Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat

Changwei Tan, Ying Du, Jian Zhou, Dunliang Wang, Ming Luo, Yongjian Zhang, Wenshan Guo

Hyperspectral remote sensing is a rapid non-destructive method for diagnosing nitrogen status in wheat crops. In this study, a quantitative correlation was associated with following parameters: leaf nitrogen accumulation (LNA), raw hyperspectral reflectance, first-order differential hyperspectra, and hyperspectral characteristics of wheat. In this study, integrated linear regression of LNA was obtained with raw hyperspectral reflectance (measurement wavelength = 790.4 nm). Furthermore, an exponential regression of LNA was obtained with first-order differential hyperspectra (measurement wavelength = 831.7 nm). Coefficients (R2) were 0.813 and 0.847; root mean squared errors (RMSE) were 2.02 g·m-2 and 1.72 g·m-2; and relative errors (RE) were 25.97% and 20.85%, respectively. Both the techniques were considered as optimal in the diagnoses of wheat LNA. Nevertheless, the better one was the new normalized variable (SD r - SD b )/(SD r + SD b ), which was based on vegetation indices of R2 = 0.935, RMSE = 0.98, and RE = 11.25%. In addition, (SD r - SD b )/(SD r + SD b ) was reliable in the application of a different cultivar or even wheat grown elsewhere. This indicated a superior fit and better performance for (SD r - SD b )/(SD r + SD b ). For diagnosing LNA in wheat, the newly normalized variable (SD r - SD b )/(SD r + SD b ) was more effective than the previously reported data of raw hyperspectral reflectance, first-order differential hyperspectra, and red-edge parameters.