Signals in the Soil

Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework

This project is an ambitious cross-disciplinary project linking Soil Scientists, Engineers and Statisticians, focused on detecting soil degradation and restoration through a novel multi-functional soil sensing platform that combines conventional and newly created sensors and a machine learning framework. 

Our work directly seeks to ‘advance our understanding
of dynamic soil processes that operate at different temporal/spatial scales.’

Through the creation of an innovative new approach to capturing and analyzing
high frequency data from in-situ sensors, this project will predict the rate
and direction of soil system functions for sites undergoing degradation or


Flourescent tracers can help us track soil particle movement

To do this, we are building  and training a new mechanistically-informed machine learning system to turn high frequency data on multiple soil functions, such as water infiltration, CO2 production, and surface soil movement, into predictions of longer term changes in soil health including the status of microbial processes, soil organic matter (SOM) content, and other properties and processes. Such an approach could be transformative: a system that will allow short-term sensor data to be used to evaluate longer term soil transformations in key ecosystem functions.


We have started our work with a suite of off-the-shelf sensors observing multiple soil functions that can be installed quickly. These data will allow us to rapidly initiate development and training of a novel mechanistically informed machine learning framework. In parallel we are developing two new soil health sensors focused on in-situ real time measurement of decomposition rates and transformation of soil color that reflects the accumulation or loss of SOM. We will then link these new sensors with a suite of conventional sensors in a novel data collection and networking system coupled to the Swarm satellite network to create a low cost sensor array that can be deployed in remote areas and used to support studies of soil degradation or progress toward restoration worldwide.

Lancaster colleagues working on the project: John Quinton, Jess Davies, Rebecca Killick, Chris Nemeth, Mengyi Gong

Project partners: Jason Neff, University of Colorado Boulder, USA, Richard Bardgett, University of Manachester, UK.

Funded by NERC and USDA