@article{fdi:010085494,
  title = {{S}entinel-2 images to assess soil surface characteristics over a rainfed {M}editerranean cropping system},
  author = {{G}omez, {C}{\'e}cile and {A}boubacar, {M}.{S}. and {I}enco, {D}. and {F}eurer, {D}enis and {J}enhaoui, {Z}akia and {R}afla, {A}. and {T}eisseire, {M}. and {B}ailly, {J}.{S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}oil surface characteristics ({SSC}s) are of high importance for water infiltration processes in crop fields. {A}s {SSC}s present strong spatiotemporal variability influenced by climatic conditions and agricultural practices, their monitor has already been explored by using {UAV} images and multispectral remote sensing. {H}owever, each technique has encountered difficulties characterizing this spatiotemporal variability. {T}he objective of this work was to explore the potential of {S}entinel-2 images to assess three {SSC}s - the green vegetation fraction, dry vegetation fraction and physical soil surface structure - at several dates. {T}his work explored two approaches for classifying these three {SSC}s from five {S}entinel-2 images acquired from {A}ugust to {N}ovember 2016. {I}n the "single-date" approach, a {R}andom {F}orest {C}lassifier ({RFC}) model was trained to classify one {SSC}j from a dataset extracted from one {S}entinel-2 image i (model noted {RF}_sdi,{SSC}j). {I}n the "multi-date" approach, a {RFC} model was trained to classify one {SSC}j from a dataset extracted from the five {S}entinel-2 images (noted {RF}_md{SSC}j). {T}he classification analysis showed that i) the {RF}_sdi,{SSC}j and {RF}_md{SSC}j models provided accurate performances (overall accuracy > 0.79) regardless of the studied {SSC}j and the tested {S}entinel-2 image, ii) the {RF}_sdi,{SSC}j model did not allow the classification of {SSC} classes that were not observed on the studied date, and iii) the {RF}_md{SSC}j model allowed the classification of all {SSC} classes observed in the five {S}entinel-2 images. {T}his indicated that several {S}entinel-2 images can favourably be used to increase knowledge of spatiotemporal representation of {SSC}s by extending results of infrequent, localized and cumbersome field work.},
  keywords = {{ZONE} {MEDITERRANEENNE} ; {TUNISIE} ; {CAP} {BON} ; {KAMECH}},
  booktitle = {},
  journal = {{C}atena},
  volume = {213},
  numero = {},
  pages = {106152 [14 ]},
  ISSN = {0341-8162},
  year = {2022},
  DOI = {10.1016/j.catena.2022.106152},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085494},
}