# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 4 Discussion
With an annual mean temperature of \(-1.1\)\({}^{\circ}\)C, a maximum temperature of \(17.3\)\({}^{\circ}\)C, a minimum temperature of \(-33.6\)\({}^{\circ}\)C, and an annual precipitation of 490 mm, 90% of which falls during the growing season, the site represents a typical wetland ecosystem of the Qinghai-Tibetan Plateau. As seen in the figure, LAI, NDVI, and EVI also show high importance in the model for the HBW site. PAR is the energy source for plant photosynthesis, directly influencing the photosynthetic efficiency and organic matter synthesis in crops. Adequate PAR enhances the photosynthetic rate, increases the accumulation of photosynthetic products, and promotes crop growth and biomass accumulation [34]. By affecting photosynthesis and respiration, PAR plays a significant role in influencing NEE at the YCA site (Figure 5D). During the day, when PAR is abundant, photosynthesis dominates over respiration, and the farmland acts as a carbon sink. However, at night or on cloudy days with insufficient PAR, respiration is relatively stronger, and NEE may decrease or shift to a carbon source. VP reflects the dryness of the air and is closely related to crop transpiration. The higher the VP, the drier the air, which increases the transpiration rate and water loss in crops. This may lead to water stress in crops when water supply is insufficient, affecting photosynthesis and growth. VP indirectly impacts the NEE of agricultural ecosystems by influencing photosynthesis and respiration. An optimal VP range benefits crop photosynthesis and growth, promoting NEE towards a carbon sink, while excessively high or low VP may weaken photosynthesis or enhance respiration, causing NEE to decrease or become a carbon source [40]. Figure 5: (**A–D**) are the summary graphs of feature importance of Dinghushan site (DHF), Haibei grassland site (HBG), Haibei wetland site (HBW) and Yucheng agriculture (YCA). The left vertical coordinate represents the input features, the lower horizontal axis represents the magnitude of the SHAP value (a positive SHAP indicates a positive impact on the output, while a negative one is the opposite), and the right vertical axis indicates that the different colors represent different magnitudes of the values of the feature value. The larger the absolute SHAP value, the greater the impact of that feature variable on the model. Temperature, a dynamic environmental variable, has a weaker influence in the model. This is primarily because surface temperature changes do not completely synchronize with vegetation growth, and the effects on vegetation vary at different growth stages. Additionally, temperature is influenced by multiple factors, such as precipitation and evapotranspiration, making its overall impact relatively weak. In the context of a multivariate data-driven model, its outcomes are not solely dependent on a subset of these features. For instance, at the HBW sites, the model-driven process derives final results from the interplay among individual variables. Although Ma, Ta, and Rn are ranked low in terms of SHAP feature importance, it suggests that these variables still hold significance. However, their impact on the model's output relies heavily on their interactions with other variables, being dominated by other important variables. This underscores the intricate relationships and dependencies between different factors within the system, leading to a more comprehensive understanding of the underlying dynamics. During the growing season, the estimated NEE usually shows significant uncertainty and is difficult to fit to the observed NEE, and the observed data match well with the observed data during the non-growing season (CBF and HBW, Figure 6). Uncertainty in the prediction of the GPP during the growing season has also been studied previously [41], confirming the conclusion of uncertainty in the prediction of carbon fluxes during the growing season.