气候变化领域集成服务门户(CCPortal): Optimization of a remote sensing energy balance method over different canopy applied at global scale

CCPortal

DOI	10.1016/j.agrformet.2019.107633
	Optimization of a remote sensing energy balance method over different canopy applied at global scale
	Chen, Xuelong; Su, Zhongbo; Ma, Yaoming; Middleton, Elizabeth M.
通讯作者	Chen, XL (通讯作者)
发表日期	2019
ISSN	0168-1923
EISSN	1873-2240
卷号	279
英文摘要	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 9Wm(-2) for H, and 92Wm(-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.
关键词	PARTICLE SWARM OPTIMIZATION SYSTEM SEBS MOMENTUM-TRANSFER EVAPOTRANSPIRATION MODEL FLUX EVAPORATION LAND ROUGHNESS ALGORITHM
英文关键词	Remote sensing energy balance; Parameter optimization; Surface energy balance system; Heat roughness length; Fluxnetwork; Particle swarm optimization
语种	英语
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
WOS类目	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000500197400049
来源期刊	AGRICULTURAL AND FOREST METEOROLOGY
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259618
推荐引用方式 GB/T 7714	Chen, Xuelong,Su, Zhongbo,Ma, Yaoming,et al. Optimization of a remote sensing energy balance method over different canopy applied at global scale[J]. 中国科学院青藏高原研究所,2019,279.
APA	Chen, Xuelong,Su, Zhongbo,Ma, Yaoming,&Middleton, Elizabeth M..(2019).Optimization of a remote sensing energy balance method over different canopy applied at global scale.AGRICULTURAL AND FOREST METEOROLOGY,279.
MLA	Chen, Xuelong,et al."Optimization of a remote sensing energy balance method over different canopy applied at global scale".AGRICULTURAL AND FOREST METEOROLOGY 279(2019).