## 5 Conclusions  
This paper describes an approach leading to an optimal VHI for drought monitoring by identifying the contributions of VCI and TCI that maximize the correlation between VHI and a reference drought index (SPEI). The VHI and associated weight \(\alpha\) are estimated for two clear-sky MFG/MSG LST climatological datasets (CM-SAF LSTs) and for one all-weather LST dataset based on merged HIRS observations and NCEP reanalysis (PU LST). The methodology also involves identifying the SPEI time-scales that maximize the correlation with VHI. It is shown that despite the use of datasets with different observational sources (the case of PU LST versus CM-SAF LST CDRs) or different algorithms (the case of the two CM-SAF LSTs), SPEI scales present a coherent spatial distribution over most of the pixels. Furthermore, VHI values and the respective contributions of VCI and TCI (quantified by the weight \(\alpha\)) reveal a fair overall consistency over the studied region among datasets. As expected, the higher agreement was found between the two datasets that only differed in the LST algorithm (i.e., CM-SAF LST CDRs). Although VHI depends on both LST and NDVI, this work focuses mostly on LST since, as already pointed out, the variability of LST in terms of observation sources (e.g., HIRS versus Meteosat) and retrieval methodologies is wider than for NDVI. It is also worth recognizing that applications of NDVI in climate studies and vegetation variability assessment are currently more consolidated than applications of LST. Nevertheless, results presented here pave the grounds to the use of an optimal VHI to monitor vegetation stress and drought events from satellite data.  
Indeed, results presented in this work may be viewed as a promising first step to the development of a future MSG/MTG SPEI-based VHI satellite drought monitoring based on a near-real time operational product, presenting the added benefit of taking into consideration the response in terms of vegetation health to moisture and temperature conditions over the area encompassed by the Meteosat viewing disk. Because of the expected global warming-driven increase in frequency and severity of drought episodes over regions like the Euro- and African- Mediterranean and Sahel, a Meteosat-based drought monitoring product may be of extreme importance to mitigate future socio-economic losses related to drought episodes.  
Further work is needed with the aim of increasing the accuracy of the quantification of VCI and TCI share to the overall vegetation health. It would be also interesting to relate the contribution of VCI and TCI to other parameters (e.g., biomes, aridity index) with the aim of filling the gaps where correlation between VHI and SPEI was found to be not significant.  
[PERSON] organized the data, produced the results and wrote the paper. [PERSON], [PERSON] and C.C.D. provided helpful insight in the discussion of the results, read the paper and contributed to its organization.  
Research by [PERSON] was funded by the Portuguese Foundation for Science and Technology (FCT) with grant number SFRH/BD/52559/2014. This research was partially supported by national funds through FCT under project IMDROFLOOD (WaterJPI/0004/2014) and by EUMETSAT in the framework of the Satellite Application Facility on Land Surface Analysis (LSA SAF).  
The authors declare no conflict of interest.