Modeling Gross Primary Production by Integrating Satellite Data and Coordinated Flux Measurements in Arid and Semi-Arid China  

作　　者：WANG He-Song JIA Gen-Suo FENG Jin-Ming ZHAO Tian-Bao MA Zhu-Guo 

机构地区：[1]Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beo＇ing 100029, China [2]Graduate University of Chinese Academy of Sciences, Beijing 100049, China

出　　处：《Atmospheric and Oceanic Science Letters》2010年第1期7-13,共7页大气和海洋科学快报（英文版）

基　　金：supported by the National Basic Research Program of China (Grant Nos. 2009CB723904 and 2006CB400500)

摘　　要：Assessing large-scale patterns of gross primary production (GPP) in arid and semi-arid (ASA) areas is important for both scientific and practical purposes.Remote sensing-based models,which integrate satellite data with input from ground-based meteorological measurements and vegetation characteristics,improve spatially extended estimates of vegetation productivity with high accuracy.In this study,the authors simulated GPP in ASA areas by integrating moderate resolution imaging spectral radiometer (MODIS) data with eddy covariance and meteorological measurements at the flux tower sites using the Vegetation Photosynthesis Model (VPM),which is a remote sensing-based model for analyzing the spatial pattern of GPP in different land cover types.The field data were collected by coordinating observations at nine stations in 2008.The results indicate that in the region during the growing season GPP was highest in cropland sites,second highest in woodland sites,and lowest in grassland sites.VPM captured the temporal and spatial characteristics of GPP for different land covers in ASA areas.Further,Enhanced Vegetation Index (EVI) had a strong liner relationship with GPP in densely vegetated areas,while the Normalized Difference Vegetation Index (NDVI) had a strong liner relationship with GPP over less dense vegetation.This study demonstrates the potential of satellite-driven models for scaling-up GPP,which is a key component for studying the carbon cycle at regional and global scales.Assessing large-scale patterns of gross primary production （GPP） in arid and semi-arid （ASA） areas is important for both scientific and practical purposes. Remote sensing-based models, which integrate satellite data with input from ground-based meteorological meas- urements and vegetation characteristics, improve spatially extended estimates of vegetation productivity with high accuracy. In this study, the authors simulated GPP in ASA areas by integrating moderate resolution imaging spectral radiometer （MODIS） data with eddy covariance and meteorological measurements at the flux tower sites using the Vegetation Photosynthesis Model （VPM）, which is a remote sensing-based model for analyzing the spatial pattern of GPP in different land cover types. The field data were collected by coordinating observations at nine sta- tions in 2008. The results indicate that in the region during the growing season GPP was highest in cropland sites, second highest in woodland sites, and lowest in grassland sites. VPM captured the temporal and spatial characteris- tics of GPP for different land covers in ASA areas. Further, Enhanced Vegetation Index （EVI） had a strong liner relationship with GPP in densely vegetated areas, while the Normalized Difference Vegetation Index （NDVI） had a strong liner relationship with GPP over less dense vegeta- tion. This study demonstrates the potential of satel- lite-driven models for scaling-up GPP, which is a key component for studying the carbon cycle at regional and global scales.

关 键 词：gross primary production vegetation photo- synthesis model eddy covariance remote sensing coordinated observation arid and semiarid areas 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] V474.2[自动化与计算机技术—控制科学与工程]