Estimation of Nitrogen Concentration in Winter Wheat Leaves Based on a Novel Spectral Index and Machine Learning Model

Summary

Assessing crop nitrogen status is crucial for optimizing fertilization strategies and promoting sustainable production. Although hyperspectral data offer significant advantages for monitoring subtle physiological changes in crops, accurately determining nitrogen status based on spectral information remains challenging. In this study, field experiments were conducted during the jointing stage of winter wheat on the Loess Plateau from 2018 to 2020. Concurrent measurements of leaf nitrogen concentration (LNC) and hyperspectral reflectance were collected to derive three types of spectral parameters: traditional vegetation indices, two-dimensional optimal spectral indices, and three-dimensional optimal spectral indices. Spectral parameters exhibiting a significant correlation with LNC (<i>p</i>