IGSNRR OpenIR
Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy
Liu, Zhengjia1,2; Wu, Chaoyang2; Peng, Dailiang3; Wang, Sisi2; Gonsamo, Alemu4,5; Fang, Bin6; Yuan, Wenping7
2017-02-15
Source PublicationAGRICULTURAL AND FOREST METEOROLOGY
ISSN0168-1923
Volume233Pages:222-234
Corresponding AuthorWu, Chaoyang(wucy@radi.ac.cn) ; Peng, Dailiang(pengdl@radi.ac.cn)
AbstractNon-photosynthetic components within the canopy (e.g., dry leaves and stem) contribute little to photosynthesis and therefore, remote sensing of gross primary production (GPP) could be improved by the removal of these components. A scaled enhanced vegetation index (EVI), which is usually regarded as a linear function of EVI, was found to have the strongest relationship with chlorophyll level fraction of absorbed photosynthetically active radiation (FPARchI) and can help improve GPP estimation in croplands compared to canopy level FPAR (FPARcanopy). However, the application of the FPARchI theory to other plant functional types (PFTs) and the underlying reasons remain largely unknown. In this study, based on standard MODIS algorithm we comprehensively assessed the performances of FPARcanopy, scaled EVI (FPARch11), normalized difference vegetation index (NDVI), scaled NDVI (FPARch12) and EVI as proxies of FPAR for estimating GPP at four forest and six non-forest sites (e.g., grasslands, croplands and wetlands) from ChinaFLUX, representing a wide range of ecosystems with different canopy structures and eco-climatic zones. Our results showed that the scaled EVI (FPARch11) as FPAR effectively improved the accuracy of estimated GPP for the entire PFTs. FPARch11 substantially improved forest GPP estimations with higher coefficient of determination (R2), lower root mean square error (RMSE) and lower bias. In comparison, for non-forest PFTs, the improvement in R2 between estimated GPP based on FPARchl1 (GPPchl1) and flux tower GPP was less evident than those between flux GPP and GPP estimations from FPARcanopy (GPPcanopy), FPARchl2, NDVI and EVI. The temperature and water attenuation scalars played important roles in reducing the difference of various GPP and indirectly reducing the impact of different FPARs on GPP in non-forest PFTs. Even so, FPARchl1 is an ecologically more meaningful parameter since FPARchl1 and flux tower GPP dropped to zero more synchronously in both forest and non-forest sites. In particular, we found that the improvement of GPPchl1 relative to GPPcanopy was positively correlated with the maximum leaf area index (LAI), implying the importance of site characteristic in regulating the strength of the improvement. This is encouraging for remote sensing of GPP for which vegetation parameter retrieval has often been found to be less successful at high LAI due to saturations in reflective and scattering domains. Our results demonstrate the significance of accurate and ecologically meaningful FPAR parameterization for improving our current capability in GPP modeling. (C) 2016 Elsevier B.V. All rights reserved.
KeywordGross primary production Light use efficiency Scaled enhanced vegetation index ChinaFLUX
DOI10.1016/j.agrformet.2016.12.001
WOS KeywordLIGHT-USE EFFICIENCY ; NET PRIMARY PRODUCTION ; EDDY COVARIANCE DATA ; DATA SET ; CHLOROPHYLL FAPAR(CHL) ; TERRESTRIAL ECOSYSTEMS ; FOREST ECOSYSTEMS ; REMOTE ESTIMATION ; ACTIVE RADIATION ; SATELLITE IMAGES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41371013] ; National Natural Science Foundation of China[41601582] ; National Natural Science Foundation of China[41522109] ; China Postdoctoral Science Foundation[2016M590149] ; Research Fund for International postdoc fund[2015PE030] ; Youth Innovation Promotion Association CAS
Funding OrganizationNational Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Research Fund for International postdoc fund ; Youth Innovation Promotion Association CAS
WOS Research AreaAgriculture ; Forestry ; Meteorology & Atmospheric Sciences
WOS SubjectAgronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS IDWOS:000393259400020
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64928
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Chaoyang; Peng, Dailiang
Affiliation1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth, Beijing 100101, Peoples R China
4.Univ Toronto, Dept Geog, 100 St George St, Toronto, ON M5S 3G3, Canada
5.Univ Toronto, Program Planning, 100 St George St, Toronto, ON M5S 3G3, Canada
6.Columbia Univ, Dept Earth & Environm Engn, 500 W 120th St, New York, NY 10027 USA
7.Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou 519082, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Liu, Zhengjia,Wu, Chaoyang,Peng, Dailiang,et al. Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy[J]. AGRICULTURAL AND FOREST METEOROLOGY,2017,233:222-234.
APA Liu, Zhengjia.,Wu, Chaoyang.,Peng, Dailiang.,Wang, Sisi.,Gonsamo, Alemu.,...&Yuan, Wenping.(2017).Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy.AGRICULTURAL AND FOREST METEOROLOGY,233,222-234.
MLA Liu, Zhengjia,et al."Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy".AGRICULTURAL AND FOREST METEOROLOGY 233(2017):222-234.
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