@article{fdi:010079541,
  title = {{I}nfrared spectroscopy approaches support soil organic carbon estimations to evaluate land degradation},
  author = {{B}arth{\`e}s, {B}ernard and {C}hotte, {J}ean-{L}uc},
  editor = {},
  language = {{ENG}},
  abstract = {{S}oil organic carbon ({SOC}) is an acknowledged indicator for land degradation, but conventional determination of {SOC} remains tedious, especially regarding {SOC} stock (in kg {C} m(-2)for a given depth layer), which is the product of {SOC} concentration (g {C} kg(-1)) by volumetric mass (kg dm(-3)). {D}iffuse reflectance infrared spectroscopy ({DRIS}) is a time- and cost-effective approach, which uses calibrations for making predictions. {T}he aim of this paper is to propose an overview of {DRIS} uses for estimating {SOC}, thus land degradation. {I}ndeed, many papers have demonstrated the precision of {DRIS} for quantifying {SOC} concentration, at different scales. {C}urrent development of large soil calibration databases and improvements in spectral data analysis pave the way for ever-wider use of {DRIS}, which should help solving the soil data crisis, regarding {SOC} especially. {T}he increasing availability of portable spectrometers allows {SOC} quantification in the field, which seems particularly promising; but large calibration databases made of soil spectra acquired in the field are difficult to build, while large collections of analyzed soil samples (air-dried, 2-mm sieved) already exist. {S}ome recent studies indicate that {DRIS} can also be used for predicting {SOC} stock, even from sieved samples, which represents an efficient option because determining the volumetric mass is particularly tedious and an obstacle for exactly specifying the role of soils in the global carbon cycle. {I}n short, {DRIS} has strong potential for supporting better evaluation of soil and land degradation, and the availability of spectrometers at increasingly affordable prices reinforces this potential.},
  keywords = {carbon concentration ; carbon stock ; mid-infrared reflectance spectroscopy ; multivariate regression ; near-infrared reflectance spectroscopy},
  booktitle = {},
  journal = {{L}and {D}egradation 2020 {D}evelopment},
  volume = {32},
  numero = {1},
  pages = {310--322},
  ISSN = {1085-3278},
  year = {2020},
  DOI = {10.1002/ldr.3718},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079541},
}