Carbon-4D: A landscape-scale model of soil organic carbon mineralization in space, depth, and time

  • Logo Carbon4D
    © Hanna Meyer


    The mineralization of soil organic carbon (SOC) is a key component of the global carbon cycle, which regulates the balance between CO2 efflux and SOC sequestration. Patterns of SOC mineralization in space, depth, and time (4D) across a landscape, however, are still poorly understood. This is mainly because SOC mineralization rates are driven by a complexity of regulators, mechanisms, and their nonlinear interactions, that is beyond human perception. The aim of “Carbon4D” is the development of a data-driven 4D model of SOC mineralization on a landscape scale that accounts for highly complex and nonlinear relationships. To reach this aim, measurements of SOC mineralization rates and of their controlling factors (SOC stocks, soil moisture and temperature) are combined with multi-source remote sensing data and weather- and soil information in a machine learning approach. Based on the learned relationships, 4D predictions are made for a typical German low mountain landscape that serves as the test area for Carbon4D. On the basis of the 4D model, a detailed analysis of temporal, spatial, and vertical patterns of SOC mineralization rates and their controlling factors will be conducted. Hence, Carbon4D will provide a first approach of a near real-time monitoring framework of SOC mineralization rates and soil CO2 efflux in all 4 dimensions, which will provide new insights into patterns and controlling factors.

  • Information

    Project lead: Hanna Meyer, Nele Meyer (University of Bayreuth)  
    : Maiken Baumberger, Hanna Meyer
    Funding: DFG
    Term: 2021 - 2024

    further information can be found on the project website

  • Publications

    Baumberger, M; Adorf, L; Haas, B; Meyer, N; and Meyer, H. 2023. Modelling Soil Temperature and Soil Moisture in Space, Depth, and Time with Machine Learning Techniques, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11958,, 2023.

    Baumberger, M; Adorf, L; Haas, B; Meyer, N; and Meyer, H. 2022. A machine learning approach to model spatiotemporal patterns of soil temperature and soil moisture as drivers for soil organic carbon mineralization. Contributed to the living planet symposium, ESA, Bonn, Germany, 25.05.2022.

    Haas, B; Baumberger, M; Meyer, H; Meyer, N. 2021. A landscape-scale model of soil organic carbon mineralization in space, depth and time. Contributed to the BayCEER Workshop, University of Bayreuth, Bayreuth, Germany, 14.10.2021.

  • Theses

    Linda Adorf: Soil temperature in space, time and depth – A landscape-scale modelling approach using remote sensing and machine learning. M.Sc. Landschchaftsökologie

    Sindhu Sivakumar: Modelling soil moisture under different land cover types using radar and optical remote sensing. M.Sc. Geoinformatik