Spatiotemporal Variations in the Sensitivity of Vegetation Growth to Typical Climate Factors on the Qinghai-Tibet Plateau  
[PERSON]  
School of Ecology and Environment, Hainan University, Haikou 570000, China  
[PERSON]  
School of Ecology and Environment, Hainan University, Haikou 570000, China  
[PERSON]  
School of Ecology and Environment, Hainan University, Haikou 570000, China
to estimate vegetation sensitivity. Although these methods are easy to implement, partial correlation analysis cannot jointly estimate the response of vegetation to multiple climate factors, and the latter two methods cannot eliminate the potential impact derived from the entangled relationship between the multiple climate factors considered. To jointly quantify vegetation sensitivity in response to multiple climate factors, researchers typically use multi-linear regression (MLR) [12]. However, given that climate factors, such as temperature, rainfall, and solar radiation, are cross-correlated with each other, it is hard to attribute vegetation variability to any one of the climatic variables considered in MLR. To address this issue, principal component regression (PCR) implemented with MLR [13; 14; 15; 16] is an effective way to disentangle the mutually correlated relationships between climate variables. In addition, slope values derived by the Theil-Sen regression method can also be used to determine vegetation sensitivity in response to a certain factor, such as near-surface and sub-surface soil moisture, as implemented by [PERSON] et al. [17]. However, this method cannot estimate vegetation sensitivity in response to multiple climate factors jointly either. Recently, several studies have tried to integrate the deep-learning method with vegetation sensitivity estimation, such as those of [PERSON] et al. [18] and [PERSON] et al. [19]. Nevertheless, such techniques are not mature enough and need further consideration. Overall, current studies on vegetation sensitivity mainly focus on the spatial domain while generally ignoring its temporal variability, i.e., assuming vegetation sensitivity to be constant over time. Previous studies generally found that temperature is the climatic factor that regulates vegetation growth in the southeastern QTP, while precipitation is the dominant climatic factor driving vegetation growth in the northwestern QTP [20; 21; 22]. The study of [PERSON] et al. [23] pointed out that alpine grasslands exhibited divergent responses to precipitation, and [PERSON] et al. [21] further reported a widespread increasing vegetation sensitivity to soil moisture on a global scale. In addition, solar radiation is also reported to significantly influence vegetation conditions and consequently has considerable impact on vegetation sensitivity [22; 24]. Nevertheless, vegetation sensitivity is known to change with time, as the temporal variabilities of factors such as climate warming [25], aridity [26], El Nino events [27], wildfires [28], biodiversity [29], and human disturbance [13; 30] have their own distinctive temporal characteristics that may trigger temporal variability in vegetation sensitivity. There are several approaches to estimating the temporal variability of vegetation sensitivity. For example, it can be determined by dividing the whole investigation period using predefined key breakpoints [31] or by considering seasonal vegetation sensitivity directly [32; 33]. However, the characterization of the temporal variability in vegetation sensitivity by these methods is rather coarse regarding temporal resolution, which means that subtle temporal fluctuations in vegetation sensitivity are ignored and cannot be detected. Therefore, [PERSON] et al. [34] estimated the temporal variability of vegetation sensitivity in response to rainfall variability using a dynamic linear model. Moreover, [PERSON] et al. [35] and [PERSON] et al. [36] applied a moving time window to the linear regression model to capture the temporal variability of vegetation sensitivity in response to temperature and rainfall.