@article{fdi:010091048,
  title = {{A} localized particle filtering approach to advance flood frequency estimation at large scale using satellite synthetic aperture radar image collection and hydrodynamic modelling},
  author = {{Z}ingaro, {M}. and {H}ostache, {R}enaud and {C}hini, {M}. and {C}apolongo, {D}. and {M}atgen, {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his study describes a method that combines synthetic aperture radar ({SAR}) data with shallow-water modeling to estimate flood hazards at a local level. {T}he method uses particle filtering to integrate flood probability maps derived from {SAR} imagery with simulated flood maps for various flood return periods within specific river sub-catchments. {W}e tested this method in a section of the {S}evern {R}iver basin in the {UK}. {O}ur research involves 11 {SAR} flood observations from {ENVISAT} {ASAR} images, an ensemble of 15 particles representing various pre-computed flood scenarios, and 4 masks of spatial units corresponding to different river segmentations. {E}mpirical results yield maps of maximum flood extent with associated return periods, reflecting the local characteristics of the river. {T}he results are validated through a quantitative comparison approach, demonstrating that our method improves the accuracy of flood extent and scenario estimation. {T}his provides spatially distributed return periods in sub-catchments, making flood hazard monitoring effective at a local scale.},
  keywords = {synthetic aperture radar ({SAR}) ; river sub-catchment ; flood monitoring ; flood hazard assessment},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {16},
  numero = {12},
  pages = {2179 [22 ]},
  year = {2024},
  DOI = {10.3390/rs16122179},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091048},
}