Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression

Abstract

Many spectral indices have been proposed to derive plant nitrogen (N) nutrient indicators based on different algorithms. However, the relationships between selected spectral indices and the canopy N content of crops are often inconsistent. The goals of this study were to test the performance of spectral indices and partial least square regression (PLSR) and to compare their use for predicting canopy N content of winter wheat. The study was conducted in cool and wet southeastern Germany and the hot and dry North China Plain for three winter wheat growing seasons. The canopy N content of winter wheat varied from 0.54% to 5.55% in German cultivars and from 0.57% to 4.84% in Chinese cultivars across growth stages and years. The best performing spectral indices and their band combinations varied across growth stages, cultivars, sites and years. Compared with the best performing spectral indices, the average value of the R2 for the PLSR models increased by 76.8% and 75.5% in the calibration and validation datasets, respectively. The results indicate that PLSR is a potentially useful approach to derive canopy N content of winter wheat across growth stages, cultivars, sites and years under field conditions when a broad set of canopy reflectance data are included in the calibration models. PLSR will be useful for real-time estimation of N status of winter wheat in the fields and for guiding farmers in the accurate application of their N fertilisation strategies.