A model-based inversion of rough soil surface parameters from radar measurements

In this paper, a model-based retrieval algorithm is developed for the remote sensing of rough surfaces. The probabilistic and sensitive issues of parameter estimation for soil surfaces are discussed and modeled. A geophysical model function (GMF) that relates the input (observation space) and output vectors (parameter space) includes both an electromagnetic scattering model and a dielectric model; the electromagnetic scattering model describes the relationship between the radar echoes and the target parameters (geometrical and electrical), while the dielectric model connects the electrical parameter (permittivity) to the geophysical parameter of interest (soil moisture). Within the framework of an integral equation model, a scattering model is devised and used as part of the GMF. To estimate the parameters from finite sets of measurements, a good approximation of an GMF inverse function of GMF is required. We apply a neural technique to do this, by exploring its many merits, including not needing an explicit function. The necessary training data sets are generated using the GMF within a pre-defined domain. In order to alleviate an ill-posed problem, in which more than one set of parameters may be mapped onto a single backscattering coefficient, (i.e, non-unique), the range of parameters must be properly selected through a sensitivity analysis. In practice, the measured data are unavoidably contaminated by inherent noise. It turns out that the measurement uncertainty becomes quite large making the inversion accuracy even worse. By properly taking the noise components into account in the training data, the noise-tolerant capability of the inversion system can be increased. A reasonably good estimation of the surface correlation length, the roughness, and the soil moisture parameters may be obtained from laboratory-controlled measurements and AIRSAR data in this study.