@article{fdi:010078940,
  title = {{P}rediction of tropical volcanic soil organic carbon stocks by visible-near- and mid-infrared spectroscopy},
  author = {{A}llo, {M}. and {T}odoroff, {P}. and {J}ameux, {M}. and {S}tern, {M}. and {P}aulin, {L}. and {A}lbrecht, {A}lain},
  editor = {},
  language = {{ENG}},
  abstract = {{A}ssessing soil organic carbon ({SOC}) stocks is a methodological issue for {SOC} monitoring at regional scale but crucial for global agendas of {SOC} sequestration to mitigate climate change and reduce food insecurity. {T}he '4 per mille {I}nitiative: {S}oils for {F}ood {S}ecurity and {C}limate', highlighted agricultural soil as a major lever for climate action and the need to assess {SOC} stock at different spatial and temporal scale. {I}nfrared spectroscopy appeared as a promising tool to address this methodological issue. {T}his work aimed to evaluate the potential of visible-near-infrared ({VNIR}) and mid-infrared ({MIR}) spectroscopic measurement methods to predict {SOC} stock and its variables ({SOC} content and bulk density) in tropical volcanic soils of '{L}a {R}eunion' island. {T}he diversity of agricultural soils of '{L}a {R}eunion' was captured in the sample set (n = 95) with soil orders such as {A}ndosols, {C}ambisols and {F}erralsols. {P}artial least squares regressions ({PLSR}) with leave-one-out cross validation were used to build prediction models. {W}ith {RPD} higher than 2, the present study showed good prediction accuracy of models by {MIR} and {VNIR} spectroscopy of {SOC} content, bulk density and {SOC} stock for measurements in the laboratory or in the field. {A}ccurate and direct {SOC} stock predictions were achieved on dried and sieved soil samples with {MIR} spectroscopy ({RPD} = 2.25; {R}-cv(2) = 0.80; {RMSE}cv = 0.69 {K}g{C} m(-2)) and {VNIR} spectroscopy ({RPD} = 2.74; {R}-cv(2) = 0.87; {RMSE}cv = 0.61 {K}g{C} m(-2)) but also directly on cores in the field with {VNIR} spectroscopy ({RPD} = 3.29; {R}-cv(2) = 0.91; {RMSE}cv = 0.51 {K}g{C} m(-2)). {T}his unexpected ability to predict directly {SOC} stocks by infrared spectroscopy can be partly explained by the high {SOC} content coupled with the large variation of {SOC} content and bulk density, providing a large range for those variables, and then a higher predictability. {Y}et these results questioned the underlying drivers of the bulk density and {SOC} stock, both being largely physical parameters supposed to be hardly predictable by infrared spectroscopy. {A}nalyses of mean spectra and regression coefficients, combined with amorphous product ({A}lo, {F}eo, {S}io, {A}lp, {F}ep) prediction models, demonstrated that these were detected in the spectra and were the drivers of {SOC} stock accurate predictions by infrared spectroscopy. {S}hort-range ordered minerals, especially allophanes, appeared as proxy of the bulk density, and amorphous products such as {A}lp and {F}ep indicated the presence of organo-mineral complexes involved in {SOC} storage. {S}uch promising results for {SOC} stock predictions from near- and mid-infrared volcanic soil spectra, confirmed that {VNIR} and {MIR} diffuse reflectance spectroscopy are an appropriate tool, rapid, low cost and non-destructive, to study {SOC} stocks in tropical volcanic soils. {U}pscaling of {SOC} stocks across the agricultural soils of the island is now just a step ahead, following external validation with additional data to validate the robustness of the prediction models.},
  keywords = {{B}ulk density ; {I}nfrared spectroscopy ; {PLSR} models ; {SOC} stock ; {SRO} ; minerals ; {V}olcanic soils ; {REUNION}},
  booktitle = {},
  journal = {{C}atena},
  volume = {189},
  numero = {},
  pages = {art. 104452 [14 p.]},
  ISSN = {0341-8162},
  year = {2020},
  DOI = {10.1016/j.catena.2020.104452},
  URL = {https://www.documentation.ird.fr/hor/fdi:010078940},
}