Abstract

Remote sensing, incorporating Sentinel-1 SAR, optical data, UAVs, and advanced machine learning, significantly improves rice cultivation monitoring and prediction. These technologies track planting dates, growth stages, phenology, and yield, crucial for food security, especially in Asia. They enable precision farming by assessing crop health, nutrient status, and detecting pests/diseases, optimizing resource use and enhancing productivity. Reviews emphasize integrating multi-source data with deep learning for robust yield forecasting and global anomaly detection. This comprehensive approach provides timely, accurate information for sustainable rice management and informed agricultural planning worldwide.