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DOI	10.1016/j.agrformet.2020.108314
	Improving the global MODIS GPP model by optimizing parameters with FLUXNET data
	Huang, Xiaojuan; Xiao, Jingfeng; Wang, Xufeng; Ma, Mingguo
通讯作者	Xiao, JF ; Ma, MG (通讯作者)，Univ New Hampshire, Earth Syst Res Ctr, Inst Study Earth Oceans & Space, Durham, NH 03824 USA.
发表日期	2021
ISSN	0168-1923
EISSN	1873-2240
卷号	300
英文摘要	The global gross primary productivity (GPP) product derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is perhaps the most widely used GPP product. However, there is still a large uncertainty associated with the MODIS GPP product partly due to the uncertainty in the default Biome specified Parameters Look-Up Table (BPLUT) of the MODIS photosynthesis (PSN) model. Here, we used the Bayesian inference with the Markov chain Monte Carlo (MCMC) approach and FLUXNET data from 110 sites to estimate the parameters of the MODIS PSN model (maximum light use efficiency: epsilon(max); temperature scalar-related parameters: Tmin(min) and Tmin(max); water scalar-related parameters: VPDmin and VPDmax) through individual and joint optimization. The spread of the posterior probability density function (PDF) of the parameters allowed for the calculation of parameter means and uncertainty estimates and also provided information on the behavior of the parameters. Each model parameter varied not only across sites but also across plant functional types (PFTs). The means of the optimized parameter values within each PFT were used to update the BPLUT. We also generated parameter estimates for wetlands and C4/C3 croplands in the BPLUT. Parameters from the joint optimization were more representative and less variable. The optimization improved the performance of the MODIS PSN model by 15% for deciduous broadleaf forests, 8% for savannas, and 3% for grasslands with well-constrained parameters. The performance of the optimized model depended on the effectiveness of parameter optimization. Our study is an effort towards quantifying and reducing parameter uncertainty of the MODIS PSN model and improving the global MODIS GPP product for better understanding global ecosystem carbon dynamics, plant productivity, and carbon-climate feedbacks.
关键词	GROSS PRIMARY PRODUCTION LIGHT-USE-EFFICIENCY NET ECOSYSTEM EXCHANGE PHOTOSYNTHETIC PARAMETERS DATA FUSION SEASONAL FLUCTUATIONS UNCERTAINTY ANALYSIS TERRESTRIAL GROSS CARBON FLUXES PRODUCTIVITY
英文关键词	Parameter optimization; Gross primary production; Light use efficiency; Eddy covariance; Uncertainty; Remote sensing
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:000635674500005
来源期刊	AGRICULTURAL AND FOREST METEOROLOGY
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254653
作者单位	[Huang, Xiaojuan; Ma, Mingguo] Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat &, Chongqing 400715, Peoples R China; [Huang, Xiaojuan] San Yat Sen Univ, Sch Atmospher Sci, Guangzhou 510245, Guangdong, Peoples R China; [Xiao, Jingfeng] Univ New Hampshire, Earth Syst Res Ctr, Inst Study Earth Oceans & Space, Durham, NH 03824 USA; [Wang, Xufeng] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China; [Ma, Mingguo] Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data Ap, Sch Geog Sci, Chongqing 400715, Peoples R China
推荐引用方式 GB/T 7714	Huang, Xiaojuan,Xiao, Jingfeng,Wang, Xufeng,et al. Improving the global MODIS GPP model by optimizing parameters with FLUXNET data[J]. 中国科学院西北生态环境资源研究院,2021,300.
APA	Huang, Xiaojuan,Xiao, Jingfeng,Wang, Xufeng,&Ma, Mingguo.(2021).Improving the global MODIS GPP model by optimizing parameters with FLUXNET data.AGRICULTURAL AND FOREST METEOROLOGY,300.
MLA	Huang, Xiaojuan,et al."Improving the global MODIS GPP model by optimizing parameters with FLUXNET data".AGRICULTURAL AND FOREST METEOROLOGY 300(2021).