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Multi-LUTs method for canopy nitrogen density estimation in winter wheat by field and UAV hyperspectral

Lookup NU author(s): Professor Zhenhong Li, Dr David Fairbairn



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Elsevier BV, 2019.

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© 2019 Elsevier B.V. Unmanned aerial vehicle (UAV) based hyperspectral images linked to a radiative transfer model can provide a promising approach for high throughput monitoring of plant nitrogen (N) status. In this study, multiple lookup tables (Multi-LUTs), each LUT corresponding to one growth stage, were constructed based on the N-PROSAIL model, a radiative transfer model, and LUT size was optimized for improving computing efficiency. The objective is to use the constructed Multi-LUTs for estimating canopy N density (CND) in winter wheat. Results suggest that Multi-LUTs of leaf area index, leaf N density and two spectral indices (MSR and MCARI/MTVI2) in winter wheat demonstrate good performance of CND estimation; and LUTs with the optimal size of 6000 rows can yield good accuracy. The R 2 and nRMSE values of the regression relationship between estimated and measured CND were 0.83 and 0.23 from field hyperspectral data, and 0.69 and 0.27 from UAV based hyperspectral imagery during the 2014–2015 growing season. CND by Multi-LUTs method was also accurately estimated from field hyperspectral data during the 2013–2014 growing season, with R 2 and nRMSE values of 0.74 and 0.26. The estimation accuracy of CND based UAV data was a slightly lower than based field data. The resultant thematic CND map accurately exhibits CND variability at varying spatial and temporal scales. Results from this study confirmed the potential of combining UAV based hyperspectral imagery and physical optics approach for estimating CND in winter wheat.

Publication metadata

Author(s): Li Z, Li Z, Fairbairn D, Li N, Xu B, Feng H, Yang G

Publication type: Article

Publication status: Published

Journal: Computers and Electronics in Agriculture

Year: 2019

Volume: 162

Pages: 174-182

Print publication date: 01/07/2019

Online publication date: 16/04/2019

Acceptance date: 07/04/2019

Date deposited: 24/06/2019

ISSN (print): 0168-1699

ISSN (electronic): 1872-7107

Publisher: Elsevier BV


DOI: 10.1016/j.compag.2019.04.005


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Funder referenceFunder name
ST/N006801/1STFC (formerly PPARC)
ST/N006801/1STFC (formerly PPARC)
ST/N006801/1STFC (formerly PPARC)