@article{fdi:010073667,
  title = {{I}mportance of the spatial extent for using soil properties estimated by laboratory {VNIR}/{SWIR} spectroscopy : examples of the clay and calcium carbonate content},
  author = {{G}omez, {C}{\'e}cile and {C}oulouma, {G}.},
  editor = {},
  language = {{ENG}},
  abstract = {{V}isible, near-infrared and short-wave infrared ({VNIR}/{SWIR}, 400-2500 nm) laboratory soil spectrometry is now considered to provide accurate estimations of primary soil properties (clay, calcium carbonate, iron, soil organic carbon, etc.). {T}he performances of primary soil property prediction models are evaluated in regard to figures of merit calculated over calibration and validation databases but not in regard to the spatial extent of predicted soil samples. {T}he objective of this study was to analyze regional model performances for soil property prediction at regional and within-field extents within contrasted representative geopedological situations. {T}his study used a database of 240 soil samples collected over eight vineyard fields located in the {L}anguedoc {R}egion (southern {F}rance) (between 20 and 36 soil samples per field) for which {VNIR}/{SWIR} laboratory spectra were acquired and two soil physico-chemical properties (clay and calcium carbonate) were measured. {S}oil property prediction models were built using the classical partial least square regression ({PLSR}) method, which links the {VNIR}/{SWIR} laboratory spectra and the physico-chemical soil property. {O}ur results showed that both clay and calcium carbonate prediction models are accurate at the regional extent, whereas prediction model performances at the within-field extent depend on the model robustness. {T}herefore, primary soil properties predicted by {VNIR}/{SWIR} laboratory spectra must be used with care at different extents.},
  keywords = {{L}aboratory {VNIR}/{SWIR} spectroscopy ; {C}lay ; {C}alcium carbonate ; {S}oil ; {P}artial least square regression ; {S}patial extent ; {FRANCE} ; {LANGUEDOC}},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {330},
  numero = {},
  pages = {244--253},
  ISSN = {0016-7061},
  year = {2018},
  DOI = {10.1016/j.geoderma.2018.06.006},
  URL = {https://www.documentation.ird.fr/hor/fdi:010073667},
}