Pedometrics

At the UFZ working group Pedometrics it is our ambition to understand and model spatial soil distribution patterns and their interaction with the biosphere and hydrosphere.

Soil-Landscape Modelling

Head

Dr. Mareike Ließ

Scientists

Anika Gebauer
Ali Sakhaee (external)
Dr. Javier Reyes

Dr. Matteo Poggio

Student Research

Lisa Krieg

Pedometrics is an interdisciplinary science integrating Soil Science, Applied Mathematics/ Statistics and Geo-Information Science. The object of investigation is spatial-temporal pedodiversity at multiple scales. Modelling approaches are used along with multiple aspects of soil sensing and geodata analysis. Common to all research approaches is that the quantification of the prediction uncertainty occupies an important space. Currently, the use of algorithms and optimization methods from the field of spatial Data Science is becoming increasingly important. The latter evolved from pattern recognition and computational learning theory and studies the application and construction of mathematical algorithms that can derive knowledge from complex data structures.


Current projects

SoilSpace3D-DE

The SoilSpace3D-DE research cooperation with the Thünen Institute of Climate-Smart Agriculture seeks to develop high resolution spatial soil-information in 2D and 3D at national scale (Germany) applying methods from data science/ pedometrics.

SOCmonit

The research project deals with the spatio-temporal monitoring of soil organic carbon with remote and proximal soil sensing methods on long-term field experiments. It is funded by the German Federal Ministry of Food and Agriculture (BMEL) according to the BMEL directive on the promotion of innovation in the field of soil as a contribution to climate protection under the Paris Agreement (COP21).

DSM-GuideEC

DSM-GuideEC is part of the "Platform for Biodiversity Monitoring and Research in South Ecuador" and investigates the soil landscapes of three mountain regions of different climate and vegetation with a focus on carbon sequestration and soil hydraulic properties. Besides research the project includes knowledge transfer of the applied methodology to local partner organisations of the public sector.

BonaRes

“BonaRes” is short for “Soil as a sustainable resource for the bioeconomy”. In this funding initiative of the German Federal Ministry for Education and Research (BMBF) the focus is on the sustainable use of soils as a limited resource. The ultimate goal of BonaRes is to extend the scientific understanding of soil ecosystems and to improve the productivity of soils and other soil functions while developing new strategies for a sustainable use and management of soils.

Hi-CAM, Project 7.1 - Agricultural Systems

The Helmholtz Climate Initative develops climate mitigation and adaptation strategies. Project 7.1 uses pedogenetic process knowledge together with national soil databases  to create spatially continuous soil information with a resolution of approx. 200 m. The focus will be essentially on those soil properties that have been identified as suitable indicators for important soil functions such as water storage, carbon sequestration, and agricultural productivity. Pedotransfer functions shall be applied in case of limited data availability. Based on the generated soil information and the derived soil functional properties, this project contributes to the development of sustainable adaptation strategies for agriculture in response to climate change. This is done at national scale (Germany), taking into account regional features with regards to soil distribution.


Recent puplications

Ellinger, M., Merbach, I., Werban, U., Ließ, M., 2019. Error propagation in spectrometric functions of soil organic carbon. Soil, 5, 275-288. https://doi.org/10.5194/soil-5-275-2019

Gebauer, A., Brito Gómez, V.M., Ließ, M., 2019. Optimisation in machine learning: An application to topsoil organic carbon stocks prediction in a dry forest ecosystem. Geoderma 354, 113846. https://doi.org/10.1016/j.geoderma.2019.07.004

Guio Blanco, C.M., Brito Gomez, V.M., Crespo, P., Ließ, M., 2018. Spatial prediction of soil water retention in a Páramo landscape : Methodological insight into machine learning using random forest. Geoderma 316, 100–114. doi:10.1016/ j.geoderma. 2017.12.002

Ließ, M., 2020. At the interface between domain knowledge and statistical sampling theory: Conditional distribution based sampling for environmental survey (CODIBAS). Catena, 187,104423. https://doi.org/10.1016/j.catena.2019.104423

Stein,S., Eberhardt, E., Grosse, M., Helming, K., Hierold, W., Hoffmann, C., Kühnert, T., Ließ, M., Russel, D.J., Schulz, S., Specka, X., Svoboda, N., Zoarder, M.A.M., Heinrich, U., 2019. Report on available soil data for German agricultural areas, 2018. BonaRes Series 2019/01. BonaRes Centre for Soil Research. DOI: 10.20387/BonaRes-CD4Q-1PEM

Vogel H-J, Eberhardt E, Franko U, Lang B, Ließ M, Weller U, Wiesmeier M, Wollschläger U, 2019. Quantitative evaluation of soil functions: potential and state. Frontiers in Environmental Science, 7 (164), https://doi.org/10.3389/fenvs.2019.00164

Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin-Spinotta, E., van Wesemael, B., Rabot, E., Ließ, M., Garcia-Franko, N., Wollschläger, U., Vogerl, H.-J., Kögel-Knabner, I., 2019. Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales. Geoderma 333, 149-162. https://doi.org/10.1016/j.geoderma.2018.07.026