## 5 Conclusions  
Compared with several previous semi-empirical models and traditional ML models, Geoman performed better (R\({}^{2}\) = 0.87, RMSE = 0.39 g C m\({}^{-2}\) yr\({}^{-1}\), MAE = 0.28 g C m\({}^{-2}\) d\({}^{-1}\)) when estimating RE in northern China's grasslands. We found that soil factors have a significant effect on the estimation of RE in the low-value period in the non-growing season, thereby suggesting that RE models should not exclude the role of soil, especially in grassland ecosystems. Our results revealed the occurrence of substantial spatiotemporal changes in RE in northern China's grasslands. On the spatial scale, the RE of alpine grassland showed a decreasing trend from southeast to northwest in the TP, and that of temperate grassland showed a decreasing trend from northeast to southwest in the IM. On the temporal scale, the RE of grasslands in northern China showed a significant increasing trend (1.81 g C m\({}^{-2}\) yr\({}^{-1}\)) from 2001 to 2015. The rapid increase in RE in alpine grasslands indicates greater response to climate change than in temperate grasslands. Biotic and climatic control over RE varied at the temporal and spatial scales. Ta was the main factor that influenced the seasonal dynamics of RE in grassland ecosystems. Ta and EVI controlled the interannual dynamics of RE in alpine grasslands, whereas PRE and EVI were the main controlling factors in temperate grasslands. EVI and soil factors were found to control the spatial changes in RE in grassland ecosystems. This study highlights the high accuracy of DL in simulating spatiotemporal changes in the carbon cycle, and enhances our understanding of the interactions between RE and climate change.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14153563/s1](https://www.mdpi.com/article/10.3390/rs14153563/s1)]([https://www.mdpi.com/article/10.3390/rs14153563/s1](https://www.mdpi.com/article/10.3390/rs14153563/s1)), Figure S1: Eight-day time series plots for observed and predicted RE at all sites; Figure S2: Predicted RE vs. observed RE in the low value period in the non-growing season; Figure S3: Interannual dynamics of annual average Ta, annual total PRE, annual average EVI, and annual total PAR from 2001-2015 at the regional scale.  
**Author Contributions:** Conceptualization, W.L. and H.H.; methodology, W.L. and X.W.; software, W.L.; validation, W.L., X.R. and L.Z.; formal analysis, X.Z.; data curation, L.F. and Y.L.; writing--original draft preparation, W.L.; writing--review and editing, H.H. and X.W.; visualization, Q.C. and Q.X.; supervision, M.Z.; project administration, Y.Z. and T.W.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (the Study on Carbon Sequestration Rate and Its Uncertainty, Stability and Sustainability of Terrestrial Ecosystems in China, No. 42141005).  
**Data Availability Statement:** All data can be accessed through the provided website and references.  
**Acknowledgments:** We thank the staff of ChinaFLUX, COIRAS and HiWATER for their dedication in observation and data processing. We also thank [PERSON] (Computer Network Information Center, Chinese Academy of Sciences) for his guidance.  
**Conflicts of Interest:** The authors declare no conflict of interest.