Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments
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 repres...
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ftmdpi:oai:mdpi.com:/2072-4292/12/11/1839/ 2023-08-20T04:08:16+02:00 Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments Jorge Vazquez-Cuervo Jose Gomez-Valdes Marouan Bouali agris 2020-06-06 application/pdf https://doi.org/10.3390/rs12111839 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs12111839 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 11; Pages: 1839 ocean fronts sea surface temperature/salinity gradients satellite observations Saildrone Text 2020 ftmdpi https://doi.org/10.3390/rs12111839 2023-07-31T23:36:19Z 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. Text North Atlantic MDPI Open Access Publishing Baja Remote Sensing 12 11 1839 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
ocean fronts sea surface temperature/salinity gradients satellite observations Saildrone |
spellingShingle |
ocean fronts sea surface temperature/salinity gradients satellite observations Saildrone Jorge Vazquez-Cuervo Jose Gomez-Valdes Marouan Bouali Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments |
topic_facet |
ocean fronts sea surface temperature/salinity gradients satellite observations Saildrone |
description |
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. |
format |
Text |
author |
Jorge Vazquez-Cuervo Jose Gomez-Valdes Marouan Bouali |
author_facet |
Jorge Vazquez-Cuervo Jose Gomez-Valdes Marouan Bouali |
author_sort |
Jorge Vazquez-Cuervo |
title |
Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments |
title_short |
Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments |
title_full |
Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments |
title_fullStr |
Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments |
title_full_unstemmed |
Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments |
title_sort |
comparison of satellite-derived sea surface temperature and sea surface salinity gradients using the saildrone california/baja and north atlantic gulf stream deployments |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12111839 |
op_coverage |
agris |
geographic |
Baja |
geographic_facet |
Baja |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Remote Sensing; Volume 12; Issue 11; Pages: 1839 |
op_relation |
Ocean Remote Sensing https://dx.doi.org/10.3390/rs12111839 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs12111839 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
11 |
container_start_page |
1839 |
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1774720453203460096 |