Integrating Sentinel _ 2 and Landsat 8 Imagery with Machine Learning Algorithms for Crop Yield Prediction and Agricultural Monitoring

This study looks into the integration of Sentinel-2 and Landsat 8 satellite imagery with machine learning algorithms for enhanced crop yield prediction and agricultural monitoring. The use of remote sensing technologies has transformed precision agriculture through real-time assessment of vegetation health, soil conditions, and environmental changes. A good complement to this long-term history and thermal imagery from Landsat 8, Sentinel-2 has high spatial resolution and high revisit cycles to enable a robust dataset for accurate yield estimation. Machine learning models, which include decision trees, random forests, and neural networks, have started processing vast datasets in agriculture that offer predictive insights into crop growth patterns, resource optimization, and risk management. Data pre-processing techniques such as atmospheric correction and cloud removal are very essential in making the satellite imagery reliable, improving the accuracy of vegetation indices and predictive models. Even though data quality, model interpretability, and high implementation costs are still issues, advances in artificial intelligence and deep learning have been refining remote sensing applications. The study highlights the transformative potential of integrating satellite technology and machine learning to enhance food security, optimize resource utilization, and promote sustainable farming practices and pave the way for more precise and data-driven agricultural decision-making.