  Computationally expensive Radiative Transfer Models (RTMs) are widely used}
to realistically reproduce the light interaction with the Earth surface and
atmosphere. Because these models take long processing time, the common practice
is to first generate a sparse look-up table (LUT) and then make use of
interpolation methods to sample the multi-dimensional LUT input variable space.
However, the question arise whether common interpolation methods perform most
accurate. As an alternative to interpolation, this work proposes to use
emulation, i.e., approximating the RTM output by means of statistical learning.
Two experiments were conducted to assess the accuracy in delivering spectral
outputs using interpolation and emulation: (1) at canopy level, using PROSAIL;
and (2) at top-of-atmosphere level, using MODTRAN. Various interpolation
(nearest-neighbour, inverse distance weighting, piece-wice linear) and
emulation (Gaussian process regression (GPR), kernel ridge regression, neural
networks) methods were evaluated against a dense reference LUT. In all
experiments, the emulation methods clearly produced more accurate output
spectra than classical interpolation methods. GPR emulation performed up to ten
times more accurately than the best performing interpolation method, and this
with a speed that is competitive with the faster interpolation methods. It is
concluded that emulation can function as a fast and more accurate alternative
to commonly used interpolation methods for reconstructing RTM spectral data.
