## 4 Discussion
### Analysis of Influencing Factors of Net Ecosystem Carbon Exchange in Rice Paddies  
The net ecosystem carbon exchange (NEE) is a critical parameter for quantifying the rice ecosystem and its contributions to climate change. [PERSON] et al. [37] indicated that both the XGBoost and RF models were applicable machine learning algorithms for predicting ecosystem NEE. However, the XGBoost model had higher computational efficiency than the RF model. Among environmental input factors, NETRAD, SWC, and TS were the most important factors, while precipitation and WS were less important in predicting NEE. It was different with this study.  
The feature importance of the seven sites is shown in Figure 5, and it can be seen that the input factor importance ranking is different among sites. However, both NETRAD and NDVI were in the top three for all sites (except for the absence of net radiation factor at the JP-MSE site). It showed that NETRAD and NDVI were important factors in predicting NEE in rice paddies. [PERSON] et al. [23] indicated that the most effective inputs on the NEE were NETRAD and LAI for irrigated sites through sensitivity analysis, similar to our findings.  
The reason for this phenomenon is that NETRAD and NDVI, respectively, reflect the input of solar radiation energy and the degree of vegetation coverage.  
[PERSON] et al. [38] indicated that SWC was one of the most important factors based on the RF model. However, in this study, SWC only performed better at the US-HRC site, ranking first. Five sites (no data available at the other site) showed a relatively moderate effect of SWC on the NEE at the half-hour time scale, which is similar to [PERSON] et al. [39].  
Temperature affects both photosynthesis and respiration, thus affecting NEE. Rice sensitivity to temperature varies in different regions. Too high or too low a temperature can affect the growth of rice. Among them, IT-Cas, JP-Mse, US-HRA, and US-HRC belong to humid subtropical climates, while KR-CRK, PH-RiF, and US-Twt have humid continental, tropical monsoon, and Mediterranean climates, respectively. Rice originates in tropical or subtropical regions, so in humid subtropical areas, the climate is warm and humid, which is very conducive to the growth of rice. However, in these humid subtropical regions, only the IT-Cas site was sensitive to temperature when predicting NEE, with a gain value of 0.3916. This may be due to the fact that the temperature at the IT-Cas site varied more than at other sites, ranging from \(-\)14.05 \({}^{\circ}\)C to 34.41 \({}^{\circ}\)C, resulting in NEE's increased sensitivity to temperature.  
Differences in geographical location and climatic conditions lead to different effects of LW on NEE in different regions. In this study, when modeling JP-Mse and KR-CRK, LW had the greatest impact, and the remaining sites had a smaller impact (except for two sites missing LW data). These two sites are located in East Asia. The range of LW at these two sites was 299.71-509.32 W m\({}^{-2}\) and 196.94-517.96 W m\({}^{-2}\), respectively. Compared with other sites, their LW variation span was larger. It was possible that this changing trend affected the growth of rice, and the respiration and photosynthesis of rice were also affected, which made the prediction of NEE more sensitive to LW.