@article{fdi:010071235,
  title = {{T}he role of atmospheric correction algorithms in the prediction of soil organic carbon from {H}yperion data},
  author = {{M}inu, {S}. and {S}hetty, {A}. and {M}inasny, {B}. and {G}omez, {C}{\'e}cile},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n this study, the role of atmospheric correction algorithm in the prediction of soil organic carbon ({SOC}) from spaceborne hyperspectral sensor ({H}yperion) visible near-infrared (vis-{NIR}, 400-2500 nm) data was analysed in fields located in two different geographical settings, viz. {K}arnataka in {I}ndia and {N}arrabri in {A}ustralia. {A}tmospheric correction algorithms, (1) {AT}mospheric {COR}ection ({ATCOR}), (2) {F}ast {L}ine-of-sight {A}tmospheric {A}nalysis of {S}pectral {H}ypercubes ({FLAASH}), (3) 6{S}, and (4) {QU}ick {A}tmospheric {C}orrection ({QUAC}), were employed for retrieving spectral reflectance from radiance image. {T}he results showed that {ATCOR} corrected spectra coupled with partial least square regression prediction model, produced the best {SOC} prediction performances, irrespective of the study area. {C}omparing the results across study areas, {K}arnataka region gave lower prediction accuracy than {N}arrabri region. {T}his may be explained due to difference in spatial arrangement of field conditions. {A} spectral similarity comparison of atmospherically corrected {H}yperion spectra of soil samples with field-measured vis-{NIR} spectra was performed. {A}mong the atmospheric correction algorithms, {ATCOR} corrected spectra found to capture the pattern in soil reflectance curve near 2200 nm. {ATCOR}'s finer spectral sampling distance in shortwave infrared wavelength region compared to other models may be the main reason for its better performance. {T}his work would open up a great scope for accurate {SOC} mapping when future hyperspectral missions are realized.},
  keywords = {{INDE} ; {AUSTRALIE}},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {R}emote {S}ensing},
  volume = {38},
  numero = {23},
  pages = {6435--6456},
  ISSN = {0143-1161},
  year = {2017},
  DOI = {10.1080/01431161.2017.1354265},
  URL = {https://www.documentation.ird.fr/hor/fdi:010071235},
}