Faraji et al. (2025) AI-Driven Flood Mapping and Precision Rice Monitoring in Morocco Using Sentinel Satellite Data

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Springer Link (Chiba Institute of Technology)
• Year: 2025
• Date: 2025-12-12
• Authors: Ibtissam Faraji, Nicola Aimane Dimarco, Hakim Boulaassal, Miriam Wahbi, Mustapha Maatouk, Otman Yazidi Aalaoui, Omar El Kharki
• DOI: 10.1051/e3sconf/202567601008/pdf

Research Groups

[Information not provided in the paper text.]

Short Summary

This study presents a multi-sensor approach combining Sentinel-1 SAR and Sentinel-2 optical imagery to accurately map rice fields and monitor crop phenology in the Gharb plain, demonstrating its utility for precision agriculture and sustainable water management.

Objective

• To map rice fields and monitor crop phenology in the Gharb plain using a multi-sensor approach.

Study Configuration

• Spatial Scale: Gharb plain (regional scale)
• Temporal Scale: Multi-temporal (crop growing season)

Methodology and Data

• Models used: k-means clustering, morphological filtering
• Data sources: Sentinel-1 synthetic aperture radar (SAR) imagery, Sentinel-2 optical imagery

Main Results

• Accurate detection of rice parcels and effective exclusion of non-rice areas was achieved through qualitative validation using NDVI profiles and visual inspection.
• Temporal dynamics captured by NDVI and SAR backscatter effectively reflect key rice growth stages and flooding events.
• The method demonstrates agronomic relevance for monitoring irrigated crops, supporting sustainable water management and precision agriculture practices.

Contributions

• Presents a novel multi-sensor approach for precision agriculture by fusing Sentinel-1 SAR and Sentinel-2 optical imagery for rice field mapping and phenological monitoring.
• Illustrates the scalable, timely, and accurate utility of Google Earth Engine for monitoring irrigated crops.
• Supports sustainable water management and precision agriculture practices through enhanced crop monitoring capabilities.

Funding

[Information not provided in the paper text.]

Citation

@article{Faraji2025AIDriven,
  author = {Faraji, Ibtissam and Dimarco, Nicola Aimane and Boulaassal, Hakim and Wahbi, Miriam and Maatouk, Mustapha and Aalaoui, Otman Yazidi and Kharki, Omar El},
  title = {AI-Driven Flood Mapping and Precision Rice Monitoring in Morocco Using Sentinel Satellite Data},
  journal = {Springer Link (Chiba Institute of Technology)},
  year = {2025},
  doi = {10.1051/e3sconf/202567601008/pdf},
  url = {https://doi.org/10.1051/e3sconf/202567601008/pdf}
}