## 4 Result
### SHAP Values of Climatic Factors on Different Vegetation Indices  
To detect the effect of climatic changes on vegetation status, we selected six pivotal climatic variables according to their previously established significance in affecting vegetation indices. These variables include VPD, Maximum Temperature (Tmmx), Minimum Temperature (Tmmn), Solar Radiation (Srad), Soil Moisture (SM), and Precipitation (Prec). In this study, we utilized the non-linear lightGBM algorithm to simulate the monthly NDVI, kNDVI, and SIF driven by different factors. The 80% of the time series data in each grid was used for model training, while the remaining 20% data was applied for model accuracy evaluation. The lightGBM model demonstrated satisfactory performance in predicting the  
Figure 2: Correlations between vegetation indicators and SPEI3. NDVI, kNVDI, and SIF for each grid, with R\({}^{2}\) scores higher than 0.9 for all grid cells in the test dataset (Table 1). SHAP values were then employed to evaluate the influence of these climatic variables on NDVI, kNDVI, and SIF, as illustrated in Figure 3. The SHAP values serve as a metric for quantifying the contribution of different climatic factors to the accuracy of vegetation index predictions. A higher absolute SHAP Value signifies a more notable contribution of a climatic variable. From Figure 3a-c, we can observe the spatial distribution of the most influential climatic factors for NDVI, kNDVI, and SIF predictions, with key factors identified by their absolute SHAP values. These maps illuminate the spatial variability in the impacts of different climatic variables on vegetation indices. A notable observation is the more dispersed spatial influence on NDVI and kNDVI, compared to a more concentrated impact on SIF, possibly reflecting the differential sensitivities of these indices to climatic variables. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline NDVI & 0.1352 & 0.1895 & 0.9674 \\ kNDVI & 0.1347 & 0.1886 & 0.9672 \\ SIF & 0.1211 & 0.1711 & 0.9703 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R-squared) of lightGBM model. Figure 3: Spatial distribution of the maximum SHAP value environmental factors to NDVI (**a**), KNDVI (**b**), and SIF (**c**) from 2001 to 2020 and the proportion of each factor (**d–f**). VPD emerges as a predominant factor for all three indices, covering 29.44%, 30.38%, and 28.79% of the area, respectively. This is particularly evident in the central Yunnan-Guizhou Plateau. The findings suggest an acute sensitivity of vegetation in this region to moisture changes, corroborated by the high correlation areas between vegetation indices and SPEI3. For NDVI and kNDVI, precipitation is the second-largest contributing factor, while for SIF, solar radiation (Srad) takes this position, indicating distinct sensitivities of these indices to different climatic variables. Consistent with the observations in Figure 4, VPD holds the largest impact across all vegetation types, highlighting its key role in vegetation water stress. Interestingly, grasslands exhibit a smaller area influenced by VPD but a relatively greater impact from precipitation, suggesting a higher dependence of grassland vegetation on precipitation compared to other vegetation types. This underscores the diverse responses of different vegetation types to climatic variables. The spatial distribution of SHAP values for NDVI, kNDVI, and SIF concerning the six selected climatic variables were shown in Figure 5. We can observe that, in contrast to NDVI and kNDVI, the SHAP values for climatic factors associated with SIF demonstrate stronger spatial continuity, displaying a distinct spatial pattern. Specifically, SIF exhibits an elevated sensitivity to Precipitation (Prec), Solar Radiation (Srad), and Minimum Temperature (Tmmn).