## 3 Results
### Time-Invariant Response of Vegetation Dynamics to Climate Changes  
Before investigating time-variant vegetation sensitivity, it is necessary to explore time-invariant vegetation sensitivity, as previous related studies have done. In this section, the spatial patterns of time-invariant vegetation sensitivity in response to the three climate factors are investigated (Figure 5) using the obtained regression coefficients. The dominant climate factor with the largest regression coefficient for vegetation sensitivity is revealed in Figure 6.  
Except for LAI, the spatial patterns were similar for EVI, NDVI, and SIF products regarding their sensitivity to the three climatic factors. In general, solar radiation had a negative impact on vegetation dynamics, as associated regression coefficients were mostly \(<\)0. Positive regression coefficients were found in the southeastern QTP, and associated coefficient values were relatively small (\(<\)0.2). Compared with EVI, NDVI, and SIF, LAI displayed a contrasting spatial pattern, as shown in Figure 5, as regression coefficients were relatively large in the southeastern QTP, while they were negative and relatively small in the western QTP. The vegetation sensitivity in response to temperature was similar for all four vegetation products (the second column in Figure 5). Positive regression coefficients were observed in the central to eastern QTP, and associated values were relatively large (\(>\)0.3). By contrast, negative regression values were found for parts of regions located in the southwestern QTP. As for water availability, shown in the third column in Figure 5, except for LAI, the other three proxies revealed that their sensitivity to water availability was small, as corresponding values were in a range from \(-\)0.1 to 0.1. Relatively large and positive regression coefficients were found in parts of areas distributed in the western and northeastern QTP. Nevertheless, LAI indicated that negative regression values (\(>\)\(-\)0.1) were observed for most pixels in southern and central-to-eastern areas of the QTP. Figure 4: Spatial distribution of the trend values for (**a**) temperature, (**b**) water availability, and (**c**) solar radiation during 2000–2021 on the QTP. The black dots denote trend values for corresponding areas that passed the significance test (\(p<0.1\)). (**d–f**) show the corresponding interannual variability of the three climate factors by considering the spatial average values for each year. Figure 5: Spatial distribution of regression coefficients derived from the PCR for vegetation sensitivity in response to solar radiation (the first column), temperature (the second column), and water availability (the third column), based on the (**a1–a3**) LAI, (**b1–b3**) EVI, (**c1–c3**) NDVI, and (**d1–d3**) SIF products. Regression coefficients are dimensionless, since all the vegetation products were seasonally detrended and normalized using their Z-scores before applying them to the PCR. Figure 6: Spatial distribution of the dominant climate factor with the largest regression coefficients. The regression coefficients were intercompared using their absolute values to identify the dominant climate factor. (**a–d**) represent the evaluation results derived from the LAI, EVI, NDVI, and SIF products, respectively. The bar chart above each subfigure describes the fraction values of red, green, and blue pixels for the whole area. The four vegetation proxies in Figure 6 jointly reveal that temperature was the most important factor regulating vegetation sensitivity on the QTP, followed by solar radiation and water availability. Temperature was the dominant factor in 49.6%, 42.7%, 51.6%, and 48.8% of the total pixels for LAI, EVI, NDVI, and SIF data, respectively. Solar radiation and water availability were identified as the dominant factors in about 30% and 20% of total pixels, respectively. The spatial patterns of the dominant climatic factors were similar for EVI, NDVI, and SIF proxies, while a relatively different spatial pattern was observed for LAI data.