Atiq et al. (2026) Monitoring drought-induced degradation of olive and citrus tree crops in the Tadla plain (Morocco) with multi-sensor remote sensing

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Identification

• Journal: Springer Link (Chiba Institute of Technology)
• Year: 2026
• Authors: Jaouad El Atiq, Abderrazak El Harti, Soufiane Hajaj, Oussama Nait-tale, Sana Elomari, Insaf Ouchkir, Yassine Amrouss, Rachida Guendour, Mostafa Bimouhen
• DOI: 10.1051/bioconf/202621101006/pdf

Research Groups

• Not specified in the provided text (Study focuses on the Tadla plain, Morocco).

Short Summary

This study utilizes multi-sensor satellite data (Sentinel-1 and Sentinel-2) and Support Vector Machine (SVM) classification to monitor the degradation of citrus and olive crops in Morocco's Tadla plain. The findings reveal a significant reduction in crop area and health between 2018 and 2024 due to persistent drought and groundwater overexploitation.

Objective

• To evaluate the spatial and temporal changes in citrus orchards and olive groves and characterize their vegetation and water status using remote sensing and machine learning.

Study Configuration

• Spatial Scale: Tadla plain, central Morocco.
• Temporal Scale: 2018 to 2024.

Methodology and Data

• Models used: Support Vector Machine (SVM) supervised classification algorithm.
• Data sources: Sentinel-1 (radar) and Sentinel-2 (optical) satellite imagery.
• Biophysical Indices: Normalized Difference Vegetation Index (NDVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Moisture Index (NDMI).

Main Results

• Classification Accuracy: The integration of optical and radar data using the SVM classifier achieved an Overall Accuracy (OA) of 0.927.
• Citrus Degradation: Citrus orchards lost approximately 38% of their total area between 2019 and 2024.
• Olive Degradation: Olive groves experienced a 32% reduction in area during the same period.
• Vegetation Health: A marked decline in biophysical indices indicates a reduction in biomass, photosynthetic activity, and leaf water content across the study area.

Contributions

• Demonstrates the high reliability of combining multi-sensor (optical and radar) data with machine learning for monitoring perennial crop degradation.
• Provides a quantitative assessment of the impact of water stress and groundwater overexploitation on Mediterranean agriculture.
• Offers a strategic monitoring tool to support sustainable water management and agricultural planning in drought-vulnerable regions.

Funding

• Not specified in the provided text.

Citation

@article{Atiq2026Monitoring,
  author = {Atiq, Jaouad El and Harti, Abderrazak El and Hajaj, Soufiane and Nait-tale, Oussama and Elomari, Sana and Ouchkir, Insaf and Amrouss, Yassine and Guendour, Rachida and Bimouhen, Mostafa},
  title = {Monitoring drought-induced degradation of olive and citrus tree crops in the Tadla plain (Morocco) with multi-sensor remote sensing},
  journal = {Springer Link (Chiba Institute of Technology)},
  year = {2026},
  doi = {10.1051/bioconf/202621101006/pdf},
  url = {https://doi.org/10.1051/bioconf/202621101006/pdf}
}

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