Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"

Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbon distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions. Siqueira, R.G., Moquedace, C.M., Fernandes-Filho, E.I., Schaefer, C.E.G.R., Francelino, M.R., Sacramento, I.F., Michel, R.F.M., 2024. Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica. Catena 235, 107677. https://doi.org/10.1016/j.catena.2023.107677 The .zip file contains the following folders: 1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution 2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction 4) soil_chemistry_quantile05: quantile 5% of the soil attributes prediction 5) soil_chemistry_quantile95: quantile 95% of the soil attributes prediction本数据集涵盖基于机器学习（Machine Learning）方法、遗留土壤数据集与环境协变量，针对南极土壤的盐基总量（Bases sum）、氢铝复合体（H+Al，潜在酸度）、pH值、有效磷（phosphorous）、残留磷（P-rem）、钠与总有机碳（total organic carbon）的空间分布开展建模与预测所得到的成果。其中分位数数据与预测区间数据可反映预测结果的空间不确定性。 引用文献：Siqueira, R.G.、Moquedace, C.M.、Fernandes-Filho, E.I.、Schaefer, C.E.G.R.、Francelino, M.R.、Sacramento, I.F.、Michel, R.F.M.，2024年。《基于随机森林（Random Forest）的主要土壤化学属性建模与预测：以机器学习解析南极土壤-环境关联关系》。Catena 235，107677。https://doi.org/10.1016/j.catena.2023.107677 该压缩包包含以下文件夹： 1) soil_chemistry_antarctica：存储南极土壤化学属性分布数据的文件夹 2) soil_chemistry_prediction_interval：存储土壤属性预测的90%预测区间（即95%分位数与5%分位数的差值）对应的不确定性数据的文件夹 4) soil_chemistry_quantile05：存储土壤属性预测的5%分位数数据的文件夹 5) soil_chemistry_quantile95：存储土壤属性预测的95%分位数数据的文件夹