Special Issue "Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature"

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

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Dr. Prasanjit Dash
Website1 Website2
Guest Editor
(CIRA Research Scientist III, Colorado State University)
National Oceanic and Atmospheric Administration (NOAA)
Center for Satellite Applications and Research (STAR)
Satellite Oceanography & Climatology Division (SOCD)
NCWCP, 5830 University Research Court
College Park, MD 20740-3818 USA
Interests: Satellite infrared radiometry; Radiative transfer modeling in terrestrial infrared; Routine and synergistic study of multiple ocean parameters; Modern visualization with web-GIS applications; Inverse algorithms; Cloud detection
Dr. Marouan Bouali
Website
Guest Editor
Instituto Oceanográfico da Universidade de São Paulo (IOUSP), Praca do Oceanográfico, 191, São Paulo, SP 05508-120 Brasil
Interests: SST gradients; Ocean front detection; Cloud detection; Level 4 SST analysis; SST image quality
Dr. Korak Saha
Website
Guest Editor
Assistant Research Scientist, Coop. Inst. for Satellite Earth System Studies, Univ of MD, NOAA National Centers for Environmental Information (NCEI), E/NE41, SSMC3, 4th floor, Rm 4711, 1315 East-West Highway, Silver Spring, MD 20910, USA
Interests: Remote sensing in IR and microwave channels; SST retrieval algorithms for climate data production; Radiative transfer modeling for land and ocean retrievals; cal/val/QC of radiation measurements; Microwave propagation in navigation and meteorological applications

Special Issue Information

Dear Colleagues,

Sea surface temperature (SST) is a key variable of the Earth system that regulates the interaction between the atmosphere and the ocean through energy and gaseous exchange, thereby influencing weather and climate patterns. Operational global retrieval of reliable SST information is a challenging task, but experts around the world have made significant progress both in terms of the quality of retrievals and the timeliness of production and distribution. Coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST), data format specification and standardization of SST products have reached a high level of maturity that enables the use of these data to proliferate. Retrieval of SST is based on observations from both low-Earth orbit infrared and microwave sensors and geostationary orbit infrared imagers. Also, in situ data from moored and drifting buoys, ship-based measurements, and Argo floats play a critical role in algorithm development and product validation. Many applications with important societal benefits depend on the global and regional mapping of SST, such as weather forecasts, climate variability and change prediction, maritime safety, environmental monitoring, and management of marine ecosystems and fisheries. Changes in SST and its trend also affect immobile corals. These may be subject to mortality when exposed to long-duration temperature changes, leading to long-term consequences for the blue economy. Therefore, a further important requirement is scientific stewardship of SST data, which includes production, validation, archival, and dissemination of these products.

To summarize the progress to date and the remaining challenges in space-based SST retrievals and make the information available to a wide-reaching audience, we are calling for papers on the retrieval, operationalization, monitoring, and application of SST from various sensors. We welcome papers from the global community actively involved in this field as well as from SST users and enthusiasts. The selection of papers for publication will depend on the quality and rigor of research. Potential topics include, but are not limited to:

Algorithms to derive SST information from satellite-based observation

  • Inverse algorithms for SST retrieval
  • Cloud identification and removal
  • Data assimilation (L4)

Information for users about operational production and distribution of SST products

  • Data availability resources
  • Technological services for data distribution

Monitoring and validation

  • Validation approaches
  • Monitoring and visualization tools

Application

  • Front detection
  • Weather and climate studies
  • Integrated approaches using SST in conjunction with other information, such as salinity, color, altimetry data, and wind
  • Effects on a wide range of ecosystem components, including the effect of thermal stress on coral reefs (bleaching)
  • Potential benefits of using SST for the blue economy and biodiversity research

Next-generation platforms and sensors and technology

  • Recent or emerging concepts, technologies, and missions
  • Gaps in sensor continuity
  • Use of Artificial Intelligence (AI)/Machine Learning (ML): potential use and possible pitfalls
Dr. Prasanjit Dash
Dr. Marouan Bouali
Dr. Korak Saha
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 papers will be 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 2000 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

  • sea surface temperature (SST) retrieval algorithm
  • cloud detection
  • validation, monitoring and error characterization of SST
  • detection of SST fronts
  • SST operational production
  • temperature anomaly effects on coral bleaching and other biodiversity.

Published Papers (3 papers)

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

Research

Jump to: Other

Open AccessArticle
Use of Uncertainty Inflation in OSTIA to Account for Correlated Errors in Satellite-Retrieved Sea Surface Temperature Data
Remote Sens. 2020, 12(7), 1083; https://doi.org/10.3390/rs12071083 (registering DOI) - 27 Mar 2020
Abstract
Sea surface temperature (SST) analysis systems such as the Operational Sea Surface Temperature and Ice Analysis (OSTIA) use statistical methods to combine observations together with a first guess field to create spatially complete maps of SST. These commonly assume that observation errors are [...] Read more.
Sea surface temperature (SST) analysis systems such as the Operational Sea Surface Temperature and Ice Analysis (OSTIA) use statistical methods to combine observations together with a first guess field to create spatially complete maps of SST. These commonly assume that observation errors are uncorrelated, yet some errors (such as due to retrieval issues) can be correlated. Information about errors is used by the analysis system to determine the weighting to apply to the observations, hence this incorrect assumption could degrade the analysis. A common technique to mitigate for this is to inflate the observation uncertainties. Using information on observation error correlations provided with data produced by the European Space Agency (ESA) SST Climate Change Initiative (CCI) project, idealised tests were carried out to determine how this inflation technique can best be applied. These showed that applying inflation in situations where the observation errors are correlated over similar or larger distances to the errors in the background can cause unpredictable and sometimes negative results. However, in situations where the observation error correlation length scale is relatively small, inflation should improve the analysis. These findings were adapted to the OSTIA system and various configurations were tested. It was found that the inflation methods did not affect statistics of differences between the analyses and independent Argo reference data. However, the SST gradients were affected, particularly if some observation uncertainties were inflated but others were not. The results from both the idealised tests and the application to the real system therefore highlight that it is challenging to implement the inflation method in the case of an SST analysis system and show the need for assimilation schemes that can make full use of observation error correlation information. Full article
Show Figures

Graphical abstract

Other

Jump to: Research

Open AccessTechnical Note
The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses
Remote Sens. 2020, 12(4), 720; https://doi.org/10.3390/rs12040720 - 21 Feb 2020
Abstract
The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system generates global, daily, gap-filled foundation sea surface temperature (SST) fields from satellite data and in situ observations. The SSTs have uncertainty information provided with them and an ice concentration (IC) analysis is [...] Read more.
The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system generates global, daily, gap-filled foundation sea surface temperature (SST) fields from satellite data and in situ observations. The SSTs have uncertainty information provided with them and an ice concentration (IC) analysis is also produced. Additionally, a global, hourly diurnal skin SST product is output each day. The system is run in near real time to produce data for use in applications such as numerical weather prediction. Data production is monitored routinely and outputs are available from the Copernicus Marine Environment Monitoring Service (CMEMS; marine.copernicus.eu). As an operational product, the OSTIA system is continuously under development. For example, since the original descriptor paper was published, the underlying data assimilation scheme that is used to generate the foundation SST analyses has been updated. Various publications have described these changes but a full description is not available in a single place. This technical note focuses on the production of the foundation SST and IC analyses by OSTIA and aims to provide a comprehensive description of the current system configuration. Full article
Show Figures

Graphical abstract

Open AccessLetter
Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis
Remote Sens. 2020, 12(4), 590; https://doi.org/10.3390/rs12040590 - 11 Feb 2020
Abstract
Sea Surface Temperature (SST) is an essential climate variable (ECV) for monitoring the state and detecting changes in the climate. The concept of ECVs, developed by the Global Climate Observing System (GCOS) program of the World Meteorological Organization (WMO), has been broadly adopted [...] Read more.
Sea Surface Temperature (SST) is an essential climate variable (ECV) for monitoring the state and detecting changes in the climate. The concept of ECVs, developed by the Global Climate Observing System (GCOS) program of the World Meteorological Organization (WMO), has been broadly adopted in worldwide science and policy circles Besides being a climate change indicator, the global SST field is an essential input for atmospheric models, air-sea exchange studies, understanding marine ecosystems, operational weather, and ocean forecasting, military and defense operations, tourism, and fisheries research. It is, therefore, critical to understand the errors associated with SST measurements from both in situ measurements and satellite observations. The customary way of validating a satellite SST is to compare it with in situ measured SSTs. This method, however, will have inaccuracies due to uncertainties involving both types of measurements. A triple collocation (TC) error analysis can be implemented on three mutually independent error-prone measurements to estimate the root-mean-square error (RMSE) of each measurement. In this study, the error characterization for the Pathfinder SST version 5.3 (PF53) dataset is performed using an extended TC (ETC) method and reported to be in the range of 0.31 to 0.37 K. These values are reasonable, as is evident from corresponding very high (~0.98) unbiased signal-to-noise ratio (SNR) values. Full article
Show Figures

Graphical abstract

Back to TopTop