@article{fdi:010091255,
  title = {{O}perational sampling designs for poorly accessible areas based on a multi-objective optimization method},
  author = {{D}umont, {M}. and {B}runel, {G}. and {T}resson, {P}aul and {N}espoulous, {J}. and {B}oukcim, {H}. and {D}ucousso, {M}. and {B}oivin, {S}. and {T}augourdeau, {O}. and {T}isseyre, {B}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}ampling for {D}igital {S}oil {M}apping is an expensive and time-constrained operation. {I}t is crucial to consider these limitations in practical situations, particularly when dealing with large-scale areas that are remote and poorly accessible. {T}o address this issue, several authors have proposed methods based on cost constraints optimization to reduce the travel time between sampling sites. {T}hese methods focused on optimizing the access cost associated to each sample site, but have not explicitly addressed field work time required for the whole sampling campaign. {H}ence, an estimation of fieldwork time is of great interest to assists soil surveyors in efficiently planning and executing optimized field surveys. {T}he goal of this study is to propose, implement and test a new method named {M}ulti-{O}bjective {O}perational {S}ampling ({MOOS}), to minimize sampling route time, while ensuring that sample representativeness of the area is maintained. {I}t offers multiple optimal sampling designs, allowing practitioners to select the most suitable option based on their desired sample quality and available time resources. {T}he proposed sampling method is derived from conditioned {L}atin {H}ypercube sampling (c{LHS}) that optimizes both total field work time (travel time and on-site sampling time) and sample representativeness of the study area (c{LHS} objective function). {T}he use of a multi-objective optimization algorithm ({NSGA} {II}) provides a variety of optimal sampling designs with varying sample size. {T}he sampling route time computation is based on an access cost map derived from remote sensing images and expert annotation data. {A} least-cost algorithm is used to create a time matrix allowing precise evaluation of the time required to connect each pair of sites and thus determine an optimal path. {T}he proposed method has been implemented and tested on sampling for p{HH}2{O} mapping within a 651 points kilometric grid in the northern part of {S}audi {A}rabia, where soil analyses were conducted over a 1,069 km2 area. {MOOS} method was compared to two other common approaches: classical c{LHS} and c{LHS} incorporating access cost. {T}he performance of each method was assessed with the cross-validated {RMSE} and sampling route time in days. {R}esults show that the {MOOS} method outperforms the two others in terms of sampling route time, especially with increasing sample size, gaining up to 1 day of work for the presented case study. {I}t still ensures a relevant map accuracy and sample representativeness when compared to the two methods. {T}his approach yields promising outcomes for field sampling in digital soil mapping. {B}y simultaneously optimizing both sample representativeness and cost constraints, it holds potential as a valuable decision support tool for soil surveyors facing sampling designs in poorly accessible areas.},
  keywords = {{S}oil sampling ; c{LHS} ; {F}ield constraints ; {P}areto optimality ; {D}igital {S}oil ; {M}apping ; {ARABIE} {SAOUDITE}},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {445},
  numero = {},
  pages = {116888 [12 ]},
  ISSN = {0016-7061},
  year = {2024},
  DOI = {10.1016/j.geoderma.2024.116888},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091255},
}