Articles | Volume 5, issue 1
SOIL, 5, 107–119, 2019
https://doi.org/10.5194/soil-5-107-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
SOIL, 5, 107–119, 2019
https://doi.org/10.5194/soil-5-107-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
22 Mar 2019
Original research article
| 22 Mar 2019
Multi-source data integration for soil mapping using deep learning
Alexandre M. J.-C. Wadoux et al.
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Cited
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- An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran M. Garajeh et al. 10.1016/j.scitotenv.2021.146253
- Digital soil mapping and assessment for Australia and beyond: A propitious future R. Searle et al. 10.1016/j.geodrs.2021.e00359
- Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects O. Odebiri et al. 10.1007/s10661-021-09561-6
- Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation J. Lu et al. 10.3390/rs13173502
- Spatial statistics and soil mapping: A blossoming partnership under pressure G. Heuvelink & R. Webster 10.1016/j.spasta.2022.100639
- Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al. 10.1016/j.geoderma.2020.114794
- Measurement error-filtered machine learning in digital soil mapping S. van der Westhuizen et al. 10.1016/j.spasta.2021.100572
- Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. 10.1016/j.catena.2021.105299
- Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher et al. 10.3389/fenvs.2021.809995
- Monitoring soil organic carbon in alpine soils using in situ vis‐NIR spectroscopy and a multilayer perceptron S. Chen et al. 10.1002/ldr.3497
- 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 M. Ließ et al. 10.3389/fenvs.2021.692959
- Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches S. Zare et al. 10.1016/j.catena.2021.105702
- Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al. 10.5194/essd-12-2517-2020
- Sample design optimization for soil mapping using improved artificial neural networks and simulated annealing S. Shao et al. 10.1016/j.geoderma.2022.115749
- Soil parent material prediction through satellite multispectral analysis on a regional scale at the Western Paulista Plateau, Brazil F. Mello et al. 10.1016/j.geodrs.2021.e00412
- Mapping soil profile depth, bulk density and carbon stock in Scotland using remote sensing and spatial covariates M. Aitkenhead & M. Coull 10.1111/ejss.12916
- Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data O. Odebiri et al. 10.1016/j.geoderma.2022.115695
- Grand Challenges in Pedometrics-AI Research S. Grunwald 10.3389/fsoil.2021.714323
- Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information C. Kirkwood et al. 10.1007/s11004-021-09988-0
- Digital soil mapping and GlobalSoilMap. Main advances and ways forward D. Arrouays et al. 10.1016/j.geodrs.2020.e00265
- High resolution middle eastern soil attributes mapping via open data and cloud computing R. Poppiel et al. 10.1016/j.geoderma.2020.114890
- Geomorphometry today I. Florinsky 10.35595/2414-9179-2021-2-27-394-448
- Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran M. Emadi et al. 10.3390/rs12142234
- Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data N. Tziolas et al. 10.3390/rs12091389
- Spatiotemporal modelling of soil organic matter changes in Jiangsu, China between 1980 and 2006 using INLA-SPDE X. Sun et al. 10.1016/j.geoderma.2020.114808
- Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al. 10.1016/j.geoderma.2021.115567
- Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space R. Taghizadeh-Mehrjardi et al. 10.3390/rs12071095
- Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning G. Feng et al. 10.1155/2022/3287117
- Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review S. Lamichhane et al. 10.1016/j.geoderma.2019.05.031
- Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2020.114552
- Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al. 10.1016/j.earscirev.2020.103359
- Basic and deep learning models in remote sensing of soil organic carbon estimation: A brief review O. Odebiri et al. 10.1016/j.jag.2021.102389
- Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux 10.1016/j.geoderma.2019.05.012
33 citations as recorded by crossref.
- An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran M. Garajeh et al. 10.1016/j.scitotenv.2021.146253
- Digital soil mapping and assessment for Australia and beyond: A propitious future R. Searle et al. 10.1016/j.geodrs.2021.e00359
- Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects O. Odebiri et al. 10.1007/s10661-021-09561-6
- Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation J. Lu et al. 10.3390/rs13173502
- Spatial statistics and soil mapping: A blossoming partnership under pressure G. Heuvelink & R. Webster 10.1016/j.spasta.2022.100639
- Predicting soil properties in 3D: Should depth be a covariate? Y. Ma et al. 10.1016/j.geoderma.2020.114794
- Measurement error-filtered machine learning in digital soil mapping S. van der Westhuizen et al. 10.1016/j.spasta.2021.100572
- Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. 10.1016/j.catena.2021.105299
- Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher et al. 10.3389/fenvs.2021.809995
- Monitoring soil organic carbon in alpine soils using in situ vis‐NIR spectroscopy and a multilayer perceptron S. Chen et al. 10.1002/ldr.3497
- 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 M. Ließ et al. 10.3389/fenvs.2021.692959
- Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches S. Zare et al. 10.1016/j.catena.2021.105702
- Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al. 10.5194/essd-12-2517-2020
- Sample design optimization for soil mapping using improved artificial neural networks and simulated annealing S. Shao et al. 10.1016/j.geoderma.2022.115749
- Soil parent material prediction through satellite multispectral analysis on a regional scale at the Western Paulista Plateau, Brazil F. Mello et al. 10.1016/j.geodrs.2021.e00412
- Mapping soil profile depth, bulk density and carbon stock in Scotland using remote sensing and spatial covariates M. Aitkenhead & M. Coull 10.1111/ejss.12916
- Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data O. Odebiri et al. 10.1016/j.geoderma.2022.115695
- Grand Challenges in Pedometrics-AI Research S. Grunwald 10.3389/fsoil.2021.714323
- Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information C. Kirkwood et al. 10.1007/s11004-021-09988-0
- Digital soil mapping and GlobalSoilMap. Main advances and ways forward D. Arrouays et al. 10.1016/j.geodrs.2020.e00265
- High resolution middle eastern soil attributes mapping via open data and cloud computing R. Poppiel et al. 10.1016/j.geoderma.2020.114890
- Geomorphometry today I. Florinsky 10.35595/2414-9179-2021-2-27-394-448
- Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran M. Emadi et al. 10.3390/rs12142234
- Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data N. Tziolas et al. 10.3390/rs12091389
- Spatiotemporal modelling of soil organic matter changes in Jiangsu, China between 1980 and 2006 using INLA-SPDE X. Sun et al. 10.1016/j.geoderma.2020.114808
- Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al. 10.1016/j.geoderma.2021.115567
- Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space R. Taghizadeh-Mehrjardi et al. 10.3390/rs12071095
- Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning G. Feng et al. 10.1155/2022/3287117
- Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review S. Lamichhane et al. 10.1016/j.geoderma.2019.05.031
- Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2020.114552
- Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al. 10.1016/j.earscirev.2020.103359
- Basic and deep learning models in remote sensing of soil organic carbon estimation: A brief review O. Odebiri et al. 10.1016/j.jag.2021.102389
- Using deep learning for multivariate mapping of soil with quantified uncertainty A. Wadoux 10.1016/j.geoderma.2019.05.012
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