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Article: A New Method for Retrieving Daily Land Surface Albedo from VIIRS Data

TitleA New Method for Retrieving Daily Land Surface Albedo from VIIRS Data
Authors
KeywordsBRDF
daily albedo
diurnal
surface albedo
surface radiation budget
VIIRS
Issue Date2017
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2017, v. 55, n. 3, p. 1765-1775 How to Cite?
AbstractUnlike instantaneous albedo, daily albedo of land surfaces is currently not routinely generated from satellite data, although it is a key input parameter for calculating daily shortwave radiation budget. This paper presents a novel approach to directly retrieve daily mean values of land surface broadband blue-sky albedo from Visible Infrared Imaging Radiometer Suite clear-sky data of apparent reflectance, with the assumption that the atmospheric conditions of the satellite overpass time can represent their daily values. Training data were simulated by atmospheric radiative transfer models, with surface spectra and bidirectional reflectance distribution function data as inputs for four aerosol types and a range of aerosol loadings. Sensitivity analysis was conducted to study the effects of cloud coverage, aerosol, and surface types on retrieval accuracy. Two years of measurements at six Surface Radiation Budget Network and eight Greenland Climate Network stations were used for algorithm validation. Daily albedo of snow-free surfaces can be retrieved with very high accuracy. By excluding far off-nadir observations of snow surfaces, the overall accuracy of retrieving daily albedo has a bias of 0.003 and a root-mean-square error of 0.055.
Persistent Identifierhttp://hdl.handle.net/10722/321724
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Dongdong-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorZhou, Yuan-
dc.contributor.authorHe, Tao-
dc.contributor.authorYu, Yunyue-
dc.date.accessioned2022-11-03T02:21:02Z-
dc.date.available2022-11-03T02:21:02Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2017, v. 55, n. 3, p. 1765-1775-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/321724-
dc.description.abstractUnlike instantaneous albedo, daily albedo of land surfaces is currently not routinely generated from satellite data, although it is a key input parameter for calculating daily shortwave radiation budget. This paper presents a novel approach to directly retrieve daily mean values of land surface broadband blue-sky albedo from Visible Infrared Imaging Radiometer Suite clear-sky data of apparent reflectance, with the assumption that the atmospheric conditions of the satellite overpass time can represent their daily values. Training data were simulated by atmospheric radiative transfer models, with surface spectra and bidirectional reflectance distribution function data as inputs for four aerosol types and a range of aerosol loadings. Sensitivity analysis was conducted to study the effects of cloud coverage, aerosol, and surface types on retrieval accuracy. Two years of measurements at six Surface Radiation Budget Network and eight Greenland Climate Network stations were used for algorithm validation. Daily albedo of snow-free surfaces can be retrieved with very high accuracy. By excluding far off-nadir observations of snow surfaces, the overall accuracy of retrieving daily albedo has a bias of 0.003 and a root-mean-square error of 0.055.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectBRDF-
dc.subjectdaily albedo-
dc.subjectdiurnal-
dc.subjectsurface albedo-
dc.subjectsurface radiation budget-
dc.subjectVIIRS-
dc.titleA New Method for Retrieving Daily Land Surface Albedo from VIIRS Data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2016.2632624-
dc.identifier.scopuseid_2-s2.0-85017522273-
dc.identifier.volume55-
dc.identifier.issue3-
dc.identifier.spage1765-
dc.identifier.epage1775-
dc.identifier.isiWOS:000396106700045-

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