@article{fdi:010044032,
  title = {{S}oil organic carbon prediction by hyperspectral remote sensing and field vis-{NIR} spectroscopy : an {A}ustralian case study},
  author = {{G}omez, {C}{\'e}cile and {R}ossel, {R}. {A}. {V}. and {M}c{B}ratney, {A}. {B}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his paper compares predictions of soil organic carbon ({SOC}) using visible and near infrared reflectance (vis-{NIR}) hyperspectral proximal and remote sensing data. {S}oil samples were collected in the {N}arrabri region, dominated by {V}ertisols, in north western {N}ew {S}outh {W}ales ({NSW}), {A}ustralia. {V}is-{NIR} spectra were collected over this region proximally with an {A}gri{S}pec portable spectrometer (350-2500 nm) and remotely from the {H}yperion 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. {T}he spectral resolution of the proximal and remote sensing data did not affect prediction accuracy {H}owever, predictions of {SOC} using the {H}yperion spectra were less accurate than those of the {A}grispec data resampled to similar resolution as the {H}yperion spectra. {F}inally, the {SOC} map predicted using {H}yperion data shows similarity with field observations. {T}here is potential for the use of hyperspectral remote sensing for predictions of soil organic carbon. {T}he use of these techniques will facilitate the implementation of digital soil mapping.},
  keywords = {{V}isible and near infrared reflectance ; {S}pectroscopy ; {S}oil ; {O}rganic carbon ; {H}yperspectral satellite ; {C}arbon accounting},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {146},
  numero = {3-4},
  pages = {403--411},
  ISSN = {0016-7061},
  year = {2008},
  DOI = {10.1016/j.geoderma.2008.06.011},
  URL = {https://www.documentation.ird.fr/hor/fdi:010044032},
}