Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data

doi:10.3390/rs10050708

	Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data
	Kang, Xiaoming 1,2; Yan, Liang 1; Zhang, Xiaodong 1; Li, Yong 1; Tian, Dashuan 3; Peng, Changhui 2,4; Wu, Haidong 1; Wang, Jinzhi 1; Zhong, Lei 5
刊名	REMOTE SENSING
	2018-05-01
卷号	10 期号:5 页码:20
关键词	coastal wetland eddy covariance gross primary production MODIS vegetation indices VPM
ISSN号	2072-4292
DOI	10.3390/rs10050708
通讯作者	Wang, Jinzhi(wangjz04@126.com) ; Zhong, Lei(lei.zhong@tju.edu.cn)
英文摘要	How to effectively combine remote sensing data with the eddy covariance (EC) technique to accurately quantify gross primary production (GPP) in coastal wetlands has been a challenge and is also important and necessary for carbon (C) budgets assessment and climate change studies at larger scales. In this study, a satellite-based Vegetation Photosynthesis Model (VPM) combined with EC measurement and Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to evaluate the phenological characteristics and the biophysical performance of MODIS-based vegetation indices (VIs) and the feasibility of the model for simulating GPP of coastal wetland ecosystems. The results showed that greenness-related and water-related VIs can better identify the green-up and the senescence phases of coastal wetland vegetation, corresponds well with the C uptake period and the phenological patterns that were delineated by GPP from EC tower (GPP(EC)). Temperature can explain most of the seasonal variation in VIs and GPP(EC) fluxes. Both enhanced vegetation index (EVI) and water-sensitive land surface water index (LSWI) have a higher predictive power for simulating GPP in this coastal wetland. The comparisons between modeled GPP (GPP(VPM)) and GPP(EC) indicated that VPM model can commendably simulate the trajectories of the seasonal dynamics of GPP(EC) fluxes in terms of patterns and magnitudes, explaining about 85% of GPP(EC) changes over the study years (p < 0.0001). The results also demonstrate the potential of satellite-driven VPM model for modeling C uptake at large spatial and temporal scales in coastal wetlands, which can provide valuable production data for the assessment of global wetland C sink/source.
资助项目	National Key Research and Development Program of China[2017YFC0506203] ; Natural Sciences and Engineering Research Council of Canada (NSERC) ; National Natural Science Foundation of China[31770511] ; National Natural Science Foundation of China[41701113] ; National Natural Science Foundation of China[41403102] ; National Natural Science Foundation of China[41403073]
WOS关键词	EVERGREEN NEEDLELEAF FOREST ; SOIL ORGANIC-CARBON ; YELLOW-RIVER DELTA ; CLIMATE DATA ; NORTHEASTERN CHINA ; VEGETATION INDEXES ; TERRESTRIAL VEGETATION ; EFFICIENCY MODELS ; DECIDUOUS FOREST ; ALPINE WETLAND
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000435198400050
资助机构	National Key Research and Development Program of China ; Natural Sciences and Engineering Research Council of Canada (NSERC) ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/54597]
专题	中国科学院地理科学与资源研究所
通讯作者	Wang, Jinzhi; Zhong, Lei
作者单位	1.Chinese Acad Forestry, Inst Wetland Res, Beijing Key Lab Wetland Serv & Restorat, Beijing 100091, Peoples R China 2.Univ Quebec, Inst Environm Sci, Dept Biol Sci, Montreal, PQ C3H 3P8, Canada 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 4.Northwest Agr & Forest Univ, Coll Forestry, Ctr Ecol Forecasting & Global Change, Yangling 712100, Shaanxi, Peoples R China 5.Tianjin Univ, Sch Environm Sci & Engn, China Australia Ctr Sustainable Urban Dev, Tianjin 300072, Peoples R China
推荐引用方式 GB/T 7714	Kang, Xiaoming,Yan, Liang,Zhang, Xiaodong,et al. Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data[J]. REMOTE SENSING,2018,10(5):20.
APA	Kang, Xiaoming.,Yan, Liang.,Zhang, Xiaodong.,Li, Yong.,Tian, Dashuan.,...&Zhong, Lei.(2018).Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data.REMOTE SENSING,10(5),20.
MLA	Kang, Xiaoming,et al."Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data".REMOTE SENSING 10.5(2018):20.