@article{fdi:010077971,
  title = {{A} {S}entinel-1 based processing chain for detection of cyclonic flood impacts},
  author = {{A}lexandre, {C}yprien and {J}ohary, {R}. and {C}atry, {T}hibault and {M}ouquet, {P}ascal and {R}evillion, {C}. and {R}akotondraompiana, {S}. and {P}ennober, {G}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n the future, climate change will induce even more severe hurricanes. {N}ot only should these be better understood, but there is also a necessity to improve the assessment of their impacts. {F}looding is one of the most common powerful impacts of these storms. {A}nalyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. {T}his paper presents an automated processing chain for {S}entinel-1 synthetic aperture radar ({SAR}) data. {T}his processing chain is based on the {S}1-{T}iling algorithm and the normalized difference ratio ({NDR}). {I}t is able to download and clip {S}1 images on {S}entinel-2 tiles footprints, perform multi-temporal filtering, and threshold {NDR} images to produce a mask of flooded areas. {A}pplied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. {W}ith the rapid mapping product of {EMS} {C}opernicus ({E}mergency {M}anagement {S}ervice) as reference, the method confers up to 95% accuracy and a {K}appa value of 0.75.},
  keywords = {hurricane ; cyclone ; flood ; {S}entinel 1 time series ; change detection ; {NDR} ; {SAR}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {12},
  numero = {2},
  pages = {252 [18 ]},
  year = {2020},
  DOI = {10.3390/rs12020252},
  URL = {https://www.documentation.ird.fr/hor/fdi:010077971},
}