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With the development of new remote sensing technology, large or even massivespatial datasets covering the globe become available. Statistical analysis ofsuch data is challenging. This article proposes a semiparametric approach tomodel large or massive spatial datasets. In particular, a Gaussian process withadditive components is proposed, with its covariance structure consisting oftwo components: one component is flexible without assuming a specificparametric covariance function but is able to achieve dimension reduction; theother is parametric and simultaneously induces sparsity. The inferencealgorithm for parameter estimation and spatial prediction is devised. Theresulting spatial prediction methodology that we call fused Gaussian process(FGP), is applied to simulated data and a massive satellite dataset. Theresults demonstrate the computational and inferential benefits of FGP overcompeting methods and show that FGP is robust against model misspecificationand captures spatial nonstationarity. The supplemental materials are availableonline.

A Fused Gaussian Process Model for Very Large Spatial Data