Associating new spectral features from visible and near infrared regions with optimal combination principle to monitor leaf nitrogen concentration in barley

The paper proposed a method to monitor LNC in crop with hyperspectral remote sensing. Taking the LNC monitoring of barley that is more demanding for nitrogen fertilization as a case, this study employs new spectral features such as slopes and angles extracted from the normalized reflectance curves in Visible-Near Infrared region to evaluate LNC, At the same time, the optimal combination principle that was widely used in the combinated forecasting domains was presented to estimate LNC. The analysis resluts proved that most of the new spectral features propsoed in the study exhibited significant correlations with LNC. Among the new spectral features, the key features of slopes (K-re/K-pb and K-pb) and angles (A(delta)/A(alpha) and A(delta)/A(theta)) could well describe the dynamic pattern of LNC changes in crop. The optimal combination algorithm determined the optimal combination with K-re/K-pb and K-nir1, which could increase the spectral responding to LNC, strengthen the stability of models monitoring LNC and improve the accuracy of LNC estimates.