  Remote sensing data have been widely used to study various geophysical
processes. With the advances in remote-sensing technology, massive amount of
remote sensing data are collected in space over time. Different satellite
instruments typically have different footprints, measurement-error
characteristics, and data coverages. To combine datasets from different
satellite instruments, we propose a dynamic fused Gaussian process (DFGP) model
that enables fast statistical inference such as filtering and smoothing for
massive spatio-temporal datasets in a data-fusion context. Based upon a
spatio-temporal-random-effects model, the DFGP methodology represents the
underlying true process with two components: a linear combination of a small
number of basis functions and random coefficients with a general covariance
matrix, together with a linear combination of a large number of basis functions
and Markov random coefficients. To model the underlying geophysical process at
different spatial resolutions, we rely on the change-of-support property, which
also allows efficient computations in the DFGP model. To estimate model
parameters, we devise a computationally efficient stochastic
expectation-maximization (SEM) algorithm to ensure its scalability for massive
datasets. The DFGP model is applied to a total of 3.7 million sea surface
temperature datasets in the tropical Pacific Ocean for a one-week time period
in 2010 from MODIS and AMSR-E instruments.
