Improving global gross primary productivity estimation by fusing multi-source data products
Yahai Zhang, Aizhong Ye
Abstract
A reliable estimate of the gross primary productivity (GPP) of terrestrial vegetation is essential for both making decisions to address global climate change and understanding the global carbon balance. The lack of consistency in global terrestrial GPP estimates across various products leads to great uncertainty. In this study, we improve the quantification of global gross primary productivity by integrating multiple source GPP products without using any prior knowledge through the Bayesian-based Three-Cornered Hat (BTCH) method to generate a new weighted GPP data set. The fusion results demonstrate the superiority of weighted GPP, which greatly reduces the random error of individual datasets and fully takes advantage of the characteristics of multi-source data products. The weighted dataset can largely reproduce the interannual variation of regional GPP. Overall, the merging scheme based on the BTCH method can effectively generate a new GPP dataset that integrates information from multiple products and provides new ideas for GPP estimation on a global scale.