@article{fdi:010090926,
  title = {{C}omparison of soil organic carbon stocks predicted using visible and near infrared reflectance ({VNIR}) spectra acquired in situ vs. on sieved dried samples : synthesis of different studies},
  author = {{C}ambou, {A}. and {A}llory, {V}. and {C}ardinael, {R}. and {C}arvalho {V}ieira, {L}. and {B}arth{\`e}s, {B}ernard},
  editor = {},
  language = {{ENG}},
  abstract = {{T}here is increasing demand for data on soil organic carbon ({SOC}) stock ({SSOC}; kg{C} m-2), but the acquisition of such data, which relies on the determination of volumetric {SOC} content ({SOC}v; g{C} dm-3), is often tedious or complex. {V}isible and near infrared reflectance spectroscopy ({VNIRS}) has proven useful for soil characterization, but has rarely been used for direct prediction of {SOC}v. {T}he objectives of this work were: (i) to compare {SOC}v predictions using {VNIR} spectra collected in situ vs. on 2-mm sieved air-dried soil (laboratory conditions), on three sample sets separately (with in situ spectra collected differently for each set); and (ii) to assess {SOC}v prediction in independent validation using laboratory spectra from all sets.{P}redictions of {SOC}v were more accurate using laboratory than in situ spectra for two sets, but not for the third set, where coarse particles content was rather high and variable. {C}onsidering the total set of laboratory spectra, predictions in independent validation (leave-one-site-out) yielded accurate {SOC}v and {SSOC} predictions (standard errors of prediction were 1.9 g{C} dm-3 and 0.36 kg{C} m-2 at 0-30 cm depth, respectively). {T}his result was achieved using local partial least squares regression ({PLSR}), based on spectral neighbors, which noticeably outperformed global {PLSR} (which uses all calibration samples equally), as often reported when using large soil spectral libraries for independent validation. {F}inally, this work demonstrated that {SSOC} could be quantified accurately using a {VNIRS} library built on archive soil samples, which offers important perspectives for {SSOC} accounting.},
  keywords = {{FRANCE}},
  booktitle = {},
  journal = {{S}oil {S}ecurity},
  volume = {5},
  numero = {},
  pages = {100024 [15 ]},
  ISSN = {2667-0062},
  year = {2021},
  DOI = {10.1016/j.soisec.2021.100024},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090926},
}