Monitoring Leaf Nitrogen Content in Wheat With Canopy Hyperspectra As Influenced By Soil Background.

Abstract: Hyperspectral sensing technique can provide an effective means for fast and non-destructive estimation of nitrogen status in crop plants, but its accuracy is often influenced by soil background. Under different fractions of soil noise, the canopy spectra and leaf nitrogen content (LNC) in wheat (Triticum aestivum L.) were obtained on the basis of field experiments with varied nitrogen rates and planting densities in three years. Five types of spectral indices (VIs) of RVI, NDVI, SAVI, OSAVI and PVI were constructed using three methods based on the original spectrum and first derivative spectrum, the fractional vegetation coverage, and the pure spectrum extracted by the method of linear mixed model. Then, comprehensive relationships were quantified between five types of VIs and LNC in wheat, and the monitoring models were further developed. Finally, the spatial distribution of LNC was generated from the satellite remote images in case study areas of wheat production in Jiangsu. The results indicated that all five types of VIs derived from the original spectrum and first derivative spectrum were significantly affected by soil background, with R2 around 0.55 for LNC estimation, although the OSAVI (R514, R469) L=0.04 exhibited the best performance. Base on the fractional vegetation coverage, however, the adjusted spectral index NDVI(R513, R481)/(1+ FVcover) gave the higher R2 of 0.62, with the lower RRMSE of 0.13, being less sensitive to the LAI, LDW, FVcover and LNA. In addition, the linear mixture model did not show a good accuracy because of the complicated field circumstance, but appeared to be potentially useful. Comparison of our new spectral parameter with published spectral parameters and its application to predicting LNC in wheat production proved that the adjusted spectral index NDVI(R513, R481)/(1+ FVcover) could effectively estimate LNC of wheat under varied vegetation coverage and soil noise. Key words: Wheat canopy; Leaf nitrogen content; Vegetation coverage; Soil background; Spectral parameter; Monitoring model