Surface Reflectance From Commercial Very High Resolution Multispectral Imagery Estimated Empirically With Synthetic Landsat (2023)
Scientific analysis of Earth's land surface change benefits from well-characterized multispectral remotely sensed data for which models estimate and remove the effects of the atmosphere and sun-sensor geometry. Top-of-atmosphere (TOA) reflectance in commercial very high resolution (
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| Veröffentlicht in: | IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.16526-16534 |
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| container_title | IEEE journal of selected topics in applied earth observations and remote sensing |
| container_volume | 17 |
| creator | Montesano, Paul M. Macander, Matthew J. Caraballo-Vega, Jordan Alexis Frost, Melanie J. Neigh, Christopher S. R. Frost, Gerald V. Tamkin, Glenn S. Carroll, Mark L. |
| description | Scientific analysis of Earth's land surface change benefits from well-characterized multispectral remotely sensed data for which models estimate and remove the effects of the atmosphere and sun-sensor geometry. Top-of-atmosphere (TOA) reflectance in commercial very high resolution ( |
| doi_str_mv | 10.1109/JSTARS.2024.3456587 |
| format | Article |
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R. ; Frost, Gerald V. ; Tamkin, Glenn S. ; Carroll, Mark L.</creator><creatorcontrib>Montesano, Paul M. ; Macander, Matthew J. ; Caraballo-Vega, Jordan Alexis ; Frost, Melanie J. ; Neigh, Christopher S. R. ; Frost, Gerald V. ; Tamkin, Glenn S. ; Carroll, Mark L.</creatorcontrib><description>Scientific analysis of Earth's land surface change benefits from well-characterized multispectral remotely sensed data for which models estimate and remove the effects of the atmosphere and sun-sensor geometry. Top-of-atmosphere (TOA) reflectance in commercial very high resolution (<5 m; VHR) spaceborne imagery routinely varies for unchanged surfaces because of signal variation from these effects. To reliably identify critical broad-scale environmental change, consistency from surface reflectance (SR) versions of this imagery must be sufficient to identify and track the change or stability of fine-scale features that, though small, may be widely distributed across remote and heterogeneous domains. Commercial SR products are available, but typically the model employed is proprietary and their use is prohibitively costly for large spatial extents. Here, we 1) describe and apply an open-source workflow for the scientific community for fine-scaled empirical estimation of SR from multispectral VHR imagery using reference from synthetic Landsat SR, 2) examine SR model results and compare with corresponding TOA estimates for a large batch with varying acquisitions in Arctic and Sub-Arctic regions, 3) assess its consistency at pseudoinvariant calibration sites, and 4) quantify improvements in classification of land cover in a Sahelian region. Results show this workflow is best for longer wavelength optical bands, identifies poor estimates associated with image acquisition variation using context provided from large batches of VHR, improves estimates with robust regression models, produces consistent estimates for non-varying sites through time, and can increase the accuracy of land cover assessments.</description><identifier>ISSN: 1939-1404</identifier><identifier>EISSN: 2151-1535</identifier><identifier>DOI: 10.1109/JSTARS.2024.3456587</identifier><identifier>CODEN: IJSTHZ</identifier><language>eng</language><publisher>IEEE</publisher><subject>Atmospheric modeling ; Clouds ; Data models ; Estimation ; Land surface ; Landsat ; Reflectivity ; Remote sensing ; surface reflectance (SR) ; very high resolution (VHR)</subject><ispartof>IEEE journal of selected topics in applied earth observations and remote sensing, 2024, Vol.17, p.16526-16534</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c261t-cc6dcc6cc79baf04f8be8cbd0f3457c54dc86bb498964e88517c6439131eb5913</cites><orcidid>0000-0003-1695-2439 ; 0000-0002-5322-6340 ; 0000-0002-5134-0334 ; 0000-0001-6829-7961 ; 0000-0001-9125-5591 ; 0009-0005-7779-1159 ; 0000-0003-2808-208X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,2096,4010,27900,27901,27902</link.rule.ids></links><search><creatorcontrib>Montesano, Paul M.</creatorcontrib><creatorcontrib>Macander, Matthew J.</creatorcontrib><creatorcontrib>Caraballo-Vega, Jordan Alexis</creatorcontrib><creatorcontrib>Frost, Melanie J.</creatorcontrib><creatorcontrib>Neigh, Christopher S. R.</creatorcontrib><creatorcontrib>Frost, Gerald V.</creatorcontrib><creatorcontrib>Tamkin, Glenn S.</creatorcontrib><creatorcontrib>Carroll, Mark L.</creatorcontrib><title>Surface Reflectance From Commercial Very High Resolution Multispectral Imagery Estimated Empirically With Synthetic Landsat (2023)</title><title>IEEE journal of selected topics in applied earth observations and remote sensing</title><addtitle>JSTARS</addtitle><description>Scientific analysis of Earth's land surface change benefits from well-characterized multispectral remotely sensed data for which models estimate and remove the effects of the atmosphere and sun-sensor geometry. Top-of-atmosphere (TOA) reflectance in commercial very high resolution (<5 m; VHR) spaceborne imagery routinely varies for unchanged surfaces because of signal variation from these effects. To reliably identify critical broad-scale environmental change, consistency from surface reflectance (SR) versions of this imagery must be sufficient to identify and track the change or stability of fine-scale features that, though small, may be widely distributed across remote and heterogeneous domains. Commercial SR products are available, but typically the model employed is proprietary and their use is prohibitively costly for large spatial extents. Here, we 1) describe and apply an open-source workflow for the scientific community for fine-scaled empirical estimation of SR from multispectral VHR imagery using reference from synthetic Landsat SR, 2) examine SR model results and compare with corresponding TOA estimates for a large batch with varying acquisitions in Arctic and Sub-Arctic regions, 3) assess its consistency at pseudoinvariant calibration sites, and 4) quantify improvements in classification of land cover in a Sahelian region. Results show this workflow is best for longer wavelength optical bands, identifies poor estimates associated with image acquisition variation using context provided from large batches of VHR, improves estimates with robust regression models, produces consistent estimates for non-varying sites through time, and can increase the accuracy of land cover assessments.</description><subject>Atmospheric modeling</subject><subject>Clouds</subject><subject>Data models</subject><subject>Estimation</subject><subject>Land surface</subject><subject>Landsat</subject><subject>Reflectivity</subject><subject>Remote sensing</subject><subject>surface reflectance (SR)</subject><subject>very high resolution (VHR)</subject><issn>1939-1404</issn><issn>2151-1535</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkUtLAzEUhYMoWB-_QBdZ6mJqMkkmk6WUqpWKYKsuQyaPNmWmU5J00a2_3NSKuLjcSzjn44QDwBVGQ4yRuHueze_fZsMSlXRIKKtYzY_AoMQMF5gRdgwGWBBRYIroKTiLcYVQVXJBBuBrtg1OaQvfrGutTmqd74fQd3DUd50N2qsWftiwg09-scyq2Lfb5Ps1fNm2ycdN9oQsmXRqsVeNY_KdStbAcbfxwWvVtjv46dMSznbrtLTJazhVaxNVgjc5MLm9ACdOtdFe_u5z8P4wno-eiunr42R0Py10WeFUaF2ZPFpz0SiHqKsbW-vGIJd_zDWjRtdV01BRi4raumaY64oSgQm2DcvrHEwOXNOrldyEnDPsZK-8_Hnow0KqkOO1VjKRaYQ3SNWCUqaEMoQY5jgnxhHsMoscWDr0MQbr_ngYyX0l8lCJ3FcifyvJruuDy1tr_zkqjkohyDc_Ooo7</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Montesano, Paul M.</creator><creator>Macander, Matthew J.</creator><creator>Caraballo-Vega, Jordan Alexis</creator><creator>Frost, Melanie J.</creator><creator>Neigh, Christopher S. 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R.</au><au>Frost, Gerald V.</au><au>Tamkin, Glenn S.</au><au>Carroll, Mark L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Surface Reflectance From Commercial Very High Resolution Multispectral Imagery Estimated Empirically With Synthetic Landsat (2023)</atitle><jtitle>IEEE journal of selected topics in applied earth observations and remote sensing</jtitle><stitle>JSTARS</stitle><date>2024</date><risdate>2024</risdate><volume>17</volume><spage>16526</spage><epage>16534</epage><pages>16526-16534</pages><issn>1939-1404</issn><eissn>2151-1535</eissn><coden>IJSTHZ</coden><abstract>Scientific analysis of Earth's land surface change benefits from well-characterized multispectral remotely sensed data for which models estimate and remove the effects of the atmosphere and sun-sensor geometry. Top-of-atmosphere (TOA) reflectance in commercial very high resolution (<5 m; VHR) spaceborne imagery routinely varies for unchanged surfaces because of signal variation from these effects. To reliably identify critical broad-scale environmental change, consistency from surface reflectance (SR) versions of this imagery must be sufficient to identify and track the change or stability of fine-scale features that, though small, may be widely distributed across remote and heterogeneous domains. Commercial SR products are available, but typically the model employed is proprietary and their use is prohibitively costly for large spatial extents. Here, we 1) describe and apply an open-source workflow for the scientific community for fine-scaled empirical estimation of SR from multispectral VHR imagery using reference from synthetic Landsat SR, 2) examine SR model results and compare with corresponding TOA estimates for a large batch with varying acquisitions in Arctic and Sub-Arctic regions, 3) assess its consistency at pseudoinvariant calibration sites, and 4) quantify improvements in classification of land cover in a Sahelian region. 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| source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Atmospheric modeling Clouds Data models Estimation Land surface Landsat Reflectivity Remote sensing surface reflectance (SR) very high resolution (VHR) |
| title | Surface Reflectance From Commercial Very High Resolution Multispectral Imagery Estimated Empirically With Synthetic Landsat (2023) |
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