  Accurate estimates of Instrument Spectral Response Functions (ISRFs) are
crucial in order to have a good characterization of high resolution
spectrometers. Spectrometers are composed of different optical elements that
can induce errors in the measurements and therefore need to be modeled as
accurately as possible. Parametric models are currently used to estimate these
response functions. However, these models cannot always take into account the
diversity of ISRF shapes that are encountered in practical applications. This
paper studies a new ISRF estimation method based on a sparse representation of
atoms belonging to a dictionary. This method is applied to different
high-resolution spectrometers in order to assess its reproducibility for
multiple remote sensing missions. The proposed method is shown to be very
competitive when compared to the more commonly used parametric models, and
yields normalized ISRF estimation errors less than 1%.
