@article{fdi:010061877,
  title = {{C}haracterization and space-time downscaling of the inundation extent over the inner {N}iger delta using giems and modis data},
  author = {{A}ires, {F}. and {P}apa, {F}abrice and {P}rigent, {C}. and {C}retaux, {J}. {F}. and {M}uriel, {B}. {N}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he objective in this work is to develop downscaling methodologies to obtain a long time record of inundation extent at high spatial resolution based on the existing low spatial resolution results of the {G}lobal {I}nundation {E}xtent from {M}ulti-{S}atellites ({GIEMS}) dataset. {I}n semiarid regions, high-spatial-resolution a priori information can be provided by visible and infrared observations from the {M}oderate {R}esolution {I}maging {S}pectroradiometer ({MODIS}). {T}he study concentrates on the {I}nner {N}iger {D}elta where {MODIS}-derived inundation extent has been estimated at a 500-m resolution. {T}he space-time variability is first analyzed using a principal component analysis ({PCA}). {T}his is particularly effective to understand the inundation variability, interpolate in time, or fill in missing values. {T}wo innovative methods are developed (linear regression and matrix inversion) both based on the {PCA} representation. {T}hese {GIEMS} downscaling techniques have been calibrated using the 500-m {MODIS} data. {T}he downscaled fields show the expected space-time behaviors from {MODIS}. {A} 20-yr dataset of the inundation extent at 500 m is derived from this analysis for the {I}nner {N}iger {D}elta. {T}he methods are very general and may be applied to many basins and to other variables than inundation, provided enough a priori high-spatial-resolution information is available. {T}he derived high-spatial-resolution dataset will be used in the framework of the {S}urface {W}ater {O}cean {T}opography ({SWOT}) mission to develop and test the instrument simulator as well as to select the calibration validation sites (with high space-time inundation variability). {I}n addition, once {SWOT} observations are available, the downscaled methodology will be calibrated on them in order to downscale the {GIEMS} datasets and to extend the {SWOT} benefits back in time to 1993.},
  keywords = {{P}rincipal components analysis ; {R}ivers ; {I}nterpolation schemes ; {R}emote sensing ; {L}and surface ; {MALI}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrometeorology},
  volume = {15},
  numero = {1},
  pages = {171--192},
  ISSN = {1525-755{X}},
  year = {2014},
  DOI = {10.1175/jhm-d-13-032.1},
  URL = {https://www.documentation.ird.fr/hor/fdi:010061877},
}