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Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study
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E-mail Addresses: gomez@supagro.inra.fr. International audience. This paper compares predictions of soil organic carbon (SOC) using visible and near infrared reflectance (vis-NIR) hyperspectral proximal and remote sensing data. Soil samples were collected in the Narrabri region, dominated by Vertisols, in north western New South Wales (NSW), Australia. Vis-NIR spectra were collected over this region proximally with an AgriSpec portable spectrometer (350-2500 nm) and remotely from the Hyperion hyperspectral sensor onboard satellite (400-2500 nm). SOC contents were predicted by partial least-squares regression (PLSR) using both the proximal and remote sensing spectra. The spectral resolution of the proximal and remote sensing data did not affect prediction accuracy However, predictions of SOC using the Hyperion spectra were less accurate than those of the Agrispec data resampled to similar resolution as the Hyperion spectra. Finally, the SOC map predicted using Hyperion data shows similarity with field observations. There is potential for the use of hyperspectral remote sensing for predictions of soil organic carbon. The use of these techniques will facilitate the implementation of digital soil mapping.
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