remotesensing-logo

Journal Browser

Journal Browser

Deep Ocean Remote Sensing and Its Application in the Ocean Warming Study in Recent Decades

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 August 2022) | Viewed by 19088

Special Issue Editors

Center for Remote Sensing, College of Earth, Ocean and Environment, University of Delaware, Newark, DE 19716, USA
Interests: physical oceanography; ocean remote sensing; climate change; air-sea interaction; ocean circulation; image processing; environmental monitoring; deep learning/big data/data science
Special Issues, Collections and Topics in MDPI journals
Laboratoire d'Océanographie Physique et Spatiale, UMR6523 CNRS/IFREMER/IRD/UBOIfremer, Centre de Bretagne, ZI de la Pointe du Diable, CS10070, F-29280 Plouzané, France
Interests: sea level change; ocean heat/freshwater content changes; ocean circulation and dynamics; intrinsic ocean variability; climate change
Special Issues, Collections and Topics in MDPI journals
The Academy of Digital China, Fuzhou University, Fuzhou 350108, China
Interests: ocean remote sensing; coastal remote sensing; deep ocean remote sensing; global climate change; AI oceanography
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361005, China
Interests: physical oceanography; ocean circulation; climate change and variability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Observing the subsurface and deeper ocean has become extremely important in recent decades following recent evidence suggesting the presence of widespread warming in the ocean’s interior as a response to the significant global warming (Earth’s Energy Imbalance—EEI). Indeed, EEI grew to unprecedented amounts in 2010–2018, much bigger than it was in previous years and decades. The ocean’s responses to this increase in EEI must be understood to fully evaluate the actual impact of global warming. Satellite remote sensing has helped us to conduct multiple sea-surface observations at various spatiotemporal scales for several decades, but these observations are confined to the ocean surface and cannot directly detect information beneath the surface, where many significant dynamic processes and features are located. Deeper ocean remote sensing has the ability to detect and depict the processes and features in the subsurface and deeper layer within the ocean, as well as their implications for climate systems on a large scale. However, the lack of consistent long-term and large-scale subsurface observation hinders the inference and recognition of subsurface and deeper ocean processes. Although deeper ocean remote sensing based on satellite sensors has been successfully developed to detect the ocean’s interior, many important ocean processes in the interior still need to be observed and studied from space, such as deeper ocean warming, climate variability, heat redistribution processes, internal dynamics, mixed layer variability, ocean circulation, biogeochemical processes, and so on, which relate to and greatly impact recent global climate change and ocean internal warming.

This Special Issue invites contributions to advances in deeper ocean remote sensing and its application in ocean warming studies based on satellite remote sensing and reports on recent attempts to combine all kinds of satellite sensors and remote sensing big data with other observations and techniques using empirical statistics, machine learning, deep learning, dynamic model, and data assimilation techniques to retrieve and reconstruct multidimensional and multiscale physical and biogeochemical parameters in the ocean’s interior, and further applied to the ocean warming and variability study. Furthermore, investigations on internal variability (from the coupled Earth–atmosphere system) and ocean intrinsic variability contributions that can blur the interpretation of ocean warming processes due to global warming are also welcomed.

Prof. Dr. Xiao-Hai Yan
Dr. William Llovel
Prof. Dr. Hua Su
Prof. Dr. Wei Zhuang
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

  • Deep ocean remote sensing
  • Multisource/multisensor remote sensing
  • Subsurface ocean processes
  • Thermohaline structure
  • Physical and biogeochemical coupling processes
  • Global ocean warming
  • Climate variability and change
  • Machine learning and deep learning

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 2919 KiB  
Article
Analysis of Global Sea Level Change Based on Multi-Source Data
by Yongjun Jia, Kailin Xiao, Mingsen Lin and Xi Zhang
Remote Sens. 2022, 14(19), 4854; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194854 - 28 Sep 2022
Cited by 3 | Viewed by 1949
Abstract
Global sea level rise is both a major indicator and consequence of global warming. At present, global warming is causing sea level rise in two main ways: one is the thermal expansion of sea water, and the other is the injection of large [...] Read more.
Global sea level rise is both a major indicator and consequence of global warming. At present, global warming is causing sea level rise in two main ways: one is the thermal expansion of sea water, and the other is the injection of large amounts of fresh water into the ocean after glaciers and ice sheets melt. In this paper, satellite altimeter data are used to study the total changes of global sea level from 2002 to 2020. Different from most previous studies, this study proposes a calculation method of sea level anomaly using only the along track altimetry data, which is similar to considering the satellite points as tide gauges, in order to avoid the error caused by interpolation in the map data. In addition, GRACE satellite data are used to calculate the changes of global sea level caused by water increase; temperature and salinity data are used to calculate the changes from ocean thermal expansion. Next, using satellite altimetry data, the calculation results show that the global sea level rise rate in the period of 2002–2020 is 3.3 mm/a. During this period, the sea level change caused by the increase of sea water calculated with GRACE satellite data is 2.07 mm/a, and that caused by the thermal expansion of seawater is 0.62 mm/a. The sea level rise caused by the increase of water volume accounts for 62.7% of the total sea level rise. Full article
Show Figures

Figure 1

14 pages, 6332 KiB  
Communication
Seasonal and Interannual Variability of Tidal Mixing Signatures in Indonesian Seas from High-Resolution Sea Surface Temperature
by Raden Dwi Susanto and Richard D. Ray
Remote Sens. 2022, 14(8), 1934; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081934 - 16 Apr 2022
Cited by 10 | Viewed by 2167
Abstract
With their complex narrow passages and vigorous mixing, the Indonesian seas provide the only low-latitude pathway between the Pacific and Indian Oceans and thus play an essential role in regulating Pacific-Indian Ocean exchange, regional air-sea interaction, and ultimately, global climate phenomena. While previous [...] Read more.
With their complex narrow passages and vigorous mixing, the Indonesian seas provide the only low-latitude pathway between the Pacific and Indian Oceans and thus play an essential role in regulating Pacific-Indian Ocean exchange, regional air-sea interaction, and ultimately, global climate phenomena. While previous investigations using remote sensing and numerical simulations strongly suggest that this mixing is tidally driven, the impacts of monsoon and El Niño Southern Oscillation (ENSO) on tidal mixing in the Indonesian seas must play an important role. Here we use high-resolution sea surface temperature from June 2002 to June 2021 to reveal monsoon and ENSO modulations of mixing. The largest spring-neap (fortnightly) signals are found to be localized in the narrow passages/straits and sills, with more vigorous tidal mixing during the southeast (boreal summer) monsoon and El Niño than that during the northwest (boreal winter monsoon) and La Niña. Therefore, tidal mixing, which necessarily responds to seasonal and interannual changes in stratification, must also play a feedback role in regulating seasonal and interannual variability of water mass transformations and Indonesian throughflow. The findings have implications for longer-term variations and changes of Pacific–Indian ocean water mass transformation, circulation, and climate. Full article
Show Figures

Figure 1

19 pages, 5254 KiB  
Article
On Investigating the Dynamical Factors Modulating Surface Chlorophyll-a Variability along the South Java Coast
by Samiran Mandal, Raden Dwi Susanto and Balaji Ramakrishnan
Remote Sens. 2022, 14(7), 1745; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071745 - 05 Apr 2022
Cited by 9 | Viewed by 2614
Abstract
Twelve years of remotely sensed all-sat merged chlorophyll-a concentration unveils strong signatures of chlorophyll-a blooms along the south Java coast. An unprecedented three-times increase in chlorophyll-a concentration is significantly observed along the south Java coast during the southeast monsoon (June–October) than the northwest [...] Read more.
Twelve years of remotely sensed all-sat merged chlorophyll-a concentration unveils strong signatures of chlorophyll-a blooms along the south Java coast. An unprecedented three-times increase in chlorophyll-a concentration is significantly observed along the south Java coast during the southeast monsoon (June–October) than the northwest monsoon (December–April). The multiple regression analysis of dynamic factors evidently indicates that seasonal upwelling is predominantly controlled by the seasonally evolving coastal eddies associated with the seasonally reversing south Java coastal currents (SJCC) and Ekman mass transport (EMT), followed by the relative roles of sea surface temperature (SST) and wind stress curl. The eddy-induced upwelling and EMT-induced coastal upwelling lead to chlorophyll-a blooms during southeast monsoon, well-supported by the entrainment of cold and saline waters (thermocline doming) with low spiciness. On the other hand, the coastal eddies associated with SJCC and SST anomalies play a significant role in modulating the interannual surface chlorophyll-a variability in the domain. Intense chlorophyll-a blooms are observed during the positive IOD years, whereas the least chlorophyll-a concentration is observed during the negative IOD years. The unprecedentedly least chlorophyll-a concentrations during 2010 and 2016 are attributed to the intense and prolonged surface marine heatwaves. Full article
Show Figures

Figure 1

19 pages, 4639 KiB  
Article
Improved Perceptron of Subsurface Chlorophyll Maxima by a Deep Neural Network: A Case Study with BGC-Argo Float Data in the Northwestern Pacific Ocean
by Jianqiang Chen, Xun Gong, Xinyu Guo, Xiaogang Xing, Keyu Lu, Huiwang Gao and Xiang Gong
Remote Sens. 2022, 14(3), 632; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030632 - 28 Jan 2022
Cited by 9 | Viewed by 2966
Abstract
Subsurface chlorophyll maxima (SCMs), commonly occurring beneath the surface mixed layer in coastal seas and open oceans, account for main changes in depth-integrated primary production and hence significantly contribute to the global carbon cycle. To fill the gap of previous methods (in situ [...] Read more.
Subsurface chlorophyll maxima (SCMs), commonly occurring beneath the surface mixed layer in coastal seas and open oceans, account for main changes in depth-integrated primary production and hence significantly contribute to the global carbon cycle. To fill the gap of previous methods (in situ measurement, remote sensing, and the extrapolating function based on surface-ocean data) for obtaining SCM characteristics (intensity, depth, and thickness), we developed an improved deep neural network (IDNN) model using a Gaussian radial basis activation function to retrieve the vertical profile of chlorophyll a concentration (Chl a) and associated SCM characteristics from surface-ocean data. The annually averaged SCM depth was further incorporated into the bias term and the Gaussian activation function to improve the estimation accuracy of the IDNN model. Based on the Biogeochemical-Argo (BGC-Argo) data acquired for three regions in the northwestern Pacific Ocean, vertical Chl a profiles produced by our improved DNN model using sea surface Chl a and sea surface temperature (SST) were in good agreement with the observations, especially in regions with low surface Chl a. Compared to other neural-network-based models with one hidden layer and a sigmoid activation function, the IDNN model retrieved vertical Chl a profiles well in more eutrophic subpolar regions. Furthermore, the application of the IDNN model to infer vertical Chl a profiles from remote-sensing information was validated in the northwestern Pacific Ocean. Full article
Show Figures

Graphical abstract

17 pages, 28910 KiB  
Article
Weak Mesoscale Variability in the Optimum Interpolation Sea Surface Temperature (OISST)-AVHRR-Only Version 2 Data before 2007
by Yanan Zhu, Yuanlong Li, Fan Wang and Mingkun Lv
Remote Sens. 2022, 14(2), 409; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020409 - 17 Jan 2022
Cited by 3 | Viewed by 1799
Abstract
Mesoscale sea surface temperature (SST) variability triggers mesoscale air–sea interactions and is linked to ocean subsurface mesoscale dynamics. The National Oceanic and Atmospheric Administration (NOAA) daily Optimum Interpolation SST (OISST) products, based on various satellite and in situ SST data, are widely utilized [...] Read more.
Mesoscale sea surface temperature (SST) variability triggers mesoscale air–sea interactions and is linked to ocean subsurface mesoscale dynamics. The National Oceanic and Atmospheric Administration (NOAA) daily Optimum Interpolation SST (OISST) products, based on various satellite and in situ SST data, are widely utilized in the investigation of multi-scale SST variabilities and reconstruction of subsurface and deep-ocean fields. The quality of OISST datasets is subjected to temporal inhomogeneity due to alterations in the merged data. Yet, whether this issue can significantly affect mesoscale SST variability is unknown. The analysis of this study detects an abrupt enhancement of mesoscale SST variability after 2007 in the OISST-AVHRR-only version 2 and version 2.1 datasets (hereafter OI.v2-AVHRR-only and OI.v2.1-AVHRR-only). The contrast is most stark in the subtropical western boundary current (WBC) regions, where the average mesoscale SST variance during 2007–2018 is twofold larger than that during 1993–2006. Further comparisons with other satellite SST datasets (TMI, AMSR-E, and WindSAT) suggest that the OISST-AVHRR-only datasets have severely underestimated mesoscale SST variability before 2007. An evaluation of related documents of the OISST data indicates that this bias is mainly caused by the change of satellite AVHRR instrument in 2007. There are no corresponding changes detected in the associated fields, such as the number and activity of mesoscale eddies or the background SST gradient in these regions, confirming that the underestimation of mesoscale SST variability before 2007 is an artifact. Another OISST product, OI.v2-AVHRR-AMSR, shows a similar abrupt enhancement of mesoscale SST variability in June 2002, when the AMSR-E instrument was incorporated. This issue leaves potential influences on scientific research that utilize the OISST datasets. The composite SST anomalies of mesoscale eddies based on the OI.v2-AVHRR-only data are underestimated by up to 37% before 2007 in the subtropical WBC regions. The underestimation of mesoscale variability also affects the total (full-scale) SST variability, particularly in winter. Other SST data products based on the OISST datasets were also influenced; we identify suspicious changes in J-OFURO3 and CFSR datasets; the reconstructed three-dimensional ocean products using OISST data as input may also be inevitably affected. This study reminds caution in the usage of the OISST and relevant data products in the investigation of mesoscale processes. Full article
Show Figures

Figure 1

16 pages, 9666 KiB  
Article
Global Wave Height Slowdown Trend during a Recent Global Warming Slowdown
by Yuhan Cao, Changming Dong, Ian R. Young and Jingsong Yang
Remote Sens. 2021, 13(20), 4096; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204096 - 13 Oct 2021
Cited by 7 | Viewed by 2938
Abstract
It has been reported that global warming results in the increase of globally averaged wave heights. What happened to the global-averaged wave heights during the global warming slowdown period (1999–2013)? Using reanalysis products, together with remote sensing and in situ observational data, it [...] Read more.
It has been reported that global warming results in the increase of globally averaged wave heights. What happened to the global-averaged wave heights during the global warming slowdown period (1999–2013)? Using reanalysis products, together with remote sensing and in situ observational data, it was found that the temporal variation pattern of the globally averaged wave heights was similar to the slowdown trend in the increase in global mean surface temperature during the same period. The analysis of the spatial distribution of trends in wave height variation revealed different rates in global oceans: a downward trend in the northeastern Pacific and southern Indian Ocean, and an upward trend in other regions. The decomposition of waves into swells and wind waves demonstrates that swells dominate global wave height variations, which indicates that local sea surface winds indirectly affect the slowdown in the rate of wave height growth. Full article
Show Figures

Figure 1

21 pages, 6429 KiB  
Article
Reconstructing Ocean Heat Content for Revisiting Global Ocean Warming from Remote Sensing Perspectives
by Hua Su, Tian Qin, An Wang and Wenfang Lu
Remote Sens. 2021, 13(19), 3799; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193799 - 22 Sep 2021
Cited by 10 | Viewed by 2736
Abstract
Global ocean heat content (OHC) is generally estimated using gridded, model and reanalysis data; its change is crucial to understanding climate anomalies and ocean warming phenomena. However, Argo gridded data have short temporal coverage (from 2005 to the present), inhibiting understanding of long-term [...] Read more.
Global ocean heat content (OHC) is generally estimated using gridded, model and reanalysis data; its change is crucial to understanding climate anomalies and ocean warming phenomena. However, Argo gridded data have short temporal coverage (from 2005 to the present), inhibiting understanding of long-term OHC variabilities at decadal to multidecadal scales. In this study, we utilized multisource remote sensing and Argo gridded data based on the long short-term memory (LSTM) neural network method, which considers long temporal dependence to reconstruct a new long time-series OHC dataset (1993–2020) and fill the pre-Argo data gaps. Moreover, we adopted a new machine learning method, i.e., the Light Gradient Boosting Machine (LightGBM), and applied the well-known Random Forests (RFs) method for comparison. The model performance was measured using determination coefficients (R2) and root-mean-square error (RMSE). The results showed that LSTM can effectively improve the OHC prediction accuracy compared with the LightGBM and RFs methods, especially in long-term and deep-sea predictions. The LSTM-estimated result also outperformed the Ocean Projection and Extension neural Network (OPEN) dataset, with an R2 of 0.9590 and an RMSE of 4.45 × 1019 in general in the upper 2000 m for 28 years (1993–2020). The new reconstructed dataset (named OPEN-LSTM) correlated reasonably well with other validated products, showing consistency with similar time-series trends and spatial patterns. The spatiotemporal error distribution between the OPEN-LSTM and IAP datasets was smaller on the global scale, especially in the Atlantic, Southern and Pacific Oceans. The relative error for OPEN-LSTM was the smallest for all ocean basins compared with Argo gridded data. The average global warming trends are 3.26 × 108 J/m2/decade for the pre-Argo (1993–2004) period and 2.67 × 108 J/m2/decade for the time-series (1993–2020) period. This study demonstrates the advantages of LSTM in the time-series reconstruction of OHC, and provides a new dataset for a deeper understanding of ocean and climate events. Full article
Show Figures

Graphical abstract

Back to TopTop