## 1 Introduction  
Climate Data Records (CDRs) [1] have been continuously and incrementally used in the last decade, and agencies such as the European Space Agency (ESA) or the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) provide datasets [2; 3; 4] for a diverse range of Essential Climate Variables (ECVs) that can be generated using satellite observations. These ECV CDRs cover parameters in various domains of the climate system, like the atmosphere (e.g., total column water vapor [5], Top-of-Atmosphere (TOA) radiance [6]), the ocean [7], and the land (e.g., albedo [8; 9], Land Surface Temperature (LST) [10]). LST has recently been added as an ECV following the recognition of its role in processes within the interface between the soil and the atmosphere. The use of ECV CDRs--consisting of land surface and/or atmospheric variables over large regions with spatially and temporally homogeneous information over decades of Earth observation--may contribute to a more performant monitoring of climate elements like snow cover and extreme events like drought episodes [11; 12; 13]. Indeed, the latter are a recurrent climate event with severe impacts and consequences on society [14; 15] that may result in famine, death [16], and large economic losses [17]. Several different drought indices were developed in the last decades with the aim of characterizing and monitoring the different types of drought [18; 19; 20]. Examples of such indices are the Palmer Drought Severity Index (PDSI) [21], the Standardized Precipitation Index (SPI) [22], and the Standardized Precipitation-Evapotranspiration Index (SPEI) [23; 24]. Satellite-based remote sensing is also largely suited for monitoring drought conditions since its instruments and sensors allow for both regional and global scale observation of the land surface with spatially continuous measurements and a high repeat visit frequency in the case of geostationary satellites. Several indices based on satellite thermal infrared (TIR) data were developed through the years, such as the Apparent Thermal Inertia (ATI) [25; 26], the Temperature Vegetation Dryness Index (TVDI) [27], or the Vegetation Health Index (VHI) [28; 29], among others [30]. A more performant drought monitoring at the continental or global scales will contribute to the adoption of more efficient measures to mitigate the adverse impacts. In this context, a crucial role is played by indices that, although suitable to be applied at the global scale, are tuned for the different biomes and regions so that they optimally reflect the stress of vegetation. Such tuning will benefit from CDRs since they provide consistent information at the large scale, whereas traditional meteorological observations are usually sparser and more limited. VHI is a widely used remote sensing-based drought index designed as the weighted sum of two components: the Vegetation Condition Index (VCI) and the Thermal Condition Index (TCI). The first component characterizes moisture conditions and is typically based on information from the visible and near infra-red windows of the electromagnetic spectrum, whereas the latter characterizes the thermal condition and is based on information from the thermal infra-red window. The Normalized Difference Vegetation Index (NDVI) and LST (or TOA brightness temperature) are commonly used to estimate VCI and TCI, respectively. The optimum weights that are to be attributed to VCI and TCI are usually not known and VHI is typically estimated by assuming equal weights of 0.5 to both components. Results from a recent study [13] point out that it is possible to improve the estimation of VCI and TCI weights by comparing the resulting VHI against a non-satellite derived multi-scalar drought indicator such as SPEI. The study was based on the use of an LST CDR developed by Princeton University (PU hereafter) [31; 32] where satellite observations were merged with reanalyzes to derive an all-weather LST, assuming land surface emissivity equal to one.