Comparison of multiple models for estimating gross primary production using remote sensing data and fluxnet observations

Abstract. In this study, gross primary production (GPP) estimated from a temperature and greenness (TG) model, a greenness and radiation (GR) model, a vegetation photosynthesis model (VPM), and a MODIS product have been compared with eddy covariance measurements in cropland during 2003–2005. Results showed that the determination coefficients (R²) between fluxnet GPP and estimated GPP were all greater than 0.74, indicating that all these models offered reliable estimates of GPP. We also found that the VPM-based GPP estimates performed a bit better (R² is 0.82, and RMSE is 16.75 gC m⁻² (8 day)⁻¹) than other models, mainly due to its comprehensive consideration of the stresses from light, temperature and water. The actual GPP was overestimated in the non-growing season and underestimated in the growing season by MOD_GPP. The validation confirms that the above three models may be used to estimate crop production in the North China Plain, but there are still significant uncertainties.