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Thorsten Behrens

Thorsten Behrens

D-Index & Metrics

Earth Science

D-Index
35
Citations
7792
World Ranking
7451
National Ranking
545

Overview

Thorsten Behrens is affiliated with the University of Tübingen in Germany and contributes primarily to the fields of Environmental Science and Agricultural and Biological Sciences. Their work spans several subfields including Environmental Engineering, Soil Science, Artificial Intelligence, Ecology, and Civil and Structural Engineering.

The researcher's major topics encompass Soil Geostatistics and Mapping, Geochemistry and Geologic Mapping, Soil Erosion and Sediment Transport, Soil Carbon and Nitrogen Dynamics, Soil and Unsaturated Flow, Soil Moisture and Remote Sensing, and Spectroscopy and Chemometric Analyses.

Thorsten Behrens has published multiple papers across notable scientific journals. Some recent papers include:

  • Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space, 2020, Remote Sensing
  • Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models, 2020, Geoderma
  • Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran, 2020, Geoderma
  • Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century, 2022, European Journal of Soil Science
  • Deep transfer learning of global spectra for local soil carbon monitoring, 2022, ISPRS Journal of Photogrammetry and Remote Sensing

Their frequent co-authors include Raphael A. Viscarra Rossel, Karsten Schmidt, Thomas Scholten, Leonardo Ramírez-López, and Ruhollah Taghizadeh-Mehrjardi.

Publications are predominantly found in these venues: Geoderma, Remote Sensing, European Journal of Soil Science, Scientific Reports, and arXiv (Cornell University).

Best Publications

  • Using data mining to model and interpret soil diffuse reflectance spectra.

    R.A. Viscarra Rossel;T. Behrens

  • A global spectral library to characterize the world’s soil

    R.A. Viscarra Rossel;T. Behrens;E. Ben-Dor;D.J. Brown

  • Soil organic carbon concentrations and stocks on Barro Colorado Island — Digital soil mapping using Random Forests analysis

    R. Grimm;T. Behrens;M. Marker;Helmut Elsenbeer;Helmut Elsenbeer

  • Impacts of species richness on productivity in a large-scale subtropical forest experiment.

    Yuanyuan Huang;Yuxin Chen;Nadia Castro-Izaguirre;Martin Baruffol;Martin Baruffol

  • Multi-scale digital terrain analysis and feature selection for digital soil mapping

    Thorsten Behrens;A-Xing Zhu;A-Xing Zhu;Karsten Schmidt;Thomas Scholten

  • Digital soil mapping using artificial neural networks

    Thorsten Behrens;Helga Förster;Thomas Scholten;Ulrich Steinrücken

  • An approach to computing topographic wetness index based on maximum downslope gradient

    Cheng-Zhi Qin;A-Xing Zhu;Tao Pei;Bao-Lin Li

  • Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

    Ruhollah Taghizadeh-Mehrjardi;Karsten Schmidt;Alireza Amirian-Chakan;Tobias Rentschler

  • Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls

    R. A. Viscarra Rossel;R. A. Viscarra Rossel;J. Lee;J. Lee;T. Behrens;Z. Luo

  • Spatial modelling with Euclidean distance fields and machine learning

    T. Behrens;K. Schmidt;R. A. Viscarra Rossel;P. Gries

  • Digital soil mapping in Germany—a review

    Thorsten Behrens;Thomas Scholten

  • Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia

    Hongfen Teng;Raphael A. Viscarra Rossel;Zhou Shi;Thorsten Behrens

  • Multi-scale digital soil mapping with deep learning.

    Thorsten Behrens;Karsten Schmidt;Robert A. MacMillan;Raphael A. Viscarra Rossel

  • Predictive soil mapping with limited sample data

    A. X. Zhu;J. Liu;F. Du;S. J. Zhang

  • Hyper-scale digital soil mapping and soil formation analysis

    Thorsten Behrens;Karsten Schmidt;Leonardo Ramirez-Lopez;John Gallant

  • Sampling optimal calibration sets in soil infrared spectroscopy

    Leonardo Ramirez-Lopez;Leonardo Ramirez-Lopez;Leonardo Ramirez-Lopez;Karsten Schmidt;Thorsten Behrens;Bas van Wesemael

  • Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models

    Ruhollah Taghizadeh-Mehrjardi;Karsten Schmidt;Norair Toomanian;Brandon Heung

  • Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran

    R. Taghizadeh-Mehrjardi;M. Mahdianpari;F. Mohammadimanesh;T. Behrens

  • Distance and similarity-search metrics for use with soil vis-NIR spectra

    L. Ramirez-Lopez;L. Ramirez-Lopez;T. Behrens;K. Schmidt;R.A. Viscarra Rossel

  • Assessing the USLE crop and management factor C for soil erosion modeling in a large mountainous watershed in Central China

    Sarah Schönbrodt;Patrick Saumer;Thorsten Behrens;Christoph Seeber

Frequent Co-Authors

Thomas Scholten
Thomas Scholten University of Tübingen
Karsten Schmidt
Karsten Schmidt University of Tübingen
Peter Dietrich
Peter Dietrich University of Tübingen
A-Xing Zhu
A-Xing Zhu University of Wisconsin–Madison
R. A. Viscarra Rossel
R. A. Viscarra Rossel Curtin University
Xuezheng Shi
Xuezheng Shi Chinese Academy of Sciences
José Alexandre Melo Demattê
José Alexandre Melo Demattê Universidade de São Paulo
Bas van Wesemael
Bas van Wesemael Université Catholique de Louvain
Zhou Shi
Zhou Shi Zhejiang University
Peter Kühn
Peter Kühn University of Tübingen

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