Modeling Net Ecosystem Carbon Exchange of Alpine Grasslands with a Satellite-Driven Model

	Modeling Net Ecosystem Carbon Exchange of Alpine Grasslands with a Satellite-Driven Model
	Yan, Wei ; Hu, Zhongmin ; Zhao, Yuping ; Zhang, Xianzhou ; Fan, Yuzhi ; Shi, Peili ; He, Yongtao ; Yu, Guirui ; Li, Yingnian
刊名	PLOS ONE
	2015-04-07
英文摘要	Estimate of net ecosystem carbon exchange (NEE) between the atmosphere and terrestrial ecosystems, the balance of gross primary productivity (GPP) and ecosystem respiration (Reco) has significant importance for studying the regional and global carbon cycles. Using models driven by satellite data and climatic data is a promising approach to estimate NEE at regional scales. For this purpose, we proposed a semi-empirical model to estimate NEE in this study. In our model, the component GPP was estimated with a light response curve of a rectangular hyperbola. The component Reco was estimated with an exponential function of soil temperature. To test the feasibility of applying our model at regional scales, the temporal variations in the model parameters derived from NEE observations in an alpine grassland ecosystem on Tibetan Plateau were investigated. The results indicated that all the inverted parameters exhibit apparent seasonality, which is in accordance with air temperature and canopy phenology. In addition, all the parameters have significant correlations with the remote sensed vegetation indexes or environment temperature. With parameters estimated with these correlations, the model illustrated fair accuracy both in the validation years and at another alpine grassland ecosystem on Tibetan Plateau. Our results also indicated that the model prediction was less accurate in drought years, implying that soil moisture is an important factor affecting the model performance. Incorporating soil water content into the model would be a critical step for the improvement of the model.; Estimate of net ecosystem carbon exchange (NEE) between the atmosphere and terrestrial ecosystems, the balance of gross primary productivity (GPP) and ecosystem respiration (Reco) has significant importance for studying the regional and global carbon cycles. Using models driven by satellite data and climatic data is a promising approach to estimate NEE at regional scales. For this purpose, we proposed a semi-empirical model to estimate NEE in this study. In our model, the component GPP was estimated with a light response curve of a rectangular hyperbola. The component Reco was estimated with an exponential function of soil temperature. To test the feasibility of applying our model at regional scales, the temporal variations in the model parameters derived from NEE observations in an alpine grassland ecosystem on Tibetan Plateau were investigated. The results indicated that all the inverted parameters exhibit apparent seasonality, which is in accordance with air temperature and canopy phenology. In addition, all the parameters have significant correlations with the remote sensed vegetation indexes or environment temperature. With parameters estimated with these correlations, the model illustrated fair accuracy both in the validation years and at another alpine grassland ecosystem on Tibetan Plateau. Our results also indicated that the model prediction was less accurate in drought years, implying that soil moisture is an important factor affecting the model performance. Incorporating soil water content into the model would be a critical step for the improvement of the model.
内容类型	期刊论文
源URL	[http://ir.nwipb.ac.cn/handle/363003/5565]
专题	西北高原生物研究所_中国科学院西北高原生物研究所
推荐引用方式 GB/T 7714	Yan, Wei,Hu, Zhongmin,Zhao, Yuping,et al. Modeling Net Ecosystem Carbon Exchange of Alpine Grasslands with a Satellite-Driven Model[J]. PLOS ONE,2015.
APA	Yan, Wei.,Hu, Zhongmin.,Zhao, Yuping.,Zhang, Xianzhou.,Fan, Yuzhi.,...&Li, Yingnian.(2015).Modeling Net Ecosystem Carbon Exchange of Alpine Grasslands with a Satellite-Driven Model.PLOS ONE.
MLA	Yan, Wei,et al."Modeling Net Ecosystem Carbon Exchange of Alpine Grasslands with a Satellite-Driven Model".PLOS ONE (2015).