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Environmental Sciences

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35
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Overview

Karsten Schmidt is affiliated with the University of Tübingen in Germany, where they contribute to research primarily in environmental science and agricultural and biological sciences. Their work is focused on various topics related to soil science and environmental engineering.

The scientist's research spans multiple subfields, including:

  • Environmental Engineering
  • Soil Science
  • Atmospheric Science
  • Civil and Structural Engineering
  • Plant Science

Their main topics of study cover:

  • Soil Geostatistics and Mapping
  • Soil erosion and sediment transport
  • Soil and Unsaturated Flow
  • Soil Carbon and Nitrogen Dynamics
  • Soil Moisture and Remote Sensing
  • Remote Sensing and LiDAR Applications
  • Remote Sensing in Agriculture

Karsten Schmidt has published research in several academic venues. Their frequent publication venues include:

  • Remote Sensing
  • Geoderma
  • Journal of Plant Nutrition and Soil Science
  • Vadose Zone Journal
  • PLoS ONE

Some of the recent papers authored or coauthored by Schmidt are:

  • "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
  • "Enhancing the accuracy of machine learning models using the super learner technique in digital soil mapping," 2021, Geoderma
  • "3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning," 2020, Vadose Zone Journal

Karsten Schmidt frequently collaborates with several researchers, most notably:

  • Thomas Scholten
  • Thorsten Behrens
  • Ruhollah Taghizadeh-Mehrjardi
  • Tobias Rentschler
  • Sandra Teuber

This profile reflects Schmidt's contributions to advancing understanding in soil science and environmental engineering through a combination of machine learning techniques, remote sensing, and geospatial analysis. Their multidisciplinary approach covers significant aspects of soil spatial variability, erosion, moisture, carbon dynamics, and related environmental processes.

Best Publications

  • 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

  • Pedogenesis, permafrost, and soil moisture as controlling factors for soil nitrogen and carbon contents across the Tibetan Plateau

    Frank Baumann;Jin-Sheng He;Karsten Schmidt;Peter Kühn

  • The spectrum-based learner: A new local approach for modeling soil vis–NIR spectra of complex datasets

    Leonardo Ramirez-Lopez;Leonardo Ramirez-Lopez;Thosten Behrens;Karsten Schmidt;Antoine Stevens

  • 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

  • On the combined effect of soil fertility and topography on tree growth in subtropical forest ecosystems - a study from SE China

    Thomas Scholten;Philipp Goebes;Peter Kühn;Steffen Seitz

  • Spatial modelling with Euclidean distance fields and machine learning

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

  • Multi-scale digital soil mapping with deep learning.

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

  • Pedogenic and microbial interrelations to regional climate and local topography: New insights from a climate gradient (arid to humid) along the Coastal Cordillera of Chile

    Nadine Bernhard;Lisa-Marie Moskwa;Karsten Schmidt;Ralf A. Oeser

  • 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

  • Enhancing the accuracy of machine learning models using the super learner technique in digital soil mapping

    Ruhollah Taghizadeh-Mehrjardi;Nikou Hamzehpour;Nikou Hamzehpour;Maryam Hassanzadeh;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

  • The ConMap approach for terrain-based digital soil mapping

    T. Behrens;K. Schmidt;A. X. Zhu;A. X. Zhu;T. Scholten

  • Instance selection and classification tree analysis for large spatial datasets in digital soil mapping

    Karsten Schmidt;Thorsten Behrens;Thomas Scholten

  • Bryophyte-dominated biological soil crusts mitigate soil erosion in an early successional Chinese subtropical forest

    Steffen Seitz;Martin Nebel;Martin Nebel;Philipp Goebes;Kathrin Käppeler

  • Spatio-temporal land use dynamics and soil organic carbon in Swiss agroecosystems

    Felix Stumpf;Armin Keller;Karsten Schmidt;Andreas Mayr

  • Spatial and Temporal Dynamics of Hillslope-Scale Soil Moisture Patterns: Characteristic States and Transition Mechanisms

    Edoardo Martini;Ute Wollschläger;Simon Kögler;Thorsten Behrens

Frequent Co-Authors

Thomas Scholten
Thomas Scholten University of Tübingen
Thorsten Behrens
Thorsten Behrens University of Tübingen
Peter Kühn
Peter Kühn University of Tübingen
Peter Dietrich
Peter Dietrich University of Tübingen
Peter Kuhn
Peter Kuhn University of Southern California
Jin-Sheng He
Jin-Sheng He Peking University
Xuezheng Shi
Xuezheng Shi Chinese Academy of Sciences
Helge Bruelheide
Helge Bruelheide Martin Luther University Halle-Wittenberg
A-Xing Zhu
A-Xing Zhu University of Wisconsin–Madison
Keping Ma
Keping Ma Chinese Academy of Sciences

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