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Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature
Land surface temperature (LST) and its diurnal variation are important when evaluating climate change, the land–atmosphere energy budget, and the global hydrological cycle. However, the available satellite LST products have either a coarse spatial resolution or a low temporal resolution, which const...
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Published in: | Remote sensing of environment 2015-01, Vol.156, p.169-181 |
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description | Land surface temperature (LST) and its diurnal variation are important when evaluating climate change, the land–atmosphere energy budget, and the global hydrological cycle. However, the available satellite LST products have either a coarse spatial resolution or a low temporal resolution, which constrains their potential applications. This paper proposes a spatio-temporal integrated temperature fusion model (STITFM) for the retrieval of high temporal and spatial resolution LST from multi-scale polar-orbiting and geostationary satellite observations. Compared with the traditional fusion methods for LST with two different sensors, the proposed method is able to fuse the LST from arbitrary sensors in a unified framework. The model was tested using LST with fine, moderate, and coarse-resolutions. Data from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS), the Geostationary Operational Environmental Satellite (GOES) Imager, and the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) were used. The fused LST values were evaluated with in situ LST obtained from the Surface Radiation Budget Network (SURFRAD) and the Land Surface Analysis Satellite Application Facility (LSA SAF) project. The final validation results indicate that the STITFM is accurate to within about 2.5K.
•We propose a spatio-temporal integrated fusion model for land surface temperature.•The fusion model is able to fuse data from an arbitrary number of sensors.•Multi-scale polar-orbiting and geostationary satellite observations are used.•MODIS narrows the scale difference between the Landsat and GOES.•The validation results indicate that the method is accurate to within about 2.5K. |
doi_str_mv | 10.1016/j.rse.2014.09.013 |
format | article |
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•We propose a spatio-temporal integrated fusion model for land surface temperature.•The fusion model is able to fuse data from an arbitrary number of sensors.•Multi-scale polar-orbiting and geostationary satellite observations are used.•MODIS narrows the scale difference between the Landsat and GOES.•The validation results indicate that the method is accurate to within about 2.5K.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2014.09.013</identifier><language>eng</language><publisher>Elsevier Inc</publisher><subject>Climate change ; Geostationary satellite ; Geostationary satellites ; Integrated fusion ; Land surface temperature ; Multi-scale ; Polar-orbiting satellite ; Resolution ; Satellites ; Sensors ; Spatial resolution ; Spinning ; Temporal resolution</subject><ispartof>Remote sensing of environment, 2015-01, Vol.156, p.169-181</ispartof><rights>2014 Elsevier Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c404t-941eac6879b997563ab50d8de7f1be959fb39aeee7a5ba20edfbedd97ca7b9e33</citedby><cites>FETCH-LOGICAL-c404t-941eac6879b997563ab50d8de7f1be959fb39aeee7a5ba20edfbedd97ca7b9e33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Wu, Penghai</creatorcontrib><creatorcontrib>Shen, Huanfeng</creatorcontrib><creatorcontrib>Zhang, Liangpei</creatorcontrib><creatorcontrib>Göttsche, Frank-Michael</creatorcontrib><title>Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature</title><title>Remote sensing of environment</title><description>Land surface temperature (LST) and its diurnal variation are important when evaluating climate change, the land–atmosphere energy budget, and the global hydrological cycle. However, the available satellite LST products have either a coarse spatial resolution or a low temporal resolution, which constrains their potential applications. This paper proposes a spatio-temporal integrated temperature fusion model (STITFM) for the retrieval of high temporal and spatial resolution LST from multi-scale polar-orbiting and geostationary satellite observations. Compared with the traditional fusion methods for LST with two different sensors, the proposed method is able to fuse the LST from arbitrary sensors in a unified framework. The model was tested using LST with fine, moderate, and coarse-resolutions. Data from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS), the Geostationary Operational Environmental Satellite (GOES) Imager, and the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) were used. The fused LST values were evaluated with in situ LST obtained from the Surface Radiation Budget Network (SURFRAD) and the Land Surface Analysis Satellite Application Facility (LSA SAF) project. The final validation results indicate that the STITFM is accurate to within about 2.5K.
•We propose a spatio-temporal integrated fusion model for land surface temperature.•The fusion model is able to fuse data from an arbitrary number of sensors.•Multi-scale polar-orbiting and geostationary satellite observations are used.•MODIS narrows the scale difference between the Landsat and GOES.•The validation results indicate that the method is accurate to within about 2.5K.</description><subject>Climate change</subject><subject>Geostationary satellite</subject><subject>Geostationary satellites</subject><subject>Integrated fusion</subject><subject>Land surface temperature</subject><subject>Multi-scale</subject><subject>Polar-orbiting satellite</subject><subject>Resolution</subject><subject>Satellites</subject><subject>Sensors</subject><subject>Spatial resolution</subject><subject>Spinning</subject><subject>Temporal resolution</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqNkc1u3CAURq2qkTJN8wDZsezG7mWMzaCuqqg_kSJ1064RxpcZRti4XBypj9S3LJ7puuoKoXvOJy5fVT1waDjw_v25SYTNHrhoQDXA21fVjh-kqkGCeF3tAFpRi30nb6s3RGcA3h0k31W_n-aMx2Qyjsyt5OPMomPTGrKvyZqAbInBpDqmwWc_H5mZR3bESNnkApv0i1GRQ_AZWRwI08tlQMzFxPIJ2WSWZRNL7MkfT4yWAphwCco4LTGVS0KKYd1EFrYBrckZixcAy-vWhG-rG2cC4f3f86768fnT98ev9fO3L0-PH59rK0DkWgmOxvZl9UEp2fWtGToYDyNKxwdUnXJDqwwiStMNZg84ugHHUUlr5KCwbe-qd9fcJcWfK1LWkydbNjQzxpU073uA_sAl_w9UyMKC2FL5FbUpEiV0ekl-Kt-nOeitQX3WpUG9NahB6dJgcT5cHSzrvnhMmqzH2eLoE9qsx-j_Yf8BXU6qRQ</recordid><startdate>201501</startdate><enddate>201501</enddate><creator>Wu, Penghai</creator><creator>Shen, Huanfeng</creator><creator>Zhang, Liangpei</creator><creator>Göttsche, Frank-Michael</creator><general>Elsevier Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7U6</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>SOI</scope><scope>7SU</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>201501</creationdate><title>Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature</title><author>Wu, Penghai ; Shen, Huanfeng ; Zhang, Liangpei ; Göttsche, Frank-Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c404t-941eac6879b997563ab50d8de7f1be959fb39aeee7a5ba20edfbedd97ca7b9e33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Climate change</topic><topic>Geostationary satellite</topic><topic>Geostationary satellites</topic><topic>Integrated fusion</topic><topic>Land surface temperature</topic><topic>Multi-scale</topic><topic>Polar-orbiting satellite</topic><topic>Resolution</topic><topic>Satellites</topic><topic>Sensors</topic><topic>Spatial resolution</topic><topic>Spinning</topic><topic>Temporal resolution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Penghai</creatorcontrib><creatorcontrib>Shen, Huanfeng</creatorcontrib><creatorcontrib>Zhang, Liangpei</creatorcontrib><creatorcontrib>Göttsche, Frank-Michael</creatorcontrib><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Penghai</au><au>Shen, Huanfeng</au><au>Zhang, Liangpei</au><au>Göttsche, Frank-Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature</atitle><jtitle>Remote sensing of environment</jtitle><date>2015-01</date><risdate>2015</risdate><volume>156</volume><spage>169</spage><epage>181</epage><pages>169-181</pages><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Land surface temperature (LST) and its diurnal variation are important when evaluating climate change, the land–atmosphere energy budget, and the global hydrological cycle. However, the available satellite LST products have either a coarse spatial resolution or a low temporal resolution, which constrains their potential applications. This paper proposes a spatio-temporal integrated temperature fusion model (STITFM) for the retrieval of high temporal and spatial resolution LST from multi-scale polar-orbiting and geostationary satellite observations. Compared with the traditional fusion methods for LST with two different sensors, the proposed method is able to fuse the LST from arbitrary sensors in a unified framework. The model was tested using LST with fine, moderate, and coarse-resolutions. Data from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS), the Geostationary Operational Environmental Satellite (GOES) Imager, and the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) were used. The fused LST values were evaluated with in situ LST obtained from the Surface Radiation Budget Network (SURFRAD) and the Land Surface Analysis Satellite Application Facility (LSA SAF) project. The final validation results indicate that the STITFM is accurate to within about 2.5K.
•We propose a spatio-temporal integrated fusion model for land surface temperature.•The fusion model is able to fuse data from an arbitrary number of sensors.•Multi-scale polar-orbiting and geostationary satellite observations are used.•MODIS narrows the scale difference between the Landsat and GOES.•The validation results indicate that the method is accurate to within about 2.5K.</abstract><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2014.09.013</doi><tpages>13</tpages></addata></record> |
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subjects | Climate change Geostationary satellite Geostationary satellites Integrated fusion Land surface temperature Multi-scale Polar-orbiting satellite Resolution Satellites Sensors Spatial resolution Spinning Temporal resolution |
title | Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature |
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