Ablila et al. (2026) A multi-scale remote sensing approach for assessing drought impacts on rainfed and irrigated cereal yields in Lleida, Spain

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-09-29
• Authors: Youness Ablila, Maria‐José Escorihuela, Abdelhakim Amazirh, Jose Antonio Martinez Casasnovas, El Houssaine Bouras, Saïd Khabba, Zaineb Bouswir, Salah Er‐Raki
• DOI: 10.1016/j.agwat.2026.110826

Research Groups

• AgroBiotech Center, Faculty of Sciences and Technics, Cadi Ayyad University, Marrakech, Morocco
• Research Group in AgroICT and Precision Agriculture, Universitat de Lleida and Agrotecnio CERCA Center, Av. Alcalde Rovira Roure, 191, Lleida, Catalonia, E25198, Spain
• isardSAT, Doctor Trueta 113 1er, Barcelona, 08005, Spain
• CRSA, Centre for Remote Sensing Applications, Mohammed VI Polytechnic University, Benguerir, Morocco
• LMFE, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco

Short Summary

This study evaluates the performance of agricultural drought indices at provincial and field scales in the Lleida region (northeastern Spain) from 2010 to 2024. The analysis reveals a strong dependence on crop type, phenological stage, spatial scale, and water management practices.

Objective

• Evaluate remote sensing-based agricultural drought indices at both field and provincial scales.
• Quantify their ability to explain cereal yield variability under rainfed and irrigated conditions, considering barley and wheat separately.

Study Configuration

• Spatial Scale: Field-scale (30 m) and provincial scale analysis in the Lleida region, northeastern Spain.
• Temporal Scale: 2010-2024 study period with monthly temporal resolution.

Methodology and Data

• Models used: DISPATCH methodology for soil moisture downscaling, anomaly indices computation using standardized anomalies of key variables.
• Data sources: Landsat 5, 7, 8, 9 missions’ level 2 NDVI and LST, SMOS surface soil moisture data, in-situ agricultural datasets (ESYRCE, IDESCAT).

Main Results

• The Combined Drought Anomaly Index (CDAI) showed the closest temporal correspondence with interannual yield variability.
• Composite indices outperform single-variable indices, particularly for barley.
• CDAI demonstrated consistently high and stable performance across crops (r > 0.81, p < 0.01 for barley) and seasons (r > 0.72, p < 0.01 for both crops).
• Under irrigated conditions, the performance of SMCI and VCI declines substantially.

Contributions

• This study provides a comprehensive overview of drought index performance across multiple dependency scales.
• The use of high-resolution remote sensing data enables drought–yield relationships to be evaluated at the individual-field scale while maintaining a direct comparison with provincial-scale relationships.

Funding

• Not specified in the provided text.

Citation

@article{Ablila2026multiscale,
  author = {Ablila, Youness and Escorihuela, Maria‐José and Amazirh, Abdelhakim and Casasnovas, Jose Antonio Martinez and Bouras, El Houssaine and Khabba, Saïd and Bouswir, Zaineb and Er‐Raki, Salah},
  title = {A multi-scale remote sensing approach for assessing drought impacts on rainfed and irrigated cereal yields in Lleida, Spain},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110826},
  url = {https://doi.org/10.1016/j.agwat.2026.110826}
}