Please use this identifier to cite or link to this item: https://doi.org/10.3390/rs12244120
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dc.titleImprovement and impacts of forest canopy parameters on noah-mp land surface model from uav-based photogrammetry
dc.contributor.authorChang, M.
dc.contributor.authorZhu, S.
dc.contributor.authorCao, J.
dc.contributor.authorChen, B.
dc.contributor.authorZhang, Q.
dc.contributor.authorChen, W.
dc.contributor.authorJia, S.
dc.contributor.authorKrishnan, P.
dc.contributor.authorWang, X.
dc.date.accessioned2021-08-10T03:10:11Z
dc.date.available2021-08-10T03:10:11Z
dc.date.issued2020
dc.identifier.citationChang, M., Zhu, S., Cao, J., Chen, B., Zhang, Q., Chen, W., Jia, S., Krishnan, P., Wang, X. (2020). Improvement and impacts of forest canopy parameters on noah-mp land surface model from uav-based photogrammetry. Remote Sensing 12 (24) : 1-19. ScholarBank@NUS Repository. https://doi.org/10.3390/rs12244120
dc.identifier.issn2072-4292
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/196290
dc.description.abstractTaking a typical forest’s underlying surface as our research area, in this study, we employed unmanned aerial vehicle (UAV) photogrammetry to explore more accurate canopy parameters including the tree height and canopy radius, which were used to improve the Noah-MP land surface model, which was conducted in the Dinghushan Forest Ecosystem Research Station (CN-Din). While the canopy radius was fitted as a Burr distribution, the canopy height of the CN-Din forest followed a Weibull distribution. Then, the canopy parameter distribution was obtained, and we improved the look-up table values of the Noah-MP land surface model. It was found that the influence on the simulation of the energy fluxes could not be negligible, and the main influence of these canopy parameters was on the latent heat flux, which could decrease up to ?11% in the midday while increasing up to 15% in the nighttime. Additionally, this work indicated that the description of the canopy characteristics for the land surface model should be improved to accurately represent the heterogeneity of the underlying surface. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2020
dc.subjectForest canopy parameters
dc.subjectLand surface modeling
dc.subjectUAV-based photogrammetry
dc.typeArticle
dc.contributor.departmentCIVIL AND ENVIRONMENTAL ENGINEERING
dc.description.doi10.3390/rs12244120
dc.description.sourcetitleRemote Sensing
dc.description.volume12
dc.description.issue24
dc.description.page1-19
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