@article{fdi:010067251,
  title = {{N}ational calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy},
  author = {{C}lairotte, {M}. and {G}rinand, {C}lovis and {K}ouakoua, {E}rnest and {T}h{\'e}bault, {A}. and {S}aby, {N}.{P}.{A}. and {B}ernoux, {M}artial and {B}arth{\`e}s, {B}ernard},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his study presents the potential of infrared diffuse reflectance spectroscopy ({DRS}) to predict soil organic carbon ({SOC}) content. {A} large national soil library was used, including about 3800 samples collected at two soil depths (0-30 and 30-50 cm) using a 16 x 16 km plot grid over the {F}rench metropolitan territory (552,000 km(2)). {R}eflectance spectra were collected in the laboratory using visible and near infrared ({VNIR}), near infrared ({NIR}) and mid infrared ({MIR}) spectrophotometers. {T}he soil data library was broken down into calibration and validation sets through sample selection at random or based on spectral representativeness. {T}he calibration intensity was investigated in order to assess the optimum number of calibration samples required to obtain accurate models. {P}redictions were achieved using global or local partial least square regression ({PLSR}) built using {VNIR}, {NIR} and {MIR} spectra separately or in combination. {L}ocal {PLSR} uses only calibration samples that are spectral neighbors of each validation sample, thus builds one model per validation sample. {M}odel performance was evaluated on the validation set based on the standard error of prediction ({SEP}), the ratio of performance to deviation ({RPD}v), and the ratio of performance to interquartile range ({RPIQ}(v)). {U}sing all calibration samples, the global {PLSR} model provided the most precise predictions of {SOC} content with the {MIR} spectra, then with the {NIR} spectra, and less accurate predictions with the {VNIR} spectra ({SEP} = 2.6, 4.4 and 4.8 g kg(-1), {RPD}v = 2.7, 23 and 1.5, and {RPIQ}(v) = 33, 22 and 1.9, respectively). {T}he combination of spectral libraries did not improve model performance noticeably. {L}ocal {PLSR} provided better models than global {PLSR}, allowing accurate predictions with only 30% of the calibration set, whatever the spectral library ({RPD}, and {RPIQ}(v) > 2.0). {O}ptimum calibration intensity was estimated at about 60% for {MIR} spectra with both global and local {PLSR}, 30-40% for {VNIR} and {NIR} spectra with global {PLSR}, but 50% for {VNIR} spectra and 70% for {NIR} spectra with local {PLSR}. {T}he most accurate models, which were obtained using the {MIR} spectra and local {PLSR} with calibration intensity higher than 50%, allowed very good {SOC} determination for the most frequent {F}rench soils ({SEP} < 2 g kg(-1)). {T}his highlights the potential of infrared {DRS} for national {SOC} monitoring, provided that calibration database is strengthened with samples from less frequent soil types.},
  keywords = {{FRANCE}},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {276},
  numero = {},
  pages = {41--52},
  ISSN = {0016-7061},
  year = {2016},
  DOI = {10.1016/j.geoderma.2016.04.021},
  URL = {https://www.documentation.ird.fr/hor/fdi:010067251},
}