Articles | Volume 5, issue 1
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.
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.
Multi-source data integration for soil mapping using deep learning
Alexandre M. J.-C. Wadoux
CORRESPONDING AUTHOR
Soil Geography and Landscape Group, Wageningen University & Research, Wageningen, the Netherlands
José Padarian
Sydney Institute of Agriculture, the University of Sydney, Sydney, Australia
Budiman Minasny
Sydney Institute of Agriculture, the University of Sydney, Sydney, Australia
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69 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
- Soil texture prediction with automated deep convolutional neural networks and population-based learning O. Omondiagbe et al. 10.1016/j.geoderma.2023.116521
- Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al. 10.1111/ejss.13463
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- A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information J. Wang et al. 10.1016/j.scitotenv.2023.166112
- Consequences of spatial structure in soil–geomorphic data on the results of machine learning models D. Kim et al. 10.1080/10106049.2023.2245381
- A two-step soil modeling approach by integrating pedological classification in digital mapping with non-stationary geostatistics F. Abbaszadeh Afshar et al. 10.1080/15324982.2024.2317399
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- Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping S. Chen et al. 10.1016/j.geoderma.2023.116467
- Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks O. Odebiri et al. 10.1016/j.scitotenv.2022.161150
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- High-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management M. van Doorn et al. 10.1016/j.geoderma.2024.116838
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- An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning algorithms X. Meng et al. 10.1016/j.rse.2022.113166
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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 M. Ließ & A. Sakhaee 10.3390/agriculture14081230
- Semi-supervised learning for the spatial extrapolation of soil information R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2022.116094
- Digital soil mapping and assessment for Australia and beyond: A propitious future R. Searle et al. 10.1016/j.geodrs.2021.e00359
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- National-scale spatial prediction of soil organic carbon and total nitrogen using long-term optical and microwave satellite observations in Google Earth Engine T. Zhou et al. 10.1016/j.compag.2023.107928
- 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
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- 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
- 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
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. 10.5194/essd-16-2941-2024
- 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
Latest update: 20 Nov 2024