Multispectral Indices for Crop Water Stress Assessment and Precision Irrigation in Arid Agriculture: A Case Study from Béchar, Algeria

Water scarcity combined with increasing food demand poses significant challenges to agricultural sustainability, particularly in arid regions such as Béchar province in Algeria, where irrigation accounts for approximately 70% of total water consumption. This study presents an innovative satellite-based precision irrigation system leveraging multispectral imagery from Landsat 8 and Sentinel-2 satellites to optimize water use efficiency while maintaining crop productivity. The proposed approach integrates multiple biophysical indices, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Automated Water Extraction Index (AWEI), and Soil Moisture Index (SMI), to accurately assess crop water stress and irrigation requirements in near real-time. Validation was conducted over four agricultural seasons within the Ouakda zone, encompassing 17 crop types such as lettuce, beetroot, and turnip, over an area of 5.33 km². Results indicate a strong correlation between NDVI values and water stress levels: NDVI below 0.33 signals critical irrigation needs, whereas values above 0.66 correspond to optimal vegetation health. Land cover classification revealed a vegetation coverage of 36.3%, while spectral indices effectively tracked seasonal variations in crop vigor. Winter crops demonstrated enhanced growth under regulated irrigation regimes, whereas summer crops exhibited pronounced water stress. This system delivers actionable irrigation recommendations that could reduce water consumption by up to 30% without compromising yields. By combining remote sensing data with multispectral analysis, this research offers a scalable, adaptable framework for precision irrigation in arid environments, fostering sustainable water management and agricultural resilience. The methodology holds potential for global application in similar water-scarce agroecosystems.