## 3 Results
### Biome Stratification  
Results presented in the previous section indicate that \(\alpha\) is organized in clusters of similar values over the different regions. With the aim of assessing whether these clusters are associated with land cover, the ATSR LST biome classification derived from GlobCover was used to analyze the distribution of land cover among pixels with significant correlation between VHI and SPEI (Figure 4). From the 27 biomes of the original classification, 21 are found among the significant pixels and only 9 contain more than 100 pixels (Figure 4). These 9 biomes are therefore selected to assess the dependence of VHI on the moisture and temperature conditions and to assess the differences between the contributions of these two components when different LST CDRs are used to estimate TCI. Figure 5 presents results obtained for the biome stratification in the form of box plots for \(\alpha\) (top), \(\Delta\rho\) (middle) and \(\Delta\) VHI (bottom) as derived using PU LST (black), CM-SAF PMW LST (blue) and CM-SAF SMW LST (red) to estimate TCI. As for \(\Delta\rho\), \(\Delta\) VHI represents the differences between VHI using \(\alpha\) estimated with the iterative maximization based on SPEI (VHI\({}_{\text{SPEI-based}}\)) and VHI using \(\alpha\) set to the standard value of 0.5 as originally proposed by [PERSON] (VHI\({}_{\text{K}}\)):  
\[\Delta\text{VHI}=\text{VHI}_{\text{SPEI-based}}-\text{VHI}_{\text{K}}, \tag{5}\]  
The box delimits the 25 th and 75 th percentiles and the inner segment represents the median, whereas the whiskers represent percentiles 10 and 90. Table 1 presents percentiles 25 and 75 and the median of the distribution of \(\alpha\) estimated with the three LST datasets, for the different biomes presented in Figure 4.  
Figure 3: Maps (**top**) and histograms (**bottom**) showing the increase in VHI-SPEI correlations, when \(\alpha\) is changed from 0.5 to the optimum values estimated with the iterative procedure: VHI estimated with TCI derived from PU LST (**left**), CM-SAF PMW LST (**middle**), and CM-SAF SMW LST (**right**). As shown in Table 1 and as suggested by the box plots of Figure 5 (top panel), the distributions tend to be shifted towards larger values of \(\alpha\) (box plots on the top panel of Figure 5; Table 1) with the median being above 0.5 for most cases. Over open broadleaved deciduous forests (biome 07) with \(\alpha\) estimated with PU LST and over closed to open shrublands (biome 13) with \(\alpha\) estimated with both CM-SAF LSTs, the median of optimum \(\alpha\) falls slightly below 0.5 (and is slightly above 0.5 for the same biomes but when computed with a different LST dataset). The highest values of \(\alpha\) may be found in southwestern Africa over closed to open grasslands (biome 14) as well as over Iberia and northern Africa over sparse vegetation (biome 15, which also contains grasslands, as well as shrubs and woody vegetation). The closed to open grasslands present medians of \(\alpha\) near 0.9 and percentile 25 is higher than 0.6 for the three LST datasets and, when \(\alpha\) is estimated with PU LST percentile 10 is also above 0.5, whereas when \(\alpha\) is estimated with CM-SAF LSTs it is close to 0.4. Sparse vegetation shows medians of \(\alpha\) of about 0.7 for the three LST datasets (with PU LST showing \(\alpha\) higher by 0.05 than the remaining). Figure 4: Advanced Along-Track Scanning Radiometer (AATSR) LST biome classification version 2 (ALB-2) derived from the GlobCover classification for pixels with significant correlation between VHI and SPEL. Figure 5: Distribution of \(\alpha\) (**top** panel), \(\Delta\rho\) (**middle** panel), and \(\Delta\)VHI (**bottom** panel) as estimated with PU LST (black), CM-SAF PMW LST (blue), and CM-SAF SMW LST (red) for the different biomes (identified by the labels described in Figure 4). Box and whiskers represent percentiles 10, 25, 50, 75, and 90. For each biome, the numbers indicate the respective number of pixels of the sample.