Aphids (Aphis gossypii) are one of the main pests of cotton. The annual loss of China’s cotton yield due to aphids is as high as 5%～10%. Monitoring the grading profile of field-scale aphid damage can assist the precise application of quantified pesticide and reduce environmental pollution. The hyperspectral imaging data obtained by unmanned aerial vehicle (UAV)-mounted imaging spectrometer has the advantages of high resolution, high timeliness and low cost. The method based on derivative of ratio spectroscopy has the advantages of simple, efficient and highly accurate, which can be effectively applied to the remote sensing spectrum unmixing process, and extract more sensitive bands to the target information, providing an effective means for the establishment of pest monitoring model. Therefore, in this study, the Korla region of Xinjiang, a typical cotton production area, was selected as the experimental area to carry out the following work. (1) to use a low-altitude-unmanned-aerial-vehicle-based hyperspectral imaging instrument to acquire hyperspectral images of cotton (Gossypium) canopies. Spectral data of 76 sample points and severity of aphid damage were obtained (including 16 healthy plants, and 15 were selected from each grade of 1～4 severity of aphid damage. (2) to use the derivative of ratio spectroscopy (DRS) to select sensitive spectral bands from cotton canopy spectra to detect aphid damage at the bud stage, band 514 nm, band 566 nm band 698 nm; (3) to construct unary-linear-regression and partial-least-squares models based on the sensitive bands of reflectance spectra and derivative of ratio (DR) spectra for rating aphid damage. The rexperiments’ results revealed the following: (1) aphid damage had a significant effect on the spectral reflectance of the cotton canopy. The more seriously cotton plants were affected by the aphid, the higher the reflectivity in the visible region and the lower the reflectivity in the near-infrared band, and the “blue shift” occurred in the red envelope region. (2) sensitive bands for detecting aphid damage were effectively extracted from the DR spectra of the cotton canopy, and the three selected bands (with wavelengths of 514, 566, and 698 nm) were consistent with the sensitive bands extracted by using the correlation coefficient method; (3) )the precisions of the aphid-damage-rating models constructed using DR spectra of the sensitive bands were better than those of the models constructed using of the sensitive bands from the reflectance spectra, among which the model constructed with 698 nm band had the best accuracy（R2=0.612, RMSE=0.89）; (4) based on derivative of ratio spectroscopy method, the UAV imaging spectral monitoring model of cotton aphid infestation can obtain the spatial distribution map of different severity aphid infestation on the field scale, which is of great significance for precisely quantified pesticide.