# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 5 Conclusions  
This research demonstrates the viability of employing deep learning models for predicting daily-scale net ecosystem exchange. The findings reveal that the A-DMLP model outperforms the random forest model in more than half of the site assessments. Moreover, the CNN model exhibits significant promise in simulating time-series NEE data. Furthermore, this study employed the SHAP model to analyze the relationship between the NEE and environmental drivers. The outcomes indicate that the NDVI, LAI, and EVI emerge as the dominant factors influencing the daily variations in the NEE at most sites. Additionally, it was observed that the NEE displays uncertainty during the growing season. The results of this investigation offer valuable insights for applying deep learning methods in NEE modeling. The introduction of the SHAP model enhances the interpretability of the deep learning model's outcomes, shedding light on the role of input variables in relation to the predicted NEE. Consequently, this research provides novel ideas and approaches for analyzing the intricate relationship between the NEE and environmental factors, and helps to achieve more accurate carbon estimates. Ultimately, this contributes to a better understanding of ecosystem dynamics and the potential implications for future environmental research and modeling efforts. In this research, we employed 11 input features to model NEE, with NDVI, EVI, and LAI recognized as crucial factors influencing carbon exchange in plants. However, due to the intricate nature of photosynthesis, there are numerous factors that impact this process, and while we selected several features, these cannot fully capture the complexity of carbon exchange in plants. Consequently, we observed that increasing the number of input features generally enhances the accuracy of model simulation. Therefore, considering the addition of more relevant variables as input features may prove to be an effective approach to achieve improved results, but care needs to be taken when selecting features to be aware of the effects of redundant input parameters on the results. The urgency to develop global carbon flux modeling has become more pronounced, aiming to aid humanity in achieving carbon neutrality and mitigating the adverse impacts of climate change. Nonetheless, the current scale of eddy covariance sites remains limited, emphasizing the importance of expanding the number, quality, and coverage of flux sites. Moreover, aside from data acquisition, there is ongoing exploration of more advanced time series modeling methods, such as migration and hybrid approaches, within the realm of deep learning. The establishment of novel and effective strategies is crucial to enhance the accuracy of carbon flux modeling using Earth observation data. By combining these efforts, we can pave the way toward a more comprehensive understanding of carbon exchange dynamics, facilitating global efforts to combat climate change. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs17010092/s1](https://www.mdpi.com/article/10.3390/rs17010092/s1)]([https://www.mdpi.com/article/10.3390/rs17010092/s1](https://www.mdpi.com/article/10.3390/rs17010092/s1)), Figure S1: (A-E) are the summary graphs of feature importance of BNF, CBF, DXG, NMG and QYF. Figure S2: (A,B) are the time series of original and predicted NEE of BNF and DHF sites. Figure S3: (A,B) are the time series of original and predicted NEE of DHF and DXG sites. Figure S4: (A,B) are the time series of original and predicted NEE of NMG and QYF sites. **Author Contributions:** L.W. : writing--original draft; methodology. Z.C. : review and editing; project administration. N.C.: project administration; funding acquisition. K.W. : methodology. All authors have read and agreed to the published version of the manuscript.