Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

Shufen Pan, Chinese Academy of Sciences
Hanqin Tian, Auburn University Main Campus
Shree R. S. Dangal, Auburn University Main Campus
Zhiyun Ouyang, Chinese Academy of Sciences
Bo Tao, Auburn University Main Campus
Wei Ren
Chaoqun Lu, Auburn University Main Campus
Steven W. Running, University of Montana - Missoula

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2 and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment.