Nevertheless, these methods only investigate vegetation sensitivity in response to a certain climate factor. We argue that current studies are typically focused on the response of a certain climate driver while neglecting the cross-correlated relationships between climatic variables. This may lead to biased conclusions, as the relationships between vegetation and other climatic factors are concealed. Recently, applying a moving time window to the MLR is a popular approach to characterize the temporal variability of vegetation sensitivity, such as the 15-year moving time window used in some studies [37; 38; 39]. However, vegetation sensitivity is reported to have high temporal variability [7; 17]. Consequently, a 15-year moving time window may not well capture the interannual variability of vegetation sensitivity. Moreover, there is a tradeoff between the kernel size of a moving window and the available samples therein. Specifically, samples in a short-term time window are typically limited and may lead to unreliable estimates of vegetation sensitivity. Currently, there is very little knowledge about temporal variability in response to climate change on the QTP. We argue that a moving window with a small kernel size, e.g., 5 years, may provide new insight into the temporal variability of vegetation sensitivity on the QTP. Here, evaluating the temporal variability of vegetation sensitivity in response to ongoing climate change is instructive to dynamically identify the ecologically fragile areas with high vegetation sensitivity and learn about the triggering mechanisms operating behind the scenes. This is highly needed for the QTP's vegetation, which is characterized by high vulnerability. Moreover, estimating vegetation sensitivity in the temporal domain provides evidential support for related studies within the ecology community, as it provides more intrinsic information about factors underlying vegetation sensitivity. Regarding the vegetation of the QTP, such investigations could serve as a basis for policy formulation and ecological protection. Based on the above considerations, this study aims at articulating the interannual variability of the vegetation sensitivity in response to typical climate factors, including temperature, solar radiation, and water availability, during 2000-2021 on the QTP using a variety of indicators characterizing vegetation dynamics. To this end, we first intercompared the spatiotemporal patterns of vegetation dynamics described by the four vegetation proxies in Section 3.1. Time-invariant and time-variant vegetation sensitivity in response to the three typical climate factors are revealed in Sections 3.2 and 3.3, respectively. Time-invariant vegetation sensitivity denotes the overall vegetation sensitivity during the whole investigation period, and time-variant vegetation sensitivity refers to interannual vegetation sensitivity obtained from the 5-year time window centered at the year to be processed. The three climate factors were chosen in our analysis since they are typical and popular variables used in related studies. In Sections 3.2 and 3.3, vegetation sensitivity estimates derived from the four vegetation products were intercompared regarding their spatiotemporal variability. Moreover, the dominant climatic factor that exerted the greatest impact on vegetation sensitivity is also revealed in these two sections.