Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'

• Main Authors: Siqueira, Rafael, Moquedace, Cássio, Francelino, Márcio, Schaefer, Carlos, Elpídio, Fernandes-Filho
• Format: Dataset
• Language: English
• Published: 2023
• Subjects: Digital Soil Mapping; Antarctica; Soil texture; Antarctic; Antarctic Peninsula; Schaefer; Antarc*
• Online Access: https://zenodo.org/record/8346735; https://doi.org/10.5281/zenodo.8346735

Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article: Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405 The .zip file has the following folders: 1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents 2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction 3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction 4) soil_texture_quantile05: quantile 5% of the soil texture prediction 5) soil_texture_quantile95: quantile 95% of the soil texture prediction