Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model

doi:10.1016/j.ecolind.2018.01.042

	Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model
	Wang, Jian 1,2; Wu, Chaoyang 2,3; Zhang, Chunhua 4; Ju, Weimin 5; Wang, Xiaoyue 1,2; Chen, Zhi 3; Fang, Bin 6
刊名	ECOLOGICAL INDICATORS
	2018-05-01
卷号	88 页码:332-340
关键词	China InTEC Phonology NDVI GPP
ISSN号	1470-160X
DOI	10.1016/j.ecolind.2018.01.042
通讯作者	Wu, Chaoyang(wucy@igsnrr.ac.cn)
英文摘要	Phenology is a significant indicator of ecosystem functioning and is one of the most important controllers of gross primary productivity (GPP). The Integrated Terrestrial Ecosystem C-budget model (InTEC) predicts carbon cycling by modeling a number of ecosystem processes, and in particularly, phenology derived from a degree-day metric. However, empirical temperature thresholds may not well represent ecosystem growth at low latitudes. Here, using 30-year Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index 3rd generation (NDVI3g) data (1983-2012), we obtained the start (SOS), end (EOS) and length of growing season (LOS) with three algorithms from time series of NDVI for forests ecosystems of China. The phenology module was then incorporated into the InTEC model before validation using ground observations from eddy covariance measurements. Our results showed that compared with temperature-based phenology of the original model, using NDVI-based phenology improved modeling of GPP. The modified InTEC model was used to analyze the spatial and temporal patterns of GPP for forest ecosystems of China during 1983 to 2012. We found that remote sensing-based phenology was more reliable than temperature-based phenology for large-scale analysis. Using the modified InTEC model, we revealed that the GPP of China's forests ecosystems increased over 1983-2012 with high spatial heterogeneity, with a mean of 1.31 Pg Cyr(-1). Our results demonstrated the significance of remotely sensed phenology for improving the accuracy of GPP modeling with ecosystem models, which is enlightening for the large-scale evaluation of carbon sequestration.
资助项目	National Natural Science Foundation of China[41522109] ; National Natural Science Foundation of China[41601054] ; Research Program of Frontier Sciences, CAS[QYZDB-SSW-DQC011]
WOS关键词	NET PRIMARY PRODUCTIVITY ; LAND-SURFACE PHENOLOGY ; TERRESTRIAL ECOSYSTEMS ; GROWING-SEASON ; VEGETATION PHENOLOGY ; CARBON BALANCE ; NORTH-AMERICA ; CHINA FORESTS ; TIME-SERIES ; SATELLITE
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000430760800034
资助机构	National Natural Science Foundation of China ; Research Program of Frontier Sciences, CAS
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/55019]
专题	中国科学院地理科学与资源研究所
通讯作者	Wu, Chaoyang
作者单位	1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Ludong Univ, Sch Resources & Environm Engn, Yantai 264025, Peoples R China 5.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Jiangsu, Peoples R China 6.Columbia Univ, Dept Earth & Environm Engn, 500 W 120th St, New York, NY 10027 USA
推荐引用方式 GB/T 7714	Wang, Jian,Wu, Chaoyang,Zhang, Chunhua,et al. Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model[J]. ECOLOGICAL INDICATORS,2018,88:332-340.
APA	Wang, Jian.,Wu, Chaoyang.,Zhang, Chunhua.,Ju, Weimin.,Wang, Xiaoyue.,...&Fang, Bin.(2018).Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model.ECOLOGICAL INDICATORS,88,332-340.
MLA	Wang, Jian,et al."Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model".ECOLOGICAL INDICATORS 88(2018):332-340.