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http://onlinelibrary.wiley.com/doi/10.1002/jgrg.20056/abstract
利用MODIS数据估算陆地生态系统总初级生产力GPP的新方法
基于不同类型陆地生态系统LST与EVI的关系,建立了基于矩形EVI-LST空间的GPP估算模型TGR。该模型参数具有明确的物理意义,计算中用到的多地面观测数据较少,且避免了由于解释变量相关性造成的信息重复考虑。北美地区30个生态站的模型率定、检验结果表明,本模型的精度高于目前常用的TG、GR模型。
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China
School of the Environment, Flinders University, Adelaide, South Australia, Australia
National Centre for Groundwater Research and Training, Adelaide, South Australia, Australia
2013), A novel algorithm to assess gross primary production forterrestrial ecosystems from MODIS imagery, J. Geophys. Res. Biogeosci., 118, 590–605, doi:10.1002/jgrg.20056.
, , , and (Issue published online: 9 JUL 2013
Article first published online: 30 APR 2013
Accepted manuscript online: 5 APR 2013 06:03PM EST
Manuscript Accepted: 3 APR 2013
Manuscript Revised: 26 MAR 2013
Manuscript Received: 30 NOV 2012
National Key Technology R&D Program of China. Grant Number: 2011BAD25B05
National Natural Science Foundation of China. Grant Number: 51279077
gross primary production;
MODIS;
satellite remote sensing;
light use efficiency;
North America
[1] Quantifying carbon fluxes at large spatial scales has attracted considerable scientific attentions. In this study, a novel approach was proposed to estimate the terrestrial ecosystem gross primary production (GPP) using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The new model (named Temperature and Greenness Rectangle, TGR) uses a combination of MODIS Enhanced Vegetation Index and Land Surface Temperature products as well as in situ measurement of photosynthetically active radiation to estimate GPP at a 16 day interval. Three major advantages are included in the model: (1) the model follows strictly the logic of the light use efficiency model and each parameter has physical meaning; (2) the model reduces the dependency on ground-based meteorological measurements; and (3) the overlap of information in correlated explanatory variables is avoided. The model was calibrated with data from 17 sites within the Ameriflux network and validated at another 13 sites, covering a wide range of climates and eight major vegetation types. Results show that the TGR model explains reasonably well the tower-based measurements of GPP for all vegetation types, except for the evergreen broadleaf forest, with the coefficient of determination in a range from 0.67 to 0.91 and the root mean square error from 9.0 to 31.9 g C/m2/16 days. Comparisons with other two models (the TG and GR model) show that the TGR model generally gives better GPP estimates in nearly all vegetation types, especially under dry climate conditions. These results indicate that the TGR model can be potentially used to estimate GPP at regional scale.
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