Integrating Sentinel-1 and Sentinel-2 for Enhanced Agricultural Monitoring and Environmental Classification

This collection focuses on the integration of Sentinel-1 and Sentinel-2 satellite data for agricultural and environmental monitoring. The studies cover a range of applications, including crop classification and rice extraction, detection of small-scale forest fires, and mapping of early and late sowing for soybean and cotton crops using deep learning. The research highlights the benefits of fusing Sentinel-1's radar and Sentinel-2's multispectral data for improved land use classification and monitoring. These methodologies provide valuable insights for researchers, remote sensing professionals, and students in precision agriculture, environmental management, and disaster monitoring.