Data and script for "A Novel Methodological Framework for Predicting and Mapping Agriculture-Related Soil Attributes Using Euclidean Distance, Regular Grids, and Machine Learning Algorithms"
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Article data and scripts: "A Novel Methodological Framework for Predicting and Mapping Agriculture-Related Soil Attributes Using Euclidean Distance, Regular Grids, and Machine Learning Algorithms" First Unzip the Necessary_folders.zip filesOpen the Dead_Kriging.Rproj file in R or Rstudio software The scripts should be to be run in a described sequence:00_Create_covariate01_create_data_frame_analysis02_predict_xy02_predict_2x2y_center02_predict_xy_2502_predict_xy_4902_predict_xy_10002_predict_IDW02_predict_Kriging Scripts numbered 02 can be run in any order; however, to obtain accurate time measurements, it is always recommended to restart the computer and run the process on a PC that is not being used by other software. It is necessary to install the library packages "dplyr", "caret" "sp", "sf", "doParallel", "terra", "randomForest", "Cubist","earth","kernlab", "e1071", "automap","hydroGOF","raster" and "gstat" The program ran successfully on R version 4.4.2. The data are free to use and please cite also the article:Veloso, G. V., de Mello, D. C., Silva, L. V., Fernandes-Filho, E. I., Rosas, J. T. F., de Oliveira Mello, F. A., ... & Demattê, J. A. (2026). Strategies for predictive digital soil mapping by geophysical, remote sensing and machine learning approaches. Catena, 264, 109822.
文章配套数据与脚本：《基于欧氏距离、规则网格与机器学习算法的农业相关土壤属性预测与制图新型方法学框架》 首先解压Necessary_folders.zip压缩包。在R或RStudio软件中打开Dead_Kriging.Rproj项目文件。脚本需按照指定顺序运行：00_Create_covariate、01_create_data_frame_analysis、02_predict_xy、02_predict_2x2y_center、02_predict_xy_25、02_predict_xy_49、02_predict_xy_100、02_predict_IDW、02_predict_Kriging。 编号为02的脚本可按任意顺序运行；但若需获取精准的运行耗时测量结果，建议始终重启计算机，并在未被其他软件占用的PC上执行该流程。 需安装以下R扩展包：dplyr、caret、sp、sf、doParallel、terra、randomForest、Cubist、earth、kernlab、e1071、automap、hydroGOF、raster与gstat。 该程序在R 4.4.2版本中可正常运行。 本数据集可免费使用，引用时请标注以下文章：Veloso, G. V., de Mello, D. C., Silva, L. V., Fernandes-Filho, E. I., Rosas, J. T. F., de Oliveira Mello, F. A., 等（2026）. 基于地球物理、遥感与机器学习方法的预测性数字土壤制图策略. Catena, 264, 109822.
