Assessing canopy nitrogen and carbon content in maize by canopy spectral reflectance and uninformative variable elimination

Assessing canopy nitrogen content (CNC) and canopy carbon content (CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investi-gating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination (UVE). Vegetation indices (VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares (PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy. UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models (UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index (NDVI, based on red edge and NIR wavebands) yielded the highest correlation coeffi-cients (r > 0.88). PLS regression models showed the lowest root mean square error (RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models. UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status. (c) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study assessed canopy nitrogen and carbon content of maize using hyperspectral remote sensing data and uninformative variable elimination (UVE). The results showed that the normalized difference vegetation index (NDVI) based on red edge and NIR wavebands had the highest correlation coefficients for estimating nitrogen and carbon content. UVE-PLS regression models with retained spectral parameters improved the prediction accuracy compared to PLS regression models.