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Article: Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products

TitleEstimation of monthly-mean daily global solar radiation based on MODIS and TRMM products
Authors
KeywordsArtificial neural network
Global solar radiation
MODIS
Remote sensing
TRMM
Issue Date2011
Citation
Applied Energy, 2011, v. 88, n. 7, p. 2480-2489 How to Cite?
AbstractGlobal solar radiation (GSR) is required in a large number of fields. Many parameterization schemes are developed to estimate it using routinely measured meteorological variables, since GSR is directly measured at a limited number of stations. Even so, meteorological stations are sparse, especially, in remote areas. Satellite signals (radiance at the top of atmosphere in most cases) can be used to estimate continuous GSR in space. However, many existing remote sensing products have a relatively coarse spatial resolution and these inversion algorithms are too complicated to be mastered by experts in other research fields. In this study, the artificial neural network (ANN) is utilized to build the mathematical relationship between measured monthly-mean daily GSR and several high-level remote sensing products available for the public, including Moderate Resolution Imaging Spectroradiometer (MODIS) monthly averaged land surface temperature (LST), the number of days in which the LST retrieval is performed in 1 month, MODIS enhanced vegetation index, Tropical Rainfall Measuring Mission satellite (TRMM) monthly precipitation. After training, GSR estimates from this ANN are verified against ground measurements at 12 radiation stations. Then, comparisons are performed among three GSR estimates, including the one presented in this study, a surface data-based estimate, and a remote sensing product by Japan Aerospace Exploration Agency (JAXA). Validation results indicate that the ANN-based method presented in this study can estimate monthly-mean daily GSR at a spatial resolution of about 5. km with high accuracy. © 2011 Elsevier Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/321436
ISSN
2023 Impact Factor: 10.1
2023 SCImago Journal Rankings: 2.820
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQin, Jun-
dc.contributor.authorChen, Zhuoqi-
dc.contributor.authorYang, Kun-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorTang, Wenjun-
dc.date.accessioned2022-11-03T02:18:54Z-
dc.date.available2022-11-03T02:18:54Z-
dc.date.issued2011-
dc.identifier.citationApplied Energy, 2011, v. 88, n. 7, p. 2480-2489-
dc.identifier.issn0306-2619-
dc.identifier.urihttp://hdl.handle.net/10722/321436-
dc.description.abstractGlobal solar radiation (GSR) is required in a large number of fields. Many parameterization schemes are developed to estimate it using routinely measured meteorological variables, since GSR is directly measured at a limited number of stations. Even so, meteorological stations are sparse, especially, in remote areas. Satellite signals (radiance at the top of atmosphere in most cases) can be used to estimate continuous GSR in space. However, many existing remote sensing products have a relatively coarse spatial resolution and these inversion algorithms are too complicated to be mastered by experts in other research fields. In this study, the artificial neural network (ANN) is utilized to build the mathematical relationship between measured monthly-mean daily GSR and several high-level remote sensing products available for the public, including Moderate Resolution Imaging Spectroradiometer (MODIS) monthly averaged land surface temperature (LST), the number of days in which the LST retrieval is performed in 1 month, MODIS enhanced vegetation index, Tropical Rainfall Measuring Mission satellite (TRMM) monthly precipitation. After training, GSR estimates from this ANN are verified against ground measurements at 12 radiation stations. Then, comparisons are performed among three GSR estimates, including the one presented in this study, a surface data-based estimate, and a remote sensing product by Japan Aerospace Exploration Agency (JAXA). Validation results indicate that the ANN-based method presented in this study can estimate monthly-mean daily GSR at a spatial resolution of about 5. km with high accuracy. © 2011 Elsevier Ltd.-
dc.languageeng-
dc.relation.ispartofApplied Energy-
dc.subjectArtificial neural network-
dc.subjectGlobal solar radiation-
dc.subjectMODIS-
dc.subjectRemote sensing-
dc.subjectTRMM-
dc.titleEstimation of monthly-mean daily global solar radiation based on MODIS and TRMM products-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.apenergy.2011.01.018-
dc.identifier.scopuseid_2-s2.0-79952450805-
dc.identifier.volume88-
dc.identifier.issue7-
dc.identifier.spage2480-
dc.identifier.epage2489-
dc.identifier.isiWOS:000289497400021-

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