Drone multispectral imaging captures the effects of soil mineral nitrogen on canopy structure and nitrogen use efficiency in wheat

Abstract

Drone remote sensing offers a powerful tool for monitoring vegetation and agricultural systems. However, its effectiveness in assessing the effect of soil mineral nitrogen (Nmin) on crop canopy traits remains inadequately explored. This study investigates the relationship between soil Nmin variability and canopy characteristics, grain yield, and nitrogen use efficiency (NUE), and explores the potential to predict NUE using drone multispectral images. Multispectral data were collected across growth stages over two growing seasons. The analysis revealed that soil Nmin significantly affected canopy structure, with low Nmin inducing a ’blue shift’ of the red-edge spectral position. The multilayer perceptron regression model predicted NUE with high accuracy (R2 {\textgreater} 0.7) in early growth stages, identifying red-edge spectral indices and canopy height as key predictors. Texture features did not play a significant role in the models for predicting NUE, which remains to be further understood in future research. These findings highlight the capability of UAV remote sensing data, especially the red-edge spectral features, to capture the effects of soil Nmin on canopy traits. This study provides a proof-of-concept for mapping NUE using UAV images, with the final goal of improving crop nitrogen management and fertilizer use efficiency in agriculture.

Jie Wang

PhD student

Using remote sensing to study the impact of climate change and cropping practices on global crop yields and nitrogen use efficiency

Prof. Dr. Kang Yu

Professor of Precision Agriculture

My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.