%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Enguehard, Pauline
%A Frappart, F.
%A Zeiger, P.
%A Blarel, F.
%A Satgé, Frédéric
%A Bonnet, Marie-Paule
%T Contribution of automatically generated radar altimetry water levels from unsupervised classification to study hydrological connectivity within Amazon floodplains
%D 2023
%L fdi:010090020
%G ENG
%J Journal of Hydrology : Regional Studies
%K Radar altimetry ; Amazon ; Floodplains ; Unsupervised classification ; Automatic generation of water level gauges ; Hydrological connectivity
%K BRESIL ; PEROU ; AMAZONIE
%K AMAZONE BASSIN
%M ISI:001044634300001
%P 101397 [17 ]
%R 10.1016/j.ejrh.2023.101397
%U https://www.documentation.ird.fr/hor/fdi:010090020
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2023-09/010090020.pdf
%V 47
%W Horizon (IRD)
%X Study region. The Curuaí floodplain in the low Amazon river in the Pará state of Brazil and Juruá basin, a major Solimões tributary. Study focus. Characterizing the hydrological dynamics of Amazon floodplains is essential to better understand and preserve these environments providing important resources to local populations. Radar altimetry is an effective remote sensing tool for monitoring water levels of continental hydrosystems, including floodplains. An unsupervised classification approach on radar echoes to determine hydrological regimes has recently been tested and showed a strong potential on the Congo River basin. This method is adapted to Envisat and Saral satellite radar altimetry data on two study areas in the Amazon Basin. The aim is to improve inland water detection along altimeter tracks to automatically generate water level time series (WLTS) over rivers, lakes, and poorly monitored floodplains and wetlands. New hydrological insights. Results show a good agreement with land cover maps obtained with optical imagery over selected Amazonian wetlands (70-80% accuracies with Envisat data and 50-60% with Saral data). Automatically generated WLTS are strongly correlated to the manually generated WLTS (R²
%$ 062 ; 126