Special Issue "Synergy of Remote Sensing and Modelling Techniques for Ocean Studies"

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

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Guest Editor
Dr. Silvia Piedracoba Website E-Mail
CETMAR (Centro Tecnológico del Mar), 36208 Vigo, Pontevedra, Spain
Interests: physical oceanography, related with observational, operational, and numerical modeling and physical–biological coupling of Iberia upwelling. Operational implementation and analysis of radar-derived surface currents and radar- derived waves of the HF radar network in Spain. Quality control procedures and statistical analysis of diverse met-ocean parameters to ensure data quality for a subsequent web dissemination through the RAIA Observatory
Guest Editor
Dr. Silvia Torres-López Website E-Mail
CETMAR (Centro Tecnológico del Mar), 36208 Vigo, Pontevedra, Spain
Interests: Operational oceanography: real-time in-situ ocean observing systems and forecasting systems coupled to regional and global models. Open data sharing and application of oceanographic and meteorological information in oil spill, shipwreck, and emergency management. Integration of new technologies in the marine sector (i.e., Internet of Things, unmanned vehicles).
Guest Editor
Dr. Gang Zheng Website E-Mail
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, No.36 Baochubei Road, Xihu District, Hangzhou 310012, China
Interests: AI oceanography, satellite oceanography, microwave remote sensing, image processing, tropical cyclone remote sensing

Special Issue Information

Dear Colleagues,

An extensive variety of remote sensing techniques have emerged as a necessary observing system to acquire information about the state of the ocean and coastal areas. The ocean applications of these remote sensing devices are wide considering both research (non-real-time) and operational (near-real-time) levels. Within this framework, all the remote sensing systems, which include airborne/spaceborne sensors and ground-based sensors, are capable of providing information about ocean waves, currents, tides, winds, storm surges, temperature, salinity, suspended sediments (turbidity), chlorophyll, and bathymetry depending on the operating frequency range of the electromagnetic spectrum. These different remote sensors can be combined to provide required high spatio-temporal sampling using physically or statistically-based merging approaches.

In addition, over the two last decades significant advances in real-time ocean observing systems, ocean modelling, ocean data assimilation, and super-computing have allowed for the development and implementation of operational ocean forecasts of the global ocean.

This Special Issue aims at coupling remote sensing systems with numerical ocean models to improve our knowledge about the ocean and coastal areas placing special emphasis on a synergist approach for observing the complex circulation in the coastal ocean and understanding the physical and biological interactions. This powerful combined tool will significantly contribute to our understanding of the economic development and social impact of the coastal area, since it could be used in a broad range of applications including wave forecasting, coastal storm surge, ship routing, commercial fishing, coastal current and wave monitoring, marine environmental management, and climate change, among others.

Dr. Silvia Piedracoba
Dr. Silvia Torres-López
Dr. Gang Zheng
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 1800 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

  • Remote sensing of oceans
  • Electromagnetic/physical/hydrodynamic modeling
  • Airborne/space-borne sensors
  • Ground-band sensors or HF band radars
  • Open sea and coastal areas monitoring
  • Physical–biological interactions
  • Signal processing

Published Papers (2 papers)

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Open AccessArticle
High-Coverage Satellite-Based Coastal Bathymetry through a Fusion of Physical and Learning Methods
Remote Sens. 2019, 11(4), 376; https://doi.org/10.3390/rs11040376 - 13 Feb 2019
Cited by 1
Abstract
An up-to-date knowledge of water depth is essential for a wide range of coastal activities, such as navigation, fishing, study of coastal erosion, or the observation of the rise of water levels due to climate change. This paper presents a coastal bathymetry estimation [...] Read more.
An up-to-date knowledge of water depth is essential for a wide range of coastal activities, such as navigation, fishing, study of coastal erosion, or the observation of the rise of water levels due to climate change. This paper presents a coastal bathymetry estimation method that takes a single satellite acquisition as input, aimed at scenarios where in situ data are not available or would be too costly to obtain. The method uses free multispectral images that are easy to obtain for any region of the globe from sources such as the Sentinel-2 or Landsat-8 satellites. In order to address the shortcomings of existing image-only approaches (low resolution, scarce spatial coverage especially in the shallow water zones, dependence on specific physical conditions) we derive a new bathymetry estimation approach that combines a physical wave model with a statistical method based on Gaussian Process Regression learned in an unsupervised way. The resulting system is able to provide a nearly complete coverage of the 2–12-m-depth zone at a resolution of 80 m. Evaluated on three sites around the Hawaiian Islands, our method obtained estimates with a correlation coefficient in the range of 0.7–0.9. Furthermore, the trained models provide equally good results in nearby zones that lack exploitable waves, extending the scope of applicability of the method. Full article
(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)
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Open AccessLetter
Ocean Surface Wind Speed Retrieval Using Simulated RADARSAT Constellation Mission Compact Polarimetry SAR Data
Remote Sens. 2019, 11(16), 1876; https://doi.org/10.3390/rs11161876 - 10 Aug 2019
Abstract
We investigated the use of C-band RADARSAT Constellation Mission (RCM) synthetic aperture radar (SAR) for retrieval of ocean surface wind speeds by using four new channels (right circular transmit, vertical receive (RV); right circular transmit, horizontal receive (RH); right circular transmit, left circular [...] Read more.
We investigated the use of C-band RADARSAT Constellation Mission (RCM) synthetic aperture radar (SAR) for retrieval of ocean surface wind speeds by using four new channels (right circular transmit, vertical receive (RV); right circular transmit, horizontal receive (RH); right circular transmit, left circular transmit (RL); and right circular transmit, right circular receive (RR)) in compact polarimetry (CP) mode. Using 256 buoy measurements collocated with RADARSAT-2 fine beam quad-polarized scenes, RCM CP data was simulated using a “CP simulator”. Provided that the relative wind direction is known, our results demonstrate that wind speed can be retrieved from RV, RH and RL polarization channels using existing C-band model (CMOD) geophysical model function (GMF) and polarization ratio (PR) models. Simulated RR-polarized radar returns have a strong linear relationship with speed and are less sensitive to relative wind direction and incidence angle. Therefore, a model is proposed for the RR-polarized synthetic aperture radar (SAR) data. Our results show that the proposed model can provide an efficient methodology for wind speed retrieval. Full article
(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)
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