Assessing the representativeness of the AmeriFlux network using MODIS and GOES data | |
Yang F. H. ; Zhu A. X. ; Ichii K. ; White M. A. ; Hashimoto H. ; Nemani R. R. | |
2008 | |
关键词 | land-surface temperature gross primary production support vector machine vegetation indexes combining modis fluxes ecosystem evapotranspiration validation carbon |
英文摘要 | The AmeriFlux network of eddy covariance towers has played a critical role in the analysis of terrestrial water and carbon dynamics. It has been used to understand the general principles of ecosystem behaviors and to scale up those principles from sites to regions. To support the generalization from individual sites to large regions, it is essential that all major ecoregions in North America are represented in the AmeriFlux network. In this study, we examined the representativeness of the AmeriFlux network by comparing the climate and vegetation across the coterminous United States in 2004 with those at the AmeriFlux network in 2000-2004 on the basis of remote sensing products. We found that the AmeriFlux network generally captured the climatic and vegetation characteristics in the coterminous United States with under-representations in the Rocky Mountain evergreen needleleaf forest, the Sierra Nevada Mountains, the Sonora desert, the northern Great Plains, the Great Basin Desert, and New England. In terms of site representativeness, our analysis suggested that Indiana Morgan Monroe State Forest, Indiana, and Harvard Forest, Massachusetts, were among the forest sites with high representativeness extents; while Audubon Research Ranch, Arizona, and Sky Oaks Young Chaparral were among the nonforest sites with high representativeness extents. |
出处 | Journal of Geophysical Research-Biogeosciences |
卷 | 113 |
期 | G4 |
收录类别 | SCI |
语种 | 英语 |
ISSN号 | 0148-0227 |
内容类型 | SCI/SSCI论文 |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/22607] ![]() |
专题 | 地理科学与资源研究所_历年回溯文献 |
推荐引用方式 GB/T 7714 | Yang F. H.,Zhu A. X.,Ichii K.,et al. Assessing the representativeness of the AmeriFlux network using MODIS and GOES data. 2008. |
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