@article{fdi:010086806,
  title = {{T}opsoil clay content mapping in croplands from {S}entinel-2 data : influence of atmospheric correction methods across a season time series},
  author = {{G}omez, {C}{\'e}cile and {V}audour, {E}. and {F}eret, {J}. {B}. and de {B}oissieu, {F}. and {D}harumarajan, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{R}ecent studies demonstrated the capability of {S}entinel-2 ({S}2) data to estimate topsoil properties and highlighted the sensitivity of these estimations to soil surface conditions depending on the {S}2 acquisition date. {T}hese estimations are based on {B}ottom of {A}tmosphere ({BOA}) reflectance images, obtained from {T}op of {A}tmosphere ({TOA}) reflectance values using {A}tmospheric {C}orrection ({AC}) methods. {AC} of optical satellite imagery is an important pre-processing stage before estimating biophysical variables, and several {AC} methods are currently operational to perform such conversion. {T}his study aims at evaluating the sensitivity of topsoil clay content estimation to atmospheric corrections along an {S}2 time series. {T}hree {AC} methods were tested ({MAJA}, {S}en2{C}or, and {L}a{SRC}) on a time series of eleven {S}entinel-2 images acquired over a cultivated region in {I}ndia ({K}arnataka {S}tate) from {F}ebruary 2017 to {J}une 2017. {M}ultiple {L}inear {R}egression models were built using clay content analyzed from topsoil samples collected over bare soil pixels and corresponding {BOA} reflectance data. {T}he influence of {AC} methods was also analysed depending on bare soil pixels selections based on two spectral indices and several thresholds: the normalized difference vegetation index ({NDVI} below 0.25, 0.3 and 0.35) and the combination of {NDVI} (below 0.3) and {N}ormalized {B}urned {R}atio 2 index ({NBR}2 below 0.09, 0.12 and 0.15) for masking green vegetation, crop residues and soil moisture. {F}irst, this work highlighted that regression models were more sensitive to acquisition date than to {AC} method, suggesting that soil surface conditions were more influent on clay content estimation models than variability among atmospheric corrections. {S}econdly, no {AC} method outperformed other methods for clay content estimation, and the performances of regression models varied mostly depending on the bare soil pixels selection used to calibrate the regression models. {F}inally, differences in {BOA} reflectance among {AC} methods for the same acquisition date led to differences in {NDVI} and {NBR}2, and hence in bare soil coverage identification and subsequent topsoil clay content mapping coverage. {T}hus, selecting {S}2 images with respect to the acquisition date appears to be a more critical step than selecting an {AC} method, to ensure optimal retrieval accuracy when mapping topsoil properties assumed to be relatively stable over time.},
  keywords = {{C}lay content ; {S}entinel-2 ; {A}tmospheric correction ; {M}ultiple linear regression ; {S}oil property mapping ; {I}ndia ; {INDE}},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {423},
  numero = {},
  pages = {115959 [15 ]},
  ISSN = {0016-7061},
  year = {2022},
  DOI = {10.1016/j.geoderma.2022.115959},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086806},
}