EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

John Quinton1, Mike James1, Jess Davies1, Greg Whiting6, Christopeher Nemeth2, Rebecca Killick2, Evan Thomas4, Richard Bardgett5, and Jason Neff3
John Quinton et al.
  • 1Lancaster University, Lancaster Environment Centre, Lancaster, United Kingdom
  • 2Lancaster University. Department of Maths and Statistics, Lancaster, United Kingdom
  • 3University of Colorado, Sustainability Innovation Lab, Boulder, USA
  • 4Unversity of Colorado, Mortenson Center in Global Engineering, Boulder, USA
  • 5University of Manchester, Department of Earth and Environmental Sciences, Manchester, United Kingdom
  • 6Unversity of Colorado, Mechanical Engineering, Boulder, USA

In this poster we will outline a new  ambitious cross-disciplinary project 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  aims 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 restoration. To do this, we will build and train 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 will start 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 will develop 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.

How to cite: Quinton, J., James, M., Davies, J., Whiting, G., Nemeth, C., Killick, R., Thomas, E., Bardgett, R., and Neff, J.: Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22591,, 2020


Display file

Comments on the display

AC: Author Comment | CC: Community Comment | Report abuse

displays version 1 – uploaded on 23 Apr 2020, no comments