Comparison and implementation of the global models of terrestrial net primary productivity

This paper examines three types of models for estimating global terrestrial net primary productivity (NPP) and the comparison of the climate models, eco-physiological process models, and light use efficiency (LUE) models were conducted. The results show that the light use efficiency models are more powerful and easy to be implemented in the estimation of NPP, especially when some biogeochemistry variables are hard to be quantified. As a case study, a LUE model, CASA model is then used to estimate the NPP values of the southern border of the Junggar Basin, Xinjiang in 2009. The estimated NPP values were then verified using in-situ measurements of NPP which were collected in the study area. The result demonstrated that the CASA model can achieve relatively accurate estimation of NPP in the southern border of the Junggar Basin, Xinjiang. However, due to the fact that the LUE models such as CASA lacks of the characterization of eco-physiological processes, they consequently lead to some uncertainty and inaccuracy in the estimation of global and regional NPP. Future work will be focused on incorporating the remotely sensed data and related ecosystem processes into the light use efficiency models.