Dataset and R-codes for Publication: "Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" (accepted version)

In this upload you can find the R-codes and dataset for the Publication: 

"Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" by Simon Oberholzer Laura Summerauer, Markus Steffens and Chinwe Ifejika Speranza accepted in SOIL (https://doi.org/10.5194/egusphere-2023-1087)

To reproduce the results of the manuscript, start with the R-file “ResampleandPreprocess.R” to prepare spectral data and then continue with the R-file “PLSRmodelling_GroupedCV.R” for the modelling.

The R-files “control_train.R” and “rep_grouped_kfold_CV.R” are helper-functions for the grouped cross-validation. The file metadata.csv explains the column names in the spectral data (spcdata.RDS).

本上传包包含已被期刊SOIL录用的论文配套R代码与数据集，论文题为《低碳酸盐土壤可见-近红外模型的最优性能——瑞士田间试验》，作者为Simon Oberholzer、Laura Summerauer、Markus Steffens及Chinwe Ifejika Speranza，论文DOI链接为https://doi.org/10.5194/egusphere-2023-1087。若需复现该论文的研究结果，请先运行R脚本"ResampleandPreprocess.R"以预处理光谱数据，随后运行R脚本"PLSRmodelling_GroupedCV.R"开展建模工作。R脚本"control_train.R"与"rep_grouped_kfold_CV.R"为分组交叉验证的辅助函数。元数据（Metadata）文件metadata.csv对光谱数据文件spcdata.RDS中的各列名称进行了解释。