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.
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Document Type: Research Article
Affiliations:1: Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences, Beijing, 100101, China 2: Cold and Arid Regions Remote Sensing Observation System Experiment Station, Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences, Lanzhou, 730000, China 3: College of Geography and Environmental Science, Northwest Normal University, Lanzhou, 730070, China 4: Department of Earth and Environmental Sciences, Graduate School and Environmental Studies, Nagoya University, Nagoya, 464-8602, Japan