  Atmospheric correction of Earth Observation data is one of the most critical
steps in the data processing chain of a satellite mission for successful remote
sensing applications. Atmospheric Radiative Transfer Models (RTM) inversion
methods are typically preferred due to their high accuracy. However, the
execution of RTMs on a pixel-per-pixel basis is impractical due to their high
computation time, thus large multi-dimensional look-up tables (LUTs) are
precomputed for their later interpolation. To further reduce the RTM
computation burden and the error in LUT interpolation, we have developed a
method to automatically select the minimum and optimal set of nodes to be
included in a LUT. We present the gradient-based automatic LUT generator
algorithm (GALGA) which relies on the notion of an acquisition function that
incorporates (a) the Jacobian evaluation of an RTM, and (b) information about
the multivariate distribution of the current nodes. We illustrate the
capabilities of GALGA in the automatic construction and optimization of
MODerate resolution atmospheric TRANsmission (MODTRAN) LUTs for several input
dimensions. Our results indicate that, when compared to a pseudo-random
homogeneous distribution of the LUT nodes, GALGA reduces (1) the LUT size by
$\sim$75\% and (2) the maximum interpolation relative errors by 0.5\% It is
concluded that automatic LUT design might benefit from the methodology proposed
in GALGA to reduce computation time and interpolation errors.
