TY - JOUR
T1 - Optimization of a remote sensing energy balance method over different canopy applied at global scale
AU - Chen, Xuelong
AU - Su, Zhongbo
AU - Ma, Yaoming
AU - Middleton, Elizabeth M.
PY - 2019/12/15
Y1 - 2019/12/15
N2 - 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.
AB - 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.
KW - Fluxnetwork
KW - Heat roughness length
KW - Parameter optimization
KW - Particle swarm optimization
KW - Remote sensing energy balance
KW - Surface energy balance system
KW - ITC-ISI-JOURNAL-ARTICLE
KW - 22/4 OA procedure
UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1016/j.agrformet.2019.107633
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/isi/su_opt.pdf
U2 - 10.1016/j.agrformet.2019.107633
DO - 10.1016/j.agrformet.2019.107633
M3 - Article
AN - SCOPUS:85070889402
VL - 279
SP - 1
EP - 15
JO - Agricultural and forest meteorology
JF - Agricultural and forest meteorology
SN - 0168-1923
M1 - 107633
ER -