###### Abstract  
As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous vegetation indices (VIs) based on satellite data have participated in the construction of GPP models. However, the relative performance of various VIs in predicting GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood. We constructed two types of models (universal and plant functional type [PFT]-specific) for solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and Leaf Area Index (LAI) based on two widely used machine learning algorithms, i.e., the random forest (RF) and back propagation neural network (BPNN) algorithms. A total of thirty plant traits and environmental factors with legacy effects are considered in the model. We then systematically investigated the ancillary variables that best match each vegetation index in estimating global GPP. Four types of models (universal and PFT-specific, RF and BPNN) consistently show that SIF performs best when modeled using a single vegetation index (R\({}^{2}\) = 0.67, RMSE = 2.24 g C\(\cdot\)m\({}^{-2}\)-d\({}^{-1}\)); however, NIRv, combined with CO\({}_{2}\) plant traits, and climatic factors can achieve the highest prediction accuracy (R\({}^{2}\) = 0.87, RMSE = 1.40 g C\(\cdot\)m\({}^{-2}\)-d\({}^{-1}\)). Plant traits effectively enhance all prediction models' accuracy, and climatic variables are essential factors in improving the accuracy of NIRv- or LAI-based GPP models, but not the accuracy of SIF-based models. Our findings provide valuable information for the configuration of the data-driven models to improve the accuracy of predicting GPP and provide insights into the physiological and ecological mechanisms underpinning GPP prediction. g rmk  
dynamic processes of ecosystems and can accurately simulate vegetation photosynthetic capacity [7; 8]. However, the method is limited by the complexity of the required input parameters and parameterization over large-scale regions [9; 10]. Along with the generation of eddy covariance (EC) techniques and the advancement of remote sensing technology, LUE models based on satellite data and flux data are widely popular in ecological studies [11; 12; 13; 14]. However, such models typically do not account for the variation in LUE caused by radiation intensity and assume that maximum LUE is only related to plant functional types (PFTs), introducing great uncertainty into GPP simulations [15; 16]. Another approach to estimating GPP is data-driven, generally by establishing relationships between local vegetation parameters, climate factors, and flux tower GPP and then upscaling to regional and global scales [17; 18]. Data-driven models based on machine learning are widely used in large-scale studies for predicting vegetation productivity due to their higher accuracy than traditional regression methods [19; 20]. The most widely known MTE GPP model [21] often serves as a reference to validate and evaluate the simulation of other models [22; 23; 24]. Remote sensing vegetation indices (VIs), allowing for the effective detection of changes in large-scale vegetation growth, have been widely used to construct GPP models. Compared to simple VIs (e.g., the Normalized Difference Vegetation Index [NDVI] [25] and Enhanced Vegetation Index [EVI] [26]), Leaf Area Index (LAI) quantifies the structure and growth of vegetation and the Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) reflects the potential energy utilized by the canopy in photosynthesis, both of which are critical biophysical parameters in carbon cycle research [27; 28; 29]. They are solely driven by leaf development, are more closely related to actual ground observations, and are more sensitive to high-density biomes than simple VIs when observing vegetation phenology [30; 31].