@article{fdi:010093565,
  title = {{P}rediction of soil organic carbon stock along layers and profiles using {V}is-{NIR} laboratory spectroscopy},
  author = {{D}harumarajan, {S}. and {G}omez, {C}{\'e}cile and {K}usuma, {C}. {G}. and {V}asundhara, {R}. and {K}alaiselvi, {B}. and {L}alitha, {M}. and {H}egde, {R}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}here is a growing need to estimate soil organic carbon ({SOC}) stocks at both local and global scales. {T}his study explores the use of {V}isible-{N}ear {I}nfrared ({V}is-{NIR}) laboratory spectroscopy as an alternative to traditional wet chemistry methods for {SOC} stock estimation. {T}wo approaches were tested: an indirect method, which uses {P}artial {L}east {S}quares {R}egression ({PLSR}) models to predict {SOC} content and bulk density separately and then multiplies them by measured layer depth; and a direct method, where {PLSR} models predict {SOC} stock per layer directly. {T}he estimates were then aggregated to calculate the total {SOC} stock per profile. {W}e evaluated both approaches using 361 samples from 84 soil profiles collected across three villages in {K}erala, {I}ndia. {T}wo calibration scenarios were tested: (i) non-clustering, where 75 % of the dataset was used for calibration and 25 % for validation, and (ii) clustering, where models were trained on samples from two villages and validated on the third. {T}he results showed that the indirect approach consistently outperformed the direct approach, both at the layer and profile scale. {T}he non-clustering calibration scenario provided variable accuracy, with {R}2val values ranging from 0.52 (direct approach) to 0.70 (indirect approach). {T}he clustering scenario produced more variable results depending on the calibration set used. {O}verall, this study confirms that {V}is-{NIR} spectroscopy is a promising, rapid, nondestructive, and cost-effective method for {SOC} stock estimation. {H}owever, scaling up its application across agricultural landscapes will require substantial data collection and further methodological refinement.},
  keywords = {{SOC} stock ; {S}oil profile ; {P}rediction ; {C}lustering ; {C}alibration ; {I}ndirect approach ; {INDE}},
  booktitle = {},
  journal = {{C}atena},
  volume = {257},
  numero = {},
  pages = {109150 [13 p.]},
  ISSN = {0341-8162},
  year = {2025},
  DOI = {10.1016/j.catena.2025.109150},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093565},
}