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

Yao Yunjun, Liang Shunlin, Li Xianglan, Chen Jiquan, Liu Shaomin, Zhang Xiaopeng, Xiao Zhiqiang, Fisher Joshua B., Mu Qiaozhen, Pan Ming, Liu Meng, Cheng Jie, Jiang Bo, Xie Xianhong, Grünwald Thomas, Bernhofer Christian, Roupsard Olivier. 2017. Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. Agricultural and Forest Meteorology, 242 : pp. 55-74.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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Quartile : Q1, Sujet : FORESTRY / Quartile : Outlier, Sujet : AGRONOMY / Quartile : Q1, Sujet : METEOROLOGY & ATMOSPHERIC SCIENCES

Abstract : Terrestrial 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. (Résumé d'auteur)

Mots-clés Agrovoc : Climatologie, Évapotranspiration, Méthode statistique, Modèle mathématique, Atmosphère, Cycle hydrologique, Cycle du carbone, couverture du sol, Couverture végétale, Terre

Mots-clés complémentaires : Biosphère, Hydrosphère

Mots-clés libres : Terrestrial evapotranspiration, Machine learning methods, Bayesian model averaging method, Plant functional type

Classification Agris : P40 - Meteorology and climatology
P01 - Nature conservation and land resources
U10 - Computer science, mathematics and statistics
P33 - Soil chemistry and physics

Champ stratégique Cirad : Axe 1 (2014-2018) - Agriculture écologiquement intensive

Auteurs et affiliations

  • Yao Yunjun, Beijing Normal University (CHN)
  • Liang Shunlin, Beijing Normal University (CHN)
  • Li Xianglan, Beijing Normal University (CHN)
  • Chen Jiquan, MSU (USA)
  • Liu Shaomin, Beijing Normal University (CHN)
  • Zhang Xiaopeng, Beijing Normal University (CHN)
  • Xiao Zhiqiang, Beijing Normal University (CHN)
  • Fisher Joshua B., JPL (USA)
  • Mu Qiaozhen, University of Montana (USA)
  • Pan Ming, Princeton University (USA)
  • Liu Meng, CAAS (CHN)
  • Cheng Jie, Beijing Normal University (CHN)
  • Jiang Bo, Beijing Normal University (CHN)
  • Xie Xianhong, Beijing Normal University (CHN)
  • Grünwald Thomas, Technische Universität Dresden (DEU)
  • Bernhofer Christian, Technische Universität Dresden (DEU)
  • Roupsard Olivier, CIRAD-PERSYST-UMR Eco&Sols (CRI)

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