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Article: Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms

TitleImproving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms
Authors
KeywordsBayesian model averaging method
Machine learning methods
Plant functional type
Terrestrial evapotranspiration
Issue Date2017
Citation
Agricultural and Forest Meteorology, 2017, v. 242, p. 55-74 How to Cite?
AbstractTerrestrial evapotranspiration (ET) for each plant functional type (PFT) is a key variable for linking the energy, water and carbon cycles of the atmosphere, hydrosphere and biosphere. Process-based algorithms have been widely used to estimate global terrestrial ET, yet each ET individual algorithm has exhibited large uncertainties. In this study, the support vector machine (SVM) method was introduced to improve global terrestrial ET estimation by integrating three process-based ET algorithms: MOD16, PT-JPL and SEMI-PM. At 200 FLUXNET flux tower sites, we evaluated the performance of the SVM method and others, including the Bayesian model averaging (BMA) method and the general regression neural networks (GRNNs) method together with three process-based ET algorithms. We found that the SVM method was superior to all other methods we evaluated. The validation results showed that compared with the individual algorithms, the SVM method driven by tower-specific (Modern Era Retrospective Analysis for Research and Applications, MERRA) meteorological data reduced the root mean square error (RMSE) by approximately 0.20 (0.15) mm/day for most forest sites and 0.30 (0.20) mm/day for most crop and grass sites and improved the squared correlation coefficient (R2) by approximately 0.10 (0.08) (95% confidence) for most flux tower sites. The water balance of basins and the global terrestrial ET calculation analysis also demonstrated that the regional and global estimates of the SVM-merged ET were reliable. The SVM method provides a powerful tool for improving global ET estimation to characterize the long-term spatiotemporal variations of the global terrestrial water budget.
Persistent Identifierhttp://hdl.handle.net/10722/321727
ISSN
2023 Impact Factor: 5.6
2023 SCImago Journal Rankings: 1.677
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYao, Yunjun-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorLi, Xianglan-
dc.contributor.authorChen, Jiquan-
dc.contributor.authorLiu, Shaomin-
dc.contributor.authorJia, Kun-
dc.contributor.authorZhang, Xiaotong-
dc.contributor.authorXiao, Zhiqiang-
dc.contributor.authorFisher, Joshua B.-
dc.contributor.authorMu, Qiaozhen-
dc.contributor.authorPan, Ming-
dc.contributor.authorLiu, Meng-
dc.contributor.authorCheng, Jie-
dc.contributor.authorJiang, Bo-
dc.contributor.authorXie, Xianhong-
dc.contributor.authorGrünwald, Thomas-
dc.contributor.authorBernhofer, Christian-
dc.contributor.authorRoupsard, Olivier-
dc.date.accessioned2022-11-03T02:21:03Z-
dc.date.available2022-11-03T02:21:03Z-
dc.date.issued2017-
dc.identifier.citationAgricultural and Forest Meteorology, 2017, v. 242, p. 55-74-
dc.identifier.issn0168-1923-
dc.identifier.urihttp://hdl.handle.net/10722/321727-
dc.description.abstractTerrestrial evapotranspiration (ET) for each plant functional type (PFT) is a key variable for linking the energy, water and carbon cycles of the atmosphere, hydrosphere and biosphere. Process-based algorithms have been widely used to estimate global terrestrial ET, yet each ET individual algorithm has exhibited large uncertainties. In this study, the support vector machine (SVM) method was introduced to improve global terrestrial ET estimation by integrating three process-based ET algorithms: MOD16, PT-JPL and SEMI-PM. At 200 FLUXNET flux tower sites, we evaluated the performance of the SVM method and others, including the Bayesian model averaging (BMA) method and the general regression neural networks (GRNNs) method together with three process-based ET algorithms. We found that the SVM method was superior to all other methods we evaluated. The validation results showed that compared with the individual algorithms, the SVM method driven by tower-specific (Modern Era Retrospective Analysis for Research and Applications, MERRA) meteorological data reduced the root mean square error (RMSE) by approximately 0.20 (0.15) mm/day for most forest sites and 0.30 (0.20) mm/day for most crop and grass sites and improved the squared correlation coefficient (R2) by approximately 0.10 (0.08) (95% confidence) for most flux tower sites. The water balance of basins and the global terrestrial ET calculation analysis also demonstrated that the regional and global estimates of the SVM-merged ET were reliable. The SVM method provides a powerful tool for improving global ET estimation to characterize the long-term spatiotemporal variations of the global terrestrial water budget.-
dc.languageeng-
dc.relation.ispartofAgricultural and Forest Meteorology-
dc.subjectBayesian model averaging method-
dc.subjectMachine learning methods-
dc.subjectPlant functional type-
dc.subjectTerrestrial evapotranspiration-
dc.titleImproving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.agrformet.2017.04.011-
dc.identifier.scopuseid_2-s2.0-85018393807-
dc.identifier.volume242-
dc.identifier.spage55-
dc.identifier.epage74-
dc.identifier.isiWOS:000403988500006-

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