Skip to main content
Go to the home page of the European Commission (opens in new window)
English en
CORDIS - EU research results
CORDIS

Combining knowledge-guided process models and data-driven machine learning models for enhancing soil carbon modelling in space and time

Project description

Soil carbon modelling with knowledge-based approaches

Modelling soil organic carbon (SOC) is essential for soil health and climate change mitigation. Machine learning captures spatial variations, while process-based models address temporal dynamics. A hybrid framework combining both is needed. Supported by the Marie Skłodowska-Curie Actions programme, the HySOC project will develop a new SOC model by integrating knowledge-guided process models with data-driven machine learning techniques. This approach will enhance soil mapping methods and support future carbon-climate feedback projections. By providing high-resolution SOC maps and open-access model components, the project aims to reduce uncertainty regarding changes in European soil carbon over time. Findings are expected to contribute to the EU Soil Mission and the European Green Deal objectives.

Objective

Accurate modelling and mapping soil organic carbon (SOC) variations in space and time are crucial for supporting soil health restoration, guiding sustainable land management practices, and contributing to climate change mitigation strategies. Although data-driven approaches, such as machine learning (ML), has attracted many attention in soil mapping tasks for its powerful ability to learn from data, it is better at capturing soil spatial variation than soil temporal dynamics. By contrast, process-based models benefit from mechanistic knowledge to express physical, chemical and biological processes that govern SOC temporal changes. Therefore, integrating two types of models will promise means to represent physically plausible SOC dynamics while retaining the spatial prediction accuracy of ML models. However, a clear research gap remains in applying this concept and designing an appropriate hybrid modelling framework to quantify soil variations in space and time. This HySOC (Hybrid modelling of SOC) project aims to propose a new paradigm of space-time SOC modelling framework that hybrid knowledge-guided process models and data-driven ML models, for fulfilling the needs of the next generation of soil mapping methods and for supporting future carbon–climate feedback projections. By bridging two types of model, the project will go beyond the state of the art by providing high spatial and temporal resolution SOC maps and also the open-access hybrid model architecture, algorithms and calibrated model parameters, thus reducing the uncertainty on our understanding of European soil carbon changes in the past, present, and future. On top of model innovation, the outcomes of HySOC will further help to assess the sensitivity of European soil carbon to multiple global change drivers, and enhance the capacity to diagnose and improve the representations of soil variations in Earth system models, thereby contributing to the goals of the EU Soil Mission and the European Green Deal.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

See all projects funded under this funding scheme

Call for proposal

(opens in new window) HORIZON-MSCA-2025-PF

See all projects funded under this call

Coordinator

INSTITUT NATIONAL DES SCIENCES ET INDUSTRIES DU VIVANT ET DE L'ENVIRONNEMENT - AGROPARISTECH
Net EU contribution
€ 242 260,56
Address
22 PLACE DE L'AGRONOMIE
91120 Palaiseau
France
Region
Ile-de-France Ile-de-France Essonne
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data

Partners (1)