Dataset from "Comparison of field and imaging spectroscopy to optimize soil organic carbon and nitrogen estimation in field laboratory conditions"

The dataset is associated with the published manuscript below: Mahmud, A., Luotamo, M., Karhu, K., Pellikka, P., Tuure, J., & Heiskanen, J. Comparison of field and imaging spectroscopy to optimize soil organic carbon and nitrogen estimation in field laboratory conditions. Catena, 243, 108180. DOI: 10.1016/j.catena.2024.108180This dataset contains soil spectral measurements acquired using an SVC HR-1024i field spectroradiometer and corresponding laboratory-measured (Leco CN828) soil carbon and nitrogen reference data. The dataset was produced for studying the use of field spectroscopy to estimate soil organic carbon and soil nitrogen in agricultural soil samples collected from the Taita Hills region, Taita Taveta County, Kenya. The spectral data are provided as raw SVC HR-1024i signature files (.sig). The dataset “Soil spectra.zip” contains 575 .sig files exported from the spectroradiometer. The accompanying CSV file contains the sample-level reference information and includes the following columns: sample_code: unique code assigned to each soil samplespectra_name: spectra name or unique identifier generated from the spectroradiometercarbon: laboratory-measured soil carbon valuenitrogen: laboratory-measured soil nitrogen value The spectral measurements were collected from air-dried soil samples under controlled field-laboratory conditions using the SVC HR-1024i field spectroradiometer. Files included: Soil spectra.zip — archive containing 575 SVC HR-1024i .sig spectral files Soil_reference_data.csv — Excel sheet containing sample_code, spectra_name, carbon, and nitrogen columns Metadata.docx   Abstract of the published manuscriptRegenerative agriculture (RA) aims to improve soil health, water retention capacity, and resilience through sustainable regeneration and retention of soil organic carbon (SOC) and soil nitrogen (SN). Although laboratory analysis offers a reliable method for measuring SOC and SN, access to such facilities can be limited in remote areas and inconvenient, particularly if a large number of samples need to be analyzed. To address this, we compared two hyperspectral sensors, SVC HR-1024i field spectroradiometer (FS) and Specim IQ imaging spectrometer (IS), to estimate SOC and SN using 157 soil samples collected from agricultural sites in Taita Hills, Kenya. Reference SOC and SN content (%) were analyzed in the laboratory, and spectral measurements and images of the air-dried soil samples were generated using protocols suitable for field laboratories. Finally, we employed partial least squares regression (PLSR), Gaussian process regression (GPR), and least absolute shrinkage and selection operator (LASSO) to estimate SOC and SN from the preprocessed soil spectra over different wavelength ranges. Both SOC and SN modeling over the full wavelength and shortwave-infrared (SWIR) regions achieved considerably better predictive accuracy than visible to near-infrared regions. These results suggest that FS with the SWIR region is best suited for SOC and SN estimation to support the planning and monitoring needs of RA initiatives.