In this proposal we outline an 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 proposed work directly addresses the Signals in the Soil call 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 analysing 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 colour 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.