title: Robustness and Uncertainties of the "Temperature and Greenness" Model for Estimating Terrestrial Gross Primary Production.
abstract: Terrestrial gross primary production (GPP) plays a vital role in offsetting anthropogenic CO2emission and regulating global carbon cycle. Various remote sensing approaches for estimating GPP have attracted considerable scientific attentions, yet their robustness and uncertainties remain unclear. Here we evaluate the performance of the "temperature and greenness" (TG) model, a representative remote sensing model in estimating GPP, using the global FLUXNET GPP based on parameter sensitive analysis and optimization strategies. The results show that the minimum (xn) and optimum (xo) temperatures for photosynthesis are sensitive parameters but maximum temperature (xm) not. Optimized xnand xodiffer largely from their defaults for more than half of 12 plant functional types (PFTs). Parameter optimization significantly improves the TG's performance in forest ecosystems where temperature or solar radiation has significant contribution to GPP. For water-limited ecosystems where GPP are strongly dependent of EVI and EVI are sensitive to precipitation, parameter optimization has limited effects. These results imply that the TG model, and most likely for other kind of GPP models using same methodology, can't be significantly improved for all PFTs through parameter optimization only, and other key climatic variables should be incorporated into the model for better predicting terrestrial ecosystem GPP.
authors.affiliation: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.|Graduate School, University of Chinese Academy of Sciences, Beijing 100100, China.
authors.forename: Jiaqi
authors.initials: J
authors.lastname: Dong
authors.affiliation: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
authors.forename: Longhui
authors.initials: L
authors.lastname: Li
authors.affiliation: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A &F University, Yangling, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Shi
authors.affiliation: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.affiliation: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
authors.forename: Geping
authors.initials: G
authors.lastname: Luo
authors.affiliation: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A &F University, Yangling, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Yu