# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 3 Results
The utilization of remotely sensed data offers valuable insights into the dynamics of vegetation, providing a means to monitor and understand the behavior of ecosystems on a larger spatial scale. By identifying the LAI's substantial impact on NEE fluxes, this research highlights the critical role of vegetation structure in governing carbon exchange processes. Incorporating such crucial environmental variables in models can significantly enhance their predictive capabilities and contribute to a deeper understanding of ecosystem carbon dynamics. Overall, the findings underscore the importance of integrating remote sensing data and relevant environmental factors to improve the accuracy of NEE flux modeling, empowering researchers and policymakers with valuable information for addressing climate change challenges. In a preceding study, scientists employed four distinct shallow machine learning models, namely random Forest, decision tree, support vector machine, and gradient boosting machine, in order to assess the predictive capacity of shallow machine learning techniques for NEE across various vegetation types. The study focused on European flux sites encompassing four different vegetation cover types. Notably, the researchers validated the effectiveness of the random forest model for NEE simulation, highlighting its superior performance compared to the other models used in the analysis [18]. In another investigation, researchers utilized a gradient boosting machine to model the NEE by leveraging data from the UK-Gri and NL-Loo sites. To assess the model's performance, they conducted a comparative analysis with three other shallow machine learning models: SVM, stochastic gradient descent, and Bayesian regression. The main objective was to validate the efficacy of the gradient boosting machine model in accurately predicting the NEE when compared to the alternative machine learning approaches. The results of this study shed light on the strengths and potential applications of the gradient boosting machine model for NEE estimation in the context of UK-Gri and NL-Loo site data [24]. This study, for the first time, provides evidence that the attention mechanism demonstrates superior results in NEE simulation compared to the shallow machine learning models used in previous research [34; 3]. The attention mechanism, by delving into deeper layers of information between elements, increases the effective information content, effectively showcasing its advantages and effectiveness in NEE simulation. Similarly, the application of deep learning models such as A-DMLP and CNN in NEE simulation is also a first, as previous research articles primarily focused on shallow machine learning methods and failed to explore the potential of deep learning methods in NEE simulation applications. This research indicates that leveraging deep learning methods can significantly enhance the accuracy of NEE simulation, which is crucial for addressing global climate change. Although the A-DMLP model performs similarly to the RF model in predicting NEE, the A-DMLP model has unique architectural advantages in handling complex nonlinear relationships. Through its deep multilayer perceptron structure, the A-DMLP model can automatically learn multi-level feature representations in the data. It has a stronger ability to capture interactions at different time scales (daily, seasonal, interannual) and the subtle nonlinear coupling relationships between various environmental variables that exist in ecosystem data. For example, when dealing with the complex relationships between environmental variables such as temperature, precipitation, and sunlight and ecosystem carbon flux, the A-DMLP model is better able to fit the higher-order interactions between these variables, whereas the RF model may be limited by its decision tree ensemble structure when handling such highly complex and deeply nested nonlinear relationships. A-DMLP model also makes progress in model interpretability.