###### Abstract  
Over the last century, significant climate changes, including more intense droughts and floods, have impacted agriculture and socio-economic development, particularly in rain-dependent regions like Marrakech-Safi (MS) in Morocco. Limited data availability complicates the accurate monitoring and assessment of these natural hazards. This study evaluates the role of satellite data in drought monitoring in the MS region using rain gauge observations from 18 stations, satellite-based precipitation estimates from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and temperatures from the fifth generation of the atmospheric global climate reanalyzed Era5-Land data. The Standardized Precipitation Evaptranspiration Index (SPEI) was calculated at various timescales to characterize droughts. Statistical analysis was then performed to assess the correlation between the SPEI and the cereal yields. The results show that CHIRPS effectively monitors droughts, demonstrating strong statistically significant correlations (\(r\sim 0.9\)) with the observed data in the plains, the plateaus, Essaouira-Chichaoua Basin, and the coastal zones, along with a good BIAS score and lower root mean square error (RMSE). However, discrepancies were observed in the High Atlas foothills and the mountainous regions. Correlation analysis indicates the significant impact of droughts on agricultural productivity, with strong correlations between the Standardized Yield Residual Series (SYRS) and SPEI-6 in April and SPEI-12 in June (\(r\sim 0.80\)). These findings underscore the importance of annual and late-season precipitation for cereal yields. Analysis provides valuable insights for decision-makers in designing adaptation strategies to enhance small-scale farmers' resilience to current and projected droughts. d 20251586510339/1/20251  
The efforts to characterize droughts involve analyzing historical meteorological data using statistical techniques or climate models to simulate future scenarios. Both these methods aim to identify the drought patterns across different temporal and spatial scales and evaluate their impacts on affected regions [7; 11; 12; 13; 14; 15]. However, the complexity of drought characterization necessitates integrating diverse approaches and data sources, including precipitation records, temperature measurements, streamflow data, and groundwater level observations, to comprehensively assess the drought impacts on water resources and ecosystems [16]. Furthermore, remote sensing (RS) techniques and advanced methodologies such as machine learning (ML) algorithms have enhanced the understanding of drought trends and variability [17]. Recent research has emphasized the development of multivariate drought indices to address the multidimensional nature of droughts. These indices combine various indicators to comprehensively assess drought events [18; 19]. Some examples include the Standardized Precipitation Index (SPI) [20], the Standardized Precipitation Evaporation Index (SPEI) [21], the Standardized Drought Index (SDI) [22], the Rainfall Deficiency Index (RDI) [16], the Vegetation Condition Index (VCI) [23], the Temperature Condition Index (TCI) [23], and the Vegetation Health Index (VHI) [23]. These indices help assess and monitor drought conditions by offering objective severity measures and supporting decision-making processes. They also facilitate us comparing drought conditions across time and space [7; 8; 17; 24; 25]. However, despite their widespread use, selecting the most appropriate index for analyzing the impacts on agricultural yields remains debated [26]. Recent studies have specifically evaluated the effectiveness of these indices in assessing drought effects on various cereal types [27; 28] and overall agricultural productivity [6; 29; 30; 31; 32; 33], highlighting the need to understand each index's strengths and limitations to improve drought management and agricultural resilience.