Simulation des données SWOT haute résolution et applications à l'étude de l'estuaire de l'Amazone

Lion Christine 1
1 GOHS
LEGOS - Laboratoire d'études en Géophysique et océanographie spatiales
Abstract : The thesis is included in the preparation of SWOT mission (Surface Water and Ocean Topography) preparation process. It has been created from collaborations between NASA / JPL (National Aeronautics and Space Administration/Jet Propulsory Laboratory), CNES (Centre National d'Etudes Spatiales) and ASC-CSA (Spatial Canada Agency) and its launching is due in 2019. SWOT is a near-nadir radar interferometer in Ka band (incidences form 0.6° to 4.1°). Its purpose is to help our understanding of surface water variations (lakes volume variation, rivers discharge, detect flooded areas ...) and ocean mesoscale dynamics (whirlpool) thanks to a 10km resolution made into a 1km. In order to determine SWOT improvements in studies of Amazon estuary, several tools were developed. The first one modelize the radar cross-section of three different kind surfaces (water, bare soil and vegetation) and was made for a CNES study by Capgemini. It allows defining the limit condition for water not been discerned between other surfaces. This model emphasizes Ka band sensibility to roughness parameter. This model is not able to represent the layover phenomenon, which is a mix of information within a single pixel due to relief. Due to its near-nadir configuration, it will be more present than in actual radars. As lakes and rivers are more often sided with trees, it is needed to evaluate the error margin on surface water measurement. I developed interferometric model which includes simplified radar backscattering models for vegetation and water. Thanks to this tool I have been able to determine the Ka band sensibility to vegetation. It has even highlighted SWOT capacities to detect flooded areas underneath vegetation. In fact, during a flood, the tree heights observations are weaker than measurements in normal conditions, as an example for a 10% gap fraction (dense vegetation), we observe an 1m57 height for a 5 meters tree, instead of 4m50. To evaluate SWOT contribution in the Amazon study, I have been using a simulator developed by S. Biancamaria during his thesis (held in 2009). The instrumental errors were simulated with a white noise, with a standard deviation of 20 cm. I improved it in order to have more realistic errors, by inserting errors from performance estimations. This simulator offers the advantage of reproducing water heights directly. It has been used in several studies of which an Ohio River assimilation by K. Andreadis. For my area of study, it allowed me to determine SWOT capacity to accurately measure the river slope and to observe the tide spread within the river.
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Lion Christine. Simulation des données SWOT haute résolution et applications à l'étude de l'estuaire de l'Amazone. Hydrologie. Université Paul Sabatier - Toulouse III, 2012. Français. ⟨tel-00932791⟩

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