Integrating multi-angle and multi-scale remote sensing for precision nitrogen management in agriculture: A review
Yeying Zhou, Yuntao Ma, Syed Tahir Ata-Ul-Karim, Sheng Wang, Ignacio A. Ciampitti, Vita Antoniuk, Caicong Wu, Mathias Neumann Andersen, Davide Cammarano
Abstract
• Review of remote sensing, scale and geometry, in nitrogen management, for detection accuracy at key growth stages. • Comparison of different scales from broad satellite-captured trends to detailed crop-level via proximal and airborne sensing. • Multi-angle sensing improves nitrogen status assessment for precision management. • Integrating satellite, drone, and proximal data enables scalable nitrogen solutions. Nitrogen (N) is an essential element for crop growth, productivity, and quality, making it a fundamental component of crop nutrition. In precision agriculture, rapid and non-destructive monitoring of crop N status is crucial for formulating N management strategies to optimize N application and assessing crop performance. This review investigates the integration of remote sensing (RS) in precision N management, particularly focusing on addressing temporal, scale, and geometric consideration in RS applications. The study reviews RS monitoring techniques from three perspectives: firstly, determining optimal fertilization timing based on crop phenology; secondly, introducing RS platforms, including proximal sensing, airborne RS, and satellites for monitoring crop N status; and finally, examining the use of multi-angle RS techniques for N monitoring. The literature reviewed in this study shows that 29% of publications focus on N monitoring at joining and 24% at grain-filling stage, limiting the window for making decisions for in-season N management. This paper concludes that integrating appropriate monitoring platforms, multi-angle observations, and dynamic modeling offers a promising approach for assessing crop N status. This integrated approach provides an essential decision-making tool for N fertilization, advancing precision agriculture for its broader implication. Advancing dynamic crop models, in-field digital twins, multi-scale RS for seamless monitoring, and artificial intelligence for real-time N status diagnosis together will pave the way for precision N management in modern agriculture.