## 4 Discussion
As the aridity gradient increased (becoming more humid), the sensitivity consistently decreased. This phenomenon indicated an insensitivity relationship between productivity and greenness in more humid regions. A comparison of R\({}^{2}\) values clearly showed a fluctuation trend in the strength of coupling, with values of 0.19, 0.21, 0.24, 0.20, 0.11, and 0.11. This observation suggested that the sensitivity and the strength of coupling between NDVI and GPP did not change synergistically with aridity gradients. The discrepancy may be due to differences in soil water availability. NDVI reflects vegetation greenness through surface reflectance; it does not immediately respond to water stress. In contrast, GPP, which directly results from vegetation photosynthesis, can better reveal the physiological changes in vegetation due to water stress [69; 70]. Moreover, soil water has a significantly positive influence on 25.44% of GPP [35]. Thus, semi-arid ecosystems were the primary contributors to interannual variations in carbon sinks within terrestrial ecosystems and exhibited high sensitivity to drought trends [71; 72; 73]. Although the positive trends in GPP and NDVI across China exist (Figure 6b), negative trends are still exhibited during the growing season. Specifically, in the southeastern ENF, the NDVI-GPP slope was negative. This may be due to the dense canopy of the ENF, which causes the NDVI to reach saturation levels. This saturated state makes it difficult to detect further increases in GPP, thus leading to a weaker or negative slope. Additionally, forested areas can experience water stress or excess water, influencing the NDVI-GPP relationship. Excess water can reduce GPP despite high NDVI values due to reduced oxygen availability for roots [74]. Plants can regulate their respiration and adjust their metabolism to optimize carbon assimilation. However, when hypoxia occurs due to excess soil moisture, they experience metabolic stress that limits photosynthetic efficiency. This stress impairs their ability to absorb essential nutrients, such as nitrogen and phosphorus, which are crucial for photosynthesis and growth [75]. Under hypoxic conditions, plants may appear green based on NDVI, but their GPP could be low due to restricted CO\({}_{2}\) uptake, inefficient nutrient absorption, and reduced energy for growth [76]. Reduced CO\({}_{2}\) intake directly decreases the rate of photosynthesis, thus lowering GPP. While NDVI captures the greenness of vegetation, which may remain high even when GPP is constrained by environmental stressors like hypoxia. Therefore, relying solely on NDVI to estimate the relationship between productivity and greenness in humid areas introduces uncertainty. To more accurately reflect actual photosynthetic performance, it is essential to consider other metrics, such as solar-induced fluorescence [77]. In this study, we primarily focused on the remote sensing datasets, and thus the results on sensitivity were not fully presented. Further research endeavors will be integrating data from land surface models, which will be crucial to better linking remote sensing-based analysis to the complex interplay between ecosystem variables.