Efficient and accurate imaging spectroscopy data processing asks for perfectly consistent (i.e., ideally uniform) data in both the spectral and spatial dimensions. However, real pushbroom-type imaging spectrometers are affected by various point spread function (PSF) nonuniformity artifacts. First, individual pixels or lines may be missing in the raw data due to bad pixels originating from the detector, readout errors, or even electronic failures. Second, so-called smile and keystone optical aberrations are inherent to imaging spectrometers. Appropriate resampling strategies are required for the preprocessing of such data if emphasis is put on spatial PSF uniformity. So far, nearest neighbor interpolations have been often recommended and used for resampling. This paper shall analyze the radiometric effects if linear interpolation is used to optimize the spatial PSF uniformity. For modeling interpolation effects, an extensive library of measured surface reflectance spectra as well as real imaging spectroscopy data over various land cover types are used. The real measurements are systematically replaced by interpolated values, and the deviation between original and resampled spectra is taken as a quality measure. The effects of nearest neighbor resampling and linear interpolation methods are compared. It is found that linear interpolation methods lead to average radiometric errors below 2% for the correction of spatial PSF nonuniformity in the subpixel domain, whereas the replacement of missing pixels leads to average errors in the range of 10%–20%.