D3.3_20230919_ProbeField_Aligned_Spectra_V1

A dataset of 470 laboratory-acquired soil spectra has been aligned using the white Lucky Bay sands as an internal soil standard (ISS). Soil samples were collected in Sweden by SLU, in Italy by CNR, in France by INRAE, and in Poland by IUNG. Spectra were acquired in the laboratory on dry soil samples, and each spectrum is associated with a laboratory measurement of soil organic carbon (SOC). Each partner also scanned the ISS using the same instrument as for the soil samples, allowing the computation of a correction factor for each instrument. Along with the main dataset, an explanatory document and an R script are provided. The R script can be used to align spectral data acquired with different instruments. Within the provided file "CF_lb" you can find five correction factors corresponding to five instruments. These factors were computed using the Lucky Bay spectra scanned by each instrument (ISS) and the master Lucky Bay spectrum acquired at the CSIRO laboratory. When using this dataset, please cite the following article: Castaldi, F., Stenberg, B., Liebisch, F., Metzger, K., Ben-Dor, E., Knadel, M., Koganti, T., Wetterlind, J., Barbetti, R., Debaene, G., Klumpp, K., Lippl, M., Lorenzetti, R., Lozano Fondon, C., Sanden, T., Schaumberger, A., & Stajnko, D. (2025). Estimating soil organic carbon using field VNIR-SWIR spectroscopy and existing soil spectral libraries: Mitigating heterogeneity, roughness and moisture effects. Smart Agricultural Technology, 12, 101353. https://doi.org/10.1016/J.ATECH.2025.101353本数据集包含470条实验室采集的土壤光谱数据，以白色Lucky Bay砂作为土壤内标（internal soil standard, ISS）完成光谱对齐。土壤样品分别由瑞典SLU、意大利CNR、法国INRAE以及波兰IUNG采集。光谱均在实验室中对干燥土壤样品进行采集，每条光谱均对应一项土壤有机碳（soil organic carbon, SOC）的实验室测量值。 各参与方均使用与土壤样品扫描相同的仪器对土壤内标（ISS）进行扫描，由此可计算每台仪器的校正因子。 本数据集除主数据集外，还附带一份说明文档与一段R脚本。该R脚本可用于对齐不同仪器获取的光谱数据。在提供的"CF_lb"文件中，可获取对应5台仪器的5项校正因子，这些因子由各仪器扫描得到的Lucky Bay光谱（即ISS）与CSIRO实验室获取的Lucky Bay主光谱计算得到。 使用本数据集时，请引用以下论文： Castaldi, F., Stenberg, B., Liebisch, F., Metzger, K., Ben-Dor, E., Knadel, M., Koganti, T., Wetterlind, J., Barbetti, R., Debaene, G., Klumpp, K., Lippl, M., Lorenzetti, R., Lozano Fondon, C., Sanden, T., Schaumberger, A., & Stajnko, D. (2025). 利用野外可见-近红外/短波红外（VNIR-SWIR）光谱技术与现有土壤光谱库估算土壤有机碳：缓解异质性、粗糙度与湿度影响. 智能农业技术, 12, 101353. https://doi.org/10.1016/J.ATECH.2025.101353