Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds.
Identifieur interne : 000090 ( PubMed/Corpus ); précédent : 000089; suivant : 000091Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds.
Auteurs : Xiwei Fan ; Bo-Hui Tang ; Hua Wu ; Guangjian Yan ; Zhao-Liang LiSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2015.
Abstract
Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11.0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0.55 μm of 0.4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0.55 μm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31-34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11.0 to 2.2 K. The sensitivity analysis indicated that the total errors from all the uncertainties of input parameters, extension algorithm accuracy, and GSW algorithm accuracy were less than 2.5 K in nadir view. Finally, the Great Lakes surface water temperatures measured by buoys showed that the retrieval accuracy of the GSW algorithm was improved by at least 1.5 K using the proposed extension algorithm for cirrus skies.
DOI: 10.3390/s150509942
PubMed: 25928059
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds.</title><author><name sortKey="Fan, Xiwei" sort="Fan, Xiwei" uniqKey="Fan X" first="Xiwei" last="Fan">Xiwei Fan</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. fanxw.11b@igsnrr.ac.cn.</nlm:affiliation></affiliation></author><author><name sortKey="Tang, Bo Hui" sort="Tang, Bo Hui" uniqKey="Tang B" first="Bo-Hui" last="Tang">Bo-Hui Tang</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. tangbh@igsnrr.ac.cn.</nlm:affiliation></affiliation></author><author><name sortKey="Wu, Hua" sort="Wu, Hua" uniqKey="Wu H" first="Hua" last="Wu">Hua Wu</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. wuhua@igsnrr.ac.cn.</nlm:affiliation></affiliation></author><author><name sortKey="Yan, Guangjian" sort="Yan, Guangjian" uniqKey="Yan G" first="Guangjian" last="Yan">Guangjian Yan</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China. gjyan@bnu.edu.cn.</nlm:affiliation></affiliation></author><author><name sortKey="Li, Zhao Liang" sort="Li, Zhao Liang" uniqKey="Li Z" first="Zhao-Liang" last="Li">Zhao-Liang Li</name><affiliation><nlm:affiliation>Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China. lizhaoliang@caas.cn.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2015">2015</date><idno type="RBID">pubmed:25928059</idno><idno type="pmid">25928059</idno><idno type="doi">10.3390/s150509942</idno><idno type="wicri:Area/PubMed/Corpus">000090</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000090</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds.</title><author><name sortKey="Fan, Xiwei" sort="Fan, Xiwei" uniqKey="Fan X" first="Xiwei" last="Fan">Xiwei Fan</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. fanxw.11b@igsnrr.ac.cn.</nlm:affiliation></affiliation></author><author><name sortKey="Tang, Bo Hui" sort="Tang, Bo Hui" uniqKey="Tang B" first="Bo-Hui" last="Tang">Bo-Hui Tang</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. tangbh@igsnrr.ac.cn.</nlm:affiliation></affiliation></author><author><name sortKey="Wu, Hua" sort="Wu, Hua" uniqKey="Wu H" first="Hua" last="Wu">Hua Wu</name><affiliation><nlm:affiliation>State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. wuhua@igsnrr.ac.cn.</nlm:affiliation></affiliation></author><author><name sortKey="Yan, Guangjian" sort="Yan, Guangjian" uniqKey="Yan G" first="Guangjian" last="Yan">Guangjian Yan</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China. gjyan@bnu.edu.cn.</nlm:affiliation></affiliation></author><author><name sortKey="Li, Zhao Liang" sort="Li, Zhao Liang" uniqKey="Li Z" first="Zhao-Liang" last="Li">Zhao-Liang Li</name><affiliation><nlm:affiliation>Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China. lizhaoliang@caas.cn.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11.0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0.55 μm of 0.4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0.55 μm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31-34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11.0 to 2.2 K. The sensitivity analysis indicated that the total errors from all the uncertainties of input parameters, extension algorithm accuracy, and GSW algorithm accuracy were less than 2.5 K in nadir view. Finally, the Great Lakes surface water temperatures measured by buoys showed that the retrieval accuracy of the GSW algorithm was improved by at least 1.5 K using the proposed extension algorithm for cirrus skies.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">25928059</PMID><DateCreated><Year>2015</Year><Month>05</Month><Day>01</Day></DateCreated><DateCompleted><Year>2015</Year><Month>06</Month><Day>22</Day></DateCompleted><DateRevised><Year>2015</Year><Month>06</Month><Day>30</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1424-8220</ISSN><JournalIssue CitedMedium="Internet"><Volume>15</Volume><Issue>5</Issue><PubDate><Year>2015</Year><Month>Apr</Month><Day>28</Day></PubDate></JournalIssue><Title>Sensors (Basel, Switzerland)</Title><ISOAbbreviation>Sensors (Basel)</ISOAbbreviation></Journal><ArticleTitle>Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds.</ArticleTitle><Pagination><MedlinePgn>9942-61</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3390/s150509942</ELocationID><Abstract><AbstractText>Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11.0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0.55 μm of 0.4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0.55 μm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31-34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11.0 to 2.2 K. The sensitivity analysis indicated that the total errors from all the uncertainties of input parameters, extension algorithm accuracy, and GSW algorithm accuracy were less than 2.5 K in nadir view. Finally, the Great Lakes surface water temperatures measured by buoys showed that the retrieval accuracy of the GSW algorithm was improved by at least 1.5 K using the proposed extension algorithm for cirrus skies.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Fan</LastName><ForeName>Xiwei</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. fanxw.11b@igsnrr.ac.cn.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>University of Chinese Academy of Sciences, Beijing 100049, China. fanxw.11b@igsnrr.ac.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tang</LastName><ForeName>Bo-Hui</ForeName><Initials>BH</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. tangbh@igsnrr.ac.cn.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China. tangbh@igsnrr.ac.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Hua</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. wuhua@igsnrr.ac.cn.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China. wuhua@igsnrr.ac.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yan</LastName><ForeName>Guangjian</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China. gjyan@bnu.edu.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Zhao-Liang</ForeName><Initials>ZL</Initials><AffiliationInfo><Affiliation>Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China. lizhaoliang@caas.cn.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>ICube, UdS, CNRS, 300 Bld Sebastien Brant, CS10413, 67412 Illkirch, France. lizhaoliang@caas.cn.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>04</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Sensors (Basel)</MedlineTA><NlmUniqueID>101204366</NlmUniqueID><ISSNLinking>1424-8220</ISSNLinking></MedlineJournalInfo><OtherID Source="NLM">PMC4481960</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>03</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2015</Year><Month>04</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>5</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>5</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>5</Month><Day>1</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25928059</ArticleId><ArticleId IdType="pii">s150509942</ArticleId><ArticleId IdType="doi">10.3390/s150509942</ArticleId><ArticleId IdType="pmc">PMC4481960</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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