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Optimization of a remote sensing energy balance method over different canopy applied at global scale
Chen, XL (Chen, Xuelong)1,2; Su, ZB (Su, Zhongbo)3,6; Ma, YM (Ma, Yaoming)1,2,4; Middleton, EM (Middleton, Elizabeth M.)5
Source PublicationAGRICULTURAL AND FOREST METEOROLOGY
2019
Volume279Issue:0Pages:107633
DOI10.1016/j.agrformet.2019.107633
Abstract

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.

Subject AreaAtmospheric Sciences
WOS IDWOS:000500197400049
Language英语
Indexed BySCI
KeywordParticle Swarm Optimization System Sebs Momentum-transfer Evapotranspiration Model Flux Evaporation Land Roughness Algorithm
WOS Research AreaAgriculture ; Forestry ; Meteorology & Atmospheric Sciences
WOS SubjectAgronomy ; Forestry ; Meteorology & Atmospheric Sciences
Cooperation Status国际
ISSN0168-1923
Department环境变化与地表过程重点实验室
URL查看原文
PublisherELSEVIER
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itpcas.ac.cn/handle/131C11/9058
Collection图书馆
Corresponding AuthorChen, XL (Chen, Xuelong)
Affiliation1.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing, Peoples R China;
2.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing, Peoples R China;
3.Univ Twente, Fac Geoinformat Sci & Earth Observat, Enschede, Netherlands;
4.Univ Chinese Acad Sci, Beijing, Peoples R China;
5.NASA, Biospher Sci Lab, GSFC, Greenbelt, MD USA;
6.Changan Univ, Sch Environm Sci & Engn, Xian, Shaanxi, Peoples R China.
Recommended Citation
GB/T 7714
Chen, XL ,Su, ZB ,Ma, YM ,et al. Optimization of a remote sensing energy balance method over different canopy applied at global scale[J]. AGRICULTURAL AND FOREST METEOROLOGY,2019,279(0):107633.
APA Chen, XL ,Su, ZB ,Ma, YM ,&Middleton, EM .(2019).Optimization of a remote sensing energy balance method over different canopy applied at global scale.AGRICULTURAL AND FOREST METEOROLOGY,279(0),107633.
MLA Chen, XL ,et al."Optimization of a remote sensing energy balance method over different canopy applied at global scale".AGRICULTURAL AND FOREST METEOROLOGY 279.0(2019):107633.
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