Dr. Mareike Ließ

WG Pedometrics


phone: +49 (0)345 - 558 -5401

Department of Soil System Science
Helmholtz Centre for Environmental Research - UFZ
D-06120 Halle (Saale)

Research interest

Understanding and modelling spatial soil distribution patterns and their interaction with the biosphere and hydrosphere.

to working group Pedometrics

Research projects

HI-CAM: Helmholtz Cimate Initiative (since 2019)

SoilSpace3D-DE: Development of nation-wide (Germany) high resolution spatial soil-information with various modelling approaches (since 2018)

SOCmonit: Monitoring of soil organic carbon with remote and proximal soil sensing methods (since 2018)

BonaRes: Soil as a sustainable resource for the bioeconomy (since 2016)

DSMGuide-EC (DFG PAK 825 - C9): Towards a guideline for digital soil mapping in Ecuador (2013-2018)

TERRECO Cluster F-03: Landform classification for functional upscaling (2012 - 2015)

TERRECO Cluster F-02: Spatial analysis of soil related environmental risks in the Soyang watershed (2012-2014)

DFG FOR 816 - D3: Functional soil landscape modelling in the Andean mountain forest zone: impact of land use and natural disturbances (2010 - 2013)

DFG FOR 816 - A3.3: Spatiotemporal dynamics of shallow landslides and their biotic and abiotic controls -  an integrating synthesis including soil landscape modelling for determination and regionalisation of soil functional groups as well as forest modeling (2007 - 2010)

Selected publications

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., Sakhaee, A., Don, A., Poggio, M., Ließ, M., 2022. Topsoil Texture Regionalization for Agricultural Soils in Germany — An Iterative Approach to Advance Model Interpretation. Front. Soil Sci. 1:770326. https://doi.org/10.3389/fsoil.2021.770326

Gebauer, A., Ellinger, M., Brito Gómez, V.M., Ließ, M., 2020. Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning. Soil, 6, 215–229. https://doi.org/10.5194/soil-6-215-2020

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. https://doi.org/10.1016/j.geoderma.2017.12.002

Jeong, G., Oeverdieck, H., Park, S.J., Huwe, B., Ließ, M. (2017). Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain. Catena, 154, 73-84. https://doi.org/10.1016/j.catena.2017.02.006

Ließ, M., Gebauer, A., Don, A. (2021). Machine learning with GA optimization to model the agricultural soil-landscape of Germany: An approach involving soil functional types with their multivariate parameter distributions along the depth profile. Front. Environ. Sci. 9:692959. https://doi.org/10.3389/fenvs.2021.692959

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

Ließ, M., Schmidt, J., Glaser, B. (2016). Improving the spatial prediction of soil organic carbon stocks in a complex tropical mountain landscape by methodological specifications in machine learning approaches, PLoS ONE, 11(4): e0153673. doi:10.1371/journal.pone.0153673

Ließ, M. (2015). Sampling for regression-based digital soil mapping - Closing the gap between statistical desires and operational applicability, Spatial Statistics, 13, 106-122, https://doi.org/10.1016/j.spasta.2015.06.002

Ließ, M., Hitziger, M., Huwe, B. (2014). The Sloping Mire Soil-Landscape of Southern Ecuador - Influence of predictor resolution and model tuning on random forest predictions, Applied and Environmental Soil Science (Article ID 603132), 10 pages, doi:10.1155/2014/603132

Ließ, M., Huwe, B. (2012). Uncertainty in soil regionalisation and its influence on slope stability estimation.  In: Picarelli L, Greco R, Urciuoli G (eds.): Large slow active slope movements with a section on landslide hydrology – Hillslope hydrological modelling for a landslides prediction (Book of Proceedings, Italian Workshop on Landslides 2011), 171-177

Curriculum vitae

  since 2016

2010 - 2015

2007 - 2010

2006 - 2007

2005 - 2006

1998 - 2005 

Researcher at UFZ, Dept.of Soil System Science

Researcher and lecturer at the University of Bayreuth, Dept. of Geosciences/ Soil Physics Division, Bayreuth/ Germany

PhD "Soil-landscape modelling in an Andean mountain forest region in southern Ecuador", University of Bayreuth, Dept. of Geosciences/ Soil Physics Division, Bayreuth/ Germany

Development cooperation: Capacity building in GIS and resource assessment, DED, Rundu/ Namibia

Teaching assistance, Loja/ Ecuador

Parallel studies of Geoecology and Applied African Development Studies in Geography, Universities of Freiberg (D), Durham (UK) and Bayreuth (D)