## 4 Discussion
### Monitoring and Estimation of the FCO\({}_{2}\) Based on Remote Sensing Data  
In recent decades, the field measurement of FCO\({}_{2}\), such as chamber-based measurements, has been widely used for exploring its spatiotemporal characteristics. Meanwhile, the in-situ measurements of soil CO\({}_{2}\) flux databases have been made openly available [91]. However, there is still a big challenge for monitoring and estimating soil CO\({}_{2}\) flux at a large spatial scale because of the sparse spatial coverage and uncertainties associated with the limited observations of FCO\({}_{2}\), especially in subtropical and tropical regions [15]. The utilization of RS data for estimating and upscaling the FCO\({}_{2}\) efflux from local to regional scale becomes increasingly important [8], and many studies have proved the potential and advantage of using RS data to monitor and map the FCO\({}_{2}\) emission in boreal and temperate forests [8; 13; 39; 92]. However, the use of RS to monitor the FCO\({}_{2}\) emission in subtropical and tropical forest ecosystems is still at the exploratory stage.  
In this study, we combined RS data combined with the in-situ observations of FCO\({}_{2}\) to estimate the FCO\({}_{2}\) emission in the dry season. The results demonstrated that RS data has a great potential for estimating the FCO\({}_{2}\) emission in subtropical forests like in boreal and temperate forests (Figure 7). Based on RS data, previous studies have established statistical approaches, such as the linear and exponential models, to estimate the FCO\({}_{2}\) emission in forested areas. These studies, however, mainly focused on the site-scale FCO\({}_{2}\) study, and few upscaled the in-situ measurement of FCO\({}_{2}\) to a larger spatial scale in combination with RS data. Moreover, at a certain region, a previous study suggested that the exponential model is only suitable for the estimation of FCO\({}_{2}\) in the upper slope but the linear model can only be used to the lower slope [93], suggesting the limitation of these models in estimating FCO\({}_{2}\) over the space scale. [PERSON] [15] also argued that the traditional statistical methods have a weak explanatory ability (only about 30-40%), and the use of new techniques, such as the machine-learning algorithm, becomes necessary for understanding soil CO\({}_{2}\) efflux.  
Therefore, the machine-learning RF algorithm in this study is used to model the FCO\({}_{2}\) and map its spatiotemporal pattern in the subtropical forested watershed. Based on RS data with a combination of in-situ measurements, the RF model illustrated a high performance (training dataset: R\({}^{2}\) = 0.88 and testing dataset: R\({}^{2}\) = 0.74) in capturing the spatiotemporal variation of FCO\({}_{2}\) in the study area (Figure 8). One of the reasons is that the RF algorithm takes into account the linear or nonlinear relationship with input variables so that it can fully mine the potential information between the target variable and the feature variables in different time and space [14], particularly in the subtropical forest areas with complex terrain and environmental conditions. For example, the RF model highlighted that VIs (NDVI and EVI) also have a stronger effect in simulating the FCO\({}_{2}\) change like the abiotic factors in the subtropical forests (Figure 6). This is because the RF model may fully dig out the nonlinear phenological processes (e.g., changes in NDVI) and plant productivity variation (e.g., changes in EVI) of VIs that determine the FCO\({}_{2}\) emission from the subtropical forests [64].