Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation
Incorporation of mid-wave infrared (MWIR) channel/s into the prevalent regression-based split-window technique (SWT) for operational daytime sea surface temperature (SST) retrieval is challenging. However, the MWIR channels are highly desirable to obtain unambiguous information from the surface sinc...
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description | Incorporation of mid-wave infrared (MWIR) channel/s into the prevalent regression-based split-window technique (SWT) for operational daytime sea surface temperature (SST) retrieval is challenging. However, the MWIR channels are highly desirable to obtain unambiguous information from the surface since these channels offer high transparency with respect to the earth's atmosphere and are very sensitive to the thermal emission from the surface. On the other hand, the MWIR channel/s can be easily incorporated into any physical-based SST retrieval scheme. Daytime SST retrieval using various physical-based methods is studied and it is found that the physical deterministic sea surface temperature (PDSST) retrieval scheme is the best choice. This article discusses various scientific aspects of the daytime PDSST retrieval including MWIR channels from a theoretical point of view and its application on real data from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua. Daytime SST retrievals from PDSST, including MWIR channels, are also compared with the currently operational SWT-based SSTs from MODIS-Aqua and MODIS-Terra by NASA, without MWIR channels. The root-mean-square differences in PDSST from the in situ buoys using the global matchup data for daytime MODIS-Aqua SSTs is ~0.28 K for complete cloud-free set and is ~0.38 K for MODIS-Aqua and MODIS-Terra when quasi-deterministic cloud and error masking algorithm is applied for cloud detection. The information gain is defined by combining the two metrics, quality improvement and the increase in cloud-free data. The PDSST suite rendered two to three times as much information as the NASA-produced daytime regression-based SST. |
doi_str_mv | 10.1109/TGRS.2020.3008656 |
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However, the MWIR channels are highly desirable to obtain unambiguous information from the surface since these channels offer high transparency with respect to the earth's atmosphere and are very sensitive to the thermal emission from the surface. On the other hand, the MWIR channel/s can be easily incorporated into any physical-based SST retrieval scheme. Daytime SST retrieval using various physical-based methods is studied and it is found that the physical deterministic sea surface temperature (PDSST) retrieval scheme is the best choice. This article discusses various scientific aspects of the daytime PDSST retrieval including MWIR channels from a theoretical point of view and its application on real data from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua. Daytime SST retrievals from PDSST, including MWIR channels, are also compared with the currently operational SWT-based SSTs from MODIS-Aqua and MODIS-Terra by NASA, without MWIR channels. The root-mean-square differences in PDSST from the in situ buoys using the global matchup data for daytime MODIS-Aqua SSTs is ~0.28 K for complete cloud-free set and is ~0.38 K for MODIS-Aqua and MODIS-Terra when quasi-deterministic cloud and error masking algorithm is applied for cloud detection. The information gain is defined by combining the two metrics, quality improvement and the increase in cloud-free data. The PDSST suite rendered two to three times as much information as the NASA-produced daytime regression-based SST.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2020.3008656</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Atmospheric modeling ; Buoys ; Channels ; Data models ; Daytime ; Information retrieval ; infrared image sensor (Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua and MODIS-Terra) ; inverse problem [physical deterministic (PD)] ; MODIS ; Ocean temperature ; Predictive models ; Quality control ; radiative transfer (RT) ; Regression analysis ; remote sensing ; Retrieval ; Sea measurements ; Sea surface ; Sea surface temperature ; sea surface temperature (SST) ; Spectroradiometers ; Temperature measurement ; Thermal emission</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2021-04, Vol.59 (4), p.2833-2844</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c336t-6741b5ad86f250ac366a86e3f85cedeae014a7cb53737673aa3cee4980203bc3</citedby><cites>FETCH-LOGICAL-c336t-6741b5ad86f250ac366a86e3f85cedeae014a7cb53737673aa3cee4980203bc3</cites><orcidid>0000-0002-5999-5378</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9146669$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids></links><search><creatorcontrib>Koner, Prabhat K.</creatorcontrib><title>Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>Incorporation of mid-wave infrared (MWIR) channel/s into the prevalent regression-based split-window technique (SWT) for operational daytime sea surface temperature (SST) retrieval is challenging. However, the MWIR channels are highly desirable to obtain unambiguous information from the surface since these channels offer high transparency with respect to the earth's atmosphere and are very sensitive to the thermal emission from the surface. On the other hand, the MWIR channel/s can be easily incorporated into any physical-based SST retrieval scheme. Daytime SST retrieval using various physical-based methods is studied and it is found that the physical deterministic sea surface temperature (PDSST) retrieval scheme is the best choice. This article discusses various scientific aspects of the daytime PDSST retrieval including MWIR channels from a theoretical point of view and its application on real data from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua. Daytime SST retrievals from PDSST, including MWIR channels, are also compared with the currently operational SWT-based SSTs from MODIS-Aqua and MODIS-Terra by NASA, without MWIR channels. The root-mean-square differences in PDSST from the in situ buoys using the global matchup data for daytime MODIS-Aqua SSTs is ~0.28 K for complete cloud-free set and is ~0.38 K for MODIS-Aqua and MODIS-Terra when quasi-deterministic cloud and error masking algorithm is applied for cloud detection. The information gain is defined by combining the two metrics, quality improvement and the increase in cloud-free data. The PDSST suite rendered two to three times as much information as the NASA-produced daytime regression-based SST.</description><subject>Algorithms</subject><subject>Atmospheric modeling</subject><subject>Buoys</subject><subject>Channels</subject><subject>Data models</subject><subject>Daytime</subject><subject>Information retrieval</subject><subject>infrared image sensor (Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua and MODIS-Terra)</subject><subject>inverse problem [physical deterministic (PD)]</subject><subject>MODIS</subject><subject>Ocean temperature</subject><subject>Predictive models</subject><subject>Quality control</subject><subject>radiative transfer (RT)</subject><subject>Regression analysis</subject><subject>remote sensing</subject><subject>Retrieval</subject><subject>Sea measurements</subject><subject>Sea surface</subject><subject>Sea surface temperature</subject><subject>sea surface temperature (SST)</subject><subject>Spectroradiometers</subject><subject>Temperature measurement</subject><subject>Thermal emission</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNo9kE1Lw0AQhhdRsFZ_gHhZ8Jy6m81uEm-lai20CG3QY5huJnVLvtwkhfrr3dDiaWB43neYh5B7ziacs_gpma83E5_5bCIYi5RUF2TEpYw8poLgkowYj5XnR7F_TW7ads8YDyQPR-T3BY6dKZFuEOimtzlopAmWDVroeot0jZ01eICCLipd26Z2e1Pt6Mpk3hcckC5K2KGlK4TW8SVWXftMp8Wutqb7LukLHrCom2FPocroJxQmcxV1dUuucihavDvPMUneXpPZu7f8mC9m06WnhVCdp8KAbyVkkcp9yUALpSBSKPJIaswQ0L0Cod5KEYpQhQJAaMQgjpwMsdViTB5PtY2tf3psu3Rf97ZyF1PXJxUXinFH8ROlbd22FvO0saYEe0w5SwfD6WA4HQynZ8Mu83DKGET852MeKKVi8QcEm3lT</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Koner, Prabhat K.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-5999-5378</orcidid></search><sort><creationdate>20210401</creationdate><title>Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation</title><author>Koner, Prabhat K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c336t-6741b5ad86f250ac366a86e3f85cedeae014a7cb53737673aa3cee4980203bc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Atmospheric modeling</topic><topic>Buoys</topic><topic>Channels</topic><topic>Data models</topic><topic>Daytime</topic><topic>Information retrieval</topic><topic>infrared image sensor (Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua and MODIS-Terra)</topic><topic>inverse problem [physical deterministic (PD)]</topic><topic>MODIS</topic><topic>Ocean temperature</topic><topic>Predictive models</topic><topic>Quality control</topic><topic>radiative transfer (RT)</topic><topic>Regression analysis</topic><topic>remote sensing</topic><topic>Retrieval</topic><topic>Sea measurements</topic><topic>Sea surface</topic><topic>Sea surface temperature</topic><topic>sea surface temperature (SST)</topic><topic>Spectroradiometers</topic><topic>Temperature measurement</topic><topic>Thermal emission</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Koner, Prabhat K.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Koner, Prabhat K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2021-04-01</date><risdate>2021</risdate><volume>59</volume><issue>4</issue><spage>2833</spage><epage>2844</epage><pages>2833-2844</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract>Incorporation of mid-wave infrared (MWIR) channel/s into the prevalent regression-based split-window technique (SWT) for operational daytime sea surface temperature (SST) retrieval is challenging. However, the MWIR channels are highly desirable to obtain unambiguous information from the surface since these channels offer high transparency with respect to the earth's atmosphere and are very sensitive to the thermal emission from the surface. On the other hand, the MWIR channel/s can be easily incorporated into any physical-based SST retrieval scheme. Daytime SST retrieval using various physical-based methods is studied and it is found that the physical deterministic sea surface temperature (PDSST) retrieval scheme is the best choice. This article discusses various scientific aspects of the daytime PDSST retrieval including MWIR channels from a theoretical point of view and its application on real data from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua. Daytime SST retrievals from PDSST, including MWIR channels, are also compared with the currently operational SWT-based SSTs from MODIS-Aqua and MODIS-Terra by NASA, without MWIR channels. The root-mean-square differences in PDSST from the in situ buoys using the global matchup data for daytime MODIS-Aqua SSTs is ~0.28 K for complete cloud-free set and is ~0.38 K for MODIS-Aqua and MODIS-Terra when quasi-deterministic cloud and error masking algorithm is applied for cloud detection. The information gain is defined by combining the two metrics, quality improvement and the increase in cloud-free data. The PDSST suite rendered two to three times as much information as the NASA-produced daytime regression-based SST.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2020.3008656</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-5999-5378</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Atmospheric modeling Buoys Channels Data models Daytime Information retrieval infrared image sensor (Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua and MODIS-Terra) inverse problem [physical deterministic (PD)] MODIS Ocean temperature Predictive models Quality control radiative transfer (RT) Regression analysis remote sensing Retrieval Sea measurements Sea surface Sea surface temperature sea surface temperature (SST) Spectroradiometers Temperature measurement Thermal emission |
title | Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation |
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