Assessments of gross primary productivity estimations with satellite data-driven models using eddy covariance observation sites over the northern hemisphere

doi:10.1016/j.agrformet.2019.107771

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所

	Assessments of gross primary productivity estimations with satellite data-driven models using eddy covariance observation sites over the northern hemisphere
	Xie Xinyao 2,3; Li Ainong 2; Tan Jianbo 1; Jin Huaan 2; Nan Xi 2; Zhang Zhengjian 2,3; Bian Jinhu 2; Lei Guangbin 2
刊名	AGRICULTURAL AND FOREST METEOROLOGY
	2020-01-15
卷号	280 页码:107771
关键词	Gross photosynthesis productivity (GPP) Eddy covariance (EC) Satellite data-driven model Boreal ecosystem productivity simulator(BEPS) Light use efficiency (LUE)
ISSN号	0168-1923
DOI	10.1016/j.agrformet.2019.107771
产权排序	1
文献子类	Article
英文摘要	The accurate quantification of gross primary productivity (GPP) has been a major challenge in global climate change research. Satellite data-driven models have been universally used as scientific tools for investigating the carbon cycle, including vegetation index (VI)-based models, light use efficiency (LUE) models, and process-based models. However, inconsistencies and uncertainties have been found in the GPP estimations from various models. The understanding of model behaviors under different climatic conditions remains unclear. In this study, three typical satellite data-driven models, namely, Moderate Resolution Imaging Spectroradiometer (MODIS) GPP (MOD17) model, Temperature and Greenness (TG) model and Boreal Ecosystem Productivity Simulator (BEPS), respectively, were compared to better understand discrepancies and uncertainties in GPP estimations at 119 northern eddy covariance (EC) sites. Due to the variations in climatic drivers of GPP, temperature, precipitation and incoming solar radiation were selected to describe climatic conditions. The results showed that BEPS and MOD17 exhibited similar performance in simulating GPP, with root-mean-square error (RMSE) values of 2.50 g C m(-2) d(-1) and 2.53 g C m(-2) d(-1), respectively, and performed slightly better than TG (RMSE = 2.98 g C m(-2) d(-1)). Comparison between simulated GPP and EC GPP also revealed that model performance varied substantially among different vegetation types. The three models performed better for deciduous broadleaf forest, evergreen needleleaf forest, and mixed forest, in comparison to the results from evergreen broadleaf forest and crop. Specifically, all three models showed poor performance under the conditions of high temperature and low precipitation, revealing the models' inability to characterize the impact of water stress on photosynthesis when drought occurs. Furthermore, our results indicated that GPP estimations from satellite data-driven models were also sensitive to remotely sensed data, suggesting that the high accuracy of remotely sensed data in describing vegetation canopy is important for carbon modeling. This study highlights the importance of understanding model behaviors in different vegetation types and climatic conditions, so that the model performances may be improved in future carbon cycle studies.
电子版国际标准刊号	1873-2240
资助项目	National Natural Science Foundation of China[41631180] ; National Natural Science Foundation of China[41571373] ; National Natural Science Foundation of China[41801370] ; National Key Research and Development Program of China[2016YFA0600103] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19030303]
WOS关键词	LIGHT-USE EFFICIENCY ; LEAF-AREA INDEX ; NET PRIMARY PRODUCTIVITY ; ENHANCED VEGETATION INDEX ; FLUX DATA ; INTERANNUAL VARIABILITY ; TERRESTRIAL BIOSPHERE ; ECOSYSTEM EXCHANGE ; RESPONSE CURVE ; CARBON-DIOXIDE
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000525807000008
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/34289]
专题	中国科学院水利部成都山地灾害与环境研究所
通讯作者	Li Ainong
作者单位	1.Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha 410114, Peoples R China 2.Chinese Acad Sci, Res Ctr Digital Mt & Remote Sensing Applicat, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China; 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
推荐引用方式 GB/T 7714	Xie Xinyao,Li Ainong,Tan Jianbo,et al. Assessments of gross primary productivity estimations with satellite data-driven models using eddy covariance observation sites over the northern hemisphere[J]. AGRICULTURAL AND FOREST METEOROLOGY,2020,280:107771.
APA	Xie Xinyao.,Li Ainong.,Tan Jianbo.,Jin Huaan.,Nan Xi.,...&Lei Guangbin.(2020).Assessments of gross primary productivity estimations with satellite data-driven models using eddy covariance observation sites over the northern hemisphere.AGRICULTURAL AND FOREST METEOROLOGY,280,107771.
MLA	Xie Xinyao,et al."Assessments of gross primary productivity estimations with satellite data-driven models using eddy covariance observation sites over the northern hemisphere".AGRICULTURAL AND FOREST METEOROLOGY 280(2020):107771.