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

Frontiers of Earth Science 2018年第4期12卷 739-749页

作者： Xiaojuan HUANG Mingguo MA Xufeng WANG Xuguang TANG Hong YANG Chongqing Engineering Research Center for Remote Sensing Big Data Application Southwest UniversityChongqing 400715China Research Base of Karst Eco-environments at Nanchuan in Chongqing Ministry of Natural ResourcesSchool of Geographical SciencesSouthwest UniversityChongqing 400715China Northwest Institute of Eco-environment and Resources Chinese Academy of SciencesLanzhou 730000China Department of Geography and Environmental Science University of ReadingReading RG66ABUK

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 (GPPMOD) were compared with the observed GPP (GPPEC) values from site-level eddy covariance measurements over seven maize flux sites in different areas around the world. The results indicate that the annual GPPMOD 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 GPPMOD algorithm in steps, the accuracies of GPPMOD estimates were improved greatly, albeit to varying degrees. The differences between the GPPMOD and the GPPEC 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 GPPMOD 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 GPPMOD 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 GPPMOD was improved by approximately 14%. Therefore, it is crucial to further improve the accuracy of global GPPMOD, especially for the maize crop ecosystem, to maintain food security and better understand global carbon cycle.

关键词： MODIS GPP eddy covariance maize cropland validation improvement

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