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Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring

Sensors (Basel, Switzerland), 2024-01, Vol.24 (3), p.849

2024

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Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales

Geoderma, 2019-01, Vol.333, p.149-162

2019

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Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PloS one, 2016-04, Vol.11 (4), p.e0153673-e0153673

2016

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Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units

Agriculture (Basel), 2022-11, Vol.12 (11), p.1784

Ließ, Mareike

2022

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Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and Random Forest models

Geoderma, 2012-01, Vol.170, p.70-79

2012

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On-the-Go Vis-NIR Spectroscopy for Field-Scale Spatial-Temporal Monitoring of Soil Organic Carbon

Agriculture (Basel), 2023-08, Vol.13 (8), p.1611

2023

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Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain

Catena (Giessen), 2017-07, Vol.154, p.73-84

2017

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Payments by modelled results: A novel design for agri-environmental schemes

Land use policy, 2021-03, Vol.102, p.105230, Article 105230

2021

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Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest

Geoderma, 2018-04, Vol.316, p.100-114

2018

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Sampling for regression-based digital soil mapping: Closing the gap between statistical desires and operational applicability

Spatial statistics, 2015-08, Vol.13, p.106-122

Ließ, Mareike

2015

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Deep Learning with a Multi-Task Convolutional Neural Network to Generate a National-Scale 3D Soil Data Product: The Particle Size Distribution of the German Agricultural Soil Landscape

Agriculture (Basel), 2024-08, Vol.14 (8), p.1230

2024

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Quantitative Evaluation of Soil Functions: Potential and State

Frontiers in environmental science, 2019-10, Vol.7

2019

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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

Frontiers in environmental science, 2021-06, Vol.9

2021

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Optimisation in machine learning: An application to topsoil organic stocks prediction in a dry forest ecosystem

Geoderma, 2019-11, Vol.354, p.113846, Article 113846

2019

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Environmental drivers of spatial patterns of topsoil nitrogen and phosphorus under monsoon conditions in a complex terrain of South Korea

PloS one, 2017-08, Vol.12 (8), p.e0183205-e0183205

2017

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Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms

Soil, 2022-09, Vol.8 (2), p.587-604

2022

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Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation

Frontiers in Soil Science (Online), 2022-01, Vol.1

2022

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On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization

Soil, 2022-08, Vol.8 (2), p.541-558

2022

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Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes

Applied and Environmental Soil Science, 2014, Vol.2014 (2014), p.1-12

2014

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Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils

Agriculture (Basel), 2024-08, Vol.14 (8), p.1298

2024

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Error propagation in spectrometric functions of soil organic carbon

Soil, 2019-09, Vol.5 (2), p.275-288

2019

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Making use of the World Reference Base diagnostic horizons for the systematic description of the soil continuum — Application to the tropical mountain soil-landscape of southern Ecuador

Catena (Giessen), 2012-10, Vol.97, p.20-30

2012

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Functional soil-landscape modelling to estimate slope stability in a steep Andean mountain forest region

Geomorphology (Amsterdam, Netherlands), 2011-09, Vol.132 (3), p.287-299

2011

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Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning

Soil, 2020-06, Vol.6 (1), p.215-229

2020

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The Sloping Mire Soil-Landscape of Southern Ecuador: Influence of Predictor Resolution and Model Tuning on Random Forest Predictions

Applied and Environmental Soil Science, 2014, Vol.2014 (2014), p.1-10

2014

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Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring

Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units

Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and Random Forest models

On-the-Go Vis-NIR Spectroscopy for Field-Scale Spatial-Temporal Monitoring of Soil Organic Carbon

Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain

Payments by modelled results: A novel design for agri-environmental schemes

Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest

Sampling for regression-based digital soil mapping: Closing the gap between statistical desires and operational applicability

Deep Learning with a Multi-Task Convolutional Neural Network to Generate a National-Scale 3D Soil Data Product: The Particle Size Distribution of the German Agricultural Soil Landscape

Quantitative Evaluation of Soil Functions: Potential and State

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

Optimisation in machine learning: An application to topsoil organic stocks prediction in a dry forest ecosystem

Environmental drivers of spatial patterns of topsoil nitrogen and phosphorus under monsoon conditions in a complex terrain of South Korea

Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms

Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation

On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization

Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes

Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils

Error propagation in spectrometric functions of soil organic carbon

Making use of the World Reference Base diagnostic horizons for the systematic description of the soil continuum — Application to the tropical mountain soil-landscape of southern Ecuador

Functional soil-landscape modelling to estimate slope stability in a steep Andean mountain forest region

Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning

The Sloping Mire Soil-Landscape of Southern Ecuador: Influence of Predictor Resolution and Model Tuning on Random Forest Predictions

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