@article{fdi:010084729,
  title = {{P}rediction of soil carbon and nitrogen contents using visible and near infrared diffuse reflectance spectroscopy in varying salt-affected soils in {S}ine {S}aloum ({S}enegal)},
  author = {{C}ambou, {A}urelie and {B}arthes, {B}ernard and {M}oulin {E}smard, {P}atricia and {C}hauvin, {L}. and {F}aye, {E}. and {M}asse, {D}ominique and {C}hevallier, {T}iphaine and {C}hapuis {L}ardy, {L}ydie},
  editor = {},
  language = {{ENG}},
  abstract = {{S}oil organic carbon ({C}) and nitrogen ({N}) contents have an essential role in soil fertility, but they may be affected by salinity, which is especially responsible for land degradation in arid and semiarid regions. {T}he objective of this work was to study the ability of visible and near infrared diffuse reflectance spectroscopy ({VNIRS}) to predict soil {C} and {N} contents and electrical conductivity ({EC}, a proxy for soil salinity) in variably salt-affected topsoils of the {S}ine {S}aloum region ({S}enegal). {D}ifferent calibration procedures and spectral pretreatments were compared, and variable log-transformation usefulness was evaluated for prediction optimization. {P}redictions involved three calibration procedures: global partial least squares regression ({PLSR}), which used all calibration samples similarly; locally weighted (local) {PLSR}, with target samples predicted individually by giving higher weight to closest calibration spectra; and global {PLSR} per salinity class, after spectral discrimination of these classes. {P}redictions were performed with possible spectrum pretreatments (e.g., derivatization) and variable decimal log-transformation.& nbsp;{T}he study was performed on 311 topsoil samples (0-25 cm depth), either unsalted to slightly salty ({S}alt-, {EC} <=2 m{S} cm(-1); 262 samples) or medium to highly salty ({S}alt+, {EC} > 2 m{S} cm(-1); 49 samples). {S}oil salinity was accurately discriminated using spectra: in validation, 100% and 95% of {S}alt-and {S}alt+ samples were correctly assigned on average, respectively. {B}est {C} and {N} content predictions were achieved after log-transformation using calibration by class ({R}-{VAL}(2) = 0.87) and local calibration ({R}-{VAL}(2) = 0.77), respectively; best {EC} prediction was achieved without log-transformation using global calibration ({R}-{VAL}(2) = 0.90). {T}his suggested {C} and {N} content predictions were affected by salinity; log{C} and log{N} distributions were almost symmetrical, hence log-transformation usefulness, while log{EC} distribution was very asymmetrical. {N}o pretreatment yielded systematically good predictions; nevertheless, first-order derivative using 31-point gap often yielded good predictions, and second-order derivatives poor results.},
  keywords = {{E}lectrical conductivity ; {S}pectral pretreatment ; {P}artial least squares regression ; {L}ocally weighted regression ; {D}iscriminant analysis ; {S}emiarid ; {W}est {A}frica ; {SENEGAL} ; {ZONE} {SEMIARIDE} ; {SINE} {SALOUM}},
  booktitle = {},
  journal = {{C}atena},
  volume = {212},
  numero = {},
  pages = {106075 [14 ]},
  ISSN = {0341-8162},
  year = {2022},
  DOI = {10.1016/j.catena.2022.106075},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084729},
}