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

Identification

• Journal: BIO Web of Conferences
• Year: 2026
• Date: 2026-01-01
• 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

Research Groups

• Geomatics, Georesources and Environment Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco.
• Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco.
• Laboratory of Data Science for Sustainable Earth, Sultan Moulay Slimane University, Beni Mellal, Morocco.

Short Summary

This study utilizes multi-sensor satellite integration (Sentinel-1 and Sentinel-2) and Support Vector Machine (SVM) classification to monitor the degradation of perennial crops in Morocco's Tadla plain. The findings reveal a significant reduction in citrus (38%) and olive (32%) cultivation areas between 2019 and 2024 driven by severe drought and groundwater overexploitation.

Objective

• To quantify the spatio-temporal degradation of citrus orchards and olive groves in the Tadla plain from 2018 to 2024.
• To evaluate the effectiveness of combining optical and radar remote sensing data with machine learning for monitoring crop health under water stress.

Study Configuration

• Spatial Scale: Tadla plain, central Morocco (regional agricultural basin).
• Temporal Scale: 2018–2024 (multi-year diachronic analysis).

Methodology and Data

• Models used: Support Vector Machine (SVM) supervised classifier; Gray-Level Co-occurrence Matrix (GLCM) for radar texture extraction (variance, correlation, entropy).
• Data sources: Sentinel-2 optical imagery (10–20 m resolution); Sentinel-1 SAR data (VV and VH polarizations); Google Earth Engine (GEE) platform for processing.
• 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 SVM classifier achieved a high overall accuracy (OA) of 0.927 for discriminating crop types.
• Areal Regression: Citrus orchards declined from 9,845 ha in 2019 to 6,115 ha in 2024 (38% loss). Olive groves experienced a 32% reduction in area over the same period.
• Vegetation Vigor: NDVI and MSAVI values for citrus dropped from 0.54 to 0.38, while olive values dropped from 0.51 to 0.34, signaling a sharp decline in photosynthetic activity and biomass.
• Water Stress: Moisture indices (NDMI and NDWI) for olive trees decreased from 0.12 to 0.07, highlighting significant loss in leaf water content despite the species' known drought tolerance.

Contributions

• Provides a robust methodological framework for monitoring perennial crop degradation by integrating radar (Sentinel-1) and optical (Sentinel-2) data, which improves detection in water-stressed environments.
• Quantifies the specific impact of the 2019–2024 drought cycle on the Tadla plain, offering a strategic tool for regional water management and agricultural planning.
• Demonstrates that even traditionally resilient crops like olive trees are reaching critical thresholds due to the combined effects of climate change and aquifer depletion.

Funding

• The provided text does not explicitly list specific project codes or funding agencies; however, the research was presented at the ICWES 2025 conference and published via BIO Web of Conferences.

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 = {BIO Web of Conferences},
  year = {2026},
  doi = {10.1051/bioconf/202621101006},
  url = {https://doi.org/10.1051/bioconf/202621101006}
}

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