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Special Issue "Ocean Remote Sensing with Synthetic Aperture Radar"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 30 June 2017

Special Issue Editors

Guest Editor
Dr. Xiaofeng Yang

State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 20A Datun Rd, Beijing 100101, China
Website | E-Mail
Phone: +86-10-64806215
Interests: satellite oceanography; SAR applications; marine atmospheric boundary layer process studies; marine pollution monitoring; air–sea interactions
Guest Editor
Dr. Xiaofeng Li

National Oceanic and Atmospheric Administration, NCWCP E/RA3, 5830 University Research Ct. Office #3216, College Park, MD 20740-3818, USA
Website | E-Mail
Phone: +1-301-683-3314
Interests: ocean remote sensing; physical oceanography; boundary layer meteorology; synthetic aperture radar imaging mechanism; multiple-polarization radar applications; satellite image classification and segmentation
Guest Editor
Dr. Ferdinando Nunziata

Università degli Studi di Napoli Parthenope, Dipartimento di Ingnegneria, Centro Direzionale, isola C4 -80143 Napoli Italy
Website | E-Mail
Phone: +390815476779
Interests: SAR; polarimetric SAR; target detection; marine monitoring; coastaline extraction and land classification.
Guest Editor
Dr. Alexis Mouche

Laboratoire d’Océanographie Physique et Spatiale/ , Ifremer, CS 10070 - 29280 Plouzané, France
Website | E-Mail
Phone: +33 (0)2 98 22 49 29
Interests: interactions of electromagnetic and oceanic waves; marine atmospheric boundary layer processes; remote sensing for extreme events characterization; multi-polarization radar and sensors synergy for ocean applications

Special Issue Information

Dear Colleagues,

The oceans covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. In 1978, NASA launched the first SeaSat satellite, primilary aiming at ocean observations and the microwave synthetic aperture radar (SAR) was one of four instruments. Since then, the global oceans have been observed on SAR images, which has a high resolution (<100 m spatial resolution) and a large swath (450 km for ScanSAR mode images). The microwave SAR can image the ocean surface in all weather conditions and day or night. An increasing number of SAR satellites have become available since the early 1990s, such as the ERS-1/-2 and Envisat satellites, the Radarsat-1/-2 satellites, the COSMO-SkyMed satellites, TerraSAR-X and TanDEM-X, among others. Recently, the European Space Agency lauched a new generation of SAR satellites (Sentinel-1A in 2014 and Sentinel-1B in 2016). This operational SAR mission, for the first time, provides researchers with free and open SAR images necessary to carry out broader and deeper investigation of the global oceans.

SAR remote sensing on ocean and coast monitoring has become a research hotspot in geoscience and remote sensing. This Special Issue on “Ocean Remote Sensing with Synthetic Aperture Radar” is focused on ocean dynamical studies of sea surface phenomena, air–sea interactions, man-made object detection and radar imaging mechanisms. We would like to invite articles on ocean-related studies using state-of-the-art SAR techniques. The topics of this Special Issue include, without being limited to, the following subjects: 

  • Ocean applications with SAR imagery (wind, wave, precipataion, etc.)
  • SAR studies of physical and biological oceanography
  • Coastline extraction and inland area classification of SAR imagery
  • Methods for ship and other man-made objects’ detection
  • Remote sensing of oceanic surface and internal waves, upwellings, bathymetry, etc.
  • Cyclone–related parameters retrieval from SAR satellite observations
  • Marine atmospheric boundary layer process studies using SAR and remotely sensed data
  • Remote sensing modelling over complex sea surfaces
  • Oil spill and seep detections with SAR
  • PolSAR and InSAR application for coastal research issues

Authors are requested to check and follow the specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf

We look forward to receiving your submissions in this interesting area of specialization.

Dr. Xiaofeng Yang
Dr. Xiaofeng Li
Dr. Ferdinando Nunziata
Dr. Alexis Mouche
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 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 1600 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
  • SAR
  • microwave
  • polarization
  • coastal oceanography

Published Papers (6 papers)

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Research

Open AccessArticle Modulation Model of High Frequency Band Radar Backscatter by the Internal Wave Based on the Third-Order Statistics
Remote Sens. 2017, 9(5), 501; doi:10.3390/rs9050501
Received: 31 March 2017 / Revised: 12 May 2017 / Accepted: 17 May 2017 / Published: 19 May 2017
PDF Full-text (1713 KB) | HTML Full-text | XML Full-text
Abstract
Modulation model of radar backscatters is an important topic in the remote sensing of oceanic internal wave by synthetic aperture radar (SAR). Previous studies related with the modulation models were analyzed mainly based on the hypothesis that ocean surface waves are Gaussian distributed.
[...] Read more.
Modulation model of radar backscatters is an important topic in the remote sensing of oceanic internal wave by synthetic aperture radar (SAR). Previous studies related with the modulation models were analyzed mainly based on the hypothesis that ocean surface waves are Gaussian distributed. However, this is not always true for the complicated ocean environment. Research has showed that the measurements are usually larger than the values predicted by modulation models for the high frequency radars (X-band and above). In this paper, a new modulation model was proposed which takes the third-order statistics of the ocean surface into account. It takes the situation into consideration that the surface waves are Non-Gaussian distributed under some conditions. The model can explain the discrepancy between the measurements and the values calculated by the traditional models in theory. Furthermore, it can accurately predict the modulation for the higher frequency band. The model was verified by the experimental measurements recorded in a wind wave tank. Further discussion was made about applicability of this model that it performs better in the prediction of radar backscatter modulation compared with the traditional modulation model for the high frequency band radar or under lager wind speeds. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Ku-Band Sea Surface Radar Backscatter at Low Incidence Angles under Extreme Wind Conditions
Remote Sens. 2017, 9(5), 474; doi:10.3390/rs9050474
Received: 14 March 2017 / Revised: 18 April 2017 / Accepted: 9 May 2017 / Published: 12 May 2017
PDF Full-text (1811 KB) | HTML Full-text | XML Full-text
Abstract
This paper reports Ku-band normalized radar cross section (NRCS) at low incidence angles ranging from 0° to 18° and in the wind speed range from 6 to 70 m/s. The precipitation radar onboard the tropical rainfall measuring mission and Jason-1 and 2 have
[...] Read more.
This paper reports Ku-band normalized radar cross section (NRCS) at low incidence angles ranging from 0° to 18° and in the wind speed range from 6 to 70 m/s. The precipitation radar onboard the tropical rainfall measuring mission and Jason-1 and 2 have provided 152 hurricanes observations between 2008 and 2013 that were collocated with stepped-frequency microwave radiometer measurements. It is found that the NRCS decreases with increasing incidence angle. The decrease is more dramatic in the 40–70 m/s range of wind speeds than in the 6–20 m/s range, indicating that the NRCS is very sensitive to low incidence angles under extreme wind conditions and insensitive to the extreme wind speed. Consequently, the sea surface appears relatively “smooth” to Ku-band electromagnetic microwaves. This phenomenon validates the observed drag coefficient reduction under extreme wind conditions, from a remote sensing viewpoint. Using the NRCS dependence on incidence angle under extreme wind conditions, we also present an empirical linear relationship between NRCS and incidence angles, which may assist future-satellites missions operating at small incidence angles to measure sea surface wind and wave field. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Figure 1

Open AccessArticle An ML-Based Radial Velocity Estimation Algorithm for Moving Targets in Spaceborne High-Resolution and Wide-Swath SAR Systems
Remote Sens. 2017, 9(5), 404; doi:10.3390/rs9050404
Received: 28 February 2017 / Revised: 20 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
PDF Full-text (13295 KB) | HTML Full-text | XML Full-text
Abstract
Multichannel synthetic aperture radar (SAR) is a significant breakthrough to the inherent limitation between high-resolution and wide-swath (HRWS) compared with conventional SAR. Moving target indication (MTI) is an important application of spaceborne HRWS SAR systems. In contrast to previous studies of SAR MTI,
[...] Read more.
Multichannel synthetic aperture radar (SAR) is a significant breakthrough to the inherent limitation between high-resolution and wide-swath (HRWS) compared with conventional SAR. Moving target indication (MTI) is an important application of spaceborne HRWS SAR systems. In contrast to previous studies of SAR MTI, the HRWS SAR mainly faces the problem of under-sampled data of each channel, causing single-channel imaging and processing to be infeasible. In this study, the estimation of velocity is equivalent to the estimation of the cone angle according to their relationship. The maximum likelihood (ML) based algorithm is proposed to estimate the radial velocity in the existence of Doppler ambiguities. After that, the signal reconstruction and compensation for the phase offset caused by radial velocity are processed for a moving target. Finally, the traditional imaging algorithm is applied to obtain a focused moving target image. Experiments are conducted to evaluate the accuracy and effectiveness of the estimator under different signal-to-noise ratios (SNR). Furthermore, the performance is analyzed with respect to the motion ship that experiences interference due to different distributions of sea clutter. The results verify that the proposed algorithm is accurate and efficient with low computational complexity. This paper aims at providing a solution to the velocity estimation problem in the future HRWS SAR systems with multiple receive channels. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Directional Spreading Function of the Gravity-Capillary Wave Spectrum Derived from Radar Observations
Remote Sens. 2017, 9(4), 361; doi:10.3390/rs9040361
Received: 3 December 2016 / Revised: 19 March 2017 / Accepted: 1 April 2017 / Published: 12 April 2017
PDF Full-text (3841 KB) | HTML Full-text | XML Full-text
Abstract
Directional spreading function of the gravity-capillary wave spectrum can provide the high-wavenumber wave energy distribution among different directions on the sea surface. The existing directional spreading functions have been mainly developed for the low-wavenumber gravity wave with buoy data. In this paper, we
[...] Read more.
Directional spreading function of the gravity-capillary wave spectrum can provide the high-wavenumber wave energy distribution among different directions on the sea surface. The existing directional spreading functions have been mainly developed for the low-wavenumber gravity wave with buoy data. In this paper, we use radar observations to derive the directional spreading function of the gravity-capillary wave spectrum, which is expressed as the second-order Fourier series expansion. So far the standard form of the second-order harmonic coefficient has not been proposed to correctly unify the gravity and gravity-capillary wave. Our strategy is to introduce a correcting term to replace the inaccurate gravity-capillary spectral component in Elfouhaily’s directional spreading function. The second-order harmonic coefficient at L, C and Ku band calculated by the radar observation is used to fit the correcting term to obtain one at the full gravity-capillary wave region. According to our proposed the directional spreading function, there is a spectral region between the gravity and gravity-capillary range where it signifies the negative upwind–crosswind asymmetry at low and moderate speed range. And this is not reflected by the previous models, but has been confirmed by radar observations. The Root Mean Square Difference (RMSD) of the proposed second-order harmonic coefficient versus the radar-observed one at L, C band Ku band is 0.0438, 0.0263 and 0.0382, respectively. The overall bias and RMSD are −0.0029 and 0.0433 for the whole second-order harmonic coefficient range, respectively. The result verifies the accuracy of the proposed directional spreading function at L, C band Ku band. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Doppler Spectrum-Based NRCS Estimation Method for Low-Scattering Areas in Ocean SAR Images
Remote Sens. 2017, 9(3), 219; doi:10.3390/rs9030219
Received: 6 December 2016 / Revised: 24 February 2017 / Accepted: 25 February 2017 / Published: 28 February 2017
PDF Full-text (4769 KB) | HTML Full-text | XML Full-text
Abstract
The image intensities of low-backscattering areas in synthetic aperture radar (SAR) images are often seriously contaminated by the system noise floor and azimuthal ambiguity signal from adjacent high-backscattering areas. Hence, the image intensity of low-backscattering areas does not correctly reflect the backscattering intensity,
[...] Read more.
The image intensities of low-backscattering areas in synthetic aperture radar (SAR) images are often seriously contaminated by the system noise floor and azimuthal ambiguity signal from adjacent high-backscattering areas. Hence, the image intensity of low-backscattering areas does not correctly reflect the backscattering intensity, which causes confusion in subsequent image processing or interpretation. In this paper, a method is proposed to estimate the normalized radar cross-section (NRCS) of low-backscattering area by utilizing the differences between noise, azimuthal ambiguity, and signal in the Doppler frequency domain of single-look SAR images; the aim is to eliminate the effect of system noise and azimuthal ambiguity. Analysis shows that, for a spaceborne SAR with a noise equivalent sigma zero (NESZ) of −25 dB and a single-look pixel of 8 m × 5 m, the NRCS-estimation precision of this method can reach −38 dB at a resolution of 96 m × 100 m. Three examples are given to validate the advantages of this method in estimating the low NRCS and the filtering of the azimuthal ambiguity. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle An Improved Shape Contexts Based Ship Classification in SAR Images
Remote Sens. 2017, 9(2), 145; doi:10.3390/rs9020145
Received: 8 December 2016 / Accepted: 4 February 2017 / Published: 10 February 2017
PDF Full-text (3508 KB) | HTML Full-text | XML Full-text
Abstract
In synthetic aperture radar (SAR) imagery, relating to maritime surveillance studies, the ship has always been the main focus of study. In this letter, a method of ship classification in SAR images is proposed to enhance classification accuracy. In the proposed method, to
[...] Read more.
In synthetic aperture radar (SAR) imagery, relating to maritime surveillance studies, the ship has always been the main focus of study. In this letter, a method of ship classification in SAR images is proposed to enhance classification accuracy. In the proposed method, to fully exploit the distinguishing characters of the ship targets, both topology and intensity of the scattering points of the ship are considered. The results of testing the proposed method on a data set of three types of ships, collected via a space-borne SAR sensor designed by the Institute of Electronics, Chinese Academy of Sciences (IECAS), establish that the proposed method is superior to several existing methods, including the original shape contexts method, traditional invariant moments and the recent approach. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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