Agricultural Science Digest
SCOPUS
  • Year: 2026
  • Volume: 46
  • Issue: 1

Integrating Sentinel-1 Sar and Landsat 8 Optical Data for Crop Type Discrimination and Vegetation Monitoring in Arid Zones of Algeria

  • Author:
  • Rabah Mayouf1, Achour Mennani2,3,*, Mohamed T. Hanafi4, Noureddine Bouali5
  • Total Page Count: 8
  • Page Number: 154 to 161

1Department of Agronomy, Faculty of Life and Natural Sciences, Echahid Hamma Lakhdar University, El Oued, Algeria

2Department of Agronomic Sciences, Faculty of Sciences, University of M’sila, Algeria

3Laboratory of Biodiversity and Biotechnological Techniques for the Valorization of Plant Resources (BTB-VRV), Mohamed Boudiaf University in M’sila, Algeria

4Scientific and Technical Research Center on Arid Regions CRSTRA, Biskra, Algeria

5Faculty of Life and Natural Sciences, Department of Biology, Echahid Hamma Lakhdar University, El Oued, Algeria

*Corresponding Author: Achour Mennani, Department of Agronomic Sciences, Faculty of Sciences, University of M’sila, Algeria, Email: mennani.achour@univ-msila.dz

Online Published on 16 April, 2026.

Abstract

Accurate monitoring of crop types and vegetation health is vital for sustainable agriculture, particularly in arid and semi-arid regions where environmental constraints challenge productivity. In Algeria, the provinces of Biskra and Khenchela exhibit diverse agro-climatic conditions and cropping systems that require robust, scalable monitoring tools. This study addresses these challenges by proposing an integrated remote sensing approach to classify crop types and assess vegetation dynamics during the 2020 growing season.

The methodology combines Sentinel-1 Synthetic Aperture Radar (SAR) and Landsat 8 optical imagery to differentiate between irrigated and rainfed crops.Key Indicators, Normalized Difference Vegetation Index (NDVI) and SAR backscatter values. Temporal Strategy, monthly composites were generated to mitigate atmospheric interference. Platform, data preprocessing and analysis were conducted within the Google Earth Engine (GEE) environment. Classification Model, A Random Forest classifier was trained using ground truth data and high-resolution satellite imagery to integrate spectral and radar features for accurate crop classification.

NDVI effectively captured crop phenological patterns, with consistently higher values observed in irrigated date palm plantations in Biskra and seasonal variability in cereal and fruit orchards in Khenchela. Sentinel-1 SAR backscatter data enhanced classification accuracy, particularly in distinguishing vegetation structures. The integrated approach achieved over 85% classification accuracy, validating the synergy between SAR and optical datasets. The study demonstrates the operational potential of this methodology for large-scale, real-time agricultural monitoring and highlights the utility of GEE as a powerful platform for remote sensing in data-scarce environments. These findings contribute to improved agricultural resource management, food security and water-use efficiency.

Keywords

Crop, Satellite, Vegetation classification, Vegetation dynamics