## 5 Conclusions  
Understanding the spatiotemporal patterns of \(\text{FCO}_{2}\) and the underlying mechanisms are conducive to global carbon budgets and the accurate prediction of future climate change. In this study, we measured the \(\text{FCO}_{2}\) emission in a typical subtropical forest ecosystem of southern China during the dry season. The measured \(\text{FCO}_{2}\) showed that it has distinct variations over time and space. Our results further revealed that the variation of \(\text{FCO}_{2}\) during the dry season was driven by the changes of abiotic (ST and SM) and biotic factors (vegetation productivity). In particular, precipitation in the dry season may affect soil moisture, which may directly determine the soil \(\text{CO}_{2}\) flux in the subtropical forests.  
Moreover, we developed a machine-learning random forest (RF) model based on remote sensing data to model the \(\text{FCO}_{2}\) in two areas, and the proposed RF model shows high performance in predicting the \(\text{FCO}_{2}\) dynamic in the study areas. Based on the RF model, we mapped the spatio-temporal patterns of \(\text{FCO}_{2}\) in a large forested watershed and further examined how the topographic factors affect the \(\text{FCO}_{2}\) emission in the watershed. The results indicate that the \(\text{FCO}_{2}\) is higher in high-altitude areas, as well as in slopes of 10-25 degrees and sunny slopes. We emphasize that future studies are necessary to consider different effects of topographic factors in modeling the \(\text{FCO}_{2}\) of subtropical forests.  
Although the proposed RF model demonstrates that the machine-learning method combined with RS data has high potential in predicting and estimating the \(\text{FCO}_{2}\) emission in subtropical forest ecosystems, the use of RS to monitor \(\text{FCO}_{2}\) emissions from subtropical and tropical forest ecosystems is still at the exploratory stage and thus our study provides some preliminary reference and evaluation results. More field observations are still needed for model calibration and validation in the future. Furthermore, the fusion of the high spatial (e.g., Landsat and Sentinel) and temporal (e.g., MODIS) resolution data of satellite remote sensing may provide new ideas for improving the monitoring accuracy of \(\text{FCO}_{2}\) in subtropical and tropical forest ecosystems.  
Conceptualization, T.C. and G.T.; Data curation, T.C., Z.X. and X.C.; Methodology, T.C. and Z.X.; Formal analysis, T.C., G.T. and H.F.; Writing--original draft, T.C.; Writing--review & editing, T.C., Z.X., X.C., H.F., H.G., Y.Y., G.Z., L.J. and X.N.; Funding acquisition, G.T.; Project administration, G.T.; Software, H.G.; Visualization, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was jointly was funded by the National Natural Science Foundation of China (No. 42171025) and the Guangzhou Municipal Scientific Program (No. 42050441).  
Not applicable.  
All datasets used in the present study are available on request from the corresponding author.  
We thank the EARTHDATA ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)) (accessed on 26 October 2020)) and NASA's Land Processes Distributed Active Archive Center (LP DAAC) ([[https://lpdaac.usgs.gov/data/](https://lpdaac.usgs.gov/data/)]([https://lpdaac.usgs.gov/data/](https://lpdaac.usgs.gov/data/)) (accessed on 7 October 2020)) for providing all the remote sensing data sets. We also sincerely thank the editors and anonymous reviewers for their useful comments on our manuscript.  
The authors declare no conflict of interest.