Vis-NIR Spectral data for Texture and Soil Organic Carbon prediction - La Peyne - France

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This dataset provides 148 Vis-NIR reflectance spectra measured on soil samples collected from the La Peyne catchment (Hérault, France).

Soil samples

148 soil samples were collected in 2009 on the La Peyne catchment (Herault, France). All of these samples are composed of five sub-samples that were collected at the topsoil layer at random locations within a 10-m-wide square centered on the geographical position of a sampling plot. For each sample, approximately 1000 g of soil was extracted. The initial samples were air-dried and sieved with a 2-mm sieve before the laboratory analysis.

Spectral acquisitions

The reflectance spectra of the 148 soil samples were measured in the laboratory in the 350–2500 nm wavelength domain with an ASD pro FR portable spectroradiometer. The spectral resolution ranged from 3 nm (at 700 nm) to 10 nm (in the SWIR). The spectral sampling interval varies between 1.4 nm from 350 to 1000 nm and 2 nm from 1000 to 2500 nm. Data are resampled to 1 nm output values. ASD measurements have a SNR better than 1000 at its original spectral resolution. The measurements were made using a black room to control irradiance conditions. A white Spectralon panel (12 × 12 cm labsphere, North Sutton, USA) provided the absolute reflectance factors for laboratory measurements. Thirty individual reflectance measurements were acquired for each soil sample.
Further explanations about the methodology can be found in the Peyne2009_ReadMe.txt file.

SOC and texture determination

Granulometry was measured using pipette method and organic carbon was determined after dry combustion minus the inorganic C. Analyses were performed by the Soil Analysis Laboratory of the INRA (Arras, France), which is accredited by the French Ministry of Agriculture.

Projects & Fundings

This dataset is part of the project Digisoil-Hymed, granted by the French National research agency (ANR) (ANR-08-BLAN-0284-01) (2009 - 2012). It has been also supported by IRD and INRAE.
This dataset is also part of the ALAMOD project (2024-2027), financed by the PEPR FairCarbon .