Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.rse.2010.05.001 |
Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling | |
Wang, Hesong1,2; Jia, Gensuo1; Fu, Congbin1; Feng, Jinming1; Zhao, Tianbao1; Ma, Zhuguo1 | |
通讯作者 | Jia, Gensuo |
来源期刊 | REMOTE SENSING OF ENVIRONMENT
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ISSN | 0034-4257 |
EISSN | 1879-0704 |
出版年 | 2010 |
卷号 | 114期号:10页码:2248-2258 |
英文摘要 | Remote sensing models based on light use efficiency (LUE). provide promising tools for monitoring spatial and temporal variation of gross primary production (GPP) at regional scale. In most of current LUE-based models, maximal LUE (epsilon(max)) heavily relies on land cover types and is considered as a constant, rather than a variable for a certain vegetation type or even entire eco-region. However, species composition and plant functional types are often highly heterogeneous in a given land cover class; therefore, spatial heterogeneity of epsilon(max) must be fully considered in GPP modeling, so that a single cover type does not equate to a single epsilon(max) value. A spatial dataset of epsilon(max) accurately represents the spatial heterogeneity of maximal light use would be of significant beneficial to regional GPP models. Here, we developed a spatial dataset of epsilon(max) by integrating eddy covariance flux measurements from 14 field sites in a network of coordinated observation across northern China and satellite derived indices such as enhanced vegetation index (EVI) and visible albedo to simulate regional distribution of GPP. This dynamic modeling method recognizes the spatial heterogeneity of epsilon(max) and reduces the uncertainties in mixed pixels. Further, we simulated GPP with the spatial dataset of epsilon(max) generated above. Both epsilon(max) and growing season GPP show complex patterns over northern China that reflect influences of humidity, green vegetation fractions, and land use intensity. "Green spots" such as oasis meadow and alpine forests in dryland and "brown spots" such as build-up and heavily degraded vegetation in the east are clearly captured by the simulation. The correlation between simulated GPP and EC measured GPP indicate that the simulated GPP from this new approach is well matched with flux-measured GPP. Those results have demonstrated the importance of considering epsilon(max) as both a spatially and temporally variable values in GPP rnodeling. (C) 2010 Elsevier Inc. All rights reserved. |
英文关键词 | Maximal light use efficiency (epsilon(max)) Satellite Flux site Gross primary production (GPP) Modeling Northern China |
类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
收录类别 | SCI-E |
WOS记录号 | WOS:000281187400012 |
WOS关键词 | PHOTOCHEMICAL REFLECTANCE INDEX ; NET PRIMARY PRODUCTION ; DECIDUOUS FOREST ; GENERALIZED-MODEL ; VEGETATION INDEX ; EDDY COVARIANCE ; CLIMATE DATA ; MODIS ; CARBON ; WATER |
WOS类目 | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
来源机构 | 中国科学院大气物理研究所 |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/166246 |
作者单位 | 1.Chinese Acad Sci, Inst Atmospher Phys, RCE TEA, Beijing 100029, Peoples R China; 2.Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Hesong,Jia, Gensuo,Fu, Congbin,et al. Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling[J]. 中国科学院大气物理研究所,2010,114(10):2248-2258. |
APA | Wang, Hesong,Jia, Gensuo,Fu, Congbin,Feng, Jinming,Zhao, Tianbao,&Ma, Zhuguo.(2010).Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling.REMOTE SENSING OF ENVIRONMENT,114(10),2248-2258. |
MLA | Wang, Hesong,et al."Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling".REMOTE SENSING OF ENVIRONMENT 114.10(2010):2248-2258. |
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