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JMSE
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Sea Surface Temperature: From Observation to Applications II
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Sea Surface Temperature: From Observation to Applications II

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 13540

Special Issue Editors

Prof. Dr. Ming-An Lee

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Guest Editor
Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, 20224, 2 Pei-Ning Rd, Keelung, Taiwan
Interests: satellite oceanography; fisheries and sea conditions; fisheries; climate change; marine ecology; marine environment; marine biodiversity; aquatic ecosystems; ecology and evolution; environmental impact assessment; natural resource management
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Chang

E-Mail Website
Guest Editor
Graduate Institute of Marine Affair, National Sun Yat-sen University, No. 70, Lienhai Rd., Kaohsiung City 80424, Taiwan
Interests: marine spatial planning; coastal management; fisheries oceanography; oceanic front detection
Dr. Hone-Jay Chu

E-Mail Website
Guest Editor
Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan
Interests: environmental management; GIS application; spatial data science; spatial statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Considering an environment facing global and accelerated climate change, sea surface temperature (SST) was defined by the World Meteorological Organization as an essential climate variable contributing to the characterization of the Earth’s climate. Recent studies confirmed that a huge amount of energy is stored in the oceans; thus, SST has emerged as a proxy of this energy reservoir, aiding in deriving future trends in climate change and determining their impacts on the frequency of weather extremes and growing effect on human societies. Energy storage has a considerable impact on the atmosphere-ocean system through heat exchange. SST monitoring and analysis have advanced considerably due to the significant interest and research in these fields.

As such, original research and review papers focusing on sea surface temperature measurement techniques, data collection, and analysis are welcome. Topics of interest for this Special Issue of JMSE include, but are not limited to:

  • SST measuring techniques, both in situ or via remote sensing;
  • Measuring SST: sensor technical development and measuring techniques;
  • SST measurement networks: buoys, gliders, remote sensing, etc.;
  • SST data treatment (gap data filling, neural networks, SST series reconstruction, etc.);
  • Remote sensing: measuring and validation;
  • SST climate: variability, spatial distribution and trends;
  • Impacts on marine biodiversity, aquaculture and fisheries;
  • Impacts on atmospheric phenomena, especially extremes;
  • Teleconnection with climatic patterns;
  • Physical and dynamical oceanography: correlation with sea level and salinity;
  • SST and general ocean circulation, and its application in other marine sectors.

Prof. Dr. Ming-An Lee
Dr. Yi Chang
Dr. Hone-Jay Chu
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. Journal of Marine Science and Engineering 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 2600 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

  • SST
  • climate change
  • atmosphere-ocean interaction
  • in situ and remote sensing
  • measuring techniques
  • sensors
  • validation
  • time series
  • biodiversity
  • general oceanic circulation

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Published Papers (6 papers)

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Research

20 pages, 18647 KiB  
Article
Impacts of Sea Surface Temperature Variability in the Indian Ocean on Drought Conditions over India during ENSO and IOD Events
by Vaibhav Kumar, Hone-Jay Chu and Abhishek Anand
J. Mar. Sci. Eng. 2024, 12(1), 136; https://doi.org/10.3390/jmse12010136 - 9 Jan 2024
Cited by 3 | Viewed by 2897
Abstract
The characteristics of terrestrial droughts are closely linked to simultaneous fluctuations in climatic factors, notably influenced by sea surface temperature (SST). This study explores the response of vegetation photosynthesis, indicated by solar-induced chlorophyll fluorescence (SIF), in India during the summer monsoon period (JJAS) [...] Read more.
The characteristics of terrestrial droughts are closely linked to simultaneous fluctuations in climatic factors, notably influenced by sea surface temperature (SST). This study explores the response of vegetation photosynthesis, indicated by solar-induced chlorophyll fluorescence (SIF), in India during the summer monsoon period (JJAS) under drought conditions. Notably, statistically significant associations between SST variations in the tropical Indian Ocean and land-based drought responses (precipitation, temperature, soil moisture, and SIF) were observed, which were attributed to atmospheric teleconnections. The positive phases of El Niño and the Indian Ocean Dipole (IOD) significantly impacted SST, triggering severe droughts in India in 2009 and 2015. The results revealed that positive SST anomalies weaken monsoon flow during the onset period, reducing moisture transmission to the Indian subcontinent. In 2009, the precipitation anomaly showed severe drought conditions (<−1.5) primarily in the northwest, central northeast, and west-central subregions, respectively, with soil moisture deficit and reduced photosynthetic activity (indicated by negative SIF anomalies) mirroring precipitation anomalies. In 2015, moderate to severe drought conditions affected regions primarily in the west-central and peninsular areas, with corresponding consistency in SIF anomalies and soil moisture deficits. These conditions led to decreased photosynthetic rates and negative SIF anomalies observed across India. The findings provide insights for predicting droughts and understanding ecosystem impacts across India amidst rapidly changing climate conditions in the Indian Ocean region. Full article
(This article belongs to the Special Issue Sea Surface Temperature: From Observation to Applications II)
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14 pages, 3856 KiB  
Article
Multivariate Sea Surface Prediction in the Bohai Sea Using a Data-Driven Model
by Song Hu, Qi Shao, Wei Li, Guijun Han, Qingyu Zheng, Ru Wang and Hanyu Liu
J. Mar. Sci. Eng. 2023, 11(11), 2096; https://doi.org/10.3390/jmse11112096 - 1 Nov 2023
Cited by 3 | Viewed by 1240
Abstract
Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a significant role in influencing the evolution of the marine environment. Both internal dynamics and [...] Read more.
Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a significant role in influencing the evolution of the marine environment. Both internal dynamics and external drivers contribute to these changes. In this study, a data-driven model is proposed using sea surface height anomaly (SSHA), sea surface temperature (SST), and sea surface wind (SSW) in the Bohai Sea. This model combines multivariate empirical orthogonal functions (MEOFs) with long and short-term memory (LSTM). MEOF analysis is used on the multivariate dataset of SSHA and SST, considering the correlation among sea surface variables. SSW is introduced as a predictor to enhance the predictability of the multivariate sea surface model. In the case of the Bohai Sea, the comparative tests of the model without wind field effect, the fully coupled model, and the proposed prediction model were carried out. MEOF analysis is employed in comparative experiments for oceanic variables, atmospheric variables, and combined atmospheric and oceanic variables. The results demonstrate that using wind field as a predictor can improve the forecast accuracy of SSHA and SST in the Bohai Sea. The root mean square errors (RMSE) for SSHA and SST in a 7-day forecast are 0.016 m and 0.3200 °C, respectively. Full article
(This article belongs to the Special Issue Sea Surface Temperature: From Observation to Applications II)
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17 pages, 3528 KiB  
Article
Projected Changes in Spawning Ground Distribution of Mature Albacore Tuna in the Indian Ocean under Various Global Climate Change Scenarios
by Sandipan Mondal, Aratrika Ray, Ming-An Lee and Malagat Boas
J. Mar. Sci. Eng. 2023, 11(8), 1565; https://doi.org/10.3390/jmse11081565 - 8 Aug 2023
Cited by 5 | Viewed by 1685
Abstract
The present study utilised a geometric mean model in which sea surface temperature, oxygen, and sea surface salinity were used to predict the effects of climate change on the habitats of mature albacore tuna in the Indian Ocean under multiple representative concentration pathway [...] Read more.
The present study utilised a geometric mean model in which sea surface temperature, oxygen, and sea surface salinity were used to predict the effects of climate change on the habitats of mature albacore tuna in the Indian Ocean under multiple representative concentration pathway (RCP) scenarios. Data pertaining to the albacore tuna fishing conducted by Taiwanese longline fisheries during the October–March period in 1998–2016 were analysed. The fishery data comprised fishing location (latitude and longitude), fishing effort (number of hooks used), number of catches, fishing time (month and year), and fish weight. Nominal catch per unit effort data were standardised to mitigate the potential effects of temporal and spatial factors in causing bias and overestimation. The Habitat Suitability Index (HSI) scores of potential habitats for mature albacore in the Indian Ocean are predicted to change considerably in response to varying levels of predicted climate change. Under projected warm climate conditions (RCP 8.5), the stratification of water is predicted to cause low HSI areas to expand and potential habitats for mature albacore to shift southward by 2100. The findings derived from these mature albacore habitat forecasts can contribute to the evaluation of potential hazards and feasible adaptation measures for albacore fishery resources in the context of climate change. The distribution trends pertaining to potential habitats for mature albacore should be used with caution and can provide resource stakeholders with guidance for decision-making. Full article
(This article belongs to the Special Issue Sea Surface Temperature: From Observation to Applications II)
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16 pages, 3174 KiB  
Article
Fishing Area Prediction Using Scene-Based Ensemble Models
by Adillah Alfatinah, Hone-Jay Chu, Tatas and Sumriti Ranjan Patra
J. Mar. Sci. Eng. 2023, 11(7), 1398; https://doi.org/10.3390/jmse11071398 - 11 Jul 2023
Cited by 3 | Viewed by 1871
Abstract
This study utilized Chlorophyll-a, sea surface temperature (SST), and sea surface height (SSH) as the environmental variables to identify skipjack tuna catch hotspots. This study conducted statistical methods (decision tree, DT, and generalized linear model, GLM) as ensemble models that were employed for [...] Read more.
This study utilized Chlorophyll-a, sea surface temperature (SST), and sea surface height (SSH) as the environmental variables to identify skipjack tuna catch hotspots. This study conducted statistical methods (decision tree, DT, and generalized linear model, GLM) as ensemble models that were employed for predicting skipjack area for each time slice. Using spatial historical data, each model was trained for one of the ensemble model sets. For prediction, the correlations of historical and new inputs were applied to select the predictive model. Using the scene-based model with the highest input correlation, this study further identified the fishing area of skipjack tuna in every case whether the alterations in their environment affected their abundance or not. Overall, the performance achieved over 83% for correlation coefficients (CC) based on the accuracy assessment. This study concluded that DT appears to perform better than GLM in predicting skipjack tuna fishing areas. Moreover, the most influential environmental variable in model construction was sea surface temperature (SST), indicating that the presence of skipjack tuna was primarily influenced by regional temperature. Full article
(This article belongs to the Special Issue Sea Surface Temperature: From Observation to Applications II)
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18 pages, 31728 KiB  
Article
Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models
by Farbod Farhangi, Abolghasem Sadeghi-Niaraki, Jalal Safari Bazargani, Seyed Vahid Razavi-Termeh, Dildar Hussain and Soo-Mi Choi
J. Mar. Sci. Eng. 2023, 11(6), 1136; https://doi.org/10.3390/jmse11061136 - 29 May 2023
Cited by 5 | Viewed by 2116
Abstract
Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While SST is highly affected by different oceanic, atmospheric, and climatic parameters, few papers have investigated time-series SST prediction based on multiple features. This [...] Read more.
Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While SST is highly affected by different oceanic, atmospheric, and climatic parameters, few papers have investigated time-series SST prediction based on multiple features. This paper utilized multi features of air pressure, water temperature, wind direction, and wind speed for time-series hourly SST prediction using deep neural networks of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM. Models were trained and validated by different epochs, and feature importance was evaluated by the leave-one-feature-out method. Air pressure and water temperature were significantly more important than wind direction and wind speed. Accordingly, feature selection is an essential step for time-series SST prediction. Findings also revealed that all models performed well with low prediction errors, and increasing the epochs did not necessarily improve the modeling. While all models were similarly practical, CNN was considered the most suitable as its training speed was several times faster than the other two models. With all this, the low variance of time-series data helped models make accurate predictions, and the proposed method may have higher errors while working with more variant features. Full article
(This article belongs to the Special Issue Sea Surface Temperature: From Observation to Applications II)
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15 pages, 8651 KiB  
Article
Long-Term Observations of Sea Surface Temperature Variability in the Gulf of Mannar
by Sandipan Mondal and Ming-An Lee
J. Mar. Sci. Eng. 2023, 11(1), 102; https://doi.org/10.3390/jmse11010102 - 4 Jan 2023
Cited by 6 | Viewed by 2062
Abstract
In this study, we conducted long-term temporal and spatial observations of monthly, interannual, and decadal sea surface temperature (SST) variation in the Gulf of Mannar (GoM) for the period from 1870 to 2018. We obtained climatological data from the Met Office Hadley Centre, [...] Read more.
In this study, we conducted long-term temporal and spatial observations of monthly, interannual, and decadal sea surface temperature (SST) variation in the Gulf of Mannar (GoM) for the period from 1870 to 2018. We obtained climatological data from the Met Office Hadley Centre, UK. The monthly time series revealed that April and August were the warmest and coolest months of the year, respectively. The mean SSTs for April and August were 29.85 ± 0.44 °C and 27.15 ± 0.49 °C, respectively. The mean annual highest and lowest SSTs were observed in 2015 and 1890 with SSTs of 28.93 ± 0.31 °C and 27.45 ± 0.31 °C, respectively, and the annual time series revealed a warming SST trend of 0.004 °C. Decadal time series also revealed a warming SST trend of 0.04 °C, with the highest and lowest mean decadal SSTs being 28.56 ± 0.21 °C in 2010–2018 and 27.78 ± 0.25 °C in 1890–1889, respectively. Throughout the study period, the spatial distribution of climate trends over decades across the GoM revealed a strong spatial gradient, and the region between 6–8° N and 77–78° E was warmer than all other regions of the GoM. Full article
(This article belongs to the Special Issue Sea Surface Temperature: From Observation to Applications II)
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