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Article: Bidirectional reflectance for multiple snow-covered land types from MISR products

TitleBidirectional reflectance for multiple snow-covered land types from MISR products
Authors
KeywordsBidirectional reflectance factor (BRF)
Multi-angle Imaging SpectroRadiometer (MISR)
snow
vegetation
Issue Date2012
Citation
IEEE Geoscience and Remote Sensing Letters, 2012, v. 9, n. 5, p. 994-998 How to Cite?
AbstractBidirectional reflectance factors (BRFs) play a key role in land surface studies. Snow has a significant influence on vegetative surface BRF. To evaluate the surface reflectance behaviors of snow-covered regions, a surface BRF database has been constructed from Multi-angle Imaging SpectroRadiometer BRF products for five biomes in the mid-high latitude regions of the U.S. (evergreen needleleaf forests, shrublands, grasslands, croplands, and urban areas). Using corresponding surface snow depth data from 26 meteorological stations, BRF signatures with snow cover are derived from the database to show the effect of snow on the BRF of vegetation. Five bidirectional reflectance distribution function models' abilities of capturing vegetation-snow mixed BRF shape are evaluated by fitting all the BRF data with snow. The results show that the Rahman model, Ross-Li model, and Walthall model perform well in fitting forest, grassland, and cropland BRFs when the surface is covered by snow. The Rahman model, Ross-Li model, and Roujean model fit visible reflectance well for mixed surfaces. The Rahman model best captures the BRF shapes, followed by the Ross-Li model. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/321464
ISSN
2023 Impact Factor: 4.0
2023 SCImago Journal Rankings: 1.248
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, Hongyi-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorTong, Ling-
dc.contributor.authorHe, Tao-
dc.contributor.authorYu, Yunyue-
dc.date.accessioned2022-11-03T02:19:06Z-
dc.date.available2022-11-03T02:19:06Z-
dc.date.issued2012-
dc.identifier.citationIEEE Geoscience and Remote Sensing Letters, 2012, v. 9, n. 5, p. 994-998-
dc.identifier.issn1545-598X-
dc.identifier.urihttp://hdl.handle.net/10722/321464-
dc.description.abstractBidirectional reflectance factors (BRFs) play a key role in land surface studies. Snow has a significant influence on vegetative surface BRF. To evaluate the surface reflectance behaviors of snow-covered regions, a surface BRF database has been constructed from Multi-angle Imaging SpectroRadiometer BRF products for five biomes in the mid-high latitude regions of the U.S. (evergreen needleleaf forests, shrublands, grasslands, croplands, and urban areas). Using corresponding surface snow depth data from 26 meteorological stations, BRF signatures with snow cover are derived from the database to show the effect of snow on the BRF of vegetation. Five bidirectional reflectance distribution function models' abilities of capturing vegetation-snow mixed BRF shape are evaluated by fitting all the BRF data with snow. The results show that the Rahman model, Ross-Li model, and Walthall model perform well in fitting forest, grassland, and cropland BRFs when the surface is covered by snow. The Rahman model, Ross-Li model, and Roujean model fit visible reflectance well for mixed surfaces. The Rahman model best captures the BRF shapes, followed by the Ross-Li model. © 2012 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Geoscience and Remote Sensing Letters-
dc.subjectBidirectional reflectance factor (BRF)-
dc.subjectMulti-angle Imaging SpectroRadiometer (MISR)-
dc.subjectsnow-
dc.subjectvegetation-
dc.titleBidirectional reflectance for multiple snow-covered land types from MISR products-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LGRS.2012.2187041-
dc.identifier.scopuseid_2-s2.0-84861922043-
dc.identifier.volume9-
dc.identifier.issue5-
dc.identifier.spage994-
dc.identifier.epage998-
dc.identifier.isiWOS:000310915500041-

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