## 4 Discussion
Therefore, integrating multiple remote sensing data to capture vegetation structural and physiological changes is essential for a more accurate diagnosis of the impact of aridity gradients, especially in forest ecosystems. Mirroring the variability in PFTs, the relationships also differ significantly in their spatial patterns of vegetation distribution (Figures 1 and 4b). Low R\({}^{2}\) values were predominantly found in the southeastern and eastern humid regions of China (Figure 3a), areas primarily occupied by forests and crops (Figure 1a). A potential mechanism underlying the spatial variation in the NDVI-GPP relationship is that the tradeoff between ecosystem greenness (NDVI) and physiology (photosynthesis per unit leaf area) intensifies in morehumid climates. In general, the eastern regions are classified as humid zones, characterized by well-watered areas with predominant forest and savanna land use. This environment allows plants to perform efficient photosynthesis, resulting in higher GPP values. Research has shown that in southeastern China, soil moisture has a significant negative correlation with NDVI but an insignificant positive correlation with GPP [18]. In southeastern China, there is an asynchronous trend where NDVI increases while GPP does not, indicating enhanced vegetation greenness but slightly reduced productivity [59]. This may be attributed to ecological engineering efforts, which can quickly enhance vegetation greenness, whereas improvements in ecological functions such as productivity may take significantly longer. Additionally, increased CO\({}_{2}\) tends to benefit photosynthesis more than respiration, leading to a modest rise in carbon uptake [60]. As a result, this contributes to the observed asynchronous changes in vegetation greenness and productivity. Conversely, the western regions, characterized by relatively arid conditions, receive less precipitation and tend to have low soil moisture levels. The dominant PFTs are GRA (Figure 1a), which have shallow root systems that cannot access water from deeper soil layers. Additionally, changes in leaf and internal physiology activations are more consistent in these plants [61; 62]. Consequently, there is a distinct difference in the response of vegetation greenness and productivity to long-term climate change between the eastern and western regions. Additionally, by examining the changes in the IAV of vegetation greenness and GPP over the past three decades, quantified as the CV (see Methods), we found that the interannual variation of GPP and NDVI was not synchronized (Figure 5a). The IAV of GPP was largest in hyper-arid areas (0.81) and showed a stable decrease with increasing aridity gradients. In contrast, the IAV of NDVI initially increased and then decreased, with the largest value (0.26) occurring in semi-arid and dry sub-humid regions. A shift in IAV trends along the aridity gradients with an AI range of 0.20-0.65. Previous studies have attributed this asynchronous change to climate stress [63]. Water availability is a key factor regulating the IAV of regional and global carbon fluxes [33; 64; 65]. Reduced soil moisture due to precipitation deficit leads to decreased evapotranspiration as well as evaporative cooling, causing local temperature increases. This results in a high vapor pressure deficit (VPD) and evaporative demand, potentially further reducing soil moisture [66; 67]. Low soil moisture and high VPD are anticipated to impact vegetation greenness and productivity differently [68], causing asynchronous changes in IAV of GPP and NDVI. The findings of this study also revealed that the sensitivity of vegetation photosynthesis to greenness varied across drought gradients (Figure 7) and vegetation types (Figure 8). The regression fit to the mean multi-year NDVI and the mean multi-year GPP in each pixel revealed that the sensitivity of NDVI-GPP reached its highest (slope was 0.12) in hyper-arid regions where the aridity index was below 0.05.