@article{fdi:010086399,
  title = {{I}mpact of atmospheric correction methods parametrization on soil organic carbon estimation based on hyperion hyperspectral data},
  author = {{M}ruthyunjaya, {P}. and {S}hetty, {A}. and {U}mesh, {P}. and {G}omez, {C}{\'e}cile},
  editor = {},
  language = {{ENG}},
  abstract = {{V}isible {N}ear infrared and {S}hortwave {I}nfrared ({VNIR}/{SWIR}, 400-2500 nm) remote sensing data is becoming a tool for topsoil properties mapping, bringing spatial information for environmental modeling and land use management. {T}hese topsoil properties estimates are based on regression models, linking a key topsoil property to {VNIR}/{SWIR} reflectance data. {T}herefore, the regression model's performances depend on the quality of both topsoil property analysis (measured on laboratory over-ground soil samples) and {B}ottom-of-{A}tmosphere ({BOA}) {VNIR}/{SWIR} reflectance which are retrieved from {T}op-{O}f-{A}tmosphere radiance using atmospheric correction ({AC}) methods. {T}his paper examines the sensitivity of soil organic carbon ({SOC}) estimation to {BOA} images depending on two parameters used in {AC} methods: aerosol optical depth ({AOD}) in the {FLAASH} ({F}ast {L}ine-of-{S}ight {A}tmospheric {A}nalysis of {S}pectral {H}ypercubes) method and water vapor ({WV}) in the {ATCOR} ({AT}mospheric {COR}rection) method. {T}his work was based on {E}arth {O}bserving-1 {H}yperion {H}yperspectral data acquired over a cultivated area in {A}ustralia in 2006. {H}yperion radiance data were converted to {BOA} reflectance using seven values of {AOD} (from 0.2 to 1.4) and six values of {WV} (from 0.4 to 5 cm), in {FLAASH} and {ATCOR}, respectively. {T}hen a {P}artial {L}east {S}quares regression ({PLSR}) model was built from each {H}yperion {BOA} data to estimate {SOC} over bare soil pixels. {T}his study demonstrated that the {PLSR} models were insensitive to the {AOD} variation used in the {FLAASH} method, with {R}-cv(2) and {RMSE}cv of 0.79 and 0.4%, respectively. {T}he {PLSR} models were slightly sensitive to the {WV} variation used in the {ATCOR} method, with {R}-cv(2) ranging from 0.72 to 0.79 and {RMSE}cv ranging from 0.41 to 0.47. {R}egardless of the {AOD} values, the {PLSR} model based on the best parametrization of the {ATCOR} model provided similar {SOC} prediction accuracy to {PLSR} models using the {FLAASH} method. {V}ariation in {AOD} using the {FLAASH} method did not impact the identification of bare soil pixels coverage which corresponded to 82.35% of the study area, while a variation in {WV} using the {ATCOR} method provided a variation of bare soil pixels coverage from 75.04 to 84.04%. {T}herefore, this work recommends (1) the use of the {FLAASH} {AC} method to provide {BOA} reflectance values from {E}arth {O}bserving-1 {H}yperion {H}yperspectral data before {SOC} mapping or (2) a careful selection of the {WV} parameter when using {ATCOR}.},
  keywords = {{H}yperion ; hyperspectral imagery ; atmospheric corrections ; soil organic carbon ; {ATCOR} ; {FLAASH} ; mapping},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {14},
  numero = {20},
  pages = {5117 [24 ]},
  year = {2022},
  DOI = {10.3390/rs14205117},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086399},
}