@article{fdi:010061499,
  title = {{T}he use of radar satellite data from multiple incidence angles improves surface water mapping},
  author = {{O}'{G}rady, {D}. and {L}eblanc, {M}arc and {B}ass, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}atellite radar data has been employed extensively to monitor flood extents, where cloud cover often prohibits the use of satellite sensors operating at other wavelengths. {W}here total inundation occurs, a low backscatter return is expected due to the specular reflection of the radar signal on the water surface. {H}owever, wind-induced waves can cause a roughening of the water surface which results in a high return signal. {A}dditionally, in arid regions, very dry sand absorbs microwave energy, resulting in low backscatter returns. {W}here such conditions occur adjacent to open water, this can make the separation of water and land problematic using radar. {I}n the past, we have shown how this latter problem can be mitigated, by making use of the difference in the relationship between the incidence angle of the radar signal, and backscatter, over land and water. {T}he mitigation of wind-induced effects, however, remains elusive. {I}n this paper, we examine how the variability in radar backscatter with incidence angle may be used to differentiate water from land overcoming, to a large extent, both of the above problems. {W}e carry out regression over multiple sets of time series data, determined by a moving window encompassing consecutively-acquired {E}nvisat {ASAR} {G}lobal {M}onitoring {M}ode data, to derive three surfaces for each data set: the slope beta of a linear model fitting backscatter against local incidence angle; the backscatter normalised to 30 degrees using the linear model coefficients (sigma(0)(30)), and the ratio of the standard deviations of backscatter and local incidence angle over the window sample ({SDR}). {T}he results are new time series data sets which are characterised by the moving window sample size. {A} comparison of the three metrics shows {SDR} to provide the most robust means to segregate land from water by thresholding. {F}rom this resultant data set, using a single step water-land classification employing a simple (and consistent) threshold applied to {SDR} values, we produced monthly maps of total inundation of the variable south-western basin of the {A}ral {S}ea through 2011, with an average pixel accuracy of 94% (kappa = 0.75) when checked against {MODIS}-derived reference maps.},
  keywords = {{C}lassification ; {F}lood mapping ; {S}urface water ; {R}adar ; {ASAR} ; {I}ncidence angle ; {B}ragg resonance ; {W}ind effects ; {A}bsorption ; {R}egression ; {A}ral {S}ea ; {K}azakhstan ; {U}zbekistan ; {KAZAKHSTAN} ; {OUZBZKISTAN}},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {140},
  numero = {},
  pages = {652--664},
  ISSN = {0034-4257},
  year = {2014},
  DOI = {10.1016/j.rse.2013.10.006},
  URL = {https://www.documentation.ird.fr/hor/fdi:010061499},
}