Special Issue "Synthetic Aperture Radar Observations of Marine 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: closed (30 November 2020).

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. XiaoMing Li
E-Mail Website
Guest Editor
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, P. R. 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
Prof. Dr. Kun-Shan Chen
E-Mail Website
Guest Editor
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
Interests: microwave scattering and propagation; imaging radar; intelligent signal and image processing
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, e.g., the growing economic use of these areas, coastline changes, and recreational activities. Continuous monitoring of coastal marine environments, therefore, is of key importance for understanding 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 nighttime and its all-weather capabilities, is a sensor of choice.

The first spaceborne SAR was flown on Seasat in 1978. Since the early 1990s, continuous SAR observations of the world’s coastal regions were realized using a growing number of spaceborne SARs deployed on several national and international satellite missions and 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 both open seas and 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 and 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 the following:

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

Such processes and phenomena may be observed and studied in coastal areas, but also on the open sea.

We are looking forward to receiving your contribution to this Special Issue on ‘Synthetic Aperture Radar Observations of Marine Coastal Environments’.

Dr. Martin Gade
Prof. Xiao-Ming Li
Prof. Kun-Shan 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 papers will be 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 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 processes
  • Radar backscattering
  • Coastal marine environment
  • Sea surface currents
  • Marine pollution
  • Target detection
  • River run-off
  • Wind fields

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR
Remote Sens. 2021, 13(3), 360; https://doi.org/10.3390/rs13030360 - 21 Jan 2021
Viewed by 364
Abstract
We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The [...] Read more.
We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band). Full article
(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments)
Show Figures

Graphical abstract

Open AccessArticle
Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network
Remote Sens. 2020, 12(20), 3291; https://doi.org/10.3390/rs12203291 - 10 Oct 2020
Cited by 2 | Viewed by 613
Abstract
In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to [...] Read more.
In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve SSWS by spaceborne SAR, we introduced an alternative retrieval method based on a GMF-guided neural network. The SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of a back propagation (BP) neural network, and the output is the SSWS. The network is developed based on 11,431 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic from 2015 to 2018 and their collocated scatterometer wind measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind data for wind speeds less than approximately 30 m/s. Further comparison of the SAR retrieved SSWS with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of the retrieval when reanalysis model wind direction data are used as inputs to the neural network. By combining the detected sea ice cover information based on SAR data, sea ice and marine-meteorological parameters can be derived simultaneously by spaceborne SAR at a high spatial resolution in the Arctic. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments)
Show Figures

Graphical abstract

Open AccessArticle
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images
Remote Sens. 2020, 12(6), 1015; https://doi.org/10.3390/rs12061015 - 22 Mar 2020
Cited by 10 | Viewed by 1414
Abstract
Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named [...] Read more.
Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments)
Show Figures

Graphical abstract

Open AccessArticle
On C-Band Quad-Polarized Synthetic Aperture Radar Properties of Ocean Surface Currents
Remote Sens. 2019, 11(19), 2321; https://doi.org/10.3390/rs11192321 - 05 Oct 2019
Cited by 2 | Viewed by 1130
Abstract
We present new results for ocean surface current signatures in dual co- and cross-polarized synthetic aperture radar (SAR) images. C-band RADARSAT-2 quad-polarized SAR ocean scenes are decomposed into resonant Bragg scattering from regular (non-breaking) surface waves and scattering from breaking waves. Surface current [...] Read more.
We present new results for ocean surface current signatures in dual co- and cross-polarized synthetic aperture radar (SAR) images. C-band RADARSAT-2 quad-polarized SAR ocean scenes are decomposed into resonant Bragg scattering from regular (non-breaking) surface waves and scattering from breaking waves. Surface current signatures in dual co- and cross-polarized SAR images are confirmed to be governed by the modulations due to wave breaking. Due to their small relaxation scale, short Bragg waves are almost insensitive to surface currents. Remarkably, the contrast in sensitivity of the non-polarized contribution to dual co-polarized signals is found to largely exceed, by a factor of about 3, the contrast in sensitivity of the corresponding cross-polarized signals. A possible reason for this result is the co- and cross-polarized distinct scattering mechanisms from breaking waves: for the former, quasi-specular radar returns are dominant, whereas for the latter, quasi-resonant scattering from the rough breaking crests governs the backscatter intensity. Thus, the differing sensitivity can be related to distinct spectral intervals of breaking waves contributing to co- and cross-polarized scattering in the presence of surface currents. Accordingly, routinely observed current signatures in quad-polarized SAR images essentially originate from wave breaking modulations, and polarized contrasts can therefore help quantitatively retrieve the strength of surface current gradients. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments)
Show Figures

Graphical abstract

Open AccessArticle
Retrieval of Internal Solitary Wave Amplitude in Shallow Water by Tandem Spaceborne SAR
Remote Sens. 2019, 11(14), 1706; https://doi.org/10.3390/rs11141706 - 18 Jul 2019
Cited by 1 | Viewed by 990
Abstract
The accurate estimation of the upper layer thickness in a two-layer ocean is a crucial step in the retrieval of internal solitary wave (ISW) amplitude from synthetic aperture radar (SAR) data. In this paper, we present a method to derive the upper layer [...] Read more.
The accurate estimation of the upper layer thickness in a two-layer ocean is a crucial step in the retrieval of internal solitary wave (ISW) amplitude from synthetic aperture radar (SAR) data. In this paper, we present a method to derive the upper layer thickness and the consequent ISW amplitude by combining two consecutive SAR images with the extended Korteweg-de Vries (eKdV) equation. An ISW case observed twice by the Chinese C-band SAR GaoFen-3 (GF-3) and the German X-band SAR TerraSAR-X (TS-X) with a temporal interval of approximately 11 min in shallow water to the southeast of Hainan Island in the northwestern South China Sea was used to demonstrate the applicability of the method. Using the in situ measurements of temperature and salinity near the observed ISW, the proposed method yielded an ISW amplitude of −4.52 m, in close proximity to −5.66 ± 1.24 m derived by applying the classic Korteweg–de Vries (KdV) equation based on the continuously stratified theory. Moreover, the climatological dataset of the World Ocean Atlas 2013 (WOA13) was also used with the proposed method in the Hainan case, and the results showed that the method can still provide a reasonable estimate of ISW amplitude in shallow water even when in situ oceanic stratification measurements are absent. The application of our method to derive the ISW amplitude from consecutive SAR images seems highly promising with the increasing emergence of tandem satellites in orbits. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments)
Show Figures

Graphical abstract

Open AccessArticle
Estimation of Sea Surface Current from X-Band Marine Radar Images by Cross-Spectrum Analysis
Remote Sens. 2019, 11(9), 1031; https://doi.org/10.3390/rs11091031 - 30 Apr 2019
Cited by 3 | Viewed by 1136
Abstract
The cross-spectral correlation approach has been used to estimate the wave spectrum from optical and radar images. This work aims to improve the cross-spectral approach to derive current velocity from the X-band marine radar image sequence, and evaluate the application conditions of the [...] Read more.
The cross-spectral correlation approach has been used to estimate the wave spectrum from optical and radar images. This work aims to improve the cross-spectral approach to derive current velocity from the X-band marine radar image sequence, and evaluate the application conditions of the method. To reduce the dependency of gray levels on range and azimuth, radar images are preprocessed by the contrast-limited adaptive histogram equalization. Two-dimensional cross-spectral coherence and phase are derived from neighboring X-band marine radar images, and the phases with large coherences are used to estimate the phase velocity and angular frequency of waves, which are first fitted with the theoretical dispersion relation by different least square models, and then the current velocity can be determined. Compared with the current velocities measured by a current meter, the root-mean-square error, correlation coefficient, bias, and relative error are 0.15 m/s. 0.88, –0.05 m/s, and 7.79% for the north-south velocity, and 0.14 m/s, 0.86, 0.06 m/s, and 10.75% for the east-west velocity in the experimental area, respectively. The preprocessing, critical coherence, and the number of images for applying the cross-spectral approach, are discussed. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments)
Show Figures

Graphical abstract

Open AccessArticle
Retrieval of Sea Surface Wind Speeds from Gaofen-3 Full Polarimetric Data
Remote Sens. 2019, 11(7), 813; https://doi.org/10.3390/rs11070813 - 04 Apr 2019
Cited by 8 | Viewed by 1217
Abstract
In this paper, the sea surface wind speed (SSWS) retrieval from Gaofen-3 (GF-3) quad-polarization stripmap (QPS) data in vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) polarizations is investigated in detail based on 3170 scenes acquired from October 2016 to May 2018. The radiometric [...] Read more.
In this paper, the sea surface wind speed (SSWS) retrieval from Gaofen-3 (GF-3) quad-polarization stripmap (QPS) data in vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) polarizations is investigated in detail based on 3170 scenes acquired from October 2016 to May 2018. The radiometric calibration factor of the VV polarization data is examined first. This calibration factor generally meets the requirement of SSWS retrieval accuracy with an absolute bias of less than 0.5 m/s but shows highly dispersed characteristics. These results lead to SSWS retrievals with a small bias of 0.18 m/s, but a rather high root mean square error (RMSE) of 2.36 m/s when compared with the ERA-Interim reanalysis model data. Two refitted polarization ratio (PR) models for the QPS HH polarization data are presented. Based on a combination of the incidence angle-dependent and azimuth angle-dependent PR model and CMOD5.N, the SSWS derived from the QPS HH data shows a bias of 0.07 m/s and an RMSE of 2.26 m/s relative to the ERA-Interim reanalysis model wind speed. A linear function relating SSWS and the normalized radar cross section (NRCS) of QPS VH data is derived. The SSWS data retrieved from the QPS VH data show good agreement with the WindSat SSWS data, with a bias of 0.1 m/s and an RMSE of 2.02 m/s. We also apply the linear function to the GF-3 Wide ScanSAR data acquired for the typhoon SOULIK, which yields very good agreement with the model results. A comparison of SSWS retrievals among three different polarization datasets is also presented. The current study and our previous work demonstrate that the general accuracy of the SSWS retrieval based on GF-3 QPS data has an absolute bias of less than 0.3 m/s and an RMSE of 2.0 ± 0.2 m/s relative to various datasets. Further improvement will depend on dedicated radiometric calibration efforts. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments)
Show Figures

Figure 1

Back to TopTop