Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy
文献类型：期刊论文
| 作者 | Liu, Zhengjia5,7; Wu, Chaoyang5; Peng, Dailiang1; Wang, Sisi5; Gonsamo, Alemu4,6; Fang, Bin3; Yuan, Wenping2 |
| 刊名 | AGRICULTURAL AND FOREST METEOROLOGY
 |
| 出版日期 | 2017-02-15 |
| 卷号 | 233页码:222-234 |
| 关键词 | Gross primary production Light use efficiency Scaled enhanced vegetation index ChinaFLUX |
| ISSN号 | 0168-1923 |
| DOI | 10.1016/j.agrformet.2016.12.001 |
| 通讯作者 | Wu, Chaoyang(wucy@radi.ac.cn) ; Peng, Dailiang(pengdl@radi.ac.cn) |
| 英文摘要 | Non-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. |
| WOS关键词 | LIGHT-USE EFFICIENCY ; NET PRIMARY PRODUCTION ; EDDY COVARIANCE DATA ; DATA SET ; CHLOROPHYLL FAPAR(CHL) ; TERRESTRIAL ECOSYSTEMS ; FOREST ECOSYSTEMS ; REMOTE ESTIMATION ; ACTIVE RADIATION ; SATELLITE IMAGES |
| 资助项目 | National 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 |
| WOS研究方向 | Agriculture ; Forestry ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000393259400020 |
| 出版者 | ELSEVIER SCIENCE BV |
| 资助机构 | National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Research Fund for International postdoc fund ; Youth Innovation Promotion Association CAS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/64927]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wu, Chaoyang; Peng, Dailiang |
| 作者单位 | 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth, Beijing 100101, Peoples R China 2.Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou 519082, Guangdong, Peoples R China 3.Columbia Univ, Dept Earth & Environm Engn, 500 W 120th St, New York, NY 10027 USA 4.Univ Toronto, Program Planning, 100 St George St, Toronto, ON M5S 3G3, Canada 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 6.Univ Toronto, Dept Geog, 100 St George St, Toronto, ON M5S 3G3, Canada 7.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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