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Article: Direct-estimation algorithm for mapping daily land-surface broadband albedo from modis data

TitleDirect-estimation algorithm for mapping daily land-surface broadband albedo from modis data
Authors
KeywordsAngular bin regression
direct-estimation algorithm
land-surface broadband albedo
Moderate Resolution Imaging Spectroradiometer (MODIS)
polarization and directionality of the Earth's reflectance (POLDER) bidirectional reflectance distribution function (BRDF) database
Issue Date2014
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2014, v. 52, n. 2, p. 907-919 How to Cite?
AbstractLand surface albedo is a critical parameter in surface-energy budget studies. Over the past several decades, many albedo products are generated from remote-sensing data sets. The Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/Albedo algorithm is used to routinely produce eight day (16-day composite), 1-km resolution MODIS albedo products. When some natural processes or human activities occur, the land-surface broadband albedo can change rapidly, so it is necessary to enhance the temporal resolution of albedo product. We present a direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. The polarization and directionality of the Earth's reflectance-3/polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a Lidar BRDF database is employed as a training data set, and the 6S atmospheric radiative transfer code is used to simulate the top-of-atmosphere (TOA) reflectances. Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method. The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed. Simulation results show that the absolute error of this algorithm is ${\sim}{0.009}$ for vegetation, 0.012 for soil, and 0.030 for snow/ice. Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly. © 1980-2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/321551
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQu, Ying-
dc.contributor.authorLiu, Qiang-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorWang, Lizhao-
dc.contributor.authorLiu, Nanfeng-
dc.contributor.authorLiu, Suhong-
dc.date.accessioned2022-11-03T02:19:42Z-
dc.date.available2022-11-03T02:19:42Z-
dc.date.issued2014-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2014, v. 52, n. 2, p. 907-919-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/321551-
dc.description.abstractLand surface albedo is a critical parameter in surface-energy budget studies. Over the past several decades, many albedo products are generated from remote-sensing data sets. The Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/Albedo algorithm is used to routinely produce eight day (16-day composite), 1-km resolution MODIS albedo products. When some natural processes or human activities occur, the land-surface broadband albedo can change rapidly, so it is necessary to enhance the temporal resolution of albedo product. We present a direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. The polarization and directionality of the Earth's reflectance-3/polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a Lidar BRDF database is employed as a training data set, and the 6S atmospheric radiative transfer code is used to simulate the top-of-atmosphere (TOA) reflectances. Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method. The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed. Simulation results show that the absolute error of this algorithm is ${\sim}{0.009}$ for vegetation, 0.012 for soil, and 0.030 for snow/ice. Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly. © 1980-2012 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectAngular bin regression-
dc.subjectdirect-estimation algorithm-
dc.subjectland-surface broadband albedo-
dc.subjectModerate Resolution Imaging Spectroradiometer (MODIS)-
dc.subjectpolarization and directionality of the Earth's reflectance (POLDER) bidirectional reflectance distribution function (BRDF) database-
dc.titleDirect-estimation algorithm for mapping daily land-surface broadband albedo from modis data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2013.2245670-
dc.identifier.scopuseid_2-s2.0-84891038057-
dc.identifier.volume52-
dc.identifier.issue2-
dc.identifier.spage907-
dc.identifier.epage919-
dc.identifier.isiWOS:000328941300010-

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