Zhang Yahai, Ye Aizhong
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Heliyon. 2022 Mar 21;8(3):e09153. doi: 10.1016/j.heliyon.2022.e09153. eCollection 2022 Mar.
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.
可靠对陆地对陆地植被的总初级生产力（GPP）进行可靠估算，对于制定应对全球气候变化的决策以及理解全球碳平衡都至关重要。不同产品的全球陆地GPP估算缺乏一致性，导致了很大的不确定性。在本研究中，我们通过基于贝叶斯的三角帽（BTCH）方法，在不使用任何先验知识的情况下整合多源GPP产品，改进了全球总初级生产力的量化，以生成一个新的加权GPP数据集。融合结果证明了加权GPP的优越性，它大大降低了单个数据集的随机误差，并充分利用了多源数据产品的特征。加权数据集能够在很大程度上再现区域GPP的年际变化。总体而言，基于BTCH方法的合并方案能够有效地生成一个整合了多个产品信息的新GPP数据集，并为全球尺度的GPP估算提供了新思路。
