  In this work we evaluate multi-output (MO) Gaussian Process (GP) models based
on the linear model of coregionalization (LMC) for estimation of biophysical
parameter variables under a gap filling setup. In particular, we focus on LAI
and fAPAR over rice areas. We show how this problem cannot be solved with
standard single-output (SO) GP models, and how the proposed MO-GP models are
able to successfully predict these variables even in high missing data regimes,
by implicitly performing an across-domain information transfer.
