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Article: Global Estimates for High-Spatial-Resolution Clear-Sky Land Surface Upwelling Longwave Radiation from MODIS Data

TitleGlobal Estimates for High-Spatial-Resolution Clear-Sky Land Surface Upwelling Longwave Radiation from MODIS Data
Authors
KeywordsModerate Resolution Imaging Spectroradiometer (MODIS)
remote sensing
surface radiation budget (SRB)
surface upwelling longwave radiation (LWUP)
Issue Date2016
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2016, v. 54, n. 7, p. 4115-4129 How to Cite?
AbstractSurface upwelling longwave radiation (LWUP) is a vital component in calculating the Earth's surface radiation budget. Under the general framework of the hybrid method, we developed linear and dynamic learning neural network (DLNN) models for estimating the global 1-km instantaneous clear-sky LWUP from the top-of-atmosphere radiance of Moderate Resolution Imaging Spectroradiometer thermal infrared channels 29, 31, and 32. Extensive radiative transfer simulations were conducted to produce a large number of representative samples, from which the linear model and DLNN model were derived. These two hybrid models were evaluated using ground measurements collected at 19 sites from three networks (SURFRAD, ASRCOP, and GAME-AAN). According to the validation results, the linear model was more accurate than the DLNN model, with a bias and root-mean-square error (RMSE) of -0.31 W/m2 and 19.92 W/m2 obtained by averaging the mean bias and RMSE for the three networks. Additionally, the computational efficiency of the linear model was much higher than that of the DLNN model. We also compared our linear model to a hybrid method developed by a previous study and found ours to perform better.
Persistent Identifierhttp://hdl.handle.net/10722/321691
ISSN
2021 Impact Factor: 8.125
2020 SCImago Journal Rankings: 2.141
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCheng, Jie-
dc.contributor.authorLiang, Shunlin-
dc.date.accessioned2022-11-03T02:20:48Z-
dc.date.available2022-11-03T02:20:48Z-
dc.date.issued2016-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2016, v. 54, n. 7, p. 4115-4129-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/321691-
dc.description.abstractSurface upwelling longwave radiation (LWUP) is a vital component in calculating the Earth's surface radiation budget. Under the general framework of the hybrid method, we developed linear and dynamic learning neural network (DLNN) models for estimating the global 1-km instantaneous clear-sky LWUP from the top-of-atmosphere radiance of Moderate Resolution Imaging Spectroradiometer thermal infrared channels 29, 31, and 32. Extensive radiative transfer simulations were conducted to produce a large number of representative samples, from which the linear model and DLNN model were derived. These two hybrid models were evaluated using ground measurements collected at 19 sites from three networks (SURFRAD, ASRCOP, and GAME-AAN). According to the validation results, the linear model was more accurate than the DLNN model, with a bias and root-mean-square error (RMSE) of -0.31 W/m2 and 19.92 W/m2 obtained by averaging the mean bias and RMSE for the three networks. Additionally, the computational efficiency of the linear model was much higher than that of the DLNN model. We also compared our linear model to a hybrid method developed by a previous study and found ours to perform better.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectModerate Resolution Imaging Spectroradiometer (MODIS)-
dc.subjectremote sensing-
dc.subjectsurface radiation budget (SRB)-
dc.subjectsurface upwelling longwave radiation (LWUP)-
dc.titleGlobal Estimates for High-Spatial-Resolution Clear-Sky Land Surface Upwelling Longwave Radiation from MODIS Data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2016.2537650-
dc.identifier.scopuseid_2-s2.0-84977982102-
dc.identifier.volume54-
dc.identifier.issue7-
dc.identifier.spage4115-
dc.identifier.epage4129-
dc.identifier.isiWOS:000377478400030-

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