SOMOSPIE: A modular SOil MOisture SPatial Inference Engine based on data driven decisions
This work addresses the need for improved spatial soil moisture representation for applications in environmental sciences and precision agriculture, but it appears incremental as it builds on existing machine-learning techniques for data enhancement.
The authors tackled the problem of coarse and gappy satellite soil moisture data by developing a modular spatial inference engine that uses machine learning and environmental data to produce gap-free, high-resolution soil moisture maps, demonstrated over the Middle Atlantic Coastal Plains region.
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).