@article{fdi:010084516,
  title = {{S}atellite multi-sensor data fusion for soil clay mapping based on the spectral index and spectral bands approaches},
  author = {{G}asmi, {A}. and {G}omez, {C}{\'e}cile and {C}hehbouni, {A}bdelghani and {D}hiba, {D}. and {E}lfil, {H}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}ntegrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. {T}his study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. {T}o this end, multispectral satellite images acquired by various sensors (i.e., {L}andsat-5 {T}hematic {M}apper ({TM}), {L}andsat-8 {O}perational {L}and {I}mager ({OLI}), {A}dvanced {S}paceborne {T}hermal {E}mission and {R}eflection {R}adiometer ({ASTER}), and {S}entinel2-{M}ulti{S}pectral {I}nstrument ({S}2-{MSI})) have been used to assess their potential in identifying bare soil pixels over an area in northeastern {T}unisia, the {L}ebna and {C}hiba catchments. {A} spectral index image and a spectral bands image are generated for each satellite sensor (i.e., {TM}, {OLI}, {ASTER}, and {S}2-{MSI}). {T}hen, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. {T}he resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the {M}ultilayer {P}erceptron with backpropagation learning algorithm ({MLP}-{BP}) method. {T}he results suggest that for clay content prediction: (i) the spectral bands' images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. {S}oil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.},
  keywords = {spectral index ; spectral band ; multispectral remote sensing ; multi-sensors data fusion ; digital soil mapping ; clay content},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {14},
  numero = {5},
  pages = {1103 [22 ]},
  year = {2022},
  DOI = {10.3390/rs14051103},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084516},
}