Articles | Volume 8, issue 2
https://doi.org/10.5194/soil-8-587-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-8-587-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
22 Sep 2022
Original research article |
| 22 Sep 2022
| 22 Sep 2022
Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms
Ali Sakhaee
CORRESPONDING AUTHOR
Thünen Institute of Climate-Smart Agriculture, Braunschweig,
Germany
Anika Gebauer
Department Soil System Science, Helmholtz Centre for Environmental
Research – UFZ, Halle (Saale), Germany
Mareike Ließ
Department Soil System Science, Helmholtz Centre for Environmental
Research – UFZ, Halle (Saale), Germany
Thünen Institute of Climate-Smart Agriculture, Braunschweig,
Germany
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Cited
20 citations as recorded by crossref.
- Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils T. Broeg et al. 10.3390/rs15040876
- Optimized bare soil compositing for soil organic carbon prediction of topsoil croplands in Bavaria using Landsat S. Zepp et al. 10.1016/j.isprsjprs.2023.06.003
- 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 M. Ließ & A. Sakhaee 10.3390/agriculture14081230
- Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia A. Chinilin & I. Savin 10.1016/j.ejrs.2023.07.007
- Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland T. Broeg et al. 10.1016/j.geoderma.2024.116850
- Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units M. Ließ 10.3390/agriculture12111784
- A detailed mapping of soil organic matter content in arable land based on the multitemporal soil line coefficients and neural network filtering of big remote sensing data D. Rukhovich et al. 10.1016/j.geoderma.2024.116941
- Prediction of Water Infiltration of Three Types of Soil with Machine Learning in the Sahuayo River Basin M. Lupián-Machuca et al. 10.1155/2024/5555105
- Inclusion of fractal dimension in four machine learning algorithms improves the prediction accuracy of mean weight diameter of soil A. Sarkar et al. 10.1016/j.ecoinf.2022.101959
- A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany A. Säurich et al. 10.1007/s10661-024-12640-z
- SSL-SoilNet: A Hybrid Transformer-Based Framework With Self-Supervised Learning for Large-Scale Soil Organic Carbon Prediction N. Kakhani et al. 10.1109/TGRS.2024.3446042
- Maximizing the carbon sink function of paddy systems in China with machine learning J. Wang et al. 10.1016/j.scitotenv.2023.168542
- Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils A. Sakhaee et al. 10.3390/agriculture14081298
- A high-resolution map of soil organic carbon in cropland of Southern China B. Hu et al. 10.1016/j.catena.2024.107813
- Developing a digital mapping of soil organic carbon on a national scale using Sentinel-2 and hybrid models at varying spatial resolutions X. Ji et al. 10.1016/j.ecolind.2024.112654
- Land use and soil property effects on aggregate stability assessed by three different slaking methods C. Poeplau et al. 10.1111/ejss.13549
- Uncertainty Quantification of Soil Organic Carbon Estimation from Remote Sensing Data with Conformal Prediction N. Kakhani et al. 10.3390/rs16030438
- A machine learning approach to map the potential agroecological complexity in an indigenous community of Colombia C. Ojeda Riaños et al. 10.1016/j.jenvman.2024.122655
- Applying machine learning to model radon using topsoil geochemistry M. Banríon et al. 10.1016/j.apgeochem.2023.105790
- Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus F. Kaya et al. 10.3390/agriculture12071062
19 citations as recorded by crossref.
- Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils T. Broeg et al. 10.3390/rs15040876
- Optimized bare soil compositing for soil organic carbon prediction of topsoil croplands in Bavaria using Landsat S. Zepp et al. 10.1016/j.isprsjprs.2023.06.003
- 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 M. Ließ & A. Sakhaee 10.3390/agriculture14081230
- Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia A. Chinilin & I. Savin 10.1016/j.ejrs.2023.07.007
- Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland T. Broeg et al. 10.1016/j.geoderma.2024.116850
- Modeling the Agricultural Soil Landscape of Germany—A Data Science Approach Involving Spatially Allocated Functional Soil Process Units M. Ließ 10.3390/agriculture12111784
- A detailed mapping of soil organic matter content in arable land based on the multitemporal soil line coefficients and neural network filtering of big remote sensing data D. Rukhovich et al. 10.1016/j.geoderma.2024.116941
- Prediction of Water Infiltration of Three Types of Soil with Machine Learning in the Sahuayo River Basin M. Lupián-Machuca et al. 10.1155/2024/5555105
- Inclusion of fractal dimension in four machine learning algorithms improves the prediction accuracy of mean weight diameter of soil A. Sarkar et al. 10.1016/j.ecoinf.2022.101959
- A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany A. Säurich et al. 10.1007/s10661-024-12640-z
- SSL-SoilNet: A Hybrid Transformer-Based Framework With Self-Supervised Learning for Large-Scale Soil Organic Carbon Prediction N. Kakhani et al. 10.1109/TGRS.2024.3446042
- Maximizing the carbon sink function of paddy systems in China with machine learning J. Wang et al. 10.1016/j.scitotenv.2023.168542
- Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils A. Sakhaee et al. 10.3390/agriculture14081298
- A high-resolution map of soil organic carbon in cropland of Southern China B. Hu et al. 10.1016/j.catena.2024.107813
- Developing a digital mapping of soil organic carbon on a national scale using Sentinel-2 and hybrid models at varying spatial resolutions X. Ji et al. 10.1016/j.ecolind.2024.112654
- Land use and soil property effects on aggregate stability assessed by three different slaking methods C. Poeplau et al. 10.1111/ejss.13549
- Uncertainty Quantification of Soil Organic Carbon Estimation from Remote Sensing Data with Conformal Prediction N. Kakhani et al. 10.3390/rs16030438
- A machine learning approach to map the potential agroecological complexity in an indigenous community of Colombia C. Ojeda Riaños et al. 10.1016/j.jenvman.2024.122655
- Applying machine learning to model radon using topsoil geochemistry M. Banríon et al. 10.1016/j.apgeochem.2023.105790
Latest update: 02 Nov 2024
Short summary
As soil carbon has become a key component of climate-smart agriculture, the demand for high-resolution maps has increased drastically. Meanwhile, machine learning algorithms are becoming more widely used and are opening up new solutions in soil mapping. This paper shows which algorithms perform best, how soil inventory data can be most efficiently used for digital soil mapping, and the different available options and methods to derive high-resolution soil carbon data at the large regional scale.
As soil carbon has become a key component of climate-smart agriculture, the demand for...
