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Synergy of Remote Sensing and Modelling Techniques for Ocean Studies

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

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 26501

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Special Issue Editors

Dr. Silvia Piedracoba

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Guest Editor

CETMAR (Centro Tecnológico del Mar), 36208 Pontevedra, Spain
Interests: physical oceanography; coastal oceanography; coastal radars; physical–biological coupling; ocean observing systems; forecast systems; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Silvia Torres-López

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Guest Editor

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)

Dr. Gang Zheng

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Guest Editor

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 Issues, Collections and Topics in MDPI journals

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 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

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 (8 papers)

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Research

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32 pages, 5499 KiB

Open AccessArticle

Comparative Analysis of Summer Upwelling and Downwelling Events in NW Spain: A Model-Observations Approach

by Pablo Lorente, Silvia Piedracoba, Pedro Montero, Marcos G. Sotillo, María Isabel Ruiz and Enrique Álvarez-Fanjul

Remote Sens. 2020, 12(17), 2762; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172762 - 26 Aug 2020

Cited by 1 | Viewed by 3486

Abstract

Upwelling and downwelling processes play a critical role in the connectivity between offshore waters and coastal ecosystems, having relevant implications in terms of intense biogeochemical activity and global fisheries production. A variety of in situ and remote-sensing networks were used in concert with the Iberia–Biscay–Ireland (IBI) circulation forecast system, in order to investigate two persistent upwelling and downwelling events that occurred in the Northwestern (NW) Iberian coastal system during summer 2014. Special emphasis was placed on quality-controlled surface currents provided by a high-frequency radar (HFR), since this land-based technology can effectively monitor the upper layer flow over broad coastal areas in near-real time. The low-frequency spatiotemporal response of the ocean was explored in terms of wind-induced currents’ structures and immediacy of reaction. Mean kinetic energy, divergence and vorticity maps were also calculated for upwelling and downwelling favorable events, in order to verify HFR and IBI capabilities, to accurately resolve the prevailing surface circulation features, such as the locus of a persistent upwelling maximum in the vicinity of Cape Finisterre. This integrated approach proved to be well-founded to efficiently portray the three-dimensional characteristics of the NW Iberian coastal upwelling system regardless of few shortcomings detected in IBI performance, such as the misrepresentation of the most energetic surface dynamics or the overestimation of the cooling and warming associated with upwelling and downwelling conditions, respectively. Finally, the variability of the NW Iberian upwelling system was characterized by means of the development of a novel ocean-based coastal upwelling index (UI), constructed from HFR-derived hourly surface current observations (UI_HFR). The proposed UI_HFR was validated against two traditional UIs for 2014, to assess its credibility. Results suggest that UI_HFR was able to adequately categorize and characterize a wealth of summer upwelling and downwelling events of diverse length and strength, paving the way for future investigations of the subsequent biophysical implications. Full article

(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)

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29 pages, 1571 KiB

Open AccessArticle

Sensitivity of Modeled CO₂ Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation

by Ricardo Torres, Yuri Artioli, Vassilis Kitidis, Stefano Ciavatta, Manuel Ruiz-Villarreal, Jamie Shutler, Luca Polimene, Victor Martinez, Claire Widdicombe, E. Malcolm S. Woodward, Timothy Smyth, James Fishwick and Gavin H. Tilstone

Remote Sens. 2020, 12(12), 2038; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122038 - 25 Jun 2020

Cited by 5 | Viewed by 2956

Abstract

This work evaluates the sensitivity of CO₂ air–sea gas exchange in a coastal site to four different model system configurations of the 1D coupled hydrodynamic–ecosystem model GOTM–ERSEM, towards identifying critical dynamics of relevance when specifically addressing quantification of air–sea CO₂ exchange. The European Sea Regional Ecosystem Model (ERSEM) is a biomass and functional group-based biogeochemical model that includes a comprehensive carbonate system and explicitly simulates the production of dissolved organic carbon, dissolved inorganic carbon and organic matter. The model was implemented at the coastal station L4 (4 nm south of Plymouth, 50°15.00’N, 4°13.02’W, depth of 51 m). The model performance was evaluated using more than 1500 hydrological and biochemical observations routinely collected at L4 through the Western Coastal Observatory activities of 2008–2009. In addition to a reference simulation (A), we ran three distinct experiments to investigate the sensitivity of the carbonate system and modeled air–sea fluxes to (B) the sea-surface temperature (SST) diurnal cycle and thus also the near-surface vertical gradients, (C) biological suppression of gas exchange and (D) data assimilation using satellite Earth observation data. The reference simulation captures well the physical environment (simulated SST has a correlation with observations equal to 0.94 with a p > 0.95). Overall, the model captures the seasonal signal in most biogeochemical variables including the air–sea flux of CO₂ and primary production and can capture some of the intra-seasonal variability and short-lived blooms. The model correctly reproduces the seasonality of nutrients (correlation > 0.80 for silicate, nitrate and phosphate), surface chlorophyll-a (correlation > 0.43) and total biomass (correlation > 0.7) in a two year run for 2008–2009. The model simulates well the concentration of DIC, pH and in-water partial pressure of CO₂ (pCO₂) with correlations between 0.4–0.5. The model result suggest that L4 is a weak net source of CO₂ (0.3–1.8 molCm⁻² year⁻¹). The results of the three sensitivity experiments indicate that both resolving the temperature profile near the surface and assimilation of surface chlorophyll-a significantly impact the skill of simulating the biogeochemistry at L4 and all of the carbonate chemistry related variables. These results indicate that our forecasting ability of CO₂ air–sea flux in shelf seas environments and their impact in climate modeling should consider both model refinements as means of reducing uncertainties and errors in any future climate projections. Full article

(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)

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22 pages, 13107 KiB

Open AccessArticle

Variability of Kuroshio Surface Axis Northeast of Taiwan Island Derived from Satellite Altimeter Data

by Zhanpeng Zhuang, Quanan Zheng, Xi Zhang, Guangbing Yang, Xinhua Zhao, Lei Cao, Ting Zhang and Yeli Yuan

Remote Sens. 2020, 12(7), 1059; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071059 - 25 Mar 2020

Cited by 7 | Viewed by 3575

Abstract

The spatial and temporal variability of the Kuroshio surface axis northeast of Taiwan Island is investigated using 24 years of surface geostrophic currents derived from satellite altimeter data from 1993 to 2016. The Kuroshio surface axis is derived by an extraction method with three selected parameters, including the length of the subsidiary line, the intervals between two adjacent points, and the distance between the two adjacent subsidiary lines. The empirical mode decomposition analysis on the 24-year Kuroshio axes reveals that the mean periods of intra-seasonal and inter-annual variability, which are the two dominant components, are about 3.2 months and 1.3 years, respectively. The self-organizing map analysis reveals that the variation of Kuroshio axis northeast of Taiwan Island has four best matching unit (BMU) patterns: straight-path (BMU_S), meandering-path (BMU_M) and two transition stages (BMU_T1 and BMU_T2). The straight-path pattern shows strong seasonality: more likely occurring in summer. The meandering-path pattern is less frequent than straight-path pattern. During a typical period from November 26, 2012 to January 27, 2013, which is chosen as an independent example, the analysis on the satellite altimeter and sea surface temperature data shows that the patterns of the Kuroshio axis change successively in order of BMU_T1→BMU_M→BMU_T2→BMU_S, i.e., the Kuroshio axis migrates from the meandering-path to the straight-path pattern. During the typical period the warm water intrusion and a mesoscale eddy occur at the second stage corresponding to BMU_M and migrate northwestward gradually at the last two stages corresponding to BMU_T2 and BMU_S. The transient order appears only during this typical period but it is not common for the whole study period. The monthly mean relatively vorticity is calculated and analyzed to evaluate the impact of the eddies on the Kuroshio surface axis variability, the results show that the anticyclonic (cyclonic) eddies can promote the Kuroshio surface axis to present the meandering-path (straight-path) pattern because of the potential vorticity conservation. The impacts of the anticyclonic eddies and the cyclonic eddies on the variability of the Kuroshio surface axis are opposite. The long-term day-to-day detection contributes to improving understanding the variability of Kuroshio surface axis northeast of Taiwan Island. Full article

(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)

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24 pages, 4581 KiB

Open AccessArticle

Quality Assessment and Practical Interpretation of the Wave Parameters Estimated by HF Radars in NW Spain

by Ana Basañez, Pablo Lorente, Pedro Montero, Enrique Álvarez-Fanjul and Vicente Pérez-Muñuzuri

Remote Sens. 2020, 12(4), 598; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040598 - 11 Feb 2020

Cited by 13 | Viewed by 3160

Abstract

High-frequency (HF) radars are efficient tools for measuring vast areas and gathering ocean parameters in real-time. However, the accuracy of their wave estimates is under analysis. This paper presents a new methodology for analyzing and validating the wave data estimated by two CODAR SeaSonde radars located on the Galician coast (NW Spain). Approximately one and a half years of wave data (January, 2014–April, 2015) were obtained for ten range cells employing two different sampling times used by the radar software. The resulting data were screened by an updated method, and their abundance and quality were described for each radar range cell and different wave regime; the latter were defined using the spectral significant wave height (Hm0) and mean wave direction (Dm) estimated by two buoys and three SIMAR points (SImulación MARina in Spanish, from the wave reanalysis model by Puertos del Estado (PdE)). The correlation between the results and the particularities of the different sea states (broadband or bimodal), the wind and the operation of the devices are discussed. Most HF radar wave parameters’ errors occur for waves from the NNE and higher than 6 m. The best agreement between the Vilán radar and the Vilano-Sisargas buoy wave data was obtained for the dominant wave regime (from the northwest) and the southwest wave regime. However, relevant contradictions regarding wave direction were detected. The possibilities of reducing the wave parameters’ processing time by one hour and increasing the numbers of range cells of the radars have been validated. Full article

(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)

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22 pages, 8707 KiB

Open AccessArticle

Assessing the Impact of Tides and Atmospheric Fronts on Submesoscale Physical and Bio-Optical Distributions near a Coastal Convergence Zone

by Richard W. Gould, Jr., Stephanie Anderson, M. David Lewis, W. David Miller, Igor Shulman, Geoffrey B. Smith, Travis A. Smith, David W. Wang and Hemantha W. Wijesekera

Remote Sens. 2020, 12(3), 553; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030553 - 07 Feb 2020

Cited by 1 | Viewed by 2534

Abstract

Optically-active constituents vary over short time and space scales in coastal waters, and they are impacted by a variety of complex, inter-related forcing processes. As part of the Integrated Coastal Bio-Optical Dynamics (ICoBOD) project, we conducted a field campaign in Mississippi Sound in the northern Gulf of Mexico during spring 2018 to examine the impact of the passage of atmospheric and tidal fronts on fine-scale physical and bio-optical property distributions in a shallow, dynamic, coastal environment. During a 25-day experiment, we deployed eight moorings over a roughly 7 × 7 km box encompassing a frontal zone, to collect a time series of physical and bio-optical measurements. We describe changes in diver visibility related to the passage of a short-duration, high-turbidity surface plume and nepheloid layer development/decay during a tidal cycle. Maximum nepheloid layer development was observed during low tide and lasted about 9–12 h. The strongest turbidity signal extended about 4–5 m above the bottom (approximately half of the water column), although anomalously elevated values were observed all the way to the surface. In addition, high-resolution (50 m) hydrodynamic model simulations provide insight into the frontal dynamics and aid interpretation of the observed patterns. Mooring observations confirmed model-predicted heat flux changes associated with the passage of an atmospheric cold front. Full article

(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)

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29 pages, 9539 KiB

Open AccessArticle

Optimal Assimilation of Daytime SST Retrievals from SEVIRI in a Regional Ocean Prediction System

by Andrea Storto and Paolo Oddo

Remote Sens. 2019, 11(23), 2776; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11232776 - 25 Nov 2019

Cited by 10 | Viewed by 3217

Abstract

Exploiting the potential of space-borne oceanic measurements to characterize the sub-surface structure of the ocean becomes critical in areas where deployment of in situ sensors might be difficult or expensive. Sea Surface Temperature (SST) observations potentially provide enormous amounts of information about the upper ocean variability. However, the assimilation of daytime SST retrievals, e.g., from infrared sensors into ocean prediction systems, requires a specific treatment of the diurnal cycle of skin SST, which is generally under-estimated in current ocean models due to poor vertical resolution at the air–sea interface and lack of proper parameterizations. To this end, a simple off-line bias correction scheme is proposed, where the bias predictors include, among others, the warm layer and cool skin warming/cooling deduced from a prognostic model. Furthermore, a localization procedure that limits the vertical penetration of the SST information in a hybrid variational-ensemble data assimilation system is formulated. These two novelties are implemented and assessed within a regional ocean prediction system in the Ligurian Sea for the assimilation of daytime SST data retrieved with hourly frequency from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary satellite Meteosat-10. Experiments are validated against independent measurements collected by gliders, moorings, and drifters during the Long-term Glider Missions for Environmental Characterization (LOGCMEC17) sea trial. Results suggest that the simple bias correction scheme is effective in improving both the sea surface and mixed layer accuracy, correctly thinning the mixed layer compared to the control experiment, outperforming experiments with night-only data assimilation, and improving the forecast skill scores. Localization further improves the prediction of the mixed layer depth. It is therefore recommended that sophisticated bias correction and localization procedures are adopted for fruitfully assimilating daytime SST data in operational oceanographic analysis systems. Full article

(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)

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16 pages, 11592 KiB

Open AccessArticle

High-Coverage Satellite-Based Coastal Bathymetry through a Fusion of Physical and Learning Methods

by Céline Danilo and Farid Melgani

Remote Sens. 2019, 11(4), 376; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11040376 - 13 Feb 2019

Cited by 20 | Viewed by 3663

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 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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Other

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14 pages, 5732 KiB

Open AccessLetter

Ocean Surface Wind Speed Retrieval Using Simulated RADARSAT Constellation Mission Compact Polarimetry SAR Data

by He Fang, William Perrie, Guosheng Zhang, Tao Xie, Shahid Khurshid, Kerri Warner, Jingsong Yang and Yijun He

Remote Sens. 2019, 11(16), 1876; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161876 - 10 Aug 2019

Cited by 6 | Viewed by 3039

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 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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