Optimizing Agriculture in Arid and Semi-Arid Region Using Remote Sensing: A review

Arid and semi-arid regions cover a significant portion of the Earth’s surface, characterized by limited water resources and vulnerable ecosystems. Efficient agricultural management in these regions is crucial to mitigate the impacts of climate change, desertification, and soil degradation. This review explores the integration of remote sensing and machine learning technologies to enhance agricultural productivity in water-scarce environments. Satellite-based techniques, such as vegetation indices (NDVI, EVI) and spectral mixture analysis, provide large-scale monitoring of vegetation health and surface dryness. Coupled with advanced machine learning models like Random Forest and Convolutional Neural Networks, these technologies enable precise crop yield predictions, soil moisture mapping, and optimized irrigation practices. Despite progress, challenges remain in data quality, model accuracy, and realtime adaptability, particularly in heterogeneous landscapes. This paper highlights current methodologies, their applications, and the potential for improved resource management, aiming to support sustainable agricultural practices in dryland regions.