  The estimation of land cover fractions from remote sensing images is a
frequently used indicator of the environmental quality. This paper focuses on
the quantification of land cover fractions in an urban area of Berlin, Germany,
using simulated hyperspectral EnMAP data with a spatial resolution of
30m$\times$30m. We use constrained sparse representation, where each pixel with
unknown surface characteristics is expressed by a weighted linear combination
of elementary spectra with known land cover class. We automatically determine
the elementary spectra from image reference data using archetypal analysis by
simplex volume maximization, and combine it with reversible jump Markov chain
Monte Carlo method. In our experiments, the estimation of the automatically
derived elementary spectra is compared to the estimation obtained by a manually
designed spectral library by means of reconstruction error, mean absolute error
of the fraction estimates, sum of fractions, $R^2$, and the number of used
elementary spectra. The experiments show that a collection of archetypes can be
an adequate and efficient alternative to the manually designed spectral library
with respect to the mentioned criteria.
