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Special Issue "Applications of Remote Sensing in Oceanography: Prospects and Challenges II"

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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: 31 October 2023 | Viewed by 2498

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Special Issue Editors

Prof. Dr. Changming Dong

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Oceanic Modeling and Observation Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: oceanic eddy; front; sea surface wind; wave; current; altimeter; turbulence
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Prof. Dr. Chunyan Li

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Guest Editor

Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: physical oceanography; coastal and estuarine circulations; extreme weather; induced water level changes including storm surges
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Prof. Dr. Jingsong Yang

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State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Interests: oceanographic techniques; remote sensing; atmospheric boundary layer; backscatter
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Dr. Guoqi Han

E-Mail Website
Guest Editor

Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, BC V8L 5T5, Canada
Interests: ocean circulation; numerical modeling; satellite oceanography; ocean climate variability and change
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Special Issue Information

Dear Colleagues,

Remote sensing provides several advantages over in situ measurements, such as the ability to provide measurements of the ocean over a large spatial range at high temporal resolutions, but for a significantly reduced cost. Indeed, with the development and launch of a new generation of satellite platforms and their sensors, exciting avenues in the marine sciences are being opened at a rapid pace. Nevertheless, numerous challenges and unsolved problems related to the development of remote sensing applications within oceanography remain. This Special Issue seeks to explore the current state of the art, future developments, and open questions of the applications of remote sensing in oceanography. Authors are encouraged to provide submissions covering all aspects of ocean remote sensing, ranging from observation techniques used in high-frequency coastal radar or satellite studies to new applications of artificial intelligence methods for pre- and postprocessing remotely sensed data. Review articles are also welcomed, on the condition that they are authoritative. Submissions that contribute towards the United Nations’ Decade of Ocean Science for Sustainable Development (2021–2030), Sustainability Development Goals, Small Island Developing States, and those that consider the Blue Economy are especially welcomed. Articles related to the following topics are invited for submission:

Innovative or improved methods and algorithms of remote sensing for oceanography applications.
Observations using drone/unmanned aircraft system (UAS) imagery, high-frequency coastal radar, airborne LiDAR, satellites.
Applications of artificial intelligence for pre- and post-processing remotely sensed data.
Advances in big data management including the exploitation of cloud platforms for marine studies.
Climate and anthropogenic influence on marine systems.
Marine pollution and water quality monitoring.
Detection and monitoring of ocean circulations, wave properties, ocean temperatures, and sea ice mapping.
Coastal applications such as tracking shoreline changes, nearshore topography mapping, erosion assessment, sediment transport.
Integration of remoting sensing data with in situ measurements or numerical ocean modeling for data assimilation.
Research works related to the Surface Water and Ocean Topography (SWOT) mission.

Prof. Dr. Changming Dong
Prof. Dr. Chunyan Li
Prof. Dr. Jingsong Yang
Dr. Guoqi Han
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 2500 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

SWOT
artificial intelligence
big remotely sensed data
sustainability
climate change
ocean numerical modeling
data assimilation

Published Papers (4 papers)

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Research

Open AccessArticle

Characteristics of Internal Solitary Waves in the Timor Sea Observed by SAR Satellite

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Remote Sens. 2023, 15(11), 2878; https://doi.org/10.3390/rs15112878 - 01 Jun 2023

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Abstract

Internal solitary waves (ISWs) with features such as large amplitude, short period, and fast speed have great influence on underwater thermohaline structure, nutrient transport, and acoustic signal propagation. The characteristics of ISWs in hotspot areas have been revealed by satellite images combined with mooring observation. However, the ISWs in the Timor Sea, which is located in the outflow of the ITF, have not been studied yet and the characteristics are unrevealed. In this study, by employing the Synthetic Aperture Radar (SAR) images taken by the Sentinel-1 satellite from 2017 to 2022, the temporal and spatial distribution characteristics of ISWs in the Timor Sea are analyzed. The results show that most of ISWs appear in Bonaparte basin and its vicinity. The average wavelength of the ISWs is 248 m, and most of the wave lengths are less than 400 m. The peak line of ISWs is longer in deeper water. The underwater structures of two typical ISWs are reconstructed based on the Korteweg–de Vries (KdV) equation combined with mooring observation. This shows that, compared with the two-layer model, the continuous layered model is more suitable for reconstructing the underwater structures of ISWs. Further analysis shows that both the rough topography and the spring-neap tides contribute to the generation of ISWs in the Timor Sea. This study fills a gap in knowledge of ISWs in regional seas, such as the Timor Sea. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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Open AccessArticle

Analysis of Seasonal and Long-Term Variations in the Surface and Vertical Structures of the Lofoten Vortex

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Remote Sens. 2023, 15(7), 1903; https://doi.org/10.3390/rs15071903 - 02 Apr 2023

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Abstract

The Lofoten Vortex (LV) is a quasi-permanent anticyclonic eddy with the characteristic of periodic regeneration in the Lofoten Basin (LB), which is one of the major areas of deep vertical mixing in the Nordic Sea. Our analysis of the LV contributes to our understanding of the variations in convective mixing in the LB. Based on drifter data and satellite altimeter data, the climatological results show that the LV has the sea surface characteristics of relative stability in terms of its spatial position and significant seasonal variations in its physical characteristics. Combined with the temperature and salinity data of Argo profiles, the vertical structures of the LV are presented here in terms of their spatial distribution and monthly variations. The wavelet analysis of the satellite sea surface temperature (SST) data shows that the period of SST anomaly (SSTA) in the LV sea area is 8–16 years. In the stage marked by a decreasing (increasing) trend of SSTA, the vertical mixing is strengthened (weakened). Current vertical mixing is clearly revealed by the Argo profiles, and the SSTA shows a significant impact of cooling. However, against a background of warming and freshening, this vertical mixing will be greatly weakened in the next increasing trending stage of the SSTA. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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Open AccessArticle

Remote Sensing Analysis of Typhoon-Induced Storm Surges and Sea Surface Cooling in Chinese Coastal Waters

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Remote Sens. 2023, 15(7), 1844; https://doi.org/10.3390/rs15071844 - 30 Mar 2023

Cited by 1 | Viewed by 585

Abstract

Inthis study, remote sensing measurements were utilized to examine the characteristics of storm surges and sea surface cooling in Chinese coastal waters caused by typhoons. Altimetric data from satellite altimeters were used to determine the magnitude, cross-shelf decaying scale, and propagating speed of storm surges from typhoons. The results were in agreement with estimates obtained from a theoretical model and tide gauge data, showing that the two storm surges propagated as continental shelf waves along the southeastern coast of China. The sea surface cooling, driven by Typhoons 1319Usagi and 1323Fitow, was analyzed using the remote sensing sea surface temperature product, named the global 1 km sea surface temperature (G1SST) dataset, revealing a considerable decrease in the temperature, with the largest decrease reaching 4.5 °C after the passage of 1319Usagi, in line with buoy estimates of 4.6 °C. It was found that 1323Fitow and 1324Danas jointly impacted the southeastern coast of China, resulting in a significant temperature drop of 4.0 °C. Our study shows that incorporating remotely sensed measurements into the study of oceanic responses to typhoons has significant benefits and complements the traditional tide gauge network and buoy data. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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Open AccessArticle

A Hybrid ENSO Prediction System Based on the FIO−CPS and XGBoost Algorithm

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Remote Sens. 2023, 15(7), 1728; https://doi.org/10.3390/rs15071728 - 23 Mar 2023

Viewed by 763

Abstract

Accurate prediction of the El Niño–Southern Oscillation (ENSO) is crucial for climate change research and disaster prevention and mitigation. In recent decades, the prediction skill for ENSO has improved significantly; however, accurate forecasting at a lead time of more than six months remains challenging. By using a machine learning method called eXtreme Gradient Boosting (XGBoost), we corrected the ENSO predicted results from the First Institute of Oceanography Climate Prediction System version 2.0 (FIO−CPS v2.0) based on the satellite remote sensing sea surface temperature data, and then developed a dynamic and statistical hybrid prediction model, named FIO−CPS−HY. The latest 15 years (2007–2021) of independent testing results showed that the average anomaly correlation coefficient (ACC) and root mean square error (RMSE) of the Niño3.4 index from FIO−CPS v2.0 to FIO−CPS−HY for 7− to 13−month lead times could be increased by 57.80% (from 0.40 to 0.63) and reduced by 24.79% (from 0.86 °C to 0.65 °C), respectively. The real−time predictions from FIO−CPS−HY indicated that the sea surface state of the Niño3.4 area would likely be in neutral conditions in 2023. Although FIO−CPS−HY still has some biases in real−time prediction, this study provides possible ideas and methods to enhance short−term climate prediction ability and shows the potential of integration between machine learning and numerical models in climate research and applications. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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Special Issue "Applications of Remote Sensing in Oceanography: Prospects and Challenges II"

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