globalcomputinglab/somospie repository overview

⁠Overview

Soil moisture is a critical variable that links climate dynamics with water and food security. It regulates land-atmosphere interactions (e.g., via evapotranspiration--the loss of water from evaporation and plant transpiration to the atmosphere), and it is directly linked with plant productivity and survival. Information on soil moisture is important to design appropriate irrigation strategies to increase crop yield, and long-term soil moisture coupled with climate information provides insights into trends and potential agricultural thresholds and risks. Thus, information on soil moisture is a key factor to inform and enable precision agriculture.

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 farming and controlled burn), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information at a finer spatial resolution (i.e., increased granularity).

SOMOSPIE is a spatial inference engine consisting of modular stages for processing spatial environmental data, generating fine-grained soil moisture predictions with machine-learning techniques, and analyzing these predictions. The Jupyter Notebook in this repository allows users to demonstrate the functionality of our prediction approach and the effects of data processing choices via multiple prediction maps over the United States ecological regions with diverse soil moisture profiles.

The relevance of this 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 farming (e.g., optimizing irrigation practices and other land management decisions).

This engine is a result of a collaboration between computer scientists of the Global Computing Laboratory at the University of Tennessee, Knoxville, and soil scientists at the University of Delaware (funded by NSF awards #1724843 and #1854312).

⁠Webpage

https://globalcomputing.group/somospie/⁠