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DOI: 10.14569/IJACSA.2020.0110176
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Towards an Intelligent Approach for the Restitution of Physical Soil Parameters

Author 1: Ibtissem HOSNI
Author 2: Lilia BENNACEUR FARAH
Author 3: Imed Riadh FARAH
Author 4: Raouf BENNACEUR
Author 5: Mohamed Saber NACEUR

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.

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Abstract: The analysis of the radar response on natural surfaces has been subject of intense research during the last decades in the field of remote sensing. Unless the availability of accurate values of surface roughness parameter, the restitution of soil moisture from radar backscattering signal can constantly provide inaccurate estimates. Characterization of soil roughness is not fully understood, so a wide range of roughness values can be obtained for the same studied surface when using different measurement methodologies. Various studies have shown a weak agreement between experimental measurements of soil physical parameters and theoretical values under natural conditions. Due to this nonlinearity and its ill-posedness, the inversion of backscattering radar signal on soils for restitution of physical soil parameters is particularly complex. The aim of the present work is the restitution of soil physical parameters from backscattered radar signal using an adapted backscattering model to the soil proposed description. As our study focuses on little rough soils, we have adopted in this work a multi-layered modified multiscale bi-dimensional Small Perturbation Model (2D MLS SPM). Subsequently, we propose a new way of describing the dielectric constant, with the aim of including air fractions in the multiscale multilayer description of the soil. Calculating the dielectric constant is based on the consideration of a soil comprising two phases, a fraction of soil, and an air fraction. For the inversion method, a methodology of coupling between neural networks (NN) and genetic algorithms (GA) was carried on in order to restitute the physical properties of the soil. Samples were generated by the original MLS 2D SPM followed by a neural network to obtain the statistic soil moisture and MLS roughness parameters algorithm. thereafter, these restored values were modelled by the genetic algorithms to resolve, in part or in whole, the disagreement between the retrieval and original values.

Keywords: Inversion; air fractions; multi-layered; multiscale; SPM; genetic algorithms

Ibtissem HOSNI, Lilia BENNACEUR FARAH, Imed Riadh FARAH, Raouf BENNACEUR and Mohamed Saber NACEUR, “Towards an Intelligent Approach for the Restitution of Physical Soil Parameters” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110176

@article{HOSNI2020,
title = {Towards an Intelligent Approach for the Restitution of Physical Soil Parameters},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110176},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110176},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Ibtissem HOSNI and Lilia BENNACEUR FARAH and Imed Riadh FARAH and Raouf BENNACEUR and Mohamed Saber NACEUR}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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