Abstract
Abstract. In this paper, a multi-layered multi-scale backscattering model for a lossy medium and a neural network inversion procedure has been presented. We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat algorithm to describe natural surface roughness. An adapted three layers 2D MLS small perturbations (SPM) model has been used to describe radar backscattering response of semiarid sub-surfaces. The total reflection coefficients of the natural soil are computed using the multilayer model, and volumetric scattering is approximated by the internal reflections between layers. The original multi-scale SPM model includes only the surface scattering of the natural bare soil, while the multilayer soil modified 2D MLS SPM model includes both the surface scattering and the volumetric scattering within the soil. This multi-layered model has been used to calculate the total surface reflection coefficients of a natural soil surface for both horizontal and vertical co-polarizations. A parametric analysis presents the dependence of the backscattering coefficient on multi scale roughness and soil. The overall objective of this work is to retrieve soil surfaces parameters namely roughness and soil moisture related to the dielectric constant by inverting the radar backscattered signal from natural soil surfaces. To perform the inversion of the modified three layers 2D MLS SPM model, we used a multilayer neural network (NN) architecture trained by a back-propagation learning rule.
Highlights
Over the last two decades, microwave remote sensing has become an efficient tool for indirectly estimating soil moisture and soil properties in the top few centimeters of soils at different spatial and temporal scales
The objective of this paper is to develop and test an inversion algorithm for soil moisture and multi-scale roughness parameters retrieval from radar backscattering coefficients simulated by the modified small perturbation model (SPM) model using a neural network inversion procedure based on a multilayer neural network (NN) architecture trained by a back propagation learning rule
To illustrate the inversion techniques described in the previous section, we apply them to the data simulated by the SPM
Summary
Over the last two decades, microwave remote sensing has become an efficient tool for indirectly estimating soil moisture and soil properties in the top few centimeters of soils at different spatial and temporal scales. In that context, modeling radar backscattering through natural surfaces has become an important theme of research and active remote sensing and has shown its utility for many applications in hydrology, geology, astrophysics, etc. Many previous works have been devoted to the analysis of the backscattering characteristics of bare soils and several backscattering models (theoretical, semi- empirical and empirical) were developed ([1] [2] [6] [9]). They used the classical statistical description of natural surfaces and characterized roughness by statistical parameters namely correlation length and standard deviation. Several works have proposed various approaches for the improvement of roughness descriptions
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