## 4 Discussion
### Environmental Factors and Plant Traits Paired with VIs  
Most machine learning-based GPP estimation models indirectly represent the effects of CO\({}_{2}\) fertilization on GPP by incorporating FPAR or other vegetation indices [21; 79; 80], although multiple pieces of evidence show that the rising atmospheric CO\({}_{2}\) concentration is one of the dominant factors driving vegetation growth [81; 82; 83]. We explicitly tested the contribution of incorporating the atmospheric CO\({}_{2}\) concentration into our study's GPP models and how it affected performance. We found that incorporating CFE did contribute to the performance of the VI-based machine learning GPP model, especially for models developed based on LAI (Figure 5). Our analysis (Figure 2) shows that preseason temperature is pivotal for all three VIs-based models. Temperature affects photosynthetic intensity at the leaf scale by altering the activity of enzymes in the chloroplast, with either high or low temperatures inhibiting enzyme activity [84]. [PERSON] et al. [85] evaluated the flux tower data and found that the relationship between vegetation photosynthesis and temperature at the ecosystem scale followed the same pattern as the leaf scale. Numerous studies have revealed that temperature is one of the dominant limiting factors in high northern latitudes [86; 87; 88; 89]. Temperature has been confirmed as the key environmental factor controlling the rising GPP of northern Eurasia [90]. Furthermore, preseason temperature also strongly regulates the phenology of plants, which essentially determines the length of the photosynthetic active period and, subsequently, annual GPP [86; 91; 92; 93; 94]. However, it has also been found that warming leads to earlier spring phenology, reducing vegetation's peak growth in North American boreal forests [95]. Solar radiation is crucial for providing energy for photosynthesis. On the one hand, incoming shortwave radiation regulates leaf development and controls the process of leaf senescence by affecting plant hormones (e.g., ethylene and abscisic acid). On the other hand, radiation intensity determines the photosynthetic rate of vegetation [96]. Several recent reports have suggested that when reflectance-based VIs incorporating biophysical and biochemical properties are paired with incoming shortwave radiation, changes in GPP are more easily captured [97; 98]. This is in line with our findings that radiation participates as the most important climatic variable in NIR\({}_{\rm v}\)- and LAI-based models (Figure 2). In the SIF-based models, we also discovered that radiation is insignificant. Solar radiation has proven to be a dominant driver of SIF yield based on theoretical and experimental analysis [70; 99; 100]. Fluorescence signals are considered the most direct response to radiation absorbed by the vegetation canopy during photosynthesis, which is coupled with the radiation. [PERSON] et al. [35] concluded that the performance of models using remote sensing data alone is comparable to the best model, implying that climate factors have a minor effect. However, we found that there is some room for improving model performance when predicting GPP using remote sensing data alone, which is achieved by selecting an optimal combination of features. Previous studies implied that NIR\({}_{v}\) to some extent, captures the influences of climate on canopy development. In other words, the accuracy of NIR\({}_{v}\) in predicting GPP is not significantly increased by meteorological data [101]. Nevertheless, our results suggest that incorporating meteorological information increased accuracy significantly for the NIR\({}_{v}\)- and LAI-based GPP estimation models (Figure 3). This is likely due to the fact that NIR\({}_{v}\) and LAI describe vegetation canopy structure well but cannot fully reflect photosynthetic activity, and the machine learning algorithm can represent the nonlinear interactions between the climate variable and the VIs.