The uncertainty analysis of the MODIS GPP product in global maize croplands

Abstract

Gross primary productivity (GPP) is very important in the global carbon cycle. Currently, the newly released estimates of 8-day GPP at 500 m spatial resolution (Collection 6) are provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Science Team for the global land surface via the improved light use efficiency (LUE) model. However, few studies have evaluated its performance. In this study, the MODIS GPP products (GPP_MOD) were compared with the observed GPP (GPP_EC) values from site-level eddy covariance measurements over seven maize flux sites in different areas around the world. The results indicate that the annual GPP_MOD was underestimated by 6%‒58% across sites. Nevertheless, after incorporating the parameters of the calibrated LUE, the measurements of meteorological variables and the reconstructed Fractional Photosynthetic Active Radiation (FPAR) into the GPP_MOD algorithm in steps, the accuracies of GPP_MOD estimates were improved greatly, albeit to varying degrees. The differences between the GPP_MOD and the GPP_EC were primarily due to the magnitude of LUE and FPAR. The underestimate of maize cropland LUE was a widespread problem which exerted the largest impact on the GPP_MOD algorithm. In American and European sites, the performance of the FPAR exhibited distinct differences in capturing vegetation GPP during the growing season due to the canopy heterogeneity. In addition, at the DE-Kli site, the GPP_MOD abruptly produced extreme low values during the growing season because of the contaminated FPAR from a continuous rainy season. After correcting the noise of the FPAR, the accuracy of the GPP_MOD was improved by approximately 14%. Therefore, it is crucial to further improve the accuracy of global GPP_MOD, especially for the maize crop ecosystem, to maintain food security and better understand global carbon cycle.

Keywords

MODIS GPP / eddy covariance / maize cropland / validation / improvement

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Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (Grant No. 41771453), National Key Technology R&D Program of China (2016YFC0500106), Special Project of Science and Technology Basic Work (2014FY210800-5), and the Fundamental Research Funds for the Central Universities in China (SWU116088). We are grateful to the availability of the tower flux data of maize sites from Fluxnet and ChinaFlux.

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