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Estimation of the vertically integrated leaf nitrogen content in maize using canopy hyperspectral red edge parameters

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Abstract

Real-time monitoring of leaf nitrogen (N) content by remote sensing can accurately diagnose crop nutrient status and facilitate precision N management. However, the methods used to estimate of vertically integrated leaf N content do not consider different cropping systems, in which the maize growth stages are not synchronized, resulting in decreased practical value of the results. The purpose of this study was to propose an optimized red-edge absorption area (OREA) index in which the prediction accuracy of vertically integrated leaf N content is improved within spring- and summer-sown maize canopies. The results showed that vertical distributions of N existed regardless of variations in the maize growth stages, that is, the leaf N density of the upper and middle layers was higher than that of the lower layers. These published vegetation indices (VIs) provided relatively good correlations with leaf N density at different layers across all of the datasets. When predicting leaf N density of each leaf layer, an optimal VI is generated, and inconsistent VIs will limit its practical application. To further overcome the drawbacks of the inconsistency of each VI when estimating the leaf N density at different layers, a new OREA index was proposed based on red-edge absorption area parameter. The OREA index showed the highest prediction accuracy with leaf N density for entire canopies (r2 = 0.811, RMSE = 0.374, RE = 13.17%) and canopies without top first leaves (r2 = 0.795, RMSE = 0.269, RE = 15.20%) compared with the other published VIs. It is concluded that the vertically integrated leaf N content under different field experiments can be accurately estimated by the OREA index.

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Acknowledgments

The authors would like to thank Jianchu Zhu and Qiuhong Sun for providing help during sample analysis.

Funding

Funding was provided by the National Key Technology R&D Program of China (No. 2015BAD22B02), the National High Technology Research and Development Program of China (863 Program) (No. 2013AA102902), and the National Natural Science Foundation of China (Nos. 31571620, and 31671641).

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Authors and Affiliations

  1. College of Agronomy, Northwest A&F University, Yang Ling, Xianyang, 712100, PR China

    Pengfei Wen, Zujiao Shi, Ao Li, Fang Ning, Yuanhong Zhang, Rui Wang & Jun Li

  2. College of Agronomy, Henan Agricultural University, Zhengzhou, 450046, PR China

    Pengfei Wen

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  1. Pengfei Wen
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Contributions

J.L., R.W. and P.W. conceived and designed the experiments and provided suggestions for manuscript; P.W. and Z.S. performed the experiments and wrote the manuscript. A.L., F.N., and Y. Z. measured field data and analyzed the data. All authors read and approved the manuscript.

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Correspondence to Jun Li.

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Wen, P., Shi, Z., Li, A. et al. Estimation of the vertically integrated leaf nitrogen content in maize using canopy hyperspectral red edge parameters. Precision Agric 22, 984–1005 (2021). https://doi.org/10.1007/s11119-020-09769-5

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