@article{fdi:010086673,
  title = {{S}oil order knowledge as a driver in soil properties estimation from {V}is-{NIR} spectral data : case study from northern {K}arnataka ({I}ndia)},
  author = {{D}harumarajan, {S}. and {G}omez, {C}{\'e}cile and {L}alitha, {M}. and {K}alaiselvi, {B}. and {V}asundhara, {R}. and {H}egde, {R}.},
  editor = {},
  language = {{ENG}},
  abstract = {{V}isible and near-infrared ({V}is-{NIR}, 350-2500 nm) laboratory spectroscopy has been proven to provide soil properties estimations, such as clay or organic carbon ({OC}). {H}owever, the performances of such estimations may be dependent on pedological and spectral similarities between calibration and validation datasets. {T}he objective of this study was to analyze how the soil order knowledge can be used to increase regression models performance for soil properties estimation. {F}or this purpose, {R}andom {F}orest regression models were calibrated and validated from both regional database (called regional models) and subsets stratified by soil order from the regional database (called soil-order models). {T}he regional database contained 482 soil samples belonging to four soil orders ({A}lfisols, {V}ertisols, {I}nceptisols and {E}ntisols) and associated with {V}is-{NIR} laboratory spectra and six soil properties: {OC}, sand, silt, clay, cation exchange capacity ({CEC}) and p{H}. {F}irst, regional models provided i) high accuracy of some soil properties estimations when considering the regional strategy in the validation step (e.g., {R}2val of 0.74, 0.76 and 0.74 for clay, {CEC} and sand, respectively) but ii) modest accuracy of these same soil properties when considering subsets stratified by soil order from the regional database in validation step (e.g., {R}2val of 0.48, 0.58 and 0.38 over {V}ertisol for clay, {CEC} and sand, respectively). {S}o the estimation accuracy appreciation is highly depending on the validation database as there is a risk of over-appreciated prediction accuracies at the soil-order scale when figures of merit are based on a regional validation dataset. {S}econd, this work highlighted that the benefit of a soil-order model compared to a regional model for calibration depends on both soil property and soil order. {S}o no recommendations for choosing between both models for calibration may be given. {F}inally, while {V}is-{NIR} laboratory spectroscopy is becoming a popular way to estimate soil physico-chemical properties worldwide, this work highlights that this technique may be used discreetly depending on the targeted scale and targeted soil type.},
  keywords = {{V}isible near-infrared ; {R}egional model ; {S}oil-order model ; {R}andom forest ; {S}oil variability ; {P}rediction accuracy ; {INDE}},
  booktitle = {},
  journal = {{G}eoderma {R}egional},
  volume = {32},
  numero = {},
  pages = {e00596 [11 ]},
  ISSN = {2352-0094},
  year = {2023},
  DOI = {10.1016/j.geodrs.2022.e00596},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086673},
}