Optimization of a remote sensing energy balance method over different canopy applied at global scale

Xuelong Chen*, Zhongbo Su, Yaoming Ma, Elizabeth M. Middleton

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)
2 Downloads (Pure)


Parameterization methods which calculate turbulent heat and water fluxes with thermal remote sensing data were evaluated in the revised remote sensing surface energy balance system (SEBS) model (Chen et al., 2013). The model calculates sensible heat (H) based on the Monin-Obukhov similarity theory (MOST) and determines latent heat (LE) as the residual of energy balance. We examined the uncertainties of H and LE in the SEBS model due to five key parameters at the local station point scale. Observations at 27 flux towers located in seven land cover types (needle-leaf forest, broadleaf forest, shrub, savanna, grassland, cropland, and sparsely vegetated land) and an artificial intelligence particle swarm optimization (PSO) algorithm was combined to calibrate the five parameters (leaf drag coefficient, leaf heat transfer coefficients, roughness length for soil, and two parameters for ground heat calculation) in the SEBS model. The root-mean-square error at the site scale was reduced by 9 Wm−2 for H, and 92 Wm−2 for LE, and their correlation coefficients were increased by 0.07 (H) and 0.11 (LE) after using the calibrated parameters. The updated model validation was further conducted globally for the remotely sensed evapotranspiration (ET) calculations. Overestimation of SEBS global ET was significantly improved by using the optimized values of the parameters. The results suggested PSO was able to consistently locate the global optimum of the SEBS model, and appears to be capable of solving the ET model optimization problem.

Original languageEnglish
Article number107633
Pages (from-to)1-15
Number of pages15
JournalAgricultural and forest meteorology
Early online date21 Aug 2019
Publication statusPublished - 15 Dec 2019


  • Fluxnetwork
  • Heat roughness length
  • Parameter optimization
  • Particle swarm optimization
  • Remote sensing energy balance
  • Surface energy balance system
  • 22/4 OA procedure


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