Abstract
Soil moisture is important to enable the growth of vegetation in the way that it also conditions the development of plant population. Additionally, its assessment is important in hydrology and agronomy, and is a warning parameter for desertification. <br><br> Furthermore, the soil moisture content affects exchanges with the atmosphere via the energy balance at the soil surface; it is significant due to its impact on soil evaporation and transpiration. Therefore, it conditions the energy transfer between Earth and atmosphere. <br><br> Many remote sensing methods were tested. For the soil moisture; the first methods relied on the optical domain (short wavelengths). Obviously, due to atmospheric effects and the presence of clouds and vegetation cover, this approach is doomed to fail in most cases. Therefore, the presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. <br><br> This paper presents a synergistic methodology of SAR and optical remote sensing data, and it’s for simulation of statistical parameters of soil from C-band radar measurements. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. <br><br> Backscattering coefficients were simulated using the established backscattering model. A two-dimensional multiscale SPM model has been employed to investigate the problem of electromagnetic scattering from an underlying soil. The water cloud model (WCM) is used to account for the effect of vegetation water content on radar backscatter data, whereof to eliminate the impact of vegetation layer and isolate the contributions of vegetation scattering and absorption from the total backscattering coefficient.
Highlights
Monitoring spatial patterns of properties of the soil in general, and the estimate of the true moisture soil in particular, is a crucial task of the Environmental Remote Sensing [Schmugge et al, 2002]
We aim to develop and test an inversion algorithm in order to retrieve the soil moisture and multi-scale roughness parameters using as input the radar backscattering coefficients simulated by the proposed synergistic methodology and using a multilayer neural network (NN) architecture trained by a back propagation learning rule
A classical simplified model used for exploring the basic microwave response of vegetation canopies is the water cloud model (WCM) whereby the canopy is modelled as a cloud of identical, randomly oriented scatterers
Summary
Monitoring spatial patterns of properties of the soil in general, and the estimate of the true moisture soil in particular, is a crucial task of the Environmental Remote Sensing [Schmugge et al, 2002]. We aim to develop and test an inversion algorithm in order to retrieve the soil moisture and multi-scale roughness parameters using as input the radar backscattering coefficients simulated by the proposed synergistic methodology and using a multilayer neural network (NN) architecture trained by a back propagation learning rule. For this purpose, we proceed as follows: The first section outlines a comparison between the Active and the Passive Microwave Remote Sensing.
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