Machine learning and soil sciences: a review aided by machine learning tools
José Padarian et al.
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- Ensemble classifier to support decisions on soil classification S. Motia & S. Reddy 10.1088/1757-899X/1022/1/012044
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- Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data M. Pezij et al. 10.1016/j.envsoft.2020.104756
- Perspectives on validation in digital soil mapping of continuous attributes—A review K. Piikki et al. 10.1111/sum.12694
- Methods and approaches to advance soil macroecology H. White et al. 10.1111/geb.13156
- Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images T. Zhou et al. 10.1016/j.scitotenv.2020.142661
- Trustworthy Predictive Algorithms for Complex Forest System Decision-Making P. Rana & L. Varshney 10.3389/ffgc.2020.587178
- Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil R. Poppiel et al. 10.3390/rs11242905
- Depth-to-bedrock map of China at a spatial resolution of 100 meters F. Yan et al. 10.1038/s41597-019-0345-6
Latest update: 08 May 2021