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16 pages, 2505 KiB

Open AccessArticle

A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature

by Francisco Pastor, Jose Antonio Valiente and Samiro Khodayar

Remote Sens. 2020, 12(17), 2687; https://doi.org/10.3390/rs12172687 - 20 Aug 2020

Cited by 113 | Viewed by 11740

Abstract

The Mediterranean basin has been classified as a hot-spot for climate change. The Mediterranean Sea plays a fundamental regulatory role in the regional climate. We have analyzed the largest available and complete time series (1982–2019) of blended sea surface temperature (SST) data to [...] Read more.

The Mediterranean basin has been classified as a hot-spot for climate change. The Mediterranean Sea plays a fundamental regulatory role in the regional climate. We have analyzed the largest available and complete time series (1982–2019) of blended sea surface temperature (SST) data to study its seasonal cycle and look for a possible warming trend in the basin. From the analysis of the Mediterranean mean SST time series, a new temporal seasonal division is derived that differs from the one used in atmospheric climatology. Then, the SST time series were decomposed into their seasonal and trend components, and a consistent warming trend of 0.035 °C/year was obtained. The nature of this trend has been investigated, indicating a higher warming trend for both maximum and high/summer SST values than for the winter/colder ones. This reinforces the consistency of the SST increase since it is not only based on the presence of extreme values, but on a homogeneous basin global increase of high SST records as well. Although warming is found throughout the Mediterranean basin, the spatial variability found leads to the division of the basin into three distinct subareas regarding warming. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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18 pages, 27259 KiB

Open AccessArticle

New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations

by Andrea Pisano, Salvatore Marullo, Vincenzo Artale, Federico Falcini, Chunxue Yang, Francesca Elisa Leonelli, Rosalia Santoleri and Bruno Buongiorno Nardelli

Remote Sens. 2020, 12(1), 132; https://doi.org/10.3390/rs12010132 - 1 Jan 2020

Cited by 153 | Viewed by 12189

Abstract

Estimating long-term modifications of the sea surface temperature (SST) is crucial for evaluating the current state of the oceans and to correctly assess the impact of climate change at regional scales. In this work, we analyze SST variations within the Mediterranean Sea and [...] Read more.

Estimating long-term modifications of the sea surface temperature (SST) is crucial for evaluating the current state of the oceans and to correctly assess the impact of climate change at regional scales. In this work, we analyze SST variations within the Mediterranean Sea and the adjacent Northeastern Atlantic box (west of the Strait of Gibraltar) over the last 37 years, by using a satellite-based dataset from the Copernicus Marine Environment Monitoring Service (CMEMS). We found a mean warming trend of

0.041 \pm 0.006

^{\circ}

C/year over the whole Mediterranean Sea from 1982 to 2018. The trend has an uneven spatial pattern, with values increasing from

0.036 \pm 0.006

^{\circ}

C/year in the western basin to

0.048 \pm 0.006

^{\circ}

C/year in the Levantine–Aegean basin. The Northeastern Atlantic box and the Mediterranean show a similar trend until the late 1990s. Afterwards, the Mediterranean SST continues to increase, whereas the Northeastern Atlantic box shows no significant trend, until ~2015. The observed change in the Mediterranean Sea affects not only the mean trend but also the amplitude of the Mediterranean seasonal signal, with consistent relative increase and decrease of summer and winter mean values, respectively, over the period considered. The analysis of SST changes occurred during the “satellite era” is further complemented by reconstructions also based on direct in situ SST measurements, i.e., the Extended Reconstructed SST (ERSST) and the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST), which go back to the 19th century. The analysis of these longer time series, covering the last 165 years, indicates that the increasing Mediterranean trend, observed during the CMEMS operational period, is consistent with the Atlantic Multidecadal Oscillation (AMO), as it closely follows the last increasing period of AMO. This coincidence occurs at least until 2007, when the apparent onset of the decreasing phase of AMO is not seen in the Mediterranean SST evolution. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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18 pages, 4833 KiB

Open AccessEditor’s ChoiceArticle

Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

by Jorge Vazquez-Cuervo, Jose Gomez-Valdes, Marouan Bouali, Luis E. Miranda, Tom Van der Stocken, Wenqing Tang and Chelle Gentemann

Remote Sens. 2019, 11(17), 1964; https://doi.org/10.3390/rs11171964 - 21 Aug 2019

Cited by 39 | Viewed by 6211

Abstract

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the [...] Read more.

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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14 pages, 4101 KiB

Open AccessArticle

Seasonal Variability of Upwelling off Central-Southern Chile

by Andre Pinochet, José Garcés-Vargas, Carlos Lara and Francisco Olguín

Remote Sens. 2019, 11(15), 1737; https://doi.org/10.3390/rs11151737 - 24 Jul 2019

Cited by 17 | Viewed by 5681

Abstract

The central and northern Chilean coasts are part of the Humboldt Current System, which sustains one of the largest fisheries in the world due to upwelling. There are several upwelling focal points along the Chilean coast; however, from a physical standpoint, the region [...] Read more.

The central and northern Chilean coasts are part of the Humboldt Current System, which sustains one of the largest fisheries in the world due to upwelling. There are several upwelling focal points along the Chilean coast; however, from a physical standpoint, the region between 39° and 41° S has not been studied in detail despite being one of the most productive zones for pelagic extraction in Chile. Here, we evaluated the seasonal variability of coastal upwelling off central-southern Chile using principally daily sea surface temperature (SST) and sea surface wind (SSW), and 8-day composite chlorophyll-a concentration between 2003 and 2017. Through the seasonal evaluation of the net surface heat flux and its relationship with the SST as well as daily SST variability, we determined the “maximum upwelling” on our area. The direction of surface winds is controlled throughout the year by the Southeast Pacific Subtropical Anticyclone, which produces a cold tongue and an upwelling shadow north of Punta Galera (40° S) in austral spring and summer. A cross-correlation analysis showed a decrease of SST follow the alongshore SSW with a lag of 2 days in the months favorable to the upwelling. However, the correlations were not as high as what would be expected, indicating that there is a large advection of waters from the south that could be related to the greater volume of subantarctic water present in the zone. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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21 pages, 1210 KiB

Open AccessArticle

Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs

by Brahim Boussidi, Peter Cornillon, Gavino Puggioni and Chelle Gentemann

Remote Sens. 2019, 11(6), 715; https://doi.org/10.3390/rs11060715 - 25 Mar 2019

Cited by 2 | Viewed by 4185

Abstract

This study was undertaken to derive and analyze the advanced microwave scanning radiometer-Earth observing satellite (EOS) (AMSR-E) sea surface temperature (SST) footprint associated with the remote sensing systems (RSS) level-2 (L2) product. The footprint, in this case, is characterized by the weight attributed [...] Read more.

This study was undertaken to derive and analyze the advanced microwave scanning radiometer-Earth observing satellite (EOS) (AMSR-E) sea surface temperature (SST) footprint associated with the remote sensing systems (RSS) level-2 (L2) product. The footprint, in this case, is characterized by the weight attributed to each

4 \times 4

km square contributing to the SST value of a given (AMSR-E) pixel. High-resolution L2 SST fields obtained from the moderate-resolution imaging spectroradiometer (MODIS), carried on the same spacecraft as AMSR-E, are used as the sub-resolution “ground truth” from which the AMSR-E footprint is determined. Mathematically, the approach is equivalent to a linear inversion problem, and its solution is pursued by means of a constrained least square approximation based on the bootstrap sampling procedure. The method yielded an elliptic-like Gaussian kernel with an aspect ratio ≈1.58, very close to the AMSR-E

6.93 GHz

channel aspect ratio, ≈1.74. (The

6.93 GHz

channel is the primary spectral frequency used to determine SST.) The semi-major axis of the estimated footprint is found to be aligned with the instantaneous field-of-view of the sensor as expected from the geometric characteristics of AMSR-E. Footprints were also analyzed year-by-year and as a function of latitude and found to be stable—no dependence on latitude or on time. Precise knowledge of the footprint is central for any satellite-derived product characterization and, in particular, for efforts to deconvolve the heavily oversampled AMSR-E SST fields and for studies devoted to product validation and comparison. A preliminary analysis suggests that use of the derived footprint will reduce the variance between AMSR-E and MODIS fields compared to the results obtained ignoring the shape and size of the footprint as has been the practice in such comparisons to date. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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16 pages, 4708 KiB

Open AccessArticle

Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis

by Boris Petrenko, Alexander Ignatov, Yury Kihai and Matthew Pennybacker

Remote Sens. 2019, 11(2), 206; https://doi.org/10.3390/rs11020206 - 21 Jan 2019

Cited by 14 | Viewed by 4778

Abstract

Monitoring of the diurnal warming cycle in sea surface temperature (SST) is one of the key tasks of the new generation geostationary sensors, the Geostationary Operational Environmental Satellite (GOES)-16/17 Advanced Baseline Imager (ABI), and the Himawari-8/9 Advanced Himawari Imager (AHI). However, such monitoring [...] Read more.

Monitoring of the diurnal warming cycle in sea surface temperature (SST) is one of the key tasks of the new generation geostationary sensors, the Geostationary Operational Environmental Satellite (GOES)-16/17 Advanced Baseline Imager (ABI), and the Himawari-8/9 Advanced Himawari Imager (AHI). However, such monitoring requires modifications of the conventional SST retrieval algorithms. In order to closely reproduce temporal and spatial variations in SST, the sensitivity of retrieved SST to SSTskin should be as close to 1 as possible. Regression algorithms trained by matching satellite observations with in situ SST from drifting and moored buoys do not meet this requirement. Since the geostationary sensors observe tropical regions over larger domains and under more favorable conditions than mid-to-high latitudes, the matchups are predominantly concentrated within a narrow range of in situ SSTs >2 85 K. As a result, the algorithms trained against in situ SST provide the sensitivity to SSTskin as low as ~0.7 on average. An alternative training method, employed in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans, matches nighttime satellite clear-sky observations with the analysis L4 SST, interpolated to the sensor’s pixels. The method takes advantage of the total number of clear-sky pixels being large even at high latitudes. The operational use of this training method for ABI and AHI has increased the mean sensitivity of the global regression SST to ~0.9 without increasing regional biases. As a further development towards improved SSTskin retrieval, the piecewise regression SST algorithm was developed, which provides optimal sensitivity in every SST pixel. The paper describes the global and the piecewise regression algorithms trained against analysis SST and illustrates their performance with SST retrievals from the GOES-16 ABI. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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12 pages, 2901 KiB

Open AccessArticle

Does Sea Surface Temperature Contribute to Determining Range Limits and Expansion of Mangroves in Eastern South America (Brazil)?

by Arimatéa C. Ximenes, Leandro Ponsoni, Catarina F. Lira, Nico Koedam and Farid Dahdouh-Guebas

Remote Sens. 2018, 10(11), 1787; https://doi.org/10.3390/rs10111787 - 11 Nov 2018

Cited by 21 | Viewed by 5940

Abstract

Low Sea Surface Temperature (SST) is a climate barrier because it may inhibit and reduce seedling growth of mangrove propagules upon dispersal through seawater. Our objective is to analyze the spatio-temporal series of daily SST data from the Multi-scale Ultra-high Resolution (MUR)-SST in [...] Read more.

Low Sea Surface Temperature (SST) is a climate barrier because it may inhibit and reduce seedling growth of mangrove propagules upon dispersal through seawater. Our objective is to analyze the spatio-temporal series of daily SST data from the Multi-scale Ultra-high Resolution (MUR)-SST in order to identify the occurrence of chilling events for mangrove plants at the Eastern South America mangrove limit and beyond. We focus our study on three key sites: (i) the Rhizophora mangle L. distribution limit (Praia do Sonho: 27°53′S), (ii) the Eastern South America mangrove limit (Laguna: 28°30′S) and (iii) one beyond mangrove areas, in Araranguá (28°55′S). Our results show that, in Araranguá, chilling events are more intense and occur more frequently than in the other two sites that have a mangrove cover. We conclude that, the chilling events of SST may play a role in restricting mangroves within their actual limits. In this sense, higher occurrences of chilling events of SST could be an explanation for the absence of R. mangle in Laguna. However, Laguncularia racemosa (L.) C.F. Gaertn. was reported to be tolerant to low temperatures, and yet it is absent from the southernmost study site. This may be an indication of the role of other factors than SST in determining a mangrove range expansion, such as dispersal constraints. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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23 pages, 3744 KiB

Open AccessArticle

Sea Surface Temperature (SST) Variability of the Eastern Coastal Zone of the Gulf of California

by Carlos Manuel Robles-Tamayo, José Eduardo Valdez-Holguín, Ricardo García-Morales, Gudelia Figueroa-Preciado, Hugo Herrera-Cervantes, Juana López-Martínez and Luis Fernando Enríquez-Ocaña

Remote Sens. 2018, 10(9), 1434; https://doi.org/10.3390/rs10091434 - 8 Sep 2018

Cited by 19 | Viewed by 6718

Abstract

The coastal zones are areas with a high flow of energy and materials where diverse ecosystems are developed. The study of coastal oceanography is important to understand the variability of these ecosystems and determine their role in biogeochemical cycles and climate change. Sea [...] Read more.

The coastal zones are areas with a high flow of energy and materials where diverse ecosystems are developed. The study of coastal oceanography is important to understand the variability of these ecosystems and determine their role in biogeochemical cycles and climate change. Sea surface temperature (SST) analysis is indispensable for the characterization of physical and biological processes, and it is affected by processes at diverse timescales. The purpose of this work is to analyze the oceanographic variability of the Eastern Coastal Zone of the Gulf of California through the study of the SST from time series analysis of monthly data obtained from remote sensors (AVHRR-Pathfinder Version 5.1 and Version 5 resolution of 4 km, MODIS-Aqua, resolution of 4 km) for the period 1981 to 2016. The descriptive analysis of SST series showed that the values decrease from south to north, as well as the amplitude of the warm period decrease from south to north (cold period increase from south to north). The minimum values occurred during January and February, and ranged between 18 and 20 °C; and maximum values, of about 32 °C, arose in August and September. Cluster analysis allowed to group the data in four regions (south, center, midriff islands and north), the spectral analysis in each region showed frequencies of variation in scales: Annual (the main), seasonal, semiannual, and interannual. The latter is associated with the El Niño and La Niña climatological phenomena. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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22 pages, 3977 KiB

Open AccessArticle

Quality Assessment of Sea Surface Temperature from ATSRs of the Climate Change Initiative (Phase 1)

by Christoforos Tsamalis and Roger Saunders

Remote Sens. 2018, 10(4), 497; https://doi.org/10.3390/rs10040497 - 21 Mar 2018

Cited by 10 | Viewed by 4979

Abstract

Sea Surface Temperature (SST) observations from space have been made by the Along Track Scanning Radiometers (ATSRs) providing 20 years (August 1991–April 2012) of high quality data. As part of the ESA Climate Change Initiative (CCI) project, SSTs have been retrieved from the [...] Read more.

Sea Surface Temperature (SST) observations from space have been made by the Along Track Scanning Radiometers (ATSRs) providing 20 years (August 1991–April 2012) of high quality data. As part of the ESA Climate Change Initiative (CCI) project, SSTs have been retrieved from the ATSRs. Here, the quality of CCI SST (Phase 1) from ATSRs is validated against drifting buoys. Only CCI ATSR SSTs (Version 1.1) are considered, to facilitate the comparison with the precursor dataset ATSR Reprocessing for Climate (ARC). The CCI retrievals compared with drifting buoys have a median difference slightly larger than 0.1 K. The median SST difference is larger in the tropics (∼0.3 K) during the day, with the night time showing a spatially homogeneous pattern. ATSR-2 and AATSR show similar performance in terms of Robust Standard Deviation (RSD) being 0.2–0.3 K during night and about 0.1 K higher during day. On the other hand, ATSR-1 shows increasing RSD with time from 0.3 K to over 0.6 K. Triple collocation analysis has been applied for the first time on TMI/ATSR-2 observations and for daytime conditions when the wind speed is greater than 10 m/s. Both day and night results indicate that since 2004, the random uncertainty of drifting buoys and CCI AATSR is rather stable at about 0.22 K. Before 2004, drifting buoys have larger values (∼0.3 K), while ATSR-2 shows slightly lower values (∼0.2 K). The random uncertainty for AMSR-E is about 0.47 K, also rather stable with time, while as expected, the TMI has higher values of ∼0.55 K. It is shown for the first time that the AMSR-E random uncertainty changes with latitude, being ∼0.3 K in the tropics and about double this value at mid-latitudes. The SST uncertainties provided with the CCI data are slightly overestimated above 0.45 K and underestimated below 0.3 K during the day. The uncertainty model does not capture correctly the periods with instrument problems after the ATSR-1 3.7

μ

m channel failed and the gyro failure of ERS-2. During the night, the uncertainties are slightly underestimated. The CCI SSTs (Phase 1) do not yet match the quality of the ARC dataset when comparing to drifting buoys. The value of the ARC median bias is closer to zero than for CCI, while the RSD is about 0.05 K lower for ARC. ARC also shows a more homogeneous geographical distribution of median bias and RSD, although the differences between the two datasets are small. The observed discrepancies between CCI and ARC during the period of ATSR-1 are unexplained given that both datasets use the same retrieval method. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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9 pages, 5511 KiB

Open AccessArticle

Confirmation of ENSO-Southern Ocean Teleconnections Using Satellite-Derived SST

by Brady S. Ferster, Bulusu Subrahmanyam and Alison M. Macdonald

Remote Sens. 2018, 10(2), 331; https://doi.org/10.3390/rs10020331 - 23 Feb 2018

Cited by 19 | Viewed by 6516

Abstract

The Southern Ocean is the focus of many physical, chemical, and biological analyses due to its global importance and highly variable climate. This analysis of sea surface temperatures (SST) and global teleconnections shows that SSTs are significantly spatially correlated with both the Antarctic [...] Read more.

The Southern Ocean is the focus of many physical, chemical, and biological analyses due to its global importance and highly variable climate. This analysis of sea surface temperatures (SST) and global teleconnections shows that SSTs are significantly spatially correlated with both the Antarctic Oscillation and the Southern Oscillation, with spatial correlations between the indices and standardized SST anomalies approaching 1.0. Here, we report that the recent positive patterns in the Antarctic and Southern Oscillations are driving negative (cooling) trends in SST in the high latitude Southern Ocean and positive (warming) trends within the Southern Hemisphere sub-tropics and mid-latitudes. The coefficient of regression over the 35-year period analyzed implies that standardized temperatures have warmed at a rate of 0.0142 per year between 1982 and 2016 with a monthly standard error in the regression of 0.0008. Further regression calculations between the indices and SST indicate strong seasonality in response to changes in atmospheric circulation, with the strongest feedback occurring throughout the austral summer and autumn. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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14 pages, 1920 KiB

Open AccessArticle

Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation

by Ronan Fablet, Phi Huynh Viet, Redouane Lguensat, Pierre-Henri Horrein and Bertrand Chapron

Remote Sens. 2018, 10(2), 310; https://doi.org/10.3390/rs10020310 - 17 Feb 2018

Cited by 15 | Viewed by 5703

Abstract

The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical [...] Read more.

The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA) for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST). We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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24 pages, 8183 KiB

Open AccessFeature PaperArticle

Optimal Estimation of Sea Surface Temperature from AMSR-E

by Pia Nielsen-Englyst, Jacob L. Høyer, Leif Toudal Pedersen, Chelle L. Gentemann, Emy Alerskans, Tom Block and Craig Donlon

Remote Sens. 2018, 10(2), 229; https://doi.org/10.3390/rs10020229 - 2 Feb 2018

Cited by 32 | Viewed by 8361

Abstract

The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to [...] Read more.

The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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11 pages, 2815 KiB

Open AccessFeature PaperArticle

Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures

by Stéphane Saux Picart, Pierre Tandeo, Emmanuelle Autret and Blandine Gausset

Remote Sens. 2018, 10(2), 224; https://doi.org/10.3390/rs10020224 - 1 Feb 2018

Cited by 11 | Viewed by 4546

Abstract

Machine learning techniques are attractive tools to establish statistical models with a high degree of non linearity. They require a large amount of data to be trained and are therefore particularly suited to analysing remote sensing data. This work is an attempt at [...] Read more.

Machine learning techniques are attractive tools to establish statistical models with a high degree of non linearity. They require a large amount of data to be trained and are therefore particularly suited to analysing remote sensing data. This work is an attempt at using advanced statistical methods of machine learning to predict the bias between Sea Surface Temperature (SST) derived from infrared remote sensing and ground “truth” from drifting buoy measurements. A large dataset of collocation between satellite SST and in situ SST is explored. Four regression models are used: Simple multi-linear regression, Least Square Shrinkage and Selection Operator (LASSO), Generalised Additive Model (GAM) and random forest. In the case of geostationary satellites for which a large number of collocations is available, results show that the random forest model is the best model to predict the systematic errors and it is computationally fast, making it a good candidate for operational processing. It is able to explain nearly 31% of the total variance of the bias (in comparison to about 24% for the multi-linear regression model). Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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20 pages, 8049 KiB

Open AccessFeature PaperArticle

The Accuracies of Himawari-8 and MTSAT-2 Sea-Surface Temperatures in the Tropical Western Pacific Ocean

by Angela L. Ditri, Peter J. Minnett, Yang Liu, Katherine Kilpatrick and Ajoy Kumar

Remote Sens. 2018, 10(2), 212; https://doi.org/10.3390/rs10020212 - 1 Feb 2018

Cited by 18 | Viewed by 7024

Abstract

Over several decades, improving the accuracy of Sea-Surface Temperatures (SSTs) derived from satellites has been a subject of intense research, and continues to be so. Knowledge of the accuracy of the SSTs is critical for weather and climate predictions, and many research and [...] Read more.

Over several decades, improving the accuracy of Sea-Surface Temperatures (SSTs) derived from satellites has been a subject of intense research, and continues to be so. Knowledge of the accuracy of the SSTs is critical for weather and climate predictions, and many research and operational applications. In 2015, the operational Japanese MTSAT-2 geostationary satellite was replaced by the Himawari-8, which has a visible and infrared imager with higher spatial and temporal resolutions than its predecessor. In this study, data from both satellites during a three-month overlap period were compared with subsurface in situ temperature measurements from the Tropical Atmosphere Ocean (TAO) array and self-recording thermometers at the depths of corals of the Great Barrier Reef. Results show that in general the Himawari-8 provides more accurate SST measurements compared to those from MTSAT-2. At various locations, where in situ measurements were taken, the mean Himawari-8 SST error shows an improvement of ~0.15 K. Sources of the differences between the satellite-derived SST and the in situ temperatures were related to wind speed and diurnal heating. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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19 pages, 17419 KiB

Open AccessArticle

Role of El Niño Southern Oscillation (ENSO) Events on Temperature and Salinity Variability in the Agulhas Leakage Region

by Morgan L. Paris and Bulusu Subrahmanyam

Remote Sens. 2018, 10(1), 127; https://doi.org/10.3390/rs10010127 - 18 Jan 2018

Cited by 6 | Viewed by 7304

Abstract

This study explores the relationship between the Agulhas Current system and El Niño Southern Oscillation (ENSO) events. Specifically, it addresses monthly to yearly variations in Agulhas leakage where the Agulhas Current sheds waters into the Atlantic Ocean, in turn affecting meridional overturning circulation [...] Read more.

This study explores the relationship between the Agulhas Current system and El Niño Southern Oscillation (ENSO) events. Specifically, it addresses monthly to yearly variations in Agulhas leakage where the Agulhas Current sheds waters into the Atlantic Ocean, in turn affecting meridional overturning circulation (MOC). Sea surface temperature (SST) data from the National Oceanic and Atmospheric Administration’s (NOAA) Advanced Very High Resolution Radiometer (AVHRR) combined with sea surface salinity (SSS) from Soil Moisture Ocean Salinity (SMOS) and Simple Ocean Data Assimilation (SODA) reanalysis are used to explore changes in Agulhas leakage dynamics. Agulhas leakage is anomalously warm in response to El Niño and anomalously cool in response to La Niña. The corresponding SSS signal shows both a primary and secondary signal response. At first, the SSS signal of Agulhas leakage is anomalously fresh in response to El Niño, but this primary signal is replaced by a secondary anomalously saline signal. In response to La Niña, the primary SSS signal of Agulhas leakage is anomalously saline, while the secondary SSS signal is anomalously fresh. The lag between the peak of ENSO and the response in SST and the corresponding primary SSS signal of Agulhas leakage is about 20 months, followed by the secondary SSS signal at a lag of about 26 months. In general, increasing ENSO strength increases the extremes of the resulting anomalous SST and SSS signal and impacts the Agulhas leakage region earlier during El Niño and slightly later during La Niña. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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22 pages, 5775 KiB

Open AccessArticle

Stability Assessment of the (A)ATSR Sea Surface Temperature Climate Dataset from the European Space Agency Climate Change Initiative

by David I. Berry, Gary K. Corlett, Owen Embury and Christopher J. Merchant

Remote Sens. 2018, 10(1), 126; https://doi.org/10.3390/rs10010126 - 18 Jan 2018

Cited by 10 | Viewed by 7042

Abstract

Sea surface temperature is a key component of the climate record, with multiple independent records giving confidence in observed changes. As part of the European Space Agencies (ESA) Climate Change Initiative (CCI) the satellite archives have been reprocessed with the aim of creating [...] Read more.

Sea surface temperature is a key component of the climate record, with multiple independent records giving confidence in observed changes. As part of the European Space Agencies (ESA) Climate Change Initiative (CCI) the satellite archives have been reprocessed with the aim of creating a new dataset that is independent of the in situ observations, and stable with no artificial drift (<0.1 K decade⁻¹ globally) or step changes. We present a method to assess the satellite sea surface temperature (SST) record for step changes using the Penalized Maximal t Test (PMT) applied to aggregate time series. We demonstrated the application of the method using data from version EXP1.8 of the ESA SST CCI dataset averaged on a 7 km grid and in situ observations from moored buoys, drifting buoys and Argo floats. The CCI dataset was shown to be stable after ~1994, with minimal divergence (~0.01 K decade⁻¹) between the CCI data and in situ observations. Two steps were identified due to the failure of a gyroscope on the ERS-2 satellite, and subsequent correction mechanisms applied. These had minimal impact on the stability due to having equal magnitudes but opposite signs. The statistical power and false alarm rate of the method were assessed. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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21 pages, 1996 KiB

Open AccessFeature PaperArticle

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

by Claire E. Bulgin, Jonathan P. D. Mittaz, Owen Embury, Steinar Eastwood and Christopher J. Merchant

Remote Sens. 2018, 10(1), 97; https://doi.org/10.3390/rs10010097 - 12 Jan 2018

Cited by 22 | Viewed by 6385

Abstract

Cloud detection is a source of significant errors in retrieval of sea surface temperature (SST). We apply a Bayesian cloud detection scheme to 37 years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data, which is an important source of [...] Read more.

Cloud detection is a source of significant errors in retrieval of sea surface temperature (SST). We apply a Bayesian cloud detection scheme to 37 years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data, which is an important source of multi-decadal global SST information. The Bayesian scheme calculates a probability of clear-sky for each image pixel, conditional on the satellite observations and prior probability. We compare the cloud detection performance to the operational Clouds from AVHRR Extended algorithm (CLAVR-x), as a measure of improvement from reduced cloud-related errors. To do this we use sea surface temperature differences between satellite retrievals and in situ observations from drifting buoys and the Global Tropical Moored Buoy Array (GTMBA). The Bayesian scheme reduces the absolute difference between the mean and median SST biases and reduces the standard deviation of the SST differences by ~10% for both daytime and nighttime retrievals. These reductions are indicative of removing cloud contaminated outliers in the distribution, as these fall only on one side of the distribution forming a cold tail. At a probability threshold of 0.9 typically used to determine a binary cloud mask for SST retrieval, the Bayesian mask also reduces the robust standard deviation by ~5–10% during the day, in comparison with the operational cloud mask. This shows an improvement in the central distribution of SST differences for daytime retrievals. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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18 pages, 613 KiB

Open AccessArticle

The Role of Advanced Microwave Scanning Radiometer 2 Channels within an Optimal Estimation Scheme for Sea Surface Temperature

by Kevin Pearson, Christopher Merchant, Owen Embury and Craig Donlon

Remote Sens. 2018, 10(1), 90; https://doi.org/10.3390/rs10010090 - 11 Jan 2018

Cited by 13 | Viewed by 6338

Abstract

We present an analysis of information content for sea surface temperature (SST) retrieval from the Advanced Microwave Scanning Radiometer 2 (AMSR2). We find that SST uncertainty of ∼0.37 K can be achieved within an optimal estimation framework in the presence of wind, water [...] Read more.

We present an analysis of information content for sea surface temperature (SST) retrieval from the Advanced Microwave Scanning Radiometer 2 (AMSR2). We find that SST uncertainty of ∼0.37 K can be achieved within an optimal estimation framework in the presence of wind, water vapour and cloud liquid water effects, given appropriate assumptions for instrumental uncertainty and prior knowledge, and using all channels. We test all possible combinations of AMSR2 channels and demonstrate the importance of including cloud liquid water in the retrieval vector. The channel combinations, with the minimum number of channels, that carry most SST information content are calculated, since in practice calibration error drives a trade-off between retrieved SST uncertainty and the number of channels used. The most informative set of five channels is 6.9 V, 6.9 H, 7.3 V, 10.7 V and 36.5 H and these are suitable for optimal estimation retrievals. We discuss the relevance of microwave SSTs and issues related to them compared to SSTs derived from infra-red observations. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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2935 KiB

Open AccessArticle

Remote Sensing of Coral Bleaching Using Temperature and Light: Progress towards an Operational Algorithm

by William Skirving, Susana Enríquez, John D. Hedley, Sophie Dove, C. Mark Eakin, Robert A. B. Mason, Jacqueline L. De La Cour, Gang Liu, Ove Hoegh-Guldberg, Alan E. Strong, Peter J. Mumby and Roberto Iglesias-Prieto

Remote Sens. 2018, 10(1), 18; https://doi.org/10.3390/rs10010018 - 22 Dec 2017

Cited by 56 | Viewed by 10526

Abstract

The National Oceanic and Atmospheric Administration’s Coral Reef Watch program developed and operates several global satellite products to monitor bleaching-level heat stress. While these products have a proven ability to predict the onset of most mass coral bleaching events, they occasionally miss events; [...] Read more.

The National Oceanic and Atmospheric Administration’s Coral Reef Watch program developed and operates several global satellite products to monitor bleaching-level heat stress. While these products have a proven ability to predict the onset of most mass coral bleaching events, they occasionally miss events; inaccurately predict the severity of some mass coral bleaching events; or report false alarms. These products are based solely on temperature and yet coral bleaching is known to result from both temperature and light stress. This study presents a novel methodology (still under development), which combines temperature and light into a single measure of stress to predict the onset and severity of mass coral bleaching. We describe here the biological basis of the Light Stress Damage (LSD) algorithm under development. Then by using empirical relationships derived in separate experiments conducted in mesocosm facilities in the Mexican Caribbean we parameterize the LSD algorithm and demonstrate that it is able to describe three past bleaching events from the Great Barrier Reef (GBR). For this limited example, the LSD algorithm was able to better predict differences in the severity of the three past GBR bleaching events, quantifying the contribution of light to reduce or exacerbate the impact of heat stress. The new Light Stress Damage algorithm we present here is potentially a significant step forward in the evolution of satellite-based bleaching products. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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12792 KiB

Open AccessArticle

Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks

by Zhihong Liao, Qing Dong, Cunjin Xue, Jingwu Bi and Guangtong Wan

Remote Sens. 2017, 9(11), 1204; https://doi.org/10.3390/rs9111204 - 22 Nov 2017

Cited by 5 | Viewed by 4966

Abstract

A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the [...] Read more.

A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the Reynolds optimum interpolation (OI) v2 daily 0.25° SST (OISST) products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC) algorithm is designed to search for the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then, the reconstructed SSTs from the RBFN method are compared with the results from the OI method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and that the average RMSE is 0.48 °C for the RBFN method, which is quite smaller than the value of 0.69 °C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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3981 KiB

Open AccessFeature PaperArticle

Submesoscale Sea Surface Temperature Variability from UAV and Satellite Measurements

by Sandra L. Castro, William J. Emery, Gary A. Wick and William Tandy

Remote Sens. 2017, 9(11), 1089; https://doi.org/10.3390/rs9111089 - 25 Oct 2017

Cited by 30 | Viewed by 6460

Abstract

Earlier studies of spatial variability in sea surface temperature (SST) using ship-based radiometric data suggested that variability at scales smaller than 1 km is significant and affects the perceived uncertainty of satellite-derived SSTs. Here, we compare data from the Ball Experimental Sea Surface [...] Read more.

Earlier studies of spatial variability in sea surface temperature (SST) using ship-based radiometric data suggested that variability at scales smaller than 1 km is significant and affects the perceived uncertainty of satellite-derived SSTs. Here, we compare data from the Ball Experimental Sea Surface Temperature (BESST) thermal infrared radiometer flown over the Arctic Ocean against coincident Moderate Resolution Imaging Spectroradiometer (MODIS) measurements to assess the spatial variability of skin SSTs within 1-km pixels. By taking the standard deviation, σ, of the BESST measurements within individual MODIS pixels, we show that significant spatial variability of the skin temperature exists. The distribution of the surface variability measured by BESST shows a peak value of O(0.1) K, with 95% of the pixels showing σ < 0.45 K. Significantly, high-variability pixels are located at density fronts in the marginal ice zone, which are a primary source of submesoscale intermittency near the surface. SST wavenumber spectra indicate a spectral slope of −2, which is consistent with the presence of submesoscale processes at the ocean surface. Furthermore, the BESST wavenumber spectra not only match the energy distribution of MODIS SST spectra at the satellite-resolved wavelengths, they also span the spectral slope of −2 by ~3 decades, from wavelengths of 8 km to <0.08 km. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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21241 KiB

Open AccessArticle

Environmental Variability and Oceanographic Dynamics of the Central and Southern Coastal Zone of Sonora in the Gulf of California

by Ricardo García-Morales, Juana López-Martínez, Jose Eduardo Valdez-Holguin, Hugo Herrera-Cervantes and Luis Daniel Espinosa-Chaurand

Remote Sens. 2017, 9(9), 925; https://doi.org/10.3390/rs9090925 - 6 Sep 2017

Cited by 24 | Viewed by 7913

Abstract

This study analyzed monthly and inter-annual variability of mesoscale phenomena, including the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) climate indexes and wind intensity considering their influence on sea surface temperature (SST) and chlorophyll a (Chl-a). These analyses were performed [...] Read more.

This study analyzed monthly and inter-annual variability of mesoscale phenomena, including the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) climate indexes and wind intensity considering their influence on sea surface temperature (SST) and chlorophyll a (Chl-a). These analyses were performed to determine the effects, if any, of climate indexes and oceanographic and environmental variability on the central and southern coastal ecosystem of Sonora in the Gulf of California (GC). Monthly satellite images of SST (°C) and Chl-a concentration were used with a 1-km resolution for oceanographic and environmental description, as well as monthly data of the climate indexes and wind intensity from 2002–2015. Significant differences (p > 0.05) were observed while analyzing the monthly variability results of mesoscale phenomena, SST and Chl-a, where the greatest percentage of anti-cyclonic gyres and filaments was correlated with a greater Chl-a concentration in the area of study, low temperatures and, thus, greater productivity. Moreover, the greatest percentage of intrusion was correlated with the increase in temperature and cyclonic gyres and a strong decrease of Chl-a concentration values, causing oligotrophic conditions in the ecosystem and a decrease in upwelling and filament occurrence. As for the analysis of the interannual variability of mesoscales phenomena, SST, Chl-a and winds, the variability between years was not significant (p > 0.05), so no correlation was observed between variabilities or phenomena. The results of the monthly analyses of climate indexes, environmental variables and wind intensity did not show significant differences for the ENSO and PDO indexes (p > 0.05). Nonetheless, an important correlation could be observed between the months of negative anomalies of the ENSO with high Chl-a concentration values and intense winds, as well as with low SST values. The months with positive ENSO anomalies were correlated with high SST values, low Chl-a concentration and moderate winds. Significant inter-annual differences were observed for climate indexes where the years with high SST values were related to the greatest positive anomaly of ENSO, of which 2002 and 2009 stood out, characterized as moderate Niño years, and 2015 as a strong El Niño year. The years with the negative ENSO anomaly were related to the years of lower SST values, of which 2007–2008 and 2010–2011 stood out, characterized as moderate Niñas. Thus, variability associated with mesoscale oceanographic phenomena and seasonal and inter-annual variations of climate indexes had a great influence on the environmental conditions of the coastal ecosystem of Sonora in the Gulf of California. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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6236 KiB

Open AccessArticle

Determining the Pixel-to-Pixel Uncertainty in Satellite-Derived SST Fields

by Fan Wu, Peter Cornillon, Brahim Boussidi and Lei Guan

Remote Sens. 2017, 9(9), 877; https://doi.org/10.3390/rs9090877 - 23 Aug 2017

Cited by 11 | Viewed by 5982

Abstract

The primary measure of the quality of sea surface temperature (SST) fields obtained from satellite-borne infrared sensors has been the bias and variance of matchups with co-located in-situ values. Because such matchups tend to be widely separated, these bias and variance estimates are [...] Read more.

The primary measure of the quality of sea surface temperature (SST) fields obtained from satellite-borne infrared sensors has been the bias and variance of matchups with co-located in-situ values. Because such matchups tend to be widely separated, these bias and variance estimates are not necessarily a good measure of small scale (several pixels) gradients in these fields because one of the primary contributors to the uncertainty in satellite retrievals is atmospheric contamination, which tends to have large spatial scales compared with the pixel separation of infrared sensors. Hence, there is not a good measure to use in selecting SST fields appropriate for the study of submesoscale processes and, in particular, of processes associated with near-surface fronts, both of which have recently seen a rapid increase in interest. In this study, two methods are examined to address this problem, one based on spectra of the SST data and the other on their variograms. To evaluate the methods, instrument noise was estimated in Level-2 Visible-Infrared Imager-Radiometer Suite (VIIRS) and Advanced Very High Resolution Radiometer (AVHRR) SST fields of the Sargasso Sea. The two methods provided very nearly identical results for AVHRR: along-scan values of approximately 0.18 K for both day and night and along-track values of 0.21 K for day and night. By contrast, the instrument noise estimated for VIIRS varied by method, scan geometry and day-night. Specifically, daytime, along-scan (along-track), spectral estimates were found to be approximately 0.05 K (0.08 K) and the corresponding nighttime values of 0.02 K (0.03 K). Daytime estimates based on the variogram were found to be 0.08 K (0.10 K) with the corresponding nighttime values of 0.04 K (0.06 K). Taken together, AVHRR instrument noise is significantly larger than VIIRS instrument noise, along-track noise is larger than along-scan noise and daytime levels are higher than nighttime levels. Given the similarity of results and the less stringent preprocessing requirements, the variogram is the preferred method, although there is a suggestion that this approach overestimates the noise for high quality data in dynamically quiet regions. Finally, simulations of the impact of noise on the determination of SST gradients show that on average the gradient magnitude for typical ocean gradients will be accurately estimated with VIIRS but substantially overestimated with AVHRR. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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5576 KiB

Open AccessArticle

Evaluation of the Multi-Scale Ultra-High Resolution (MUR) Analysis of Lake Surface Temperature

by Erik Crosman, Jorge Vazquez-Cuervo and Toshio Michael Chin

Remote Sens. 2017, 9(7), 723; https://doi.org/10.3390/rs9070723 - 13 Jul 2017

Cited by 5 | Viewed by 6110

Abstract

Obtaining accurate and timely lake surface water temperature (LSWT) analyses from satellite remains difficult. Data gaps, cloud contamination, variations in atmospheric profiles of temperature and moisture, and a lack of in situ observations provide challenges for satellite-derived LSWT for climatological analysis or input [...] Read more.

Obtaining accurate and timely lake surface water temperature (LSWT) analyses from satellite remains difficult. Data gaps, cloud contamination, variations in atmospheric profiles of temperature and moisture, and a lack of in situ observations provide challenges for satellite-derived LSWT for climatological analysis or input into geophysical models. In this study, the Multi-scale Ultra-high Resolution (MUR) analysis of LSWT is evaluated between 2007 and 2015 over a small (Lake Oneida), medium (Lake Okeechobee), and large (Lake Michigan) lake. The advantages of the MUR LSWT analyses include daily consistency, high-resolution (~1 km), near-real time production, and multi-platform data synthesis. The MUR LSWT versus in situ measurements for Lake Michigan (Lake Okeechobee) have an overall bias (MUR LSWT-in situ) of −0.20 °C (0.31 °C) and a RMSE of 0.86 °C (0.91 °C). The MUR LSWT versus in situ measurements for Lake Oneida have overall large biases (−1.74 °C) and RMSE (3.42°C) due to a lack of available satellite imagery over the lake, but performs better during the less cloudy 15 July–30 September period. The results of this study highlight the importance of calculating validation statistics on a seasonal and annual basis for evaluating satellite-derived LSWT. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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15 pages, 3897 KiB

Open AccessEditor’s ChoiceLetter

Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

by Christopher J. Merchant and Owen Embury

Remote Sens. 2020, 12(16), 2554; https://doi.org/10.3390/rs12162554 - 8 Aug 2020

Cited by 14 | Viewed by 5377

Abstract

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate [...] Read more.

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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