Satellite-based light use efficiency (LUE) models are important tools for estimating regional and global vegetation gross primary productivity (GPP). However, all LUE models assume a constant value of maximum LUE at canopy scale (LUE_maxcanopy) over a given vegetation type. This assumption is not supported by observed plant traits regulating LUE_maxcanopy, which varies greatly even within the same ecosystem type. In this study, we developed an improved satellite data driven GPP model by identifying the potential maximal GPP (GPP_POT) and their dominant climate control factor in various plant functional types (PFT), which takes into account both plant trait and climatic control inter-dependence. We selected 161 sites from the FLUXNET2015 dataset with eddy covariance CO₂ flux data and continuous meteorology to derive GPP_POT and their dominant climate control factor of vegetation growth for 42 natural PFTs. Results showed that (1) under the same phenology and incident photosynthetic active radiation, the maximal variance of GPP_POT is found in different PFTs of forests (10.9 g C m⁻² day⁻¹) and in different climatic zones of grasslands (>10 g C m⁻² day⁻¹); (2) intra-annual change of GPP in tropical and arid climate zones is mostly driven by vapor pressure deficit (VPD) changes, while temperature is the dominant climate control factor in temperate, boreal and polar climate zones; even under the same climate condition, physiological stress in photosynthesis is different across PFTs; (3) the model that takes into account the plant trait difference across PFTs had a higher agreement with flux tower-based GPP data (GPP_flux) than the GPP products that omit PFT differences. Such agreement was highest for natural vegetation cover sites (R² = 0.77, RMSE = 1.79 g C m⁻² day⁻¹). These results suggest that global scale GPP models should incorporate both plant traits and their dominant climate control factor variance in various PFT to reduce the uncertainties in terrestrial carbon assessments.

基于卫星的光利用效率（LUE）模型是估算区域和全球植被总初级生产力（GPP）的重要工具。但是，在给定的植被类型下，所有LUE模型都假设在树冠尺度上的最大LUE（LUE _maxcanopy）为常数。观察到的调节LUE _maxcanopy的植物性状不支持该假设，即使在相同的生态系统类型中，LUE _maxcanopy的变化也很大。在这项研究中，我们通过识别潜在的最大GPP（GPP _POT），开发了一种改进的卫星数据驱动GPP模型。）及其在各种植物功能类型（PFT）中的主要气候控制因素，这些因素都考虑了植物性状和气候控制的相互依存关系。我们从FLUXNET2015数据集中选择了161个地点，该地点具有涡动协方差CO ₂通量数据和连续气象学，以得出GPP _POT及其对42种自然PFT的植被生长的主要气候控制因子。结果表明（1）在相同的物候和入射的光合作用活性辐射下，GPP _POT的最大方差出现在森林的不同PFT中（10.9 g C m ^-2 day ^-1）和草地的不同气候区（> 10 g C m ^-2天^-1）; （2）在热带和干旱气候区，GPP的年内变化主要由蒸气压亏空（VPD）变化驱动，而温度是温带，北方和极地气候区的主要气候控制因素；即使在相同的气候条件下，各个PFT的光合作用中的生理压力也不同。（3）与忽略PFT差异的GPP产品相比，考虑到PFT之间的植物性状差异的模型与基于通量塔的GPP数据（GPP _flux）的一致性更高。该协议对于自然植被覆盖点最高（R ²  = 0.77，RMSE = 1.79 g C m ^-2天^-1）。这些结果表明，全球规模的GPP模型应在各种PFT中纳入植物性状及其主要的气候控制因子方差，以减少陆地碳评估中的不确定性。