  The current availability of soil moisture data over large areas comes from
satellite remote sensing technologies (i.e., radar-based systems), but these
data have coarse resolution and often exhibit large spatial information gaps.
Where data are too coarse or sparse for a given need (e.g., precision
agriculture), one can leverage machine-learning techniques coupled with other
sources of environmental information (e.g., topography) to generate gap-free
information and at a finer spatial resolution (i.e., increased granularity). To
this end, we develop a spatial inference engine consisting of modular stages
for processing spatial environmental data, generating predictions with
machine-learning techniques, and analyzing these predictions. We demonstrate
the functionality of this approach and the effects of data processing choices
via multiple prediction maps over a United States ecological region with a
highly diverse soil moisture profile (i.e., the Middle Atlantic Coastal
Plains). The relevance of our work derives from a pressing need to improve the
spatial representation of soil moisture for applications in environmental
sciences (e.g., ecological niche modeling, carbon monitoring systems, and other
Earth system models) and precision agriculture (e.g., optimizing irrigation
practices and other land management decisions).

Machine Learning