## 4 Discussion  
Results obtained show that it is possible to estimate optimal contributions of VCI and TCI to VHI (\(\alpha\) in Equation (1)) by using an independent drought index as reference. For this purpose, the multi-scalar SPEI is appropriate since it is derived using precipitation (that reflects on the moisture condition) and evapotranspiration (that reflects on the temperature condition). Based on an approach developed in a previous study [13], we show that there are indeed regions more prone to experience degraded vegetation health due to a larger contribution of either VCI or TCI. In the case of the Euro-Mediterranean window, results from the already mentioned study [13] pointed out that the contribution of moisture conditions tend to be larger over drier regions where water is a limiting factor, whereas the opposite occurs over humid regions where temperature conditions dominate, and solar energy is the limiting factor to vegetation growth. It is worth emphasizing that in Ref. [13], the approach relies on PU LST, i.e., a merged LST derived from HIRS satellite data and NCEP reanalyzed LST to fill unobserved pixels [31]. However, in a climate service related to drought monitoring, other remote sensing LST products are required, as the former is not expected to be regularly updated (not at least in an operational mode). Certainly, the PU LST CDR has the advantage, when compared to other infra-red LST products (such as the two CM-SAF LST products) to be available under all-weather conditions, however the use of a unitary emissivity may also introduce a bias in the analysis. Here, the analysis is extended by using MFG/MSG clear-sky retrievals of LST (i.e., the PMW and SMW datasets) over an area encompassing the Meteosat disk (Figure 1) and comparing the results with those obtained with PU LST. The use of these datasets to estimate the contributions of moisture (VCI) and temperature (TCI) conditions to the overall vegetation health may be an added benefit, since its practical applicability as a drought monitoring satellite product may be achieved by calibrating the contributions with the CM-SAF LST CDRs and then disseminating the product in near-real time with the currently operational MSG and future MTG. However, it is important to stress that the PU LST and CM-SAF LSTs have two fundamental differences: (1) the surface emissivity and (2) the availability under clear and all-weather conditions, respectively. The effect of emissivity may be attenuated by the fact that LSTs are used in an index such as TCI, which is a measure of temperature stress where the LST is positioned within the maximum range for a given pixel and month. Nevertheless, both effects may have an impact on the results. The three obtained sets of parameter \(\alpha\) (one with each LST CDR) present very similar spatial patterns (Figure 1, top panel). However, some differences are apparent like the ones between \(\alpha\) estimated with PU LST and the two CM-SAF LSTs over the region of Algeria/Tunisia, which may be linked with the humid climate conditions found in northern Algeria [67] that may influence the retrieval of CM-SAF LSTs; or the extended patch of very low values of \(\alpha\) over Kenya/ Tanzania when using SMW LST. On the other hand, the similarity of patterns between the three datasets is worth pointing out. For instance, the east/west differences over South Africa are very well identified in all three datasets. An analysis of obtained patterns of \(\alpha\) in terms of the different biomes (Figure 5, top panels) reveals that there are large differences between the distributions of \(\alpha\) among the biomes. South Africa is characterized by grasslands over the western part and shrublands and forests over the eastern part. Previous studies [68; 69] showed a strong relationship between NDVI and drought conditions over grasslands located in the USA and China, while Ref. [70] shows higher correlations between NDVI and SPEI over grassland regions in south Africa.