AUTHOR=Tan Changwei , Du Ying , Zhou Jian , Wang Dunliang , Luo Ming , Zhang Yongjian , Guo Wenshan TITLE=Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat JOURNAL=Frontiers in Plant Science VOLUME=9 YEAR=2018 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.00674 DOI=10.3389/fpls.2018.00674 ISSN=1664-462X ABSTRACT=

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.