## 1 Introduction
It accounts for 3-50% of annual carbon emissions and thus is crucial for determining the annual carbon cycle [22; 25; 26; 27]. Therefore, it is necessary to accurately assess the spatial and temporal changes of FCO\({}_{2}\) in subtropical and tropical forested areas, especially in the dry season. Such studies can contribute to better understand the principal components of the annual carbon cycle and to reduce uncertainties in global carbon budgets [28; 29]. With the increasing demand for mapping the global and regional FCO\({}_{2}\) at the moderate or high spatiotemporal resolution, the earth observation techniques with low-cost and long-term observation data can be used to capture the interannual and intraseasonal variation of FCO\({}_{2}\) at the local or regional scale. For example, microwave and optical remote sensing(RS) technologies are widely used to monitor the \(\text{FCO}_{2}\) in forest ecosystems. Despite the application of the microwave RS technique to detect the \(\text{FCO}_{2}\) is not subject to weather conditions [30; 31], the coarse spatial resolution of products inhibits its development. The commonly used optical RS such as the Landsat is also widely used in studying forest carbon flux. Despite the merits of the high spatial resolution of Landsat imagery, the coarse temporal resolution and lack of images for subtropical and tropical zones challenge its application in estimating \(\text{FCO}_{2}\) in these regions. In contrast, the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor generates ideal data with suitable spatial (250 m, 500 m, and 1 km) and high temporal (daily) resolution to measure the \(\text{FCO}_{2}\). Moreover, the rich bands (36 bands) of the MODIS sensor can retrieve different biophysical and environmental parameters in relation to the \(\text{FCO}_{2}\), and thus reduce parameter errors due to the inconsistency of sensors [32]. Given that the \(\text{FCO}_{2}\) in terrestrial ecosystems is mainly controlled by biotic (e.g., plant productivity and leaf area index) and abiotic factors (e.g., soil temperature and soil moisture) [5; 33], and RS can retrieve important information about factors that control the dynamics of \(\text{FCO}_{2}\), many studies used RS data to build models to extrapolate field-measured \(\text{FCO}_{2}\) to global and regional scales [34; 35; 36]. For instance, \(\text{FCO}_{2}\) was found to be strongly correlated with a MODIS-derived land surface temperature (LST) in the boreal and temperate forests [8; 37; 38]. By integrating the MODIS LST and vegetation indices (VIs), [PERSON], et al. [39] established a linear model to monitor \(\text{FCO}_{2}\) and map its spatial pattern in a Canadian temperate forest. [PERSON], et al. [13; 34] argued that remotely sensed VIs, LST, and soil moisture can significantly enhance the model's accuracy in quantifying \(\text{FCO}_{2}\) at different temperate forest sites. Although RS products are useful for monitoring \(\text{FCO}_{2}\) in combination with in-situ measured data [13; 34], related studies targeted mainly the temperate and high-latitude forest ecosystems instead of subtropical and tropical forested areas. Therefore, the potential of remote sensing-based methods needs to be further explored in subtropical and tropical forest areas. Recently, the machine-learning Random Forest (RF) algorithm was increasingly used to build the nonlinear relationships between the carbon flux and the satellite-derived variables [5; 14; 40; 41; 15]. As a non-parametric machine learning method without requirements of complicated assumptions and large numbers of parameters, the RF algorithm has the advantages of high-precision prediction and less sensitivity to outliers and noise [42; 43]. It thus offers the ability to establish the nonlinear relationships between the spatial covariates and target variables and to bridge the gaps between local and regional scale studies [44].