Spatio-Temporal Data Fusion for Massive Sea Surface Temperature Data from MODIS and AMSR-E Instruments
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Remote sensing data have been widely used to study many geophysical processes. With the advance of 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 coverage. To combine datasets from different instruments, we propose a dynamic fused Gaussian process (DFGP) model that enables fast statistical inference such as smoothing and filtering for massive spatio-temporal datasets in the data-fusion context. Based upon the 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 and a linear combination of a large number of basis functions and Markov random coefficients. To model the true process at different spatial resolutions, we rely on the change-of-support property, since it allows efficient computations in DFGP. 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 a one-week period in tropical Pacific Ocean area from MODIS and AMSR-E instruments.
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