Hyperspectral estimation models for LTN content of winter wheat canopy under stripe rust stress

The objective of this study is to diagnose the nutrition status of crops by hyperspectral data under stripe rust stress. Canopy reflectance of winter wheat infected by stripe rust was measured in situ, and the leaf total nitrogen (LTN) contents corresponding to the spectra were determined in laboratory. Linear and non-linear regression methods were used to build the regression models between derivative variables and LTN content. It is shown that LTN of disease wheat gradually decreases with disease aggravating and there is high correlation between LTN and first derivative data at 430～518, 534～608, 660～762 nm and 783～893 nm. By validation, the model consisting of the ratio of sum of the first derivative within red edge and sum of the first derivative within blue edge（SDr/SDb）had the best performance, and the RMSE was 0.3567 and the relative error was 8.33％. So it is feasible to estimate LTN content of crops under disease stress by those proposed models based on hyperspectral remote sensing and the accuracy is satisfactory . These results also provide a theoretical basis for monitoring of plant nitrogen status and for diagnosing disease severity of wheat stripe rust and precision management of nitrogen fertilization in wheat production．