Ma, PL; Kang, EL (2020). Spatio-Temporal data fusion for massive sea surface temperature data from MODIS and AMSR-E instruments. ENVIRONMETRICS, 31(2), e2594.

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 data sets 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 data sets in a data-fusion context. Based upon a spatio-temporal-random-effect 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 algorithm to ensure its scalability for massive data sets. The DFGP model is applied to a total of 3.7 million sea surface temperature data sets in the tropical Pacific Ocean for a one-week time period in 2010 from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) instruments.