# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 3 Results  
The site data used in this study are distributed in different regions of China and cover a wide range of ecosystem types. The sites have different climatic types, hydrometeorological conditions, and altitudes ranging from plains to plateaus. The wide range of geographic variations and diverse ecosystem types resulted in significant spatial heterogeneity in NEE. In order to evaluate the validity and reliability of each model in modeling NEE, we counted the performance of the models under nine data site based on four metrics, as shown in Table 2 and Figure 4. The following are the optimal results obtained by each model, among which in the Haibei wetland site, the A-DMLP model resulted in an R\({}^{2}\) value of 0.909, RMSE of 0.463 gC m\({}^{-2}\) d\({}^{-1}\), MSE of 0.215 gC m\({}^{-2}\) d\({}^{-1}\), and MAE of 0.246 gC m\({}^{-2}\) d\({}^{-1}\), which reached optimality in both the horizontal and vertical comparisons. The worst-performing A-DMLP model result was for the Xishuangbanna site (BNF), with an R\({}^{2}\) of 0.656, an RMSE of 0.902 gC m\({}^{-2}\) d\({}^{-1}\), an MSE of 0.815 gC m\({}^{-2}\) d\({}^{-1}\), and an MAE of 0.638 gC m\({}^{-2}\) d\({}^{-1}\). In fact, in all the models, the Xishuangbanna site was one of the worst sites, which we analyze below, with attribution based on the SHAP model. Overall, compared to typical existing deep learning models as well as random forest models, the A-DMLP model achieved the best results in terms of accuracy compared to the best results, at most sites, with random forests taking the lead in terms of results at the remaining one-third of the sites. Among all of the deep learning models, the worst result was the LSTM model, which is mainly used for single-step or multi-step prediction of spatio-temporal sequences; and its application to regression modeling failed to give full play to its ability to combine temporal and spatial information, which may be the reason for the poor results of its application. The CNN model shows greater potential in this experiment, although the results failed to reach optimality. Although the results were not good enough, they showed stability and reliability in several sites, and except for the BNF, the R\({}^{2}\) result of most of the sites were able to stay within the range of 0.7 to 0.9, which demonstrated great potential for daily NEE modeling. The estimation of NEE has always been very challenging, due to the limited understanding of the mechanism of its interaction with various factors of the ecosystem, the simulation accuracy is low, and the disturbances of land use change, human management, and the occurrence of fires greatly affect the accuracy of the NEE. The random forest model, DNN, CNN, and LSTM models, and five data-driven modeling methods such as the A-DMLP model were developed to simulate the daily NEE in multiple ecosystems. According to the results of the study, the A-DMLP model achieved excellent performance in all models and most of the site results, which effectively proved that the mechanism of attention can be used in the time series data, and that this model effectively utilized the interactions between data elements. The relationship between the data elements was effectively utilized, which makes the developed A-DMLP achieve the appropriate performance. The combination of satellite data and the eddy covariance site data effectively improves the daily NEE simulation accuracy of the site, as shown in Table 3. In the process of modeling NEE fluxes, incorporating remotely sensed data has emerged as a prevalent approach to enhance simulation accuracy. Therefore, the importance of vegetation in