Spatial data fusion for large non‐Gaussian remote sensing datasets. Issue 1 (23rd October 2017) - Shi, Hongxiang; Kang, Emily L.
Abstract : Remote sensing data are playing a vital role in understanding the pattern of the Earth's geophysical processes in environmental and climate sciences. We propose a spatial data‐fusion methodology that is able to take advantage of two (or potentially more) large remote sensing datasets with the exponential family of distributions. Our hierarchical model follows the generalized linear mixed model but also leverages a low‐rank spatial random effects model to allow for flexible spatial covariance and cross‐covariance structure. We take an empirical hierarchical modelling approach where any unknown parameters are estimated by maximum likelihood estimation via an efficient expectation–maximization algorithm. Through a Markov chain Monte Carlo algorithm, spatial predictions are obtained by generating samples from the empirical predictive distribution where the unknown parameters are substituted by the estimates. The performance of our proposed method is investigated through a simulation study and a real‐data example. It shows that via borrowing strength across complementary datasets, the proposed method improves spatial predictions reciprocally. Copyright © 2017 John Wiley & Sons, Ltd.