Marine Remote Sensing Group (MRSG), Department of Marine Sciences, University of the Aegean, 81100 Mytilini, Greece
Interests: analysis of remote sensing datasets, including satellite and aerial images, for marine and coastal applications; oil spill detection, automatic detection of oceanographic phenomena; object-based image analysis; image processing algorithms and coastal mapping
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

About 10% of the world’s population live in coastal zones that occupy only 2% of the world’s land surface. As such, many coastal marine environments, being invaluable ecosystems and host to many species, are under increasing pressure caused by anthropogenic impacts such as, among others, growing economic use of these areas, coastline changes, and recreational activities. Continuous monitoring of coastal marine environments, therefore, is of key importance for a better understanding of the various oceanic and atmospheric processes, for the identification of manmade hazards, and eventually for the sustainable use of those vulnerable areas. Here, synthetic aperture radar (SAR), because of its independence of day- and night-time and its all-weather capabilities, is a sensor of choice.

Since the early 1990s, several national and international satellite missions allowed for continuous SAR observations of the World’s coastal regions, deploying a growing number of spaceborne SARs working at different radar bands. Their data have helped to deepen our knowledge of various marine processes and phenomena, and of the radar backscattering from the sea surface that is caused, or influenced, by them. Based on this knowledge, new monitoring concepts for coastal waters have been designed, implemented, and constantly improved over the years.

This Special Issue focusses on the way in which SAR sensors can be used for the surveillance of the marine coastal environment, and how these sensors can detect and quantify processes and phenomena that are of importance for the local environment, fauna and flora, coastal residents, and local authorities. These processes and phenomena include, but are not restricted to:

  • Surface waves and currents
  • Wind fields
  • Marine pollution
  • Coastal run-off
  • Coastal bathymetry
  • Coastline changes
  • Coastal wetlands
  • Target detection

We are looking forward to receiving your contribution to this Special Issue entitled “Synthetic Aperture Radar Observations of Marine Coastal Environments: 2nd Edition”.

Dr. Martin Gade
Prof. Dr. XiaoMing Li
Dr. Konstantinos Topouzelis
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 2400 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
  • coastal marine environment
  • marine pollution
  • target detection
  • coastal run-off
  • coastal wetlands
  • wind fields
  • ocean surface waves

Published Papers (4 papers)

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Research

Article
Small-Sized Ship Detection Nearshore Based on Lightweight Active Learning Model with a Small Number of Labeled Data for SAR Imagery
Remote Sens. 2021, 13(17), 3400; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173400 - 27 Aug 2021
Viewed by 326
Abstract
Marine ship detection by synthetic aperture radar (SAR) is an important remote sensing technology. The rapid development of big data and artificial intelligence technology has facilitated the wide use of deep learning methods in SAR imagery for ship detection. Although deep learning can [...] Read more.
Marine ship detection by synthetic aperture radar (SAR) is an important remote sensing technology. The rapid development of big data and artificial intelligence technology has facilitated the wide use of deep learning methods in SAR imagery for ship detection. Although deep learning can achieve a much better detection performance than traditional methods, it is difficult to achieve satisfying performance for small-sized ships nearshore due to the weak scattering caused by their material and simple structure. Another difficulty is that a huge amount of data needs to be manually labeled to obtain a reliable CNN model. Manual labeling each datum not only takes too much time but also requires a high degree of professional knowledge. In addition, the land and island with high backscattering often cause high false alarms for ship detection in the nearshore area. In this study, a novel method based on candidate target detection, boundary box optimization, and convolutional neural network (CNN) embedded with active learning strategy is proposed to improve the accuracy and efficiency of ship detection in nearshore areas. The candidate target detection results are obtained by global threshold segmentation. Then, the strategy of boundary box optimization is defined and applied to reduce the noise and false alarms caused by island and land targets as well as by sidelobe interference. Finally, a lightweight CNN embedded with active learning scheme is used to classify the ships using only a small labeled training set. Experimental results show that the performance of the proposed method for small-sized ship detection can achieve 97.78% accuracy and 0.96 F1-score with Sentinel-1 images in complex nearshore areas. Full article
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Article
Automated Rain Detection by Dual-Polarization Sentinel-1 Data
Remote Sens. 2021, 13(16), 3155; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163155 - 10 Aug 2021
Viewed by 634
Abstract
Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes [...] Read more.
Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes for maritime applications. This study relies on Sentinel-1 SAR acquisitions in the Interferometric Wide swath mode and high-resolution measurements from ground-based weather radar to document the rain impact on the radar backscattered signal in both co- and cross-polarization channels. The dark and bright rain signatures are found in connection with the timeliness of the rain cells. In particular, the bright patches are demonstrated by the hydrometeors (graupels, hails) in the melting layer. In general, the radar backscatter under rain increases with rain rate for a given sea state and decreases when the sea state strengthens. The rain also has a stronger impact on the radar signal in both polarizations when the incidence angle increases. The complementary sensitivity of the SAR signal of rain in both channels is then used to derive a filter to locate the areas in SAR scenes where the signal is not dominated by rain. The filter optimized to match the rain observed by the ground-based weather radar is more efficient when both polarization channels are considered. Case studies are presented to discuss the advantages and limitations of such a filtering approach. Full article
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Article
Preliminary Significant Wave Height Retrieval from Interferometric Imaging Radar Altimeter Aboard the Chinese Tiangong-2 Space Laboratory
Remote Sens. 2021, 13(12), 2413; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122413 - 20 Jun 2021
Viewed by 535
Abstract
The interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory is the first spaceborne imaging radar working at low incidence angles. This study focuses on the retrieval of significant wave heights (SWHs) from InIRA data. The retrieved SWHs can be used [...] Read more.
The interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory is the first spaceborne imaging radar working at low incidence angles. This study focuses on the retrieval of significant wave heights (SWHs) from InIRA data. The retrieved SWHs can be used for correcting the sea state bias of InIRA-derived sea surface heights and can supplement SWH products from other spaceborne sensors. First, we analyzed tilt, range bunching and velocity bunching wave modulations at low incidence angles, and we found clear dependencies between the SWH and two defined factors, range and azimuth integration, for ocean waves in the range and azimuth directions, respectively. These dependencies were further confirmed using InIRA measurements and collocated WaveWatch III (WW3) data. Then, an empirical orthogonal SWH model using the range and azimuth integration factors as model inputs was proposed. The model was segmented by the incidence angle, and the model coefficients were estimated by fitting the collocation at each incidence angle bin. Finally, the SWHs were retrieved from InIRA data using the proposed model. The retrievals were validated using both WW3 and altimeter (JASON2, JASON3, SARAL, and HY2A) SWHs. The validation with WW3 data shows a root mean square error (RMSE) of 0.43 m, while the average RMSE with all traditional altimeter data is 0.48 m. This indicates that the InIRA can be used to measure SWHs. Full article
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Article
Detection of Biogenic Oil Films near Aquaculture Sites Using Sentinel-1 and Sentinel-2 Satellite Images
Remote Sens. 2021, 13(9), 1737; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091737 - 30 Apr 2021
Cited by 1 | Viewed by 478
Abstract
Biogenic films are very thin surface oils, frequently observed near aquaculture farms, that affect the roughness and the optical properties of the sea surface, making them visible in SAR and multispectral images. The purpose of this study is to investigate the potential of [...] Read more.
Biogenic films are very thin surface oils, frequently observed near aquaculture farms, that affect the roughness and the optical properties of the sea surface, making them visible in SAR and multispectral images. The purpose of this study is to investigate the potential of satellite SAR and multispectral sensors in the detection of biogenic oil films near aquaculture farms. Sentinel-1 SAR and Sentinel-2 multispectral data were exploited to detect the films around three aquaculture sites. The study is divided in three stages: (a) preprocessing, (b) main process and (c) accuracy assessment. The preprocessing stage includes subset, filtering, land masking and image corrections. The main process was similar for both datasets, using an adaptive thresholding method to identify dark formations, extract and classify them. Finally, the performance of the algorithm was evaluated based on the estimation of standard classification error statistics. The evaluation of the results was based on empirical photointerpretation and in situ photos. The results are successful and promising, with overall accuracy over 70%, while both sensors are proved to be effective in the detection, with Sentinel-1 SAR presenting slightly better accuracy (81%) than Sentinel-2 MSI (70%). There is no evidence of these films causing stress to the aquaculture farms or the surrounding environment; however, our knowledge on their presence, amount and dissolution is limited and further knowledge could contribute to efficient feeding management and fish welfare. Full article
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