Wheat stripe rust is one of the major diseases affecting wheat yield, and improving the remote sensing monitoring accuracy of wheat stripe rust is of great significance for disease prevention and control. Based on the inversion of full-band SIF spectra by using the F-SFM algorithm, shape characteristics were extracted, and the response characteristics of full-band SIF spectra and their shape characteristics under stripe rust stress were analyzed. A remote sensing monitoring model for wheat stripe rust was constructed by using the random forest algorithm, and compared with a single-band SIF model. The results showed that under stripe rust stress, both leaf and canopy-scale SIF spectral curves and their shape characteristics exhibited different response characteristics. At the leaf level, as the severity of wheat stripe rust increased, the peak value of far-red SIF (CFR), skewness of far-red SIF (SFR), and area of emission peak of far-red SIF (AFR) was decreased, while the peak wavelengths of red (λR) and far-red (λFR) SIF, the kurtosis of far-red SIF (KFR) was increased. At the canopy level, CFR, λR, λFR, and AFR were decreased with the severity of wheat stripe rust. Additionally, the remote sensing monitoring model for wheat stripe rust constructed with shape features such as AFR, λFR, full-band SIF kurtosis (K), skewness of redband SIF spectra (SR), and λR as independent variables showed higher accuracy compared with the model with red-band SIF peak value (CR) and CFR as independent variables, with an increase of 27.59% in R2 and a decrease of 19.83% in RMSE in the training set, and an increase of 96.43% in R2 and a decrease of 17.01% in RMSE in the testing set. The shape characteristics extracted using full-band SIF can more comprehensively and accurately reflect disease stress information.