## 3 Results
### Accuracy of \(V^{25}_{cmax,0}\) and GPP Estimation  
Figure 5 shows the performance of eight \(V^{25}_{cmax,0}\) models. Three metrics, including correlation (R), root mean square error (RMSE), and medium absolute percentage error (MdAPE), were employed to assess the model performance. When employing traditional reflectance values as input (Figure 5a), the R, RMSE, and MdAPE of \(V^{25}_{cmax,0}\) estimation on the test dataset were 0.87, 30.08 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) and 23%, respectively. In contrast, the \(V^{25}_{cmax,0}\) model driven solely by texture information displayed a noticeable performance degradation. The GLCM texture-driven model achieved \(V^{25}_{cmax,0}\) estimation with an R of 0.80, RMSE of 36.50 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) and MdAPE of 27% on the test dataset (Figure 5c). The LBPH-driven model exhibited the poorest accuracy, with R, RMSE, and MdAPE values of 0.81, 39.31, and 31% on the test dataset, respectively (Figure 5e). However, it can be seen that combining the reflectance value and texture information can significantly improve the accuracy of \(V^{25}_{cmax,0}\) estimation, while the contribution of differing texture information varies significantly. Two joint-driven models, including \(\mu_{nrf}\)-GLCM texture feature and \(\mu_{nrf}\)-LBPH texture feature models, substantially improved the \(V^{25}_{cmax,0}\) estimations, with R values reaching 0.93-0.94, and RMSE and MdAPE falling from 20.08 to 23.40 \(\mu\)mol m  
Figure 4: Seasonal variation in field-scale \(V^{25}_{cmax,0}\). s\({}^{-1}\) and 12% to 16%, respectively. However, the combination of \(\mu_{ref}\) and \(\sigma_{ref}\) provided only a marginal improvement, with an R of 0.89, RMSE of 28.70 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) and MdAPE of 22% (Figure 5b). Two CNN methods demonstrated excellent performance. ResNet is typically superior to AlexNet in the ImageNet classification challenge, whereas AlexNet slightly outperformed ResNet in this study. The former acquired the \(V_{emax,0}^{25}\) estimation with an R of 0.94, RMSE of 23.70 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), and MdAPE of 11%. The latter obtained the \(V_{emax,0}^{25}\) estimation with an R of 0.93, RMSE of 27.30 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), and MdAPE of 14%. A noteworthy observation is that the CNN methods showed an overwhelming advantage over other models on the training dataset, with R reaching 1, and RMSE and MdAPE being lower than 10 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) and 10%. Although this result indicates that the CNN methods encountered a degree of overfitting, it still suggests a strong ability to discern and utilize spatial information embedded in UAV images. The ResNet, with a more complicated structure, displays significantly higher potential than the AlexNet. The overfitting issue of CNN methods can be alleviated by training with more data. As the dataset expands, there is a promising outlook for CNN methods to transcend these challenges and realize significant success. Considering the texture information also contributes to the GPP estimation, it can be found that the superior \(V_{emax,0}^{25}\) models result in more accurate GPP estimation (Figure 6). Two CNN models acquired the best GPP estimation, with an R of 0.92, RMSE of 6.5-6.6 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), and MdAPE of 23-24%. The \(u_{ref}\)-GLCM texture feature and the \(u_{ref}\) -LBPH texture feature joint-driven models also achieved excellent GPP estimation. The former obtained the lowest RMSE, with a value of 6.43 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\). The latter also obtained promising results, with an R of 0.92, RMSE of 6.74 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), and MdAPE of 26%. The \(\mu_{ref}\)-\(\sigma_{ref}\) joint-driven model and LBPH solely driven model performed worst for GPP estimation. The RMSE of the two models was larger than 7 \(\mu\)mol m\({}^{-2}\), and the MdAPE was over 30%.