Soil properties mapping using high resolution remote sensing data in south-western Burkina Faso

Six soil properties- sand, silt, clay, cation exchange capacity, soil organic carbon, nitrogen- have been predicted using remote sensing data in the Dano Catchment (West Africa, Burkina Faso). The predictors topographical and sprectral (Landsat and RapidEye) data. These data are in support of the article entitled: "High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models". The uploaded dataset contains: (1) dataset in text file format, in excel file format as well as the R code use to to train and test the model.<br><br><br><br><br><br>

本数据集针对西非布基纳法索的达诺流域（Dano Catchment），借助遥感数据完成了6项土壤属性的预测，涵盖砂粒、粉粒、黏粒、阳离子交换量（cation exchange capacity）、土壤有机碳与氮素。
预测变量包含地形与光谱（Landsat及RapidEye）数据。
本数据集配套支持的学术论文题为《布基纳法索西南部利用遥感变量实现土壤属性高分辨率制图：机器学习与多元线性回归模型对比》。
上传的数据集包含文本格式、Excel格式的土壤属性数据集，以及用于模型训练与测试的R代码。