## 5 Conclusions  
Accurate estimation of _GPP_ estimation poses a considerable challenge due to the influence of spatial heterogeneity. High-resolution UAV images can capture spatial heterogeneity and offer new insights for _GPP_ monitoring. In this study, we proposed high-resolution UAV-imagery-driven models to quantify \(V^{25}_{cmax,0}\), a key parameter of the leaf biochemical properties-based _GPP_ model. The reflectance and texture information were extracted from UAV images and employed to build \(V^{25}_{cmax,0}\) models. A significant improvement in \(V^{25}_{cmax,0}\) and _GPP_ estimation was obtained. The main conclusions are drawn as follows:  
1. [label=(0)]
2. Combining reflectance and texture features can vastly improve the field-scale \(V^{25}_{cmax,0}\) (R = 0.94, RMSE = 19.44 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), and MdAPE = 11%) and further result in high-accuracy GPP estimation (R = 0.92, RMSE = 6.5 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), and MdAPE = 23%). The performance of different texture features varies significantly. The CNN method shows the strongest ability to monitor \(V^{25}_{cmax,0}\). The \(\mu_{ref}\)-GLCM texture features and \(\mu_{ref}\)-LBPH joint-driven models also acquire promising results. However, the standard deviation of reflectance contributes less to estimating \(V^{25}_{cmax,0}\). 3. The contribution of input features changes significantly among different models. The CNN model focuses on nir and red-edge bands and pays much attention to the subregion with high spatial heterogeneity. The \(\mu_{ref}\)-LBPH joint-driven model mainly attaches importance to the reflectance information. On the other hand, the \(\mu_{ref}\)-GLCM-based features joint-driven model emphasizes the role of GLCM texture indices, including contrast, dissimilarity and homogeneity. 4. The strong spatial heterogeneity results in the large gap difference between field-scale and leaf-scale \(V^{25}_{cmax,0}\) values and hinders the conversion of parameters across different scales  
Our study confirms the necessity of considering spatial heterogeneity information for monitoring \(V^{25}_{cmax,0}\) and GPP, which is ignored in the current understanding of the GPP model. The following perspectives should be the focus of future work. First, thefusion of satellite and UAV data offers great potential. Medium-resolution satellites, such as Landsat and Sentinel-2, can capture remote sensing images with spatial resolutions of 10-60 m, providing valuable spatial information about the underlying surface. Additionally, integrating multi-scale remote sensing images can achieve information complementarity, which is likely to enhance the accuracy of GPP monitoring. Second, further exploration of new interpretation methods is needed to better elucidate the mechanistic link between spatial heterogeneity and field-scale parameters. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs16203906/s1](https://www.mdpi.com/article/10.3390/rs16203906/s1)]([https://www.mdpi.com/article/10.3390/rs16203906/s1](https://www.mdpi.com/article/10.3390/rs16203906/s1)), Table S1: The seasonal \(u^{\star}\) threshold for filtering NEE; Table S2: The fixed parameter values in the BESP model; Table S3: The date of leaf gas-exchange experiment; Figure S1: Dynamics of daily RGB and Reflectance maps within the flux footprint climatology; Figure S2: The coefficient of stomatal conductance model; Figure S3: The process of generating LBPH; Figure S4: The architectures of AlexNet and ResNet. The blue block indicates the convolution layer; Figure S5: Dynamics of spatial distribution of SHAP value based on the AlexNet model; Figure S6. Seasonal variation of filed-scale and leaf-scale \(V^{25}_{cmax,0}\). References [32; 51; 62] are cited in the supplementary materials. Conceptualization, X.H. and L.S. ; methodology, X.H., X.D., J.B. and Z.Z.Z. ; software, X.H. ; validation, L.L., L.S. and J.B.; formal analysis, X.H. ; investigation, X.H. ; resources, S.L. ; data curation, X.H., X.D., J.L., C.S., S.D., T.W.