Madlene Nussbaum

Assistant Professor

Utrecht University

Netherlands

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Dr. Madlene Nussbaum is an Assistant Professor at Utrecht University in the Netherlands. Her research primarily focuses on soil hydraulic properties, machine learning applications in ecosystem behavior, and digital soil mapping. Recent publications include studies on UAV data for biomass predictions and the use of satellite imagery to forecast soil carbon content. She actively contributes to the field by exploring innovative methods to enhance soil property assessments and ecosystem understanding.

Articles (10)

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2025

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2025

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2024

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2024

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2023

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2022

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2022

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2021

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2021

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2020

Collaborators (4)

Andri Baltensweiler

Swiss Federal Institute for Forest, Snow and Landscape Research

SWITZERLAND

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Marc Hanewinkel

Professor, Head of Chair

Albert-Ludwigs-Universität Freiburg

GERMANY

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Hanna Meyer

GERMANY

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David Hofmeyr

Senior Lecturer

University of Lancaster

UNITED KINGDOM

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Madlene Nussbaum

Research Interests

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Articles (10)

Transferability of pedotransfer functions for estimating soil hydraulic properties: An analysis of controlling factors for forest soils in Switzerland

Machine learning emulators of dynamical systems for understanding ecosystem behaviour

Mapping soil thickness by accounting for right‐censored data with survival probabilities and machine learning

Herbage biomass predictions from UAV data using a derived digital terrain model and machine learning

Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH

Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment

Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran

Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland

Mapping the geogenic radon potential for Germany by machine learning

Evaluation of the Potential for Soil Organic Carbon Content Monitoring With Farmers

Collaborators (4)

Andri Baltensweiler

Marc Hanewinkel

Hanna Meyer

David Hofmeyr

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