Abstract
Crop nitrogen monitoring techniques, particularly choosing sensitive monitoring bands and suitable monitoring models, have great significance both in theory and in practice for achieving non-destructive monitoring of nitrogen concentration and accurate management of water and fertilizer in large-scale areas. In this study, a lysimeter experiment was carried out to examine the characteristics of canopy spectral reflectance variation of summer corn under different fertilization levels. The relationship between canopy spectral reflectance and nitrogen concentration was investigated, based on which sensitive bands for the corn canopy nitrogen monitoring were selected and a suitable spectral index model was determined. The results suggest that under different fertilization levels, the canopy spectral reflectance of summer corn decreases with the increase of the canopy nitrogen concentration in the visible light band, but varies in the opposite direction in the near-infrared band, with a premium put on a higher correlation between the spectral reflectance of the characteristic bands and their first derivatives and the canopy nitrogen concentration. The most sensitive bands for monitoring the canopy nitrogen concentration using spectral reflectance and its first derivative are found to be 762 nm and 726 nm and the correlation coefficients are 0.550 and 0.795, respectively. The optimal band combination, generated by multivariate stepwise regression analysis, is composed of 762 nm, 944 nm and 957 nm bands. From the 55 reported spectral index models of crop nitrogen concentration monitoring, the most suitable index model, NDRE, is chosen such that this index model has the highest correlation with the canopy nitrogen concentration in summer corn. This model has a significant positive correlation with the canopy nitrogen concentration at each growth period, and the correlation coefficient is up to 0.738 during the whole growth period. Spectral monitoring models of canopy nitrogen concentration are constructed using sensitive bands, and a combination of bands and the spectral index, suggesting that these models perform well in monitoring. The models arranged in descending order of simulation accuracy are as follows: the suitable spectral index model, the optimal band combination model, the sensitive band reflectance first derivative model, the sensitive band reflectance model. The determination coefficients are 0.754, 0.711, 0.639 and 0.306, respectively.
Highlights
Corn is a widely grown food crop, and nitrogen fertilizer is one of the major limiting factors affecting its growth, playing an important role in growth, yield, and quality of corn
The canopy reflectance of the plants is low due to the absorption by chlorophyll in the visible light band, but the multi-scattering effect of the canopy cell structure in the near-infrared region leads to a higher reflectance in this band
At the point of fertilization, the canopy spectral reflectance of summer corn plants in the visible light band decreases with the increase of fertilization, but the trend is reversed in the near infrared band
Summary
Corn is a widely grown food crop, and nitrogen fertilizer is one of the major limiting factors affecting its growth, playing an important role in growth, yield, and quality of corn. Aimless increase in fertilizer application and low efficiency of use are having ever greater adverse effects [1]. Rapid but effective tracking and monitoring of crop nitrogen concentration situations, and reasonable application of nitrogen fertilizer, are of great significance for improving corn quality and for sustainable land use. Compared with traditional destructive sampling methods to monitor plant canopy nitrogen concentration, non-destructive methods that are able to acquire crop hyperspectral information over a large area are more convenient, straightforward and able to provide scientific support for accurate management of modern, large-scale agricultural water and fertilizer application
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