EGU22-291, updated on 25 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Potential of remote sensing data to analyze the effect of drought on wheat yields in the Mediterranean region: study area Kairouan Tunisia and Lleida Spain

Manel Khlif1, Aicha Chahbi Bellakanji1, Maria José Escorihuela2, Vivien-Georgiana Stefan2, and Zohra Lili Chabaane1
Manel Khlif et al.
  • 1National Agronomic Institute of Tunisia, University of Carthage, LR GREEN-TEAM, Tunis, Tunisia (
  • 2isardSAT, Barcelona, Catalonia (

With climate change, mainly drought, the situation of water stress in most Mediterranean countries is worsening with the high demand for agricultural water and the scarcity of water resources. Forecasts have found that more than 33 countries, including Tunisia and Spain, will face extremely high water stress by 2040, threatening agriculture and food security. In this study, we analyze the potential of different drought indices to identify drought periods for two regions with different climates: Kairouan in Tunisia, and Lleida in Spain, and we identify the indices that give more accuracy for cereal yield prediction.

To achieve the objectives of this study, satellite data was used: MODIS (NDVI and LST) and SMOS. Spatial resolution enhancement algorithms have been applied, such as DISaggregation based on Physical And Theoretical scale Change (DISPATCh), to improve the spatial resolution of SMOS from 40 km to 1 km. In this study, we focus on two principal parameters to identify agricultural drought: Soil Moisture Anomaly Index (SMAI) calculated from soil moisture DISPATCh data, which gives an idea of the soil water status and Vegetation Anomaly Index (VAI) derived from MOD13Q1, which reflects the vegetative activity. 

Over the past 10 years, from the 2010/2011 agricultural year to 2019/2020, we have identified dry periods of agricultural drought based on VAI and SMAI. The results show that SMAI can detect more dry periods in space and time than VAI. For the study area in Tunisia, the strongest correlation obtained between wheat yield and SMAI is in November (R = 0.71). This result highlights the importance of water during this period. The correlation between wheat yield and SMAI decreased slightly in January (R=0.55), February (R=0.57), and March (R=0.63). However, the vegetation cover started to appear in January. A stronger, but later, correlation with VAI in March (R=0.63). For the second study area in Spain, Lleida, the correlation between drought index and yield anomaly of wheat and barley was studied separately. For barley, the increase in the correlation between grain yield and VAI started in February (R= 0.71), March (R=0.73), and then April where it reached its maximum (R=0.87). A more important correlation is noted in March with the SMAI which is about 0.8. Similarly, for wheat, the best correlation between yield and SMAI is recorded in March (R= 0.88) and with a slightly less important correlation with VAI of the order of 0.51 in March.

In conclusion, this study shows the interest in improving the spatial resolution of soil moisture to better study agricultural drought and its effect on cereal yield.

How to cite: Khlif, M., Chahbi Bellakanji, A., Escorihuela, M. J., Stefan, V.-G., and Lili Chabaane, Z.: Potential of remote sensing data to analyze the effect of drought on wheat yields in the Mediterranean region: study area Kairouan Tunisia and Lleida Spain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-291,, 2022.


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