  For many survey-based spatial modelling problems, responses are observed as
spatially aggregated over survey regions due to limited resources. Covariates,
from weather models and satellite imageries, can be observed at many different
spatial resolutions, making the pre-processing of covariates a key challenge
for any spatial modelling task. We propose a Gaussian process regression model
to flexibly handle multiresolution covariates by employing an additive kernel
that can efficiently aggregate features across resolutions. Compared to
existing approaches that rely on resolution matching, our approach better
maintains distributional information across resolutions, leading to better
performance and interpretability. Our model yields stronger predictive
performance and interpretability on both simulated and crop yield datasets.
