# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 4 Discussion  
The summary plot is a visually informative way to present a condensed overview of how the most influential features in a dataset influence the model's output. Each data instance's explanation is represented by a single dot on the corresponding feature row. The horizontal position of the dot is determined by the SHAP value associated with that feature. As more instances have similar SHAP values for a particular feature, the dots \"pile up\", indicating density. Additionally, color is utilized to represent the original values of the features, adding an extra layer of information to the visualization. The horizontal axis (SHAP value) shows the influence of each feature on the prediction result. The farther the observation point (or sample point) is from the center line (zero point), the greater the influence of the feature on the model output. A positive SHAP value indicates a positive influence; that is, when the feature value increases, the model prediction value will also increase. A negative SHAP value indicates a negative influence, that is, when the feature value increases, the model prediction value will decrease. The vertically arranged features are sorted by their total influence on the model output. The features at the top have a greater impact on the model output, and their SHAP values are distributed more widely, while the features at the bottom have a smaller impact, and their SHAP values are mostly concentrated near zero. From Figure 5A, the top feature (e.g., NDVI) shows a large number of positive and negative influence points, indicating that under different observation values, this feature has a significant and varied impact on the model's prediction results. The varying direction and magnitude of this impact suggest that NDVI plays an important role in the model's decision-making process. Middle features (e.g., FPAR) show points of two different colors, but the distribution is more concentrated, suggesting that the influence of this feature is relatively smaller, and its impact direction is more consistent. Bottom features (e.g., P) exhibit the smallest impact on the model, with most influence points close to zero. This indicates that these features contribute less to the model's predictions and may not be strongly related to the prediction outcomes. From Figure 5 and Figure S1, NDVI, LAI, EVI, and FPAR are typically positioned in the upper half of the summary plot across all sites, indicating that these features have a significant impact on NEE simulation. Figure 5 presents the four typical vegetation service types used in this study. The DHF (Figure 5A) site is located in a warm, humid climate with abundant precipitation and belongs to the subtropical forest ecosystem. It features rich vegetation with a certain vertical distribution. Forest vegetation is dense and grows actively, and the vegetation indices (NDVI, EVI, LAI), as key parameters for identifying vegetation status and dynamic changes, stably reflect vegetation growth, playing an important role in the model's estimation process. In the grassland (Figure 5B), the variation in NDVI and LAI is closely related to seasonal changes in precipitation and temperature. During the rainy season, vegetation grows vigorously, NDVI rises, and NEE shifts towards carbon dioxide absorption; the opposite occurs during the dry season. From a climatic perspective, in tropical humid climates, NDVI remains relatively stable and high throughout the year, continuously promoting the carbon absorption process of NEE. In contrast, in temperate continental climate grassland regions, the significant seasonal fluctuation in NDVI leads to pronounced seasonal variations in NEE [34]. The HBW site (Figure 5C) is located in a typical continental climate with a short growing season and a long non-growing season, typically starting in mid-April and ending in late October.