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Drought is a phenomenon that concerns different parts of the world. Globally, it affects more people than any other natural disaster (Denchak, 2018) causing water and food insecurity, and economic damage and loss. It is categorized into four types: agricultural, hydrological, meteorological, and socioeconomic droughts (Wilhite & Glantz, 1985).
Drought has become a major concern in Morocco due to its impact on agriculture that is important to the Moroccan economy. Agriculture is considered to generate 14% of the Moroccan Gross Domestic Product (GDP) and to provide 38.8% of employment (Ministry of Agriculture, Fisheries, Rural Development, Water and Forests, 2019) but these numbers can change each year. Besides, droughts lead to lowering the GDP growth, decreasing agricultural exports, and increasing agricultural imports to meet domestic needs and ensure food security (Verner et al., 2018). Several studies have been carried out to analyse drought using different approaches and indices for different periods. Le Page and Zribi (2019) have studied drought in Northwest of Africa (Comprising of the Northern of Morocco, Algeria, and Tunisia) for 10 years (2007-2017) using the Normalized Difference Vegetation Index (NDVI), Soil Water Index, and Land Surface Temperature (LST). The years 2007-2008 and 2015-2016 were found to be particularly dry. In another study (Zkhiri et al., 2019), drought was assessed for the Moroccan High Atlas basins using the Standardized Precipitation Index (SPI) from 1988 to 2011. They detected a succession of five wet periods and three dry periods. SPI was also used by Ouatiki et al. (2019) for trend analysis of drought for 1970-2010 over the Oum Er-Rbia River Basin. In the study, deficit periods of great spatial periods occurred in the 1980s and 1990s. These studies concerned some regions of Morocco. A recent study that concerned the whole Moroccan country for growing seasons (October-April) from January 2003 to April 2016 has developed a Moroccan personalized drought index called the Moroccan Composite Drought Indicator (Bijaber et al., 2018). In the study, the 2015-2016 growing season drought was tracked for Meknes and Settat. Another study (Laachrate et al., 2020b) has also tracked the same drought event for the same regions but using another drought index: Microwave Integrated Drought Index (MIDI) with four generating approaches of drought maps. The aim of this study was to compare the results of the four approaches to those of Bijaber et al. 2018 to choose the best approach to MIDI calculation and drought classification. In this work, agricultural drought was assessed for Morocco using different drought indices. Drought indices defined as: “typically computed numerical representations of drought severity” (World Meteorological Organization (WMO) & Global Water Partnership (GWP), 2016) are powerful tools in drought assessment. They provide useful information about drought characteristics and simplify complex relationships using hydrometeorological and climatic parameters for different applications (WMO & GWP, 2016). An overview of the main drought indices can be found in “Handbook of drought indicators and indices” (WMO & GWP, 2016). Many drought indices can be used, but in this study three composite indices were chosen: Composite Drought Indicator (CDI), Soil Moisture Agricultural Drought Index (SMADI), and MIDI. Composite indices were used rather than simple indices to have a better drought assessment based on different parameters of the hydrological cycle. These indices have in common the use of Soil Moisture (SM) which is an important parameter for such extreme events (Laachrate et al., 2020b) (especially agricultural droughts that are linked to SM shortage), and floods (Laachrate et al., 2019). Other applications of SM can be found in the review paper: “Soil Moisture Retrieval Using Microwave Remote Sensing: Review of Techniques and Applications” (Laachrate et al., 2020a) including hydrology and Land Surface Models, hazards, climate change, and agriculture. SM datasets used in this study were taken from the Google Earth Engine (GEE) data catalog based on the Soil Moisture and Ocean Salinity (SMOS) mission. This mission is about a satellite launched in 2009 by the European Spatial Agency with the aim of retrieving SM and ocean salinity parameters.