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Remote Sensing
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Linking Marine Heatwaves/Cold-Spells, Eddies, and Sea Surface Features
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Linking Marine Heatwaves/Cold-Spells, Eddies, and Sea Surface Features

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

Deadline for manuscript submissions: 30 January 2025 | Viewed by 2728

Special Issue Editors

Dr. Wenjin Sun

E-Mail Website
Guest Editor
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: mesoscale eddy; marine heatwaves; marine cold-spells; artificial intelligence
Dr. Yulong Yao

E-Mail Website
Guest Editor
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanography, Chinese Academy of Sciences, Guangzhou 510301, China
Interests: ocean’s role in climate change; marine heatwaves; coral bleaching
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Continuous changes in global climate are altering sea surface features, such as marine heatwaves, cold spells, and oceanic eddies. These physical processes have unprecedented impacts on various marine organisms and human life. Research in this area has always been a prominent topic in terms of physical oceanography, yet our understanding remains incomplete. This Special Issue aims to explore the connections between marine heatwaves, cold spells, oceanic eddies, and sea surface features, focusing on their characteristics, mechanisms of generation and dissipation, future evolutionary trends, and impacts on marine ecosystems and climate change. We welcome original research, review articles, and case studies utilizing remote sensing technologies, AI technologies, and other advanced methodologies. Contributions should cover disciplines such as physical oceanography and climatology. By integrating multidisciplinary research findings, this Special Issue hopes to reveal the driving mechanisms and interactions of these ocean phenomena.

Dr. Wenjin Sun
Dr. Yulong Yao
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

  • marine heatwaves
  • marine cold-spells
  • oceanic eddies
  • sea surface features
  • remote sensing
  • AI
  • climate change
  • ecosystem impacts

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Published Papers (4 papers)

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Research

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22 pages, 9742 KiB  
Article
Three-Dimensional Thermohaline Reconstruction Driven by Satellite Sea Surface Data Based on Sea Ice Seasonal Variation in the Arctic Ocean
by Xiangyu Wu, Jinlong Li, Xidong Wang, Zikang He, Zhiqiang Chen, Shihe Ren and Xi Liang
Remote Sens. 2024, 16(21), 4072; https://doi.org/10.3390/rs16214072 - 31 Oct 2024
Viewed by 517
Abstract
This study investigates and evaluates methods for the three-dimensional thermohaline reconstruction of the Arctic Ocean using multi-source observational data. A multivariate statistical regression model based on sea ice seasonal variation is developed, driving by satellite data, and in situ data is used to [...] Read more.
This study investigates and evaluates methods for the three-dimensional thermohaline reconstruction of the Arctic Ocean using multi-source observational data. A multivariate statistical regression model based on sea ice seasonal variation is developed, driving by satellite data, and in situ data is used to validate the model output. The study indicates that the multivariate statistical regression model effectively captures the characteristics of the three-dimensional thermohaline structure of the Arctic Ocean. Areas with large reconstruction errors are primarily observed in the salinity values of ice-free regions and the temperature values of ice-covered regions. The statistical regression experiments reveal that salinity errors in ice-free regions are caused by inaccuracies in the satellite salinity data, while temperature errors in ice-covered areas mainly result from the inadequate representation of the under-ice temperature structure of the reanalysis data. The continuous and stable thermohaline data produced using near real-time satellite data as input provide an important foundation for studying Arctic marine environmental characteristics and assessing climate change. Full article
(This article belongs to the Special Issue Linking Marine Heatwaves/Cold-Spells, Eddies, and Sea Surface Features)
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20 pages, 15528 KiB  
Article
Analysis of Lofoten Vortex Merging Based on Altimeter Data
by Jing Meng, Yu Liu, Guoqing Han, Xiayan Lin and Juncheng Xie
Remote Sens. 2024, 16(20), 3796; https://doi.org/10.3390/rs16203796 - 12 Oct 2024
Viewed by 462
Abstract
The Lofoten Vortex (LV), which is identified as a quasi-permanent anticyclonic eddy, strengthens through continuous merging with external anticyclonic eddies. Our investigation used the Lagrangian method to monitor the LV on a daily basis. Utilizing satellite altimeter data, we conducted multi-year tracking and [...] Read more.
The Lofoten Vortex (LV), which is identified as a quasi-permanent anticyclonic eddy, strengthens through continuous merging with external anticyclonic eddies. Our investigation used the Lagrangian method to monitor the LV on a daily basis. Utilizing satellite altimeter data, we conducted multi-year tracking and statistical analysis of merging events involving the LV. The results indicate a characteristic radius of approximately 42.72 km and a mean vorticity at the eddy center of approximately −2.23 × 10−5 s−1. The eddy exhibits oscillatory motion within the sea basin depression, centered at 70°N, 3°E, characterized by counterclockwise trajectories between 0.5°E and 6°E and between 69°N and 70.5°N. There are two types of merging events: fusion events (55%), in which eddies of similar strengths interact within a closed flow line and then merge to form a new eddy; and absorption events (45%), in which the stronger LV absorbs the weaker anticyclonic eddies without destroying the structure of the LV itself. The nodes where strong vorticity advection occurs correspond to the nodes where merging occurs, suggesting that their effect on merging can be well characterized by the vorticity advection time series. We also observe occasional fluctuations and substitution events involving the LV and external anticyclonic eddies, suggesting a dynamic succession rather than a single vortex entity. Full article
(This article belongs to the Special Issue Linking Marine Heatwaves/Cold-Spells, Eddies, and Sea Surface Features)
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20 pages, 10975 KiB  
Article
Numerical Weather Prediction of Sea Surface Temperature in South China Sea Using Attention-Based Context Fusion Network
by Hailun He, Benyun Shi, Yuting Zhu, Liu Feng, Conghui Ge, Qi Tan, Yue Peng, Yang Liu, Zheng Ling and Shuang Li
Remote Sens. 2024, 16(20), 3793; https://doi.org/10.3390/rs16203793 - 12 Oct 2024
Viewed by 665
Abstract
Numerical weather prediction of sea surface temperature (SST) is crucial for regional operational forecasts. Deep learning offers an alternative approach to traditional numerical general circulation models for numerical weather prediction. In our previous work, we developed a sophisticated deep learning model known as [...] Read more.
Numerical weather prediction of sea surface temperature (SST) is crucial for regional operational forecasts. Deep learning offers an alternative approach to traditional numerical general circulation models for numerical weather prediction. In our previous work, we developed a sophisticated deep learning model known as the Attention-based Context Fusion Network (ACFN). This model integrates an attention mechanism with a convolutional neural network framework. In this study, we applied the ACFN model to the South China Sea to evaluate its performance in predicting SST. The results indicate that for a 1-day lead time, the ACFN model achieves a Mean Absolute Error of 0.215 °C and a coefficient of determination (R2) of 0.972. In addition, in situ buoy data were utilized to validate the forecast results. The Mean Absolute Error for forecasts using these data increased to 0.500 °C for a 1-day lead time, with a corresponding R2 of 0.590. Comparative analyses show that the ACFN model surpasses traditional models such as ConvLSTM and PredRNN in terms of accuracy and reliability. Full article
(This article belongs to the Special Issue Linking Marine Heatwaves/Cold-Spells, Eddies, and Sea Surface Features)
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Other

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14 pages, 4295 KiB  
Technical Note
The Impact of Consecutive Tropical Cyclones on Changes in Environmental Factors and Phytoplankton Distributions in Overlapping Areas
by Ying Chen, Hui Zhao and Hui Gao
Remote Sens. 2024, 16(23), 4460; https://doi.org/10.3390/rs16234460 - 28 Nov 2024
Viewed by 466
Abstract
Tropical cyclones are known to have significant ecological impacts, particularly on marine productivity. This study investigates the effects of two tropical cyclones (TC “MARIA” and TC “AMPIL”) on changes in environmental factors and phytoplankton in overlapping marine areas during August 2024. Our findings [...] Read more.
Tropical cyclones are known to have significant ecological impacts, particularly on marine productivity. This study investigates the effects of two tropical cyclones (TC “MARIA” and TC “AMPIL”) on changes in environmental factors and phytoplankton in overlapping marine areas during August 2024. Our findings indicated that TC “MARIA”, despite its lower wind speeds, resulted in significant increases in surface chlorophyll-a (Chl-a) due to its prolonged duration, while depth-integrated Chl-a showed a declining trend, suggesting limitations on phytoplankton growth due to water column instability and reduced light availability. In contrast, TC “AMPIL”, with its higher wind speeds and faster translation speed, caused more immediate disturbances, leading to increases in surface Chl-a. However, the depth-integrated Chl-a did not significantly increase, as phytoplankton growth was hindered by the succession of the two typhoons. Additionally, we observed a pronounced cooling in sea surface temperature after both typhoons, likely linked to ongoing mixing processes and atmospheric influences. This study can provide us with more insights into the interaction between tropical cyclone dynamics and marine ecology. Full article
(This article belongs to the Special Issue Linking Marine Heatwaves/Cold-Spells, Eddies, and Sea Surface Features)
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