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

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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: closed (31 August 2020) | Viewed by 60289

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Special Issue Editors

Dr. Prasanjit Dash

E-Mail 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

E-Mail Website
Guest Editor

Instituto Oceanográfico da Universidade de São Paulo (IOUSP), Praca do Oceanográfico, 191, São Paulo, SP 05508-120, Brazil
Interests: SST gradients; ocean front detection; cloud detection; level 4 SST analysis; SST image quality

Dr. Korak Saha

E-Mail Website
Guest Editor

Assistant Research Scientist, Cooperation Institute for Satellite Earth System Studies, University of Maryland, 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 and sea surface wind 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
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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

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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.

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Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III in Remote Sensing (12 articles)

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21 pages, 8349 KiB

Open AccessArticle

Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model

by Minkyu Kim, Hyun Yang and Jonghwa Kim

Remote Sens. 2020, 12(21), 3654; https://doi.org/10.3390/rs12213654 - 7 Nov 2020

Cited by 44 | Viewed by 4968

Abstract

Recent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond to them as early as possible. In the present study, we propose an HWT prediction method that applies sea surface temperatures (SSTs) and deep-learning technology in a long short-term memory (LSTM) model based on a recurrent neural network (RNN). The LSTM model is used to predict time series data for the target areas, including the coastal area from Goheung to Yeosu, Jeollanam-do, Korea, which has experienced frequent HWT occurrences in recent years. To evaluate the performance of the SST prediction model, we compared and analyzed the results of an existing SST prediction model for the SST data, and additional external meteorological data. The proposed model outperformed the existing model in predicting SSTs and HWTs. Although the performance of the proposed model decreased as the prediction interval increased, it consistently showed better performance than the European Center for Medium-Range Weather Forecast (ECMWF) prediction model. Therefore, the method proposed in this study may be applied to prevent future damage to the aquaculture industry. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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16 pages, 4495 KiB

Open AccessArticle

Variability of Diurnal Sea Surface Temperature during Short Term and High SST Event in the Western Equatorial Pacific as Revealed by Satellite Data

by Anindya Wirasatriya, Kohtaro Hosoda, Joga Dharma Setiawan and R. Dwi Susanto

Remote Sens. 2020, 12(19), 3230; https://doi.org/10.3390/rs12193230 - 4 Oct 2020

Cited by 10 | Viewed by 3763

Abstract

Near-surface diurnal warming is an important process in the climate system, driving exchanges of water vapor and heat between the ocean and the atmosphere. The occurrence of the hot event (HE) is associated with the high diurnal sea surface temperature amplitude (δSST), which is defined as the difference between daily maximum and minimum sea surface temperature (SST). However, previous studies still show some inconsistency for the area of HE occurrence and high δSST. The present study produces global δSST data based on the SST, sea surface wind data derived from microwave radiometers, and solar radiation data obtained from visible/infrared radiometers. The value of δSSTs are estimated and validated over tropical oceans and then used for investigating HE in the western equatorial Pacific. A three-way error analysis was conducted using in situ mooring buoy arrays and geostationary SST measurements by the Himawari-8 and Geostationary Operational Environmental Satellite (GOES). The standard deviation error of daily and 10-day validation is around 0.3 °C and 0.14–0.19 °C, respectively. Our case study in the western Pacific (from 110°E to 150°W) shows that the area of HE occurrence coincided well with the area of high δSST. Climatological analysis shows that the collocated area between high occurrence rate of HE and high δSST, which coincides with the western Pacific warm pool region in all seasons. Thus, this study provides more persuasive evidence of the relation between HE occurrence and high δSST. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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23 pages, 8770 KiB

Open AccessArticle

Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager

by Rishi Kumar Gangwar and Pradeep Kumar Thapliyal

Remote Sens. 2020, 12(19), 3142; https://doi.org/10.3390/rs12193142 - 24 Sep 2020

Cited by 9 | Viewed by 3802

Abstract

Infrared (IR) radiometers from geostationary (GEO) satellites have an advantage over low-earth orbiting (LEO) satellites as they provide continuous observations to monitor the diurnal variations in the sea surface temperature (SST), typically better than 30-minute interval. However, GEO satellite observations suffer from significant diurnal and seasonal biases arising due to varying sun-earth-satellite geometry, leading to biases in SST estimates from conventional non-linear regression-based algorithms (NLSST). The midnight calibration issue occurring in GEO sensors poses a different challenge altogether. To mitigate these issues, we propose SST estimation from split-window IR observations of INSAT-3D and 3DR Imagers using One-Dimensional Variational (1DVAR) scheme. Prior to SST estimation, the bias correction in Imager observations is carried out using cumulative density function (CDF) matching. Then NLSST and 1DVAR algorithms were applied on six months of INSAT-3D/3DR observations to retrieve the SST. For the assessment of the developed algorithms, the retrieved SST was validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQuam) for the study period. The quantitative assessment confirms the superiority of the 1DVAR technique over the NLSST algorithm. However, both the schemes under-estimate the SST as compared to in-situ SST, which may be primarily due to the differences in the retrieved skin SST versus bulk in-situ SST. The 1DVAR scheme gives similar accuracy of SST for both INSAT-3D and 3DR with a bias of −0.36 K and standard deviation (Std) of 0.63 K. However, the NLSST algorithm provides slightly less accurate SST with bias (Std) of −0.18 K (0.87 K) for INSAT-3DR and −0.27 K (0.95 K) for INSAT-3D. Both the NLSST and 1DVAR algorithms are capable of producing the accurate thermal gradients from the retrieved SST as compared to the gradients calculated from daily Multiscale Ultrahigh Resolution (MUR) level-4 analysis SST acquired from Group for High-Resolution Sea Surface Temperature (GHRSST). Based on these spatial gradients, thermal fronts can be generated that are very useful for predicting potential fishery zones (PFZ), which is available from GEO satellites, INSAT-3D/3DR, in near real-time at 15-minute intervals. Results from the proposed 1DVAR and NLSST algorithms suggest a marked improvement in the SST estimates with reduced diurnal/seasonal biases as compared to the operational NLSST algorithm. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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27 pages, 12402 KiB

Open AccessArticle

Inter-Comparisons of Daily Sea Surface Temperatures and In-Situ Temperatures in the Coastal Regions

by Hye-Jin Woo and Kyung-Ae Park

Remote Sens. 2020, 12(10), 1592; https://doi.org/10.3390/rs12101592 - 16 May 2020

Cited by 29 | Viewed by 5186

Abstract

In this study, seven, global, blended, sea surface temperature (SST) analyses, including Operational SST and Sea Ice Analysis (OSTIA), Canadian Meteorological Centre (CMC) analysis, Optimum Interpolation SST (OISST), Remote Sensing System (REMSS) analysis, Multi-scale Ultra-high Resolution SST (MURSST), Merged Satellite and In situ Data Global Daily SST (MGDSST), and Geo-Polar Blended SST (Blended SST) were conducted. In-situ temperature measurements were used for the years 2014–2018, from 35 narrowly-spaced buoys distributed along the Korean Peninsula coast, to investigate how well the SST analyses represent the temperatures at the coastal regions. Contrary to the overall accuracy of the SSTs in the global ocean and offshore regions, the root-mean-square errors for the analyses were relatively large over 1.27 K. Specifically, all SST analyses resulted in warm biases over 0.31 K, which became quite distinctive in the western and the southwestern coastal regions. Investigation of the errors identified relationships with the coastal zones of vigorous tidal mixing, shallow bathymetry, and absence of microwave measurements. Overall, temporal wavelet coherency between in-situ measurements and SST products revealed high coherency of greater than 0.8 in periods longer than 180 days, however, low coherency (<0.5) in the period shorter than 10 days was observed. Inter-comparisons between the SST analyses illustrated clear spatial differences in the correlations at both the coastal regions, along the southwestern coast of the Korean Peninsula and in the frontal regions, and in the marginal seas of the Northwest Pacific. Overall, the results emphasized on the importance of using real-time in-situ measurements as much as possible, to overcome the increasing SST errors in coastal regions. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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18 pages, 9960 KiB

Open AccessArticle

Use of Uncertainty Inflation in OSTIA to Account for Correlated Errors in Satellite-Retrieved Sea Surface Temperature Data

by Rebecca Reid, Simon Good and Matthew J. Martin

Remote Sens. 2020, 12(7), 1083; https://doi.org/10.3390/rs12071083 - 27 Mar 2020

Cited by 1 | Viewed by 2564

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 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

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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10 pages, 2437 KiB

Open AccessTechnical Note

CoralTemp and the Coral Reef Watch Coral Bleaching Heat Stress Product Suite Version 3.1

by William Skirving, Benjamin Marsh, Jacqueline De La Cour, Gang Liu, Andy Harris, Eileen Maturi, Erick Geiger and C. Mark Eakin

Remote Sens. 2020, 12(23), 3856; https://doi.org/10.3390/rs12233856 - 25 Nov 2020

Cited by 100 | Viewed by 7407

Abstract

The National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has been providing resource managers, scientific researchers, and other coral reef ecosystem stakeholders with coral bleaching heat stress products for more than 20 years. The development of the CoralTemp sea surface temperature (SST) dataset has allowed CRW to produce the Coral Bleaching Heat Stress product suite with climatologies and daily SST measurements from within the same SST dataset, significantly improving data quality. Previously, the Monthly Mean (MM) SST and Maximum Monthly Mean (MMM) SST climatologies were derived using a different dataset from the near real-time SST. Here we provide an up-to-date description of how each product within the Coral Reef Watch Coral Bleaching Heat Stress product suite version 3.1 is derived, including descriptions of the MM, MMM, SST Anomaly, Coral Bleaching HotSpot and Degree Heating Week (DHW) products. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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18 pages, 10712 KiB

Open AccessLetter

High-Resolution Sea Surface Temperatures Derived from Landsat 8: A Study of Submesoscale Frontal Structures on the Pacific Shelf off the Hokkaido Coast, Japan

by Hiroshi Kuroda and Yuko Toya

Remote Sens. 2020, 12(20), 3326; https://doi.org/10.3390/rs12203326 - 13 Oct 2020

Cited by 8 | Viewed by 2869

Abstract

Coastal and offshore waters are generally separated by a barrier or “ocean front” on the continental shelf. A basic question arises as to what the representative spatial scale across the front may be. To answer this question, we simply corrected skin sea surface temperatures (SSTs) estimated from Landsat 8 imagery with a resolution of 100 m using skin SSTs estimated from geostationary meteorological satellite Himawari 8 with a resolution of 2 km. We analyzed snapshot images of skin SSTs on 13 October 2016, when we performed a simultaneous ship survey. We focused in particular on submesoscale thermal fronts on the Pacific shelf off the southeastern coast of Hokkaido, Japan. The overall spatial distribution of skin SSTs was consistent between Landsat 8 and Himawari 8; however, the spatial distribution of horizontal gradients of skin SSTs differed greatly between the two datasets. Some parts of strong fronts on the order of 1 °C km⁻¹ were underestimated with Himawari 8, mainly because of low resolution, whereas weak fronts on the order of 0.1 °C km⁻¹ were obscured in the Landsat 8 imagery because the signal-to-noise ratios were low. The widths of the strong fronts were estimated to be 114–461 m via Landsat 8 imagery and 539–1050 m via in situ ship survey. The difference was probably attributable to the difference in measurement depth of the SST, i.e., about 10-μm skin layer by satellite and a few dozen centimeters below the sea surface by the in situ survey. Our results indicated that an ocean model with a grid size of no more than ≤100–200 m is essential for realistic simulation of the frontal structure on the shelf. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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12 pages, 2073 KiB

Open AccessLetter

SST Comparison of AVHRR and MODIS Time Series in the Western Mediterranean Sea

by María José López García

Remote Sens. 2020, 12(14), 2241; https://doi.org/10.3390/rs12142241 - 13 Jul 2020

Cited by 14 | Viewed by 4459

Abstract

Sea Surface Temperature (SST) is a key parameter for understanding atmospheric and oceanic processes. Since the late 1980s, infrared satellite images have been used to complement in situ records for studying the temporal and spatial variability of SST. The Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA)’s satellite was the first sensor successfully used to compute SST following the development and validation of the atmospheric correction algorithm known as “split-window”. More recently, the MODerate-resolution Imaging Spectroradiometer (MODIS) on board the National Aeronautics and Space Administration (NASA)’s Terra and Aqua satellites, launched in 1999 and 2002, respectively, also provides SST products which can be combined with AVHRR series to complete the analysis of time series. This paper presents a comparison of the monthly SST data derived from both sensors, AVHRR and MODIS, in a series of ten years (2000–2009) in the Western Mediterranean basins. The results showed a high correlation (R² = 0.99) between the sensors when averaged values at the regional scale were compared. SST obtained from AVHRR were slightly higher (+0.18 °C ± 0.2 °C, on average) than SST from MODIS. The series were most similar during winter and spring (+0.09 °C ± 0.1 °C for January to May) with a greater difference from June to December (+0.24 °C ± 0.2 °C). The comparative analysis showed that the two sensors can be used jointly to estimate long-term trends at the regional scale. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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12 pages, 4047 KiB

Open AccessTechnical Note

Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments

by Jorge Vazquez-Cuervo, Jose Gomez-Valdes and Marouan Bouali

Remote Sens. 2020, 12(11), 1839; https://doi.org/10.3390/rs12111839 - 6 Jun 2020

Cited by 12 | Viewed by 4952

Abstract

Validation of satellite-based retrieval of ocean parameters like Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) is commonly done via statistical comparison with in situ measurements. Because in situ observations derived from coastal/tropical moored buoys and Argo floats are only representatives of one specific geographical point, they cannot be used to measure spatial gradients of ocean parameters (i.e., two-dimensional vectors). In this study, we exploit the high temporal sampling of the unmanned surface vehicle (USV) Saildrone (i.e., one measurement per minute) and describe a methodology to compare the magnitude of SST and SSS gradients derived from satellite-based products with those captured by Saildrone. Using two Saildrone campaigns conducted in the California/Baja region in 2018 and in the North Atlantic Gulf Stream in 2019, we compare the magnitude of gradients derived from six different GHRSST Level 4 SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and two SSS (JPLSMAP, RSS40km) datasets. While results indicate strong consistency between Saildrone- and satellite-based observations of SST and SSS, this is not the case for derived gradients with correlations lower than 0.4 for SST and 0.1 for SSS products. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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16 pages, 7268 KiB

Open AccessLetter

Improving the Altimeter-Derived Surface Currents Using Sea Surface Temperature (SST) Data: A Sensitivity Study to SST Products

by Daniele Ciani, Marie-Hélène Rio, Bruno Buongiorno Nardelli, Hélène Etienne and Rosalia Santoleri

Remote Sens. 2020, 12(10), 1601; https://doi.org/10.3390/rs12101601 - 17 May 2020

Cited by 20 | Viewed by 3688

Abstract

Measurements of ocean surface topography collected by satellite altimeters provide geostrophic estimates of the sea surface currents at relatively low resolution. The effective spatial and temporal resolution of these velocity estimates can be improved by optimally combining altimeter data with sequences of high resolution interpolated (Level 4) Sea Surface Temperature (SST) data, improving upon present-day values of approximately 100 km and 15 days at mid-latitudes. However, the combined altimeter/SST currents accuracy depends on the area and input SST data considered. Here, we present a comparative study based on three satellite-derived daily SST products: the Remote Sensing Systems (REMSS, 1/10

^{\circ}

resolution), the UK Met Office OSTIA (1/20

^{\circ}

resolution), and the Multiscale Ultra-High resolution SST (1/100

^{\circ}

resolution). The accuracy of the marine currents computed with our synergistic approach is assessed by comparisons with in-situ estimated currents derived from a global network of drifting buoys. Using REMSS SST, the meridional currents improve up to more than 20% compared to simple altimeter estimates. The maximum global improvements for the zonal currents are obtained using OSTIA SST, and reach 6%. Using the OSTIA SST also results in slight improvements (≃1.3%) in the zonal flow estimated in the Southern Ocean (45

^{\circ}

S to 70

^{\circ}

S). The homogeneity of the input SST effective spatial resolution is identified as a crucial requirement for an accurate surface current reconstruction. In our analyses, this condition was best satisfied by the lower resolution SST products considered. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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20 pages, 6942 KiB

Open AccessTechnical Note

The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses

by Simon Good, Emma Fiedler, Chongyuan Mao, Matthew J. Martin, Adam Maycock, Rebecca Reid, Jonah Roberts-Jones, Toby Searle, Jennifer Waters, James While and Mark Worsfold

Remote Sens. 2020, 12(4), 720; https://doi.org/10.3390/rs12040720 - 21 Feb 2020

Cited by 254 | Viewed by 10499

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 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

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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13 pages, 3191 KiB

Open AccessEditor’s ChoiceLetter

Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis

by Korak Saha, Prasanjit Dash, Xuepeng Zhao and Huai-min Zhang

Remote Sens. 2020, 12(4), 590; https://doi.org/10.3390/rs12040590 - 11 Feb 2020

Cited by 14 | Viewed by 4586

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 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

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature)

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