## 1 Introduction  
Rice is a major staple crop that feeds over 50% of the world's population. Planting rice requires many freshwater resources, coupled with increased demand from other competitive sectors and expanding sectors, which means better agricultural water management for rice. Evaptranspiration (ET) is the main component of water consumption in rice paddies. Therefore, accurate estimation of ET is essential for rice water management, which helps formulate and evaluate water-saving strategies and enhances the understanding of the rice water cycle [1; 2; 3]. In addition to the rice paddy water cycle, the carbon cycle is significant for maintaining the health and sustainable development of the rice paddy ecosystem [4]. Continuous measurement of net ecosystem CO\({}_{2}\) exchange (NEE) in rice paddy helps determine the source and sink status of the rice paddy ecosystem and analyze the temporal variation of carbon exchange [5]. Methane (CH\({}_{4}\)) is the second largest radiatively forced greenhouse gas after CO\({}_{2}\)[6]. Rice paddies will cause an anaerobic soil environment under long-term flooding conditions. Organic matter, such as primary carbon and plant residues in the soil, is gradually decomposed into soluble organic matter utilizedby methanogens. Methane is produced by acetic acid fermentation or hydrogen/carbon dioxide reduction. Thus, rice paddies are considered to be one of the main CH4 sources of the atmosphere [7; 8; 9; 10]. In order to better understand the water and carbon cycle in rice paddies, it is important to conduct field studies to measure these water and carbon fluxes.  
The eddy covariance technique [11] has been extensively used for the continuous measurements of evapotranspiration (ET) [12; 13; 14], net ecosystem carbon exchange (NEE) [15], and methane flux (FCH4) [16; 17]. This technique can provide nearly continuous measurements on the ecosystem scale without interfering with the gas exchange process between the terrestrial ecosystem and the atmosphere, thus becoming a means of measuring trace gas exchange. However, it has limitations that come from the high costs of installation and maintenance. Building more sites to observe these water and carbon fluxes is very complicated, laborious, and not sustainable. On the one hand, these instruments need calibration and servicing and are inconvenient to maintain in the field. On the other hand, the life and performance of the instrument can also be affected by some unstable factors, such as weather conditions, environmental factors, and interference from animals and insects. These aspects affect the sustainability of the measurement instruments. To address these issues and make measuring ET, NEE, and FCH4 sustainable, other ways are needed to quantify these water-carbon fluxes. Alternative machine learning algorithms for predicting these water and carbon fluxes are support vector machine (SVM) [18; 19; 20; 21] and artificial neural network (ANN) [22; 23; 24]. Apart from accurately predicting these water and carbon fluxes, it is also necessary to understand the environmental driving factors of water and carbon fluxes in rice paddies. ET, NEE, and FCH4 nonlinearly rely on multiple driving factors, such as air temperature, soil temperature, soil water content, air pressure, radiation, etc. Understanding their key factors can also provide information for subsequent lifting algorithms.  
In this study, we used the XGBoost and RF models to analyze seven rice paddy sites. The objective of this study is (1) to compare the predictive performance of ET, NEE, and FCH4 for the two models; (2) to analyze the importance of input factors of ET, NEE, and FCH4; and (3) to provide further information for subsequent water carbon flux predictions in rice paddies.