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A method for estimating the gross primary production of alpine meadows using MODIS and climate data in China

作者：

Li F 【所有文章和数据】
Wang XF 【所有文章和数据】
Zhao J 【所有文章和数据】
Zhang XQ 【所有文章和数据】
Zhao QJ 【所有文章和数据】

引用方式

Li F, Wang XF, Zhao J, Zhang XQ, Zhao QJ. A method for estimating the gross primary production of alpine meadows using MODIS and climate data in China. International Journal of Remote Sensing, 2013, 34(23): 8266-8286.

文献信息
Type Of Reference	JOUR
Title	A method for estimating the gross primary production of alpine meadows using MODIS and climate data in China
Authors	Li, Fei\| Wang, Xufeng\| Zhao, Jun\| Zhang, Xiaoqiang\| Zhao, Qianjun\|
Secondary Title	International Journal of Remote Sensing
Abstract	The use of remotely sensed data to estimate and monitor the gross primary production (GPP) of an ecosystem on regional scales is an important method in climate change research. Under the unremitting efforts of scientists, many successful remote-sensing-based GPP models have been developed for various vegetation types and regions. However, in practice, some models have been applied to a wide variety of ecosystems, and the suitability of a particular model for the environment under consideration has seldom been taken into account. Due to ecosystem diversity and climatic and environmental variation, it is often difficult to find a model that is suitable for a specific vegetation region. In this article, a new method is proposed for estimating the GPP of alpine vegetation, known as the alpine vegetation model (AVM). The accuracy of the AVM in estimating the GPP was compared to that of four other models: the vegetation photosynthesis model (VPM), eddy covariance–light use efficiency (EC–LUE) model, temperature and greenness (TG) model, and vegetation index (VI) model. The results demonstrated that the AVM displays superior accuracy in estimating the GPP of alpine vegetation. We also found that there is information redundancy in the input variables of these four models, which may account for their lower accuracy in estimating the GPP. In addition, the GPP estimates using the enhanced vegetation index are affected more in the case of low rather than high GPP by the influence of senesced grass during the early and late grassland growing season.
Date	2013///
Year	2013
DOI	10.1080/01431161.2013.834394
Database Provider	Taylor and Francis+NEJM
volume	34
Issue Number	23
Start Page	8266
Issn	0143-1161
Url	http://www.tandfonline.com/doi/abs/10.1080/01431161.2013.834394
Access Date	2013/09/28/06:40:35
file_attachments2	http://www.tandfonline.com/doi/abs/10.1080/01431161.2013.834394
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