IGSNRR OpenIR
Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data
Kang, Xiaoming1,2; Yan, Liang1; Zhang, Xiaodong1; Li, Yong1; Tian, Dashuan3; Peng, Changhui2,4; Wu, Haidong1; Wang, Jinzhi1; Zhong, Lei5
2018-05-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:5Pages:20
Corresponding AuthorWang, Jinzhi(wangjz04@126.com) ; Zhong, Lei(lei.zhong@tju.edu.cn)
AbstractHow 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.
Keywordcoastal wetland eddy covariance gross primary production MODIS vegetation indices VPM
DOI10.3390/rs10050708
WOS KeywordEVERGREEN NEEDLELEAF FOREST ; SOIL ORGANIC-CARBON ; YELLOW-RIVER DELTA ; CLIMATE DATA ; NORTHEASTERN CHINA ; VEGETATION INDEXES ; TERRESTRIAL VEGETATION ; EFFICIENCY MODELS ; DECIDUOUS FOREST ; ALPINE WETLAND
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Key Research and Development Program of China ; Natural Sciences and Engineering Research Council of Canada (NSERC) ; National Natural Science Foundation of China
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000435198400050
PublisherMDPI
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54598
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Jinzhi; Zhong, Lei
Affiliation1.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
Recommended Citation
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.
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