@article{fdi:010058877,
  title = {{D}etecting, correcting and interpreting the biases of measured soil profile data : a case study in the {C}ap {B}on {R}egion ({T}unisia)},
  author = {{C}iampalini, {R}ossano and {L}agacherie, {P}. and {G}omez, {C}{\'e}cile and {G}runberger, {O}livier and {H}amrouni, {M}. {H}. and {M}ekki, {I}. and {R}ichard, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he spatial sets of soil profiles that have been collected for these past 70 years over the world constitute a major source of soil information that are indispensable for operational applications of {D}igital {S}oil {M}apping. {H}owever, significant biases between soil profile datasets issued from different soil surveys could occur because of differences in survey methods (field data collection, laboratory analysis, etc.) or in sampling dates. {A} pre-processing is therefore needed to detect and remove these biases and then obtain adequate inputs for digital soil-mapping models. {S}uch a pre-processing of legacy soil profile datasets is proposed in this study. {T}he procedure is applied to different sets of geo-referenced legacy soil profiles available in the {C}ap {B}on {R}egion ({N}orthern {T}unisia) and use a "reference" spatial sampling of soil surface data that fits with modern standards of soil analysis and was recently collected. {T}he general approach includes three steps: i) define the comparison area (i.e. the intersection of the spatial samplings), ii) compare the distributions of soil profiles properties with the references using a conditional stochastic simulation algorithm and decide whether they are different iii) if needed, apply a correction algorithm to remove the detected biases. {V}arious implementations of this approach were undertaken and tested on theoretical and real soil sampling.},
  keywords = {{L}egacy data ; {D}igital soil mapping ; {S}oil properties ; {S}oil spatial variability ; {TUNISIE}},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {192},
  numero = {},
  pages = {68--76},
  ISSN = {0016-7061},
  year = {2013},
  DOI = {10.1016/j.geoderma.2012.07.022},
  URL = {https://www.documentation.ird.fr/hor/fdi:010058877},
}