Abstract
The Vegetation Health Index (VHI) is widely used for monitoring drought using satellite data. VHI depends on vegetation state and thermal stress, respectively assessed via (i) the Vegetation Condition Index (VCI) that usually relies on information from the visible and near infra-red parts of the spectrum (in the form of Normalized Difference Vegetation Index, NDVI); and (ii) the Thermal Condition Index (TCI), based on top of atmosphere thermal infrared (TIR) brightness temperature or on TIR-derived Land Surface Temperature (LST). VHI is then estimated as a weighted average of VCI and TCI. However, the optimum weights of the two components are usually not known and VHI is usually estimated attributing a weight of 0.5 to both. Using a previously developed methodology for the Euro-Mediterranean region, we show that the multi-scalar drought index (SPEI) may be used to obtain optimal weights for VCI and TCI over the area covered by Meteosat satellites that includes Africa, Europe, and part of South America. The procedure is applied using clear-sky Meteosat Climate Data Records (CDRs) and all-sky LST derived by combining satellite and reanalysis data. Results obtained present a coherent spatial distribution of VCI and TCI weights when estimated using clear- and all-sky LST. This study paves the way for the development of a future VHI near-real time operational product for drought monitoring based on information from Meteosat satellites.
Highlights
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
The magnitude and spatial distribution of α are coherent among these datasets, when either the Land Surface Temperature (LST) datasets were derived from the same observations with different algorithms or from different observations and methodologies (PU and CM-SAF LST CDRs)
This paper describes an approach leading to an optimal Vegetation Health Index (VHI) for drought monitoring by identifying the contributions of Vegetation Condition Index (VCI) and Thermal Condition Index (TCI) that maximize the correlation between VHI and a reference drought index (SPEI)
Summary
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. 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] 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]. 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, 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
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