The global gross primary productivity (GPP) product derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is perhaps the most widely used GPP product. However, there is still a large uncertainty associated with the MODIS GPP product partly due to the uncertainty in the default Biome specified Parameters Look-Up Table (BPLUT) of the MODIS photosynthesis (PSN) model. Here, we used the Bayesian inference with the Markov chain Monte Carlo (MCMC) approach and FLUXNET data from 110 sites to estimate the parameters of the MODIS PSN model (maximum light use efficiency: ɛ_max; temperature scalar-related parameters: Tmin_min and Tmin_max; water scalar-related parameters: VPD_min and VPD_max) through individual and joint optimization. The spread of the posterior probability density function (PDF) of the parameters allowed for the calculation of parameter means and uncertainty estimates and also provided information on the behavior of the parameters. Each model parameter varied not only across sites but also across plant functional types (PFTs). The means of the optimized parameter values within each PFT were used to update the BPLUT. We also generated parameter estimates for wetlands and C4/C3 croplands in the BPLUT. Parameters from the joint optimization were more representative and less variable. The optimization improved the performance of the MODIS PSN model by 15% for deciduous broadleaf forests, 8% for savannas, and 3% for grasslands with well-constrained parameters. The performance of the optimized model depended on the effectiveness of parameter optimization. Our study is an effort towards quantifying and reducing parameter uncertainty of the MODIS PSN model and improving the global MODIS GPP product for better understanding global ecosystem carbon dynamics, plant productivity, and carbon-climate feedbacks.

源自中等分辨率成像光谱仪（MODIS）的全球总初级生产力（GPP）产品可能是使用最广泛的GPP产品。但是，与MODIS GPP产品相关的不确定性仍然很大，部分原因是MODIS光合作用（PSN）模型的默认生物群系指定参数查找表（BPLUT）中存在不确定性。在这里，我们使用马尔可夫链贝叶斯推理蒙特卡洛（MCMC）从估计MODIS PSN模型（最大光利用效率的参数110位点的方法和FLUXNET数据：ɛ_最大;温度标量相关的参数：TMIN_分钟和Tmin _max；与水标量相关的参数：VPD _min和VPD _max），通过个体和联合优化。参数的后验概率密度函数（PDF）的扩展允许进行参数均值和不确定性估计的计算，还可以提供有关参数行为的信息。每个模型参数不仅在站点之间不同，而且在工厂功能类型（PFT）之间也不同。每个PFT中优化参数值的均值用于更新BPLUT。我们还为BPLUT中的湿地和C4 / C3农田生成了参数估计。联合优化的参数更具代表性，且变化较小。优化后，对于落叶阔叶林，MODIS PSN模型的性能提高了15％，热带稀树草原提高了8％，对于参数严格约束的草地提高了3％。优化模型的性能取决于参数优化的有效性。我们的研究致力于量化和减少MODIS PSN模型的参数不确定性，并改进全球MODIS GPP产品，以更好地了解全球生态系统的碳动态，植物生产力和碳气候反馈。