Optimum satellite remote sensing of the marine carbonate system using empirical algorithms in the global ocean, the Greater Caribbean, the Amazon Plume and the Bay of Bengal
Improving our ability to monitor ocean carbonate chemistry has become a priority as the ocean continues to absorb carbon dioxide from the atmosphere. This long-term uptake is reducing the ocean pH; a process commonly known as ocean acidification. The use of satellite Earth Observation has not yet be...
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| Veröffentlicht in: | Remote sensing of environment 2019-12, Vol.235, p.111469, Article 111469 |
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| creator | Land, Peter E. Findlay, Helen S. Shutler, Jamie D. Ashton, Ian G.C. Holding, Thomas Grouazel, Antoine Girard-Ardhuin, Fanny Reul, Nicolas Piolle, Jean-Francois Chapron, Bertrand Quilfen, Yves Bellerby, Richard G.J. Bhadury, Punyasloke Salisbury, Joseph Vandemark, Douglas Sabia, Roberto |
| description | Improving our ability to monitor ocean carbonate chemistry has become a priority as the ocean continues to absorb carbon dioxide from the atmosphere. This long-term uptake is reducing the ocean pH; a process commonly known as ocean acidification. The use of satellite Earth Observation has not yet been thoroughly explored as an option for routinely observing surface ocean carbonate chemistry, although its potential has been highlighted. We demonstrate the suitability of using empirical algorithms to calculate total alkalinity (AT) and total dissolved inorganic carbon (CT), assessing the relative performance of satellite, interpolated in situ, and climatology datasets in reproducing the wider spatial patterns of these two variables. Both AT and CTin situ data are reproducible, both regionally and globally, using salinity and temperature datasets, with satellite observed salinity from Aquarius and SMOS providing performance comparable to other datasets for the majority of case studies. Global root mean squared difference (RMSD) between in situ validation data and satellite estimates is 17 μmol kg−1 with bias |
| doi_str_mv | 10.1016/j.rse.2019.111469 |
| format | Article |
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•Satellite salinity measurements enable estimation of surface carbonate parameters.•Uncertainties within these observation-based estimates are well characterized.•Monthly satellite salinity and temperature allows synoptic monitoring.•Satellite observations allow study of seasonal, interannual and episodic variations.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2019.111469</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Acidification ; Algorithms ; Alkalinity ; Aquarius ; Bias ; Carbon dioxide ; Carbon dioxide atmospheric concentrations ; Carbonate chemistry ; Carbonates ; Climatology ; CORA ; Datasets ; Dissolved inorganic carbon ; Earth observation ; Earth observations (from space) ; Empirical analysis ; HadGEM2-ES ; Ocean acidification ; Oceans ; Organic chemistry ; Remote sensing ; Salinity ; Salinity effects ; Satellite observation ; Satellites ; Sciences of the Universe ; SMOS ; Total alkalinity</subject><ispartof>Remote sensing of environment, 2019-12, Vol.235, p.111469, Article 111469</ispartof><rights>2019</rights><rights>Copyright Elsevier BV Dec 15, 2019</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c441t-eb136f2ac73a39649bc3b2a304a112ef16a0c7082906356fa88bce0a9acefcc93</citedby><cites>FETCH-LOGICAL-c441t-eb136f2ac73a39649bc3b2a304a112ef16a0c7082906356fa88bce0a9acefcc93</cites><orcidid>0000-0001-7819-7665 ; 0000-0002-9113-9467 ; 0000-0001-6088-8775 ; 0000-0003-2405-1075 ; 0000-0003-4881-2967</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0034425719304882$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://hal.science/hal-04202447$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Land, Peter E.</creatorcontrib><creatorcontrib>Findlay, Helen S.</creatorcontrib><creatorcontrib>Shutler, Jamie D.</creatorcontrib><creatorcontrib>Ashton, Ian G.C.</creatorcontrib><creatorcontrib>Holding, Thomas</creatorcontrib><creatorcontrib>Grouazel, Antoine</creatorcontrib><creatorcontrib>Girard-Ardhuin, Fanny</creatorcontrib><creatorcontrib>Reul, Nicolas</creatorcontrib><creatorcontrib>Piolle, Jean-Francois</creatorcontrib><creatorcontrib>Chapron, Bertrand</creatorcontrib><creatorcontrib>Quilfen, Yves</creatorcontrib><creatorcontrib>Bellerby, Richard G.J.</creatorcontrib><creatorcontrib>Bhadury, Punyasloke</creatorcontrib><creatorcontrib>Salisbury, Joseph</creatorcontrib><creatorcontrib>Vandemark, Douglas</creatorcontrib><creatorcontrib>Sabia, Roberto</creatorcontrib><title>Optimum satellite remote sensing of the marine carbonate system using empirical algorithms in the global ocean, the Greater Caribbean, the Amazon Plume and the Bay of Bengal</title><title>Remote sensing of environment</title><description>Improving our ability to monitor ocean carbonate chemistry has become a priority as the ocean continues to absorb carbon dioxide from the atmosphere. This long-term uptake is reducing the ocean pH; a process commonly known as ocean acidification. The use of satellite Earth Observation has not yet been thoroughly explored as an option for routinely observing surface ocean carbonate chemistry, although its potential has been highlighted. We demonstrate the suitability of using empirical algorithms to calculate total alkalinity (AT) and total dissolved inorganic carbon (CT), assessing the relative performance of satellite, interpolated in situ, and climatology datasets in reproducing the wider spatial patterns of these two variables. Both AT and CTin situ data are reproducible, both regionally and globally, using salinity and temperature datasets, with satellite observed salinity from Aquarius and SMOS providing performance comparable to other datasets for the majority of case studies. Global root mean squared difference (RMSD) between in situ validation data and satellite estimates is 17 μmol kg−1 with bias < 5 μmol kg−1 for AT and 30 μmol kg−1 with bias < 10 μmol kg−1 for CT. This analysis demonstrates that satellite sensors provide a credible solution for monitoring surface synoptic scale AT and CT. It also enables the first demonstration of observation-based synoptic scale AT and CT temporal mixing in the Amazon plume for 2010–2016, complete with a robust estimation of their uncertainty.
•Satellite salinity measurements enable estimation of surface carbonate parameters.•Uncertainties within these observation-based estimates are well characterized.•Monthly satellite salinity and temperature allows synoptic monitoring.•Satellite observations allow study of seasonal, interannual and episodic variations.</description><subject>Acidification</subject><subject>Algorithms</subject><subject>Alkalinity</subject><subject>Aquarius</subject><subject>Bias</subject><subject>Carbon dioxide</subject><subject>Carbon dioxide atmospheric concentrations</subject><subject>Carbonate chemistry</subject><subject>Carbonates</subject><subject>Climatology</subject><subject>CORA</subject><subject>Datasets</subject><subject>Dissolved inorganic carbon</subject><subject>Earth observation</subject><subject>Earth observations (from space)</subject><subject>Empirical analysis</subject><subject>HadGEM2-ES</subject><subject>Ocean acidification</subject><subject>Oceans</subject><subject>Organic chemistry</subject><subject>Remote sensing</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Satellite observation</subject><subject>Satellites</subject><subject>Sciences of the Universe</subject><subject>SMOS</subject><subject>Total alkalinity</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kU9r20AQxUVpoG7aD9DbQk-Fyp39Y_2hJ8e0ScGQHJLzMlqP7DXSrrsrBdzvlO_YlVVy7Gngze89ZnhZ9onDkgMvvh2XIdJSAK-XnHNV1G-yBa_KOocS1NtsASBVrsSqfJe9j_EIwFdVyRfZy_1psP3Ys4gDdZ0diAXqfRqRXLRuz3zLhgOxHoN1xAyGxjuc9uc4UM_GC0T9yQZrsGPY7X2ww6GPzLqLc9_5Ji28IXRfL8ptoJQQ2CZlNs2rvO7xj3fsoRt7Yuh2F_EGz9MJN-T22H3IrlrsIn38N6-zp58_Hjd3-fb-9tdmvc2NUnzIqeGyaAWaUqKsC1U3RjYCJSjkXFDLCwRTQiVqKOSqaLGqGkOANRpqjanldfZlzj1gp0_BpufP2qPVd-utnjRQAoRS5TNP7OeZPQX_e6Q46KMfg0vnaSElgBCikIniM2WCjzFQ-xrLQU8N6qNODeqpQT03mDzfZw-lV58tBR2NJWdoZwOZQe-8_Y_7L3Y9pVU</recordid><startdate>20191215</startdate><enddate>20191215</enddate><creator>Land, Peter E.</creator><creator>Findlay, Helen S.</creator><creator>Shutler, Jamie D.</creator><creator>Ashton, Ian G.C.</creator><creator>Holding, Thomas</creator><creator>Grouazel, 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empirical algorithms in the global ocean, the Greater Caribbean, the Amazon Plume and the Bay of Bengal</title><author>Land, Peter E. ; Findlay, Helen S. ; Shutler, Jamie D. ; Ashton, Ian G.C. ; Holding, Thomas ; Grouazel, Antoine ; Girard-Ardhuin, Fanny ; Reul, Nicolas ; Piolle, Jean-Francois ; Chapron, Bertrand ; Quilfen, Yves ; Bellerby, Richard G.J. ; Bhadury, Punyasloke ; Salisbury, Joseph ; Vandemark, Douglas ; Sabia, Roberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c441t-eb136f2ac73a39649bc3b2a304a112ef16a0c7082906356fa88bce0a9acefcc93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Acidification</topic><topic>Algorithms</topic><topic>Alkalinity</topic><topic>Aquarius</topic><topic>Bias</topic><topic>Carbon dioxide</topic><topic>Carbon dioxide atmospheric concentrations</topic><topic>Carbonate chemistry</topic><topic>Carbonates</topic><topic>Climatology</topic><topic>CORA</topic><topic>Datasets</topic><topic>Dissolved inorganic carbon</topic><topic>Earth observation</topic><topic>Earth observations (from space)</topic><topic>Empirical analysis</topic><topic>HadGEM2-ES</topic><topic>Ocean acidification</topic><topic>Oceans</topic><topic>Organic chemistry</topic><topic>Remote sensing</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Satellite observation</topic><topic>Satellites</topic><topic>Sciences of the Universe</topic><topic>SMOS</topic><topic>Total alkalinity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Land, Peter E.</creatorcontrib><creatorcontrib>Findlay, Helen S.</creatorcontrib><creatorcontrib>Shutler, Jamie D.</creatorcontrib><creatorcontrib>Ashton, Ian G.C.</creatorcontrib><creatorcontrib>Holding, Thomas</creatorcontrib><creatorcontrib>Grouazel, 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in the global ocean, the Greater Caribbean, the Amazon Plume and the Bay of Bengal</atitle><jtitle>Remote sensing of environment</jtitle><date>2019-12-15</date><risdate>2019</risdate><volume>235</volume><spage>111469</spage><pages>111469-</pages><artnum>111469</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Improving our ability to monitor ocean carbonate chemistry has become a priority as the ocean continues to absorb carbon dioxide from the atmosphere. This long-term uptake is reducing the ocean pH; a process commonly known as ocean acidification. The use of satellite Earth Observation has not yet been thoroughly explored as an option for routinely observing surface ocean carbonate chemistry, although its potential has been highlighted. We demonstrate the suitability of using empirical algorithms to calculate total alkalinity (AT) and total dissolved inorganic carbon (CT), assessing the relative performance of satellite, interpolated in situ, and climatology datasets in reproducing the wider spatial patterns of these two variables. Both AT and CTin situ data are reproducible, both regionally and globally, using salinity and temperature datasets, with satellite observed salinity from Aquarius and SMOS providing performance comparable to other datasets for the majority of case studies. Global root mean squared difference (RMSD) between in situ validation data and satellite estimates is 17 μmol kg−1 with bias < 5 μmol kg−1 for AT and 30 μmol kg−1 with bias < 10 μmol kg−1 for CT. This analysis demonstrates that satellite sensors provide a credible solution for monitoring surface synoptic scale AT and CT. It also enables the first demonstration of observation-based synoptic scale AT and CT temporal mixing in the Amazon plume for 2010–2016, complete with a robust estimation of their uncertainty.
•Satellite salinity measurements enable estimation of surface carbonate parameters.•Uncertainties within these observation-based estimates are well characterized.•Monthly satellite salinity and temperature allows synoptic monitoring.•Satellite observations allow study of seasonal, interannual and episodic variations.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2019.111469</doi><orcidid>https://orcid.org/0000-0001-7819-7665</orcidid><orcidid>https://orcid.org/0000-0002-9113-9467</orcidid><orcidid>https://orcid.org/0000-0001-6088-8775</orcidid><orcidid>https://orcid.org/0000-0003-2405-1075</orcidid><orcidid>https://orcid.org/0000-0003-4881-2967</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Acidification Algorithms Alkalinity Aquarius Bias Carbon dioxide Carbon dioxide atmospheric concentrations Carbonate chemistry Carbonates Climatology CORA Datasets Dissolved inorganic carbon Earth observation Earth observations (from space) Empirical analysis HadGEM2-ES Ocean acidification Oceans Organic chemistry Remote sensing Salinity Salinity effects Satellite observation Satellites Sciences of the Universe SMOS Total alkalinity |
| title | Optimum satellite remote sensing of the marine carbonate system using empirical algorithms in the global ocean, the Greater Caribbean, the Amazon Plume and the Bay of Bengal |
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