## 3 Results  
Seasonal Variation in Carbon Fluxes, Environmental Elements, and Biophysical Indicators on the Paddy Field  
Figure 3a illustrates the seasonal variation in daily _NEE_, _GPP_, and \(R_{e\alpha}\) during the rice growth period. The _NEE_ displayed a one-valley pattern. The minimum value of daily _NEE_ was \(-12.08\) and \(-14.60\) gC m\({}^{-2}\) d\({}^{-1}\) in 2022 and 2023, respectively. The two minimum values occurred on August 25 th, 2022, and July 29 th, 2023. The cumulative _NEE_ in the growth period was 502 and 552 gC m\({}^{-2}\). The _GPP_ exhibited a one-peak mode, with peak daily values of 21.6 and 20.16 gC m\({}^{-2}\) d\({}^{-1}\) in 2022 and 2023, respectively. The peak value occurred on August 21 st, 2022, and July 29 th, 2023. Total _GPP_ during the growth period was 1429 gC m\({}^{-2}\) in 2022 and 1337 gC m\({}^{-2}\) in 2023. A strong fluctuation can be found in the dynamics of _NEE_ and _GPP_, while daily \(R_{e\alpha}\) changed smoothly. \begin{table}
\begin{tabular}{c c c} \hline  & \(V^{25}_{cmax,0}\) Model & Description \\ \hline M1 & \(V^{25}_{cmax,0}=\text{DNN}\Big{(}\mu_{ref}\Big{)}\) & \(\mu_{ref}\): mean value of reflectance within the flux footprint climatology. M1 is driven by the mean value of the 5-band reflectance. \\ M2 & \(V^{25}_{cmax,0}=\text{DNN}\Big{(}\mu_{ref},\sigma_{ref}\Big{)}\) & \(\sigma_{ref}\): standard deviation value of reflectance within the flux footprint climatology. M2 is jointly driven by the mean and standard deviation of the 5-band reflectance. \\ M3 & \(V^{25}_{c,\text{max}0}=\text{DNN}(GLCM)\) & GLCM: GLCM-based texture features. M3 is driven by the GLCM-based texture features from the RGB images. \\ M4 & \(V^{25}_{c,\text{max}0}=\text{DNN}\Big{(}\mu_{ref},GLCM\Big{)}\) & M4 is jointly driven by the mean value of the 5-band reflectance and \\ M5 & \(V^{25}_{c,\text{max}0}=\text{DNN}(LBPH)\) & GLCM-based texture features from the RGB images. \\ M6 & \(V^{25}_{c,\text{max}0}=\text{DNN}(LBPH)\) & LBPH: local binary pattern histogram. M5 is driven by the local binary pattern histogram feature from the RGB images. \\ M6 & \(V^{25}_{c,\text{max}0}=\text{DNN}\Big{(}\mu_{ref},LBPH\Big{)}\) & M6 is jointly driven by the mean value of the 5-band reflectance and the local binary pattern histogram feature from the RGB images. \\ M7 & \(V^{25}_{c,\text{max}0}=\text{AlexNet}\Big{(}Image_{ref}\Big{)}\) & _Image\({}_{ref}\)_: standardized UAV reflectance maps. M7 and M8 are driven by the 5-band reflectance maps. \\ M8 & \(V^{25}_{c,\text{max}0}=\text{ResNet}\Big{(}Image_{ref}\Big{)}\) & 5-band reflectance maps. \\ \hline \end{tabular}
\end{table} 
Table 3: The list of \(V^{25}_{cmax,0}\) models proposed in this study. The summer of 2022 witnessed a severe meteorological drought along the Yangtze River. Accordingly, the incoming shortwave radiation (\(SW_{IN}\)) and air temperature (\(T_{a}\)) in 2022 were significantly higher than those in 2023 (Figure 3b,c). The daily mean \(SW_{IN}\) and \(T_{a}\) during the summer of 2022 were 21.35 MJ m\({}^{-2}\) d\({}^{-1}\) and 28.4 \({}^{\circ}\)C. The two values at the same time in 2023 were 18.00 MJ m\({}^{-2}\) d\({}^{-1}\) and 27.1 \({}^{\circ}\)C. The difference in \(T_{a}\) on the same days between 2022 and 2023 during the summer was as large as 6 \({}^{\circ}\)C. Moreover, 2022 experienced lower precipitation (\(P\)) and relative humidity (\(RH\)) compared to 2023, with mean \(RH\) values of 71% and 76, and accumulated P values of 759 and 922 mm during the growth period for 2022 and 2023, respectively (Figure 3d,g). Precipitation was more frequent in 2023, particularly during the summer. Differences in wind speed (\(u\)) and atmospheric CO\({}_{2}\) concentration (\(C_{a}\)) between the two years were minimal (Figure 3e,f),  
Figure 3: Seasonal variation in daily carbon fluxes, environmental elements and biophysical indicators.