Modeling of maize gross primary production using MODIS imagery and flux tower data

Maize is one of the most important crops cultivated on the global scale. Accurate estimation of maize Gross Primary Production (GPP) can provide valuable information for regional and global carbon budget studies. From site level to regional/global scales, GPP estimation depends on remote sensing or eddy covariance flux data. In this research, the 8-day composite GPP of maize was estimated by Moderate Resolution Imaging Spectroradiometer (MODIS) and flux tower data at eight study sites using a Regional Production Efficiency Model (REG-PEM). The performance of the model was assessed by analyzing the linearly regression of GPP estimated from the REG-PEM model (GPPEST) with the GPP predicted from the eddy covariance data (GPPEC). The coefficient of determination, root mean squared error and mean absolute error of the regression model were calculated. The uncertainties of the model are also discussed in this research. The seasonal dynamics (phases and magnitudes) of the GPPEST reasonably agreed with those of GPPEC, indicating the potential of the satellite-driven REG-PEM model for up-scaling the GPP in maize croplands. Furthermore, the maize GPP estimated by this model is more accurate than the MODIS GPP products (MOD17A2). In particular, MOD17A2 significantly underestimated the GPP of maize croplands. The uncertainties in the REG-PEM model are mostly contributed by the maximum light use efficiency and the fraction of photosynthetically active radiation.