# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
## 4 Discussion  
This study successfully estimated wheat carbon flux across four key growth stages--green-up, jointing, heading-filling, and milk maturity stages in the Guanzhong  
Figure 5: Importance analysis of characteristics in different growth stages ((**a**) green-up stage, (**b**) jointing stage, (**c**) heading–filling stage, and (**d**) milk maturity stage). \begin{table}
\begin{tabular}{c c} \hline \hline
**Period** & **Top 5 Features** \\ \hline Green-up stage & (1) LAI (2) EVI (3) NDVI (4) Vap (5) Pr \\ Jointing stage & (1) LAI (2) EVI (3) NDVI (4) Vap (5) PDSI \\ Heading–filling stage & (1) LAI (2) NDVI (2) EVI (4) Pet (5) PDSI \\ Milk maturity stage & (1) LAI (2) NDVI (3) EVI (4) PDSI (5) SM \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Predictive model feature importance ranking in 2023. Plain using the MCFEW model. The model demonstrated strong overall performance across all growth stages, with particularly high accuracy observed during the jointing stage. This aligns with the results from [PERSON] et al. [34], who found that the prediction accuracy of crop carbon flux models is typically highest during periods of active growth. In contrast, the milk maturity stage exhibited slightly reduced accuracy, likely due to the physiological changes in the wheat crop as it nears harvest, resulting in a reduced rate of carbon assimilation and biomass accumulation, which is consistent with the findings of [PERSON] et al. [35], who observed decreased flux stability in late-stage crops. The spatial distribution maps further reinforced this, showing that while carbon flux values remain high in some areas during the milk maturity stage, they generally decrease in regions where biomass accumulation has slowed. The feature importance analysis revealed that LAI, NDVI, and EVI were the most influential features across all growth stages, with LAI consistently showing the highest SHAP values, highlighting its critical role in photosynthetic capacity and carbon assimilation, as emphasized by [PERSON] et al. [36], due to its correlation with canopy structure and light interception. EVI, less influenced by SM and atmospheric interference than NDVI, was particularly important during the jointing and heading-filling stages, reflecting its ability to capture seasonal biomass changes and variations in GPP, in line with [PERSON] et al. [37], who found EVI to be a more reliable measure of vegetation productivity in areas with high soil variability. NDVI showed significant fluctuations, peaking during the green-up and jointing stages when crop biomass and chlorophyll content were maximal, and declining in the milk maturity stage, reflecting leaf aging and reduced photosynthetic activity, thus leading to lower carbon flux. Among meteorological variables, PDSI was consistently significant across all stages, particularly during water stress periods, as it integrates soil moisture and climate conditions. Vap, representing atmospheric humidity, was also highly relevant during the jointing and heading-filling stages, underscoring the importance of atmospheric conditions in regulating transpiration and photosynthesis. Notably, Pr was more significant than Pet and PDSI during the green-up stage, reflecting wheat's greater dependence on precipitation early in growth, while in the milk maturity stage, Pr became less influential, indicating that wheat's reliance on precipitation decreases with maturation, consistent with findings by [PERSON] et al. [5]. Although this study successfully predicted wheat carbon flux in the Guanzhong Plain, several issues still need to be addressed. First, there may be inconsistencies in the quality of the data used. The resolution of the multimodal data employed for GPP prediction varies across datasets. Even with the application of aggregation and averaging methods to standardize the data, inconsistencies remain, meaning not all features are equally prominent across all agricultural areas.