This chapter explores how modern remote sensing has become central to monitoring croplands and improving agricultural decisions. It introduces the major sensing technologies—ranging from multispectral satellites to UAV-based thermal and LiDAR systems—and explains how vegetation indices and time-series patterns reveal crop health and stress. With AI and machine learning, raw sensor data turns into predictions, classifications, and fused insights that strengthen precision agriculture. Real-world cases illustrate global adoption trends and practical benefits, while also acknowledging challenges such as cloud interference, limited ground truth, and high data costs. The chapter shows how these tools now anchor digital agriculture, linking observation to action and enabling more sustainable, data-driven farming.