Abstract

In a context of high stress on water resources and agricultural production at the global level, together with climate change marked by an increase in the frequency of these events, drought is considered to be a strong threat both socially and economically. The Mediterranean region is a hot spot of climate change; it is also characterized by a scarcity of water resources that places intense pressure on agricultural productivity. This article analyzes the potential for using multiple remote sensing tools in the quantification and predictability of drought in Northwest Africa. Three satellite products are considered: the Normalized Difference Vegetation Index (NDVI), Soil Moisture Index (SWI), and Land Surface Temperature (LST). A discussion of the variability of these products and their inter-correlation is presented, illustrating a generally high consistency between them. Statistical anomaly indices are then computed and a drought severity mapping is presented. The results illustrate in particular a high percentage of dry conditions in the region studied during the last ten years (2007–2017). Finally, we propose the use of the analog statistical approach to identify similar evolutions of the three variables in the past. Although this technique is not a forecast, it provides a strong indication of the plausible future trajectory of a given hydrological season.

Highlights

  • For vegetation cover, measurements are mostly based on optical remote sensing

  • The limited correlations during December and January could be explained by the limited precision of Soil Water Index data (SWI) satellite products in forest areas where the effect of vegetation cover on microwave signals is dominant

  • This paper proposes an analysis of drought phenomena in Northwest Africa using multi-sensor time series satellite products

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Summary

Introduction

Measurements are mostly based on optical remote sensing. the NDVI satellite index, often used in vegetation analysis, is strongly correlated with vegetation cover growth[20]. The most popular is VCI, ranging between 0 to 1 (0 for the driest condition, 1 for the wettest) applied in several regions of the globe to estimate agricultural drought under varying ecological conditions[10,11] It can give the status of vegetation in comparison to the best and worst vegetation conditions for a particular monthly period over many years. After recalling the drawbacks of the PDSI index[24] and the uncertainty of precipitations they note that the DSI is subject to uncertainties including the global reanalysis input data and the MODIS Evapotranspiration algorithm In addition to this estimation of drought intensity using drought indices, it remains essential for stakeholders to predict the future dynamic of a drought event in relation to specific drought conditions. CGIAR28 has proposed a tool based on the same approach

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