# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 1 Introduction
In another study, five machine learning methods (MLR, SVM, DT, GBM, and RF) predicted NEE's daily variation range, highlighting the RF model's superior performance and the MLR model's comparatively poorer performance [18]. The above methods can be regarded as the most widely used and advanced machine learning assessment methods for ecosystem CO2 net exchange so far. However, many studies focus on comparing shallow machine learning methods, improving the accuracy of NEE simulations by increasing the driving data, and evaluating the most important driving factors between the NEE and environmental variables. Currently, shallow machine learning methods have achieved very limited accuracy improvements in many studies. In the past few decades, time series prediction methods based on machine learning have made great progress, and machine learning methods such as random forests, decision trees, and support vector machines have been applied in NEE research. However, traditional machine learning methods face limitations in estimating the complex seasonal interactions between ecosystems across different regions. In recent years, deep learning techniques have made significant advancements in this area. Convolutional neural networks (CNNs) [19] are particularly effective for processing grid-structured data, such as image data. Through convolutional layers, CNNs can automatically extract local features,and the weight-sharing mechanism significantly reduces the number of model parameters, enhancing training efficiency. Recurrent neural networks (RNNs) [20] and their variants, such as long short-term memory networks (LSTMs) [21], excel in handling sequential data and can effectively capture temporal dependencies. When applied to ecosystem time-series monitoring data, these models can learn the relationships between previous and subsequent data points, effectively modeling trends and cyclical changes. Generative adversarial networks (GANs) [22], primarily used for data generation tasks, employ adversarial training, where the generator and discriminator networks compete to produce new data samples with distributions similar to real data. This approach has promising applications in data augmentation or the simulation of ecological scenarios that are difficult to observe directly, offering valuable insights for research in ecological modeling and forecasting. So far, research on NEE using deep learning models is still in its infancy [23]. Moreover, due to the insufficient consideration of the relationships between input features in tree-structured models, we propose an A-DMLP approach based on remote sensing and eddy covariance data for NEE simulation. By incorporating the attention mechanism, this model effectively leverages the connections between input feature elements to extract richer and more relevant information, thereby enhancing the overall performance of the model. We compared the applicability and limitations of the simulation with several common time series deep learning models and the well-performing random forest model, which has been highlighted in previous studies. Additionally, we utilized the SHAP method to quantify the relative impact of environmental controls on daily NEE variations.