In the traditional characteristic spectrum extraction algorithm, the arithmetic mean value of spectra is often used to indicate the characteristic spectrum. However, by strengthening the extreme value information and weakening some characteristic information, the indication capability of the mean value is easily affected by the degree of internal differences between objects, Based on the first theorem of geography and the idea of spatial interpolation, a characteristic spectrum extraction algorithm of spectral domain interpolation is proposed. First, the spectral domain of the objects, maximum and minimum reflectances of the objects at each wavelength， are calculated on several object spectra. To obtain single-feature spectral domain spaces, normalized inverse distance interpolation is performed at the center of a single object spectrum with the range of spectral domain. Finally, as multiple spectral domains are added, the cumulative spectral domain space of ground objects is obtained, and the maximum value in the cumulative spectral domain space, which is calculated by wavelength, is taken as the reflectivity, forming the characteristic spectrum of ground objects.To verify the validity and superiority of the spectral domain interpolation characteristic extraction algorithms performance on the construction of characteristic spectral shape and amplitude, tree species spectra measured from aerial hyperspectral remote sensing images and ASD are used as data sources to calculate the mean characteristic spectrum (MCS) and spectral domain interpolation characteristic spectrum (ICS). To explore the ICSs ability to characterize the overall shape and reproduce detail features, spectral angle mapping (SAM) of aerial hyperspectral data, feature parameter extraction importance evaluation, and linear discriminant analysis (LDA) of ASD-measured data were performed.The experimental results show that ICS improves the overall accuracy by 4.24% in the SAM when indicating characteristic spectral morphology compared with MCS when it comes to the amplitude feature parameter importance evaluation and LDA, which reveals the characteristic spectral details, the amplitude parameter importance score increased by 0.35 on average, the discrimination accuracy of each tree species increased by 2.51%, and the overall accuracy increased by 2.5%. Studies have shown that ICS is superior to traditional MCS in characterizing the spectral features overall shape and reproducing detailed features. ICS can be used to refine the feature spectrum extraction process of target objects in classification scenes and improve the separability between classes. Moreover, ICS can also be used to optimize the selection of feature parameters in inversion scenes to improve the ability to characterize spectra.