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Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

N. Baghdadi, R. Cresson, M. El Hajj (CEMAGREF), R. Ludwig (LMU) and I. La Jeunesse (UT)


The purpose of this study (accepted in HESS: Hydrology and Earth System Sciences) was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on Multi-Layer Perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases in using or not a priori knowledge on soil parameters. The inversion approach was then validated in using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates whereas the precision on the surface roughness estimation remains unchanged.


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© 2010 CLIMB Project - Climate Induced Changes on the Hydrology of Mediterranean Basins
Reducing Uncertainty and Quantifying Risk through an Integrated Monitoring and Modeling System
A 7th Framework Programme Collaborative Research Project
(Environment, incl. Climate Change)